Marie Thiollet-Scholtus
email: marie.thiollet-scholtus@inrae.fr

Mark Anthony Arceño

Mateus Valduga

François Sarrazin

Typing winegrower profiles to ease agroecological change in viticulture practices in the Loire Valley, France

Keywords: Viticulture, winegrower networks, Loire Valley, agroecological change

Viticulture is a perennial agricultural system that is rooted in tradition but must also adapt to change (e.g. climate change, authorized rate of pesticide uses in the European Union). Viticulture includes a variety of practices whose objective is to produce wine grapes. To help viticulture systems adapt to changes, both field viticulture practices and value chain activities within winegrower networks must be considered. Here, we used socioeconomic and technical surveys to investigate these potential influences on winegrowers' decision-making in regard to adapting to changes, so as to develop a typology of winegrowers in the Loire Valley, France. The surveys were conducted in 2010 with 37 winegrowers managing 56 fields. To our knowledge, this study is the first to use this method to quantify viticulture networks. Our results showed that winegrowers within the same networks share similar behavioural patterns, with non-harvest viticulture practices (e.g. vine density planting, number of buds) characterizing winegrower profiles. Further, our typology reveals viable options for changing practices in a manner that aligns with the sociocultural characteristics of each profile. Agricultural extension services can especially make use of this typology as a starting point to help winegrowers adapt to changes in a more personalized and successful way.

Introduction

In response to environmental and/or legislative contextual changes brought about by various inevitable (e.g. climate change) and avoidable (e.g. air pollution) agroecological issues, farmers must decide how to adapt their growing practices accordingly. Many large-scale methods have been developed to manage the objectives and actions required [START_REF] Mcfadden | Evaluating the efficacy of adaptive management approaches: Is there a formula for success[END_REF]. The resilience-experimentalist ('with high emphasis on stakeholder and complex models') and decision-theoretical ('with simpler models') frameworks are often used in adaptive natural resource management [START_REF] Mcfadden | Evaluating the efficacy of adaptive management approaches: Is there a formula for success[END_REF]. At regional and local levels, farm profiling has also been applied (Kostrowicki, 1977;[START_REF] Landais | Modelling farm diversity: New approach to typology building in France[END_REF] to assess the current situation and evaluate adaptations and responses to innovations introduced into cropping systems.

Various practices derived from different methods have been used to characterize annual crops. [START_REF] Deffontaines | Les pratiques des agriculteurs. Point de vue sur une courant nouveau de la recherche agronomique[END_REF] defined grain and oilseed crop systems. [START_REF] Perrot | Typologie d'exploitations contruite par agrégation autour de pôles définis à dire d'experts[END_REF] developed definitions for dairy systems. [START_REF] Van Der Ploeg | Farm diversity, classification schemes and multifunctionality[END_REF] analysed and conceptualized farming diversity and demonstrated technical, societal, and multifunctional ties among farms with similar cropping systems. Several authors described cropping system practices that were believed to be logically organized to achieve specific farming goals, such as the impact of the cropping system on production quality and quantity [START_REF] Debaeke | Iterative design and evaluation of rule-based cropping systems: Methodology and case studies. A review[END_REF][START_REF] Loyce | Management planning for winter wheat with multiple objectives (2): Ethanol-wheat production[END_REF].

Such examples of farm profiling are often descriptive and qualitative and seldom include univariate or multivariate statistical analyses of cropping system practices. Moreover, they rarely account for farmers' social networks. However, socially-derived criteria may explain the observed agroecologically motivated changes in cropping practices (e.g. organic certification, grassing between vines to decrease runoff, promoting vineyard biodiversity), such as those seen in Burgundy vineyards to increase system sustainability [START_REF] Compagnone | Dynamique des changements de pratiques des viticulteurs en Bourgogne: influence de la structure du conseil et des réseaux de dialogues entre pairs[END_REF]. In the present study, we used multivariate data for technical and trade networks to develop tools to assess vineyard management, highlighting variability on the ground in terms of winegrowers' adaptive responses to a variety of agroecological issues.

Analyses of production systems are often used to quantify socioeconomic and environmental performance or output variables such as work satisfaction [START_REF] Besser | Which farm characteristics influence work satisfaction? An analysis of two agricultural systems[END_REF], profitability [START_REF] Ash | Boosting the productivity and profitability of northern Australian beef enterprises: Exploring innovation options using simulation modelling and systems analysis[END_REF], and climate change [START_REF] De Salvo | A spatial micro-econometric approach to estimating climate change impacts on wine firm performance: A case study from Moldavia region, Romania[END_REF]. For this reason, product origin labels should be designated as input variables. For many years, much of the wine in France has been produced in delimited geographical areas in accordance with local production rules and methods including (i) those related to a protected designation of origin (PDO) of the product and (ii) distribution, sales, and marketing organizations. The PDO [START_REF] Inao | Agricultural typology concept and method[END_REF]) describing France's wine production areas incorporates human factors (e.g. collective knowledge of a village's winegrowers, vine planting, and management practices) that manage economic development and environmental changes. PDO fields may be described using the precise combined knowledge of the winegrower practices and social profiles that are regarded in the present study.

Separate and combined technical analyses of viticulture practices and wines have been used to explain differences in PDOs. Indeed, several techniques are available to assess terroirs, using vineyard characteristics such as experimental plots, winegrower surveys, statistical analyses, and soil assessments in the natural environment (e.g. altitude, slope, aspect, and insolation) [START_REF] Morlat | Experience of multisite trials for the study of vineyards[END_REF]. Other studies applied more complex analytical methods involving vineyard and wine cellar practices. [START_REF] Morlat | Long-term additions of organic amendments in a Loire valley vineyard. III. Effects on fruit composition and chemical and sensory characteristics of Cabernet franc vine[END_REF] related organic matter fertilization to field-scale grape and wine quality. [START_REF] Renaud-Gentié | Choosing the most representative technical management routes within diverse management practices: Application to vineyards in the Loire Valley for environmental and quality assessment[END_REF] combined several vineyard practices at the field and farm scales to assess their environmental impacts according to the technical management route. And Thiollet-Scholtus et al. ( 2014) characterized various PDO Loire Valley wines according to viticulture and oenology practices and the sensory attributes of the wines. Moreover, Thelier-Huche and [START_REF] Thelier-Huche | Anjou wine growers' perception and development of natural land factors[END_REF] characterized differences in product quality among vineyards by combining field-scale technical and social analyses, including quantitative measurements and farmer perception surveys.

With the above in mind, understanding the diversity and practical characteristics of farm profiles helps farmers adapt their practices to agroecological and climate change. The aim of the present study was to type or define such profiles for winegrowers, identifying practices that are crucial to winegrowers' decision-making in response to challenges posed by unpredictable events. We characterized these profiles according to both viticulture practices and social networks and sought to present all possible combinations thereof, inclusive of winegrowers' memberships in technical and trade networks and product marketing strategies. To these ends, the assessment tools we developed identify levers and obstacles facing winegrowers confronted with various forms of change, specifically that of climate change [START_REF] Battaglini | European winegrowers' perception of climate change impact and options for adaptation[END_REF][START_REF] Neethling | Assessing local climate vulnerability and winegrowers' adaptative processes in the context of climate change[END_REF]. The present study may be the first to consider the often-overlooked association between viticulture practices in vineyard systems and winegrower social networks and their influences on the decisions winegrowers must take to respond and adapt to inevitable changes in their local settings. We therefore anticipate that this will be of help to winegrowers, as they seek appropriate sources of advice and guidance to adapt most effectively in their local contexts.

Materials and methods

Loire Valley vineyards as a case study

The Loire Valley encompasses four geographical areas that cover 70,000 hectares (ha) and produce four million hectolitres (hL) of wine annually. Muscadet, Sancerre, and Touraine contain 69 of the 85 Protected Designations of Origin (PDO) that yield regular-to highquality still white wines (Figure 1). Anjou was not included in the present study as it produces mainly red wines. These geographical areas have the following characteristics:

. Temperature and precipitation gradients: (i) strong oceanic influence in Muscadet, (ii) moderate oceanic influence in Touraine, and (iii) weak oceanic influence in Sancerre [START_REF] Quénol | Adaptation of viticulture to climate change: High resolution observations of adaptation scenario for viticulture: The Adviclim European Project[END_REF]. . Wine prices in the Loire Valley vary by quality and area. Sancerre produces 'highquality' wines on 2,900 ha of vineyards. Touraine and Muscadet produce 'regularquality' wines on 12,600 and 10,300 ha, respectively (InterLoire, 2019).

With the assistance of agricultural extension services in Muscadet, Sancerre, and Touraine, winegrowers and fields were selected at random to ensure heterogeneity of the regional climate and wine prices. The focus was on winegrowers who had a mean vineyard area of ≤10 ha. Initially, the winegrowers who were contacted belonged to various networks including public and private winegrower advisors, winegrower contractors, producer associations, and other winegrowers. Thereafter, snowball sampling was implemented, in which the winegrowers surveyed were asked to identify others who's technical and trade networks differed from their own. The ultimate goal was to compile a representative series of winegrowers. As snowball sampling depends on voluntary responses, however, it does not ensure full representation. The final sample of 37 winegrowers amply represented the overall area of the Loire Valley. However, results similar to those obtained here were reported by studies that assessed relatively fewer sites, such as the 22 surveyed in the Argentinean Pampa by [START_REF] Salembier | Tracking on-farm innovations to unearth alternatives to the dominant soybean-based system in the Argentinean Pampa[END_REF].

Within each vineyard, representative fields were selected according to the dominant white grape variety characteristic of that area: (i) Sauvignon Blanc in Sancerre and Touraine and (ii) Melon de Bourgogne in Muscadet. This sampling approach ensured that grape variety did not contribute to any of the diversity observed in farm profiles and practices. The winegrowers selected their field samples according to the following criteria: (i) each field represented a dominant viticulture system and (ii) no more than three fields were chosen per vineyard to represent the diversity in field quality (low-, medium-, and high-price). Of the 37 winegrowing sites, 56 individual field plots were selected that encompassed the diversity of wine-selling strategies and viticulture practices of the vineyards in each area. We defined 'wine selling strategies' as how winegrowers chose to sell their wines (e.g. 100% to a co-operative, merchants, or restaurants; 100% in direct sales from their own cellars; a mixture of 30% to restaurants, 30% to merchants, and 40% in direct sales).

Data collection

A graduate student conducted two different surveys on two separate site visits to each vineyard. The first survey was completed in April 2010. It contained open-ended questions intended to define fixed winegrower typologies (Supplementary Material 1). It also contained 95 closed questions addressing the major characteristics of the farm, sales, marketing, workers, viticulture, oenology, information, communication, public relations, and winegrower concerns. Wine production topics included geographical, historical, and human factors. There were no questions about modification of practices or the winegrower's ability or willingness to change practices (Supplementary Material 1). The initial survey collected essential information about the decision-making process that winegrowers use for viticulture practices (Kostrowicki, 1977).

The second survey contained 19 closed questions addressing the cropping system practices involved in grape production, including vineyard training, annual viticulture, and harvest (Supplementary Material 1). The surveys were completed by May 2010. They characterized viticulture and production practices in each field. Questions were related only to the general management practices associated with producing PDO white wines in each of the three geographical areas of this study. The surveys did not attempt to account for the complexities and interrelationships of cropping systems. These have since been addressed in an earlier study [START_REF] Renaud-Gentié | Choosing the most representative technical management routes within diverse management practices: Application to vineyards in the Loire Valley for environmental and quality assessment[END_REF]. The conclusions of the trade (winegrower) and technical (viticulture practice) surveys were used to propose the opportunities or restrictions to change in viticulture during ecological transitions.

We would like to acknowledge here that, though no further data collection has taken place since the completion of the study in 2010, others have begun to assess winegrowers' perceptions of their ability to adapt to agroecological and climatic changes. This includes Neethling et al.'s (2016) interviews with winegrowers in 2012 and 2013 in Anjou-Saumur, who tended to be reactive to agroecological changes. [START_REF] Teil | L'adaptation aux évolutions récentes des vins vue par des vignerons d'AOP. Enquête en Anjou et Alsace (France)[END_REF] interviews conducted between 2012 and 2015 in Anjou and Alsace with winegrowers who not only adapt regularly to weather variations but also concentrate efforts on helping the vines to adapt to perceived change. Both studies were part of the larger 2012-2016 Long-term Adaptation to Climate Change in Viticulture and Enology (LACCAVE) project organized by what is now known as the INRAE [START_REF] Ollat | Adaptation to climate change of the French wine industry: A systemic approach -Main outcomes of the project LACCAVE[END_REF], which has in part investigated future adaptation scenarios among vine-to-wine participants. In addition, [START_REF] Iliff | The influence of climate change and state regulatory systems on independent wine production[END_REF] exploratory research of interviews among winegrowers in the Pays Nantais, which were published throughout the last decade, highlights tensions between climate change and legislative/marketing restrictions. Combined with these previous efforts, we anticipate the application of our typology to ongoing and future studies will be of benefit as researchers refine or develop their own assessment tools and as winegrowers decide whether and how to implement potential adaptations [START_REF] Van Leeuwen | Soil-related terroir factors: A review[END_REF], many of which we discuss here.

Method of profiling winegrowers

Previous studies devised innovative methods to describe farm diversity [START_REF] Doorman | A social science contribution to applied agricultural research for the small farm sector: The diagnostic case study as a tool for problem identification[END_REF]. Our approach was to establish winegrower profiles based on the study of Kostrowicki (1977) and on additional parameters related to social aspects of the farming system. The survey framework and winegrower classification system were inspired by descriptions of farm style diversity of [START_REF] Van Der Ploeg | Farm diversity, classification schemes and multifunctionality[END_REF]. We assumed that winegrowers could be categorized according to an established typology by identifying the extent to which technical and trade networks supported changes in viticulture practices.

To develop a fixed typology based on winegrower networks, the first survey was textually analysed, based on the pioneering strategic analyses of [START_REF] Crozier | L'acteur et le système[END_REF] and [START_REF] Martin | L'analyse stratégique en perspective[END_REF]. The objective was to understand the patterns that might emerge when winegrower networks present opportunities for organized, strategic interactions that could enable winegrowers to respond to issues or changes arising in the field [START_REF] Olaya | The sociological theory of Crozier and Friedberg on organized action seen through a simulation model[END_REF]. We followed an agricultural constructivist approach [START_REF] Landais | Modelling farm diversity: New approach to typology building in France[END_REF]. The assumptions made for our typology were derived from analysis of our first survey and our expert opinion (F.S., an authority on Loire Valley cropping systems; [START_REF] Sarrazin | Audit stratégique et organisationnel des Anjou-Villages Brissac[END_REF]; [START_REF] Sarrazin | Audit stratégique et organisationnel de l'AOC Chinon[END_REF]). We established winegrower profiles based on criteria related to technical and trade networks (Table 1).

The initial typology allowed us to identify the logic behind the adaptive behaviour and decision-making processes of winegrowers. It also identified the organizing forces and local sociotechnical functions of winegrowers and other players. In this way, factors responsible for the cohesion and divergence observed among winegrowers could be identified, including factors that represented opportunities or obstacles to change in viticulture practices.

Statistical characterization of viticulture practices

To identify relationships between viticulture practices and winegrower profiles, discriminant factorial analysis (DFA) was performed on eight quantitative variables to identify which of these were able to discriminate among the winegrower profiles, while a factor analysis of correspondences (FAC) was performed on 11 qualitative variables (Table 2, survey answers #6 and #9). DFAs identify variables best able to discriminate between existing groups pre-determined from social profiles, i.e. the analysis does not offer any statistical verification/test of the pre-defined groups. Using DFA and FAC methods together makes it possible to take into account all the practices of the winegrowers when constructing the typology. This makes it possible not to restrict oneself to a few quantitative variables. The technical variables, from the follow-up survey, were selected according to (i) annual viticulture and vineyard training system practices, and (ii) practices that were independent of each other. The 'annual viticulture' practices were considered potential shortterm levers for agroecological change, whereas the 'vineyard training system' practices were considered potential medium-to long-term levers for agroecological change.

For DFA and FAC, we assessed winegrower (each of which is a point whose coordinates are calculated from the weighted coordinates of all weighted points in a set). In each set of winegrower fields, each field had the same weight, and the profile barycentre coincided with the centroid. Then, 'Winegrower barycentres' to refer to individual grower centres and 'Profile barycentres' to refer to the 4 group centres. DFA predicted the home cluster for new data that were spatially separated in each analysis. These statistical analyses were performed in XLStat-Pro v. 2009 (Addinsoft, Paris, France). The resulting analysis suggested specific viticulture practices that helped explain winegrower membership in the various technical and trade networks (Supplementary Material 1).

Results and discussion

In this section, we characterize winegrowers according to their social profiles (Tables 1 and3) and viticulture practices (Figures 2 and3). We then discuss the extent to which winegrower networks may present levers or obstacles to adaptive options in any context of agroecological change. For DFA and FAC, the coordinates of the variables, profile barycentres, and individual fields of the principal components are listed in Supplementary Material 2. Winegrower technical and trade networks correspond to four different profiles regardless of geographical area

In the three geographical areas of our case study (Muscadet, Touraine, and Sancerre), we identified four distinct winegrower profiles (COP, DIN, NEG, and VAL) (Table 3). However, they were not bound to any single area in the Loire Valley. Profile memberships could also overlap.

(1) COP winegrower profiles were found in the Touraine and Sancerre vineyards. COP winegrowers do not invest in additional labour as they either do the work themselves or rely on the assistance of family members throughout the year. The only technical network to which they belong is one that shares equipment with other winegrowers. This practice reduces the burden upon any individual winegrower of incurring the costs for large or specialized processing machinery. COP winegrowers do not consult with any agricultural extension services to make their management decisions. They prefer to read technical literature to educate themselves in viticulture practices. COP winegrowers are affiliated only with co-operative selling networks. Membership in a co-operative network entails (i) signing a closed contract with certain viticulture practices and only a few winegrowers, and (ii) being elected to the co-operative management board and participating in strategic decision-making for the co-operative. (2) DIN winegrower profiles occurred in the Muscadet and Touraine vineyards. The profiles of the DIN winegrowers lie between those of NEG and VAL. As a rule, they belong only to the equipment-sharing network in the Touraine region, sharing larger equipment so that individual winegrowers need not invest in their own tools. However, DIN winegrowers do not necessarily share the same viticulture practices. For example, one winegrower may prune buds at the bottom of the vine, whereas another winegrower may prefer not to do so. Most winegrowers in the Touraine and Muscadet vineyards do not consult with any agricultural extension services and thus do not rely upon them for decision-making guidance. Most of the DIN winegrowers are simultaneously engaged in several other technical and social networks, such as viticulture syndicates, city policymaking committees, or sports and school associations. DIN winegrowers are not associated with any particular selling network. They sell up to 80% of their wine to merchants and then sell the rest directly.

Most DIN winegrowers expressed the desire to improve their wine selling methods. (3) NEG winegrower profiles are located in the Muscadet and Touraine vineyards. NEG winegrowers do not share viticulture practices. Moreover, Touraine-NEG winegrowers do not invest in labour and do not consult with any agricultural extension services. They are, however, associated with wine merchants. Touraine-NEG winegrowers depend entirely upon technical and sales contracts with wine merchants and sell them >80% of their product. NEG winegrowers are not affiliated with any other technical networks. They abide by the terms of signed contracts with wine merchants rather than those of PDO-Touraine practices. They do not intend to change their viticulture practices, wine quality, or wine prices, as these parameters are not included in their wine merchant contracts. The wine merchant network encompasses both the technical and trade networks of NEG winegrowers. (4) VAL winegrower profiles apply to those whose product is highly valued. They occur in Muscadet, Touraine, and Sancerre. Labour investment is high for both the Touraine-VAL and Muscadet-VAL winegrowers. All VAL winegrowers except those in Touraine have strong technical networks based on close ties with agricultural extension services that assist with viticulture practices. Unlike the Touraine-VAL winegrowers, those in Sancerre-VAL and Muscadet-VAL have invested large amounts of time and money in oenological practices and processes to advance their business. VAL winegrowers have extensive trade networks that include direct sales, exportation, and merchants. Many VAL winegrowers have established these multiple points of contact as they believe that selling wine is becoming increasingly difficult. Most of the VAL winegrowers concurrently engage in other technical and social networks, such as viticulture syndicates, city policymaking committees, and sports and school associations. Few Touraine-VAL winegrowers are not associated with these groups.

Winegrower membership in various networks should be considered in the attempt to identify potential obstacles to adaptation to agroecological change resulting from alterations in the environment, climate, law, or culture (Table 3). [START_REF] Battaglini | European winegrowers' perception of climate change impact and options for adaptation[END_REF] demonstrated that many winegrowers recognize their need for climate change information to help them plan modifications to their viticulture practices. Technical and trade networks may be used to transmit such information. The DIN and VAL winegrowers in our study had numerous and diverse networks (Table 3). Thus, they may be better equipped to adapt their viticulture practices than the COP and NEG winegrowers. The latter belong to few or even no networks. As they are not part of any formal network, the COP, DIN, and NEG winegrowers may continue to rely on word-of-mouth or the opinions of close friends and family to change their management practices (Table 1). In contrast, VAL winegrowers associate closely with agricultural extension services and may benefit from professional expertise. Nevertheless, they may also be subjected to pressure from their peers to modify their practices accordingly. We found that VAL winegrowers such as those in Burgundy already relied upon agricultural extension services networks to help them implement more ecologically friendly viticulture practices [START_REF] Compagnone | Dynamique des changements de pratiques des viticulteurs en Bourgogne: influence de la structure du conseil et des réseaux de dialogues entre pairs[END_REF][START_REF] Compagnone | Les viticulteurs bourguignons et le respect de l'environnement. Réseaux de dialogues professionnels et dynamiques de changement[END_REF]. Moreover, winegrower profiles (Table 3) indicated that technical and commercial networks and their interconnections may favour changes in viticulture practices that increase its sustainability [START_REF] Chiffoleau | Understanding local agri-food systems through advice network analysis[END_REF].

Winegrowers share the same universal harvest practices but have different viticulture practices

To define our winegrower typology, we considered various vine-to-wine factors. Our analyses revealed a relatively wide range of viticulture practices among winegrowers. As such, winegrower profiles could be characterized based on trade and technical networks, as well as non-harvest viticulture practices.

First, we analyse the percentage of variation between barycentres explained by each axis. According to the DFA, 72% of the profile barycentres of the DIN, NEG, COP, and VAL profiles are explained on PC1. Moreover, the COP and VAL profile barycentres contrasted those of DIN and NEG on this axis. The most important variables correlated with DFA-PC1 are (i) vineyard planting density, (ii) vine top pruning height, and (iii) number of buds remaining after winter pruning. Their correlations with DFA-PC1 were 0.894, -0.352, and -0.527, respectively. According to the DFA, 20% of the individual profile barycentres of the DIN, NEG, COP, and VAL profiles were explained on PC2. The COP and NEG profile barycentres contrasted those of DIN and VAL on this axis. The most important variables correlated with DFA-PC2 were (i) the number of times the vine top was pruned, (ii) number of days between the harvest date of the study plot and those of the other white wine plots on the same farm, and (iii) vine age. Their correlations with DFA-PC2 were -0.780, -0.558, and 0.476, respectively. Our qualitative data revealed other differences among the four profiles. According to the FAC, the DIN, NEG, and VAL profile barycentres were distinguished mainly by PC1 and viticulture practices (Figure 3). In contrast, the COP profile barycentre was characterized by viticulture practices and vineyard training systems. The total inertia of the PC1 and PC2 axes of the FAC was 91% (74% and 17%, respectively). PC1 was characterized primarily by soil tillage management (24% contribution) under the vine row, grape cutting to control yield (9%), leaf thinning to increase sun exposure (9%), and the Melon de Bourgogne variety (8.5%). In contrast, only the COP profile barycentre was found on the PC2 axis, which was characterized primarily by the Melon de Bourgogne variety (16.5% contribution), no vine shoot crushing after winter pruning (12.5%), no harvested grape blending before fermentation (12.4%), and leaf thinning to increase sun exposure (10.0%).

Secondly, we analyse the percentage of each axes explained by different viticulture practices. Taken together, each profile's barycentre appeared to correlate with different viticulture practices, thus providing more evidence for typological distinctions. The COP profile barycentre was correlated mainly with vine age. The VAL profile barycentre was correlated mainly with planting density, selected winter pruning date, and the number of times the vine top was pruned. The DIN profile barycentre was correlated mainly with the number of days between the harvest date of the test plot and those of the other white wine plots on the same farm, and the number of buds remaining after winter pruning. The NEG profile barycentre was correlated mainly with vine top pruning height (Figure 2; Supplementary Material 2). Notably, these patterns differ markedly from those for the harvest variables, whose practices are similar among all winegrower networks.

Finally, we identify the 3 harvest practices that are the same among the four profiles. Three harvest practices (i.e. CRUSH, BLEND, and HARVEST) were the same among the four profiles. The HARVEST variable follows mainly the PC2 axis of the DFA, which represents only 20% of the total explanation of the axes. The BLEND variable is found on the PC2 axis of the FAC, which represents only 17% of the total explanation of the axes (Figures 2 and3; Supplementary Material 2). Our data suggest that the three harvest practices are universal levers for managing viticulture change and do not necessarily aid in typing winegrower profiles. Harvest recommendations must be applicable to all winegrower profiles (Figure 2).

Certain details of the distribution of harvest data must be emphasized to explain the similarity in harvest date but not in grape blending. Twenty-eight of the 56 fields (50%) were harvested on the same date on the same farm, yielding a value of zero for HARVEST. Of the remaining fields, 15 and 13 were harvested 2-4 d and ≥5 d, respectively, after the others on the same farm. There was a 1.2-d difference in mean harvest time between VAL fields and COP and NEG fields. There was a 2.8-d difference in mean harvest time between VAL and DIN fields. The lack of grape blending after harvest and before fermentation accounted for PC2 of the FAC and represented 0.02% of the global FAC contribution (Figure 3).

The levers and obstacles to changing viticulture practices differ among winegrower profiles HARVEST and BLEND are annual viticulture practices that seem easy to change at first glance. However, changing them would require assumingincorrectlythat all winegrowers experience the same levers and obstacles. For their part, technical networks allow information about trends in changes in harvesting practices to flow to other winegrowing farms, for example. Of course, winegrowers will not change their harvest dates with each new trend, but because of the flow of information in the network, they become aware and can then decide whether to change. Information is one of the decision-making elements of winegrowers. In comparison, sales networks allow, in particular, information flows about changes in consumer preferences. They could influence practices for choosing the harvest date and blending practices in the cellar for the acidsugar balance of future wine. In terms of climate change, all winegrowers may have to adapt to changes in harvest date. One potential obstacle to changing the traditional harvest date would be logistical concerns, such as the availability of human labour during an 'off' harvest season. [START_REF] Neethling | Assessing local climate vulnerability and winegrowers' adaptative processes in the context of climate change[END_REF] described this practice as the adjustment of 'harvest windows'. Nevertheless, the feasibility of this practice according to individual winegrower characteristics has not been investigated. The workforces for COP and VAL winegrowers are essentially family-based and thus more flexible and less costly than the salaried workforces of the DIN winegrowers (Table 1). NEG winegrowers may be the least able to change, as they often have only one harvest and little financial investment in labour (Table 2). In terms of blending fields for winemaking, winegrowers' ability to adapt to change will depend mainly on their investment in processes such as changing the collection vats used during harvest. Financial investment is less of an obstacle for DIN and VAL winegrowers than it is for COP and NEG winegrowers (Table 2).

Vineyard training practices such as planting density, vine age, and variety are important long-term levers of change, but they are not the same for all winegrower profiles. Individual VAL winegrowers may be best equipped to change their training systems as they have multiple technical networks and information sources. [START_REF] Battaglini | European winegrowers' perception of climate change impact and options for adaptation[END_REF] suggested that VAL winegrowers might also be in a favoured position to change varieties. In contrast, as COP winegrowers belong to one network each, they may be able to change viticulture practices only if other members of their co-operative make similar changes.

A winegrower typology that considers trade networks may identify opportunities and obstacles to changing viticulture practices. The results of our DFA and FAC analyses identified viticulture practices and winegrower networks that differentiate vineyards in the Loire Valley and were used to generate a typology. [START_REF] Mesiti | Specifying the farming styles in viticulture[END_REF] reported 14 grape grower styles based on winegrower interviews in Australia. They drew upon technical and sales networks to describe a 'professional scientific manager' profile and upon sales and marketing networks to describe 'experienced manager,' 'low-input grower,' and 'traditional grower' profiles. Viticulture practices were profile indicators for only seven of the 14 profiles, but they were not described in detail. In future research, we intend to list practices and network types that must be considered before proposing more context-specific recommendations for sustainable viticulture practices.

Previous studies considered either only a few different practices [START_REF] Morlat | Long-term additions of organic amendments in a Loire valley vineyard. III. Effects on fruit composition and chemical and sensory characteristics of Cabernet franc vine[END_REF][START_REF] Renaud-Gentié | Choosing the most representative technical management routes within diverse management practices: Application to vineyards in the Loire Valley for environmental and quality assessment[END_REF] or a combination of viticulture, winemaking, and ageing practices [START_REF] Thiollet-Scholtus | Use of production practices and sensory attributes to characterize Loire Valley red wines[END_REF] in their grape quality and environmental assessments. They did not mention relationships between these practices and winegrower networks. Our results (Table 3) indicate that VAL winegrowers rely upon several information sources, such as technical journals and networks, agricultural extension services networks, and advisor networks. These sources expose VAL winegrowers to many sustainable practices, making it easier for them to adapt to agroecological change than for COP or NEG winegrowers. NEG winegrowers, for example, are connected only to their trade networks; changing practices could be relatively difficult and/or gradual due to contractual obligations. Furthermore, our results indicate that DIN winegrowers are also relatively better equipped than COP or NEG winegrowers to adapt to agroecological change, given their multiple networks. Ideas can be exchanged when equipment is shared with other winegrowers (e.g. regular face-to-face contact), and innovations in viticulture practices are available in the technical journals they read. [START_REF] Chiffoleau | Learning about innovation through networks: The development of environment-friendly viticulture[END_REF] demonstrated the relationship between membership in winegrowers' professional networks and the development of more sustainable viticulture. [START_REF] Chiffoleau | Learning about innovation through networks: The development of environment-friendly viticulture[END_REF] used Daré's definition of the professional dialogue network as being composed of daily or regular technical discussions, equipment exchanges, and joint work. From a sociological viewpoint, [START_REF] Chiffoleau | Learning about innovation through networks: The development of environment-friendly viticulture[END_REF] described the types of winegrower networks that make it possible to change winegrowing practices. We thus follow her approach by demonstrating that there is an agronomic connection between networks and winegrowers' practices.

Trade networks may impede or even prevent changes in practices of COP winegrowers. They cannot modify their practices at the field scale as they have signed contracts with their co-operatives. COP winegrower practices must be adapted in and through the cooperative structure itself. Changes in viticulture practices of COP winegrowers depend on connections between the winegrowers and the upper management of their co-operatives. Further investigation into the decision-makers involved in the technical and trade networks of the co-operatives may identify levers that could effectively change COP winegrower practices. This research may also clarify how viticulture practices can be made more sustainable and climate-friendly. Such steps have already been taken for local agri-food systems [START_REF] Chiffoleau | Understanding local agri-food systems through advice network analysis[END_REF].

Conclusion

In the present study, we (i) described profiles of four types of winegrower in the Loire Valley, (ii) used simple survey methods to develop these profiles based on viticulture practices and technical and trade networks, and (iii) identified relationships between harvestand non-harvest viticulture practices of the winegrowers. Despite our data now being ten years old, we have not noticed in our previous or ongoing work indications of changes in viticulture practices or winegrower networks that veer from the typology presented here. Though, there certainly may be additional variables to now consider amidst ongoing agroecological or climatic change (e.g. pesticide usage, organic/biodynamic practices, etc.). What is of importance is our ability to have developed winegrower profiles for a wide viticulture region through our inclusion of agronomic and sociological variables, which allows for additional inputs to be considered. Our interdisciplinary approach connected technical sciences (viticulture) to social sciences (trade and technical networks) and developed a more holistic, theoretical, and data-driven perspective that enables researchers to better understand the opportunities and obstacles winegrowers encounter when they must modify their viticulture practices in response to agroecological change. The harvest and viticulture variables we used in our study to profile Loire Valley winegrowers align with those of previous studies [START_REF] Chiffoleau | Learning about innovation through networks: The development of environment-friendly viticulture[END_REF][START_REF] Lamine | Transition pathways towards a robust ecologization of agriculture and the need for system redesign. Cases from organic farming and IPM[END_REF] and may be applicable in other contexts of the changes required in viticulture practice. The initial parameters to consider in a viticulture system such as vine age, varieties, and trellising systems are crucial for constructing profiles for winegrowers who belong to technical and/or trade networks.

It was essential to recognize the diversity of winegrowers in the Loire Valley to define typologies for them. [START_REF] Mesiti | Specifying the farming styles in viticulture[END_REF] reported that it is helpful to identify specific winegrower farming styles so that the modes of communication between them and agricultural extension services can be adapted accordingly. Borne out by our survey data, we were able to characterize four distinct winegrower profiles. NEG winegrowers must produce wines in agreement with their wine merchants, while COP winegrowers are constrained by their associations with other members of the co-operative to which they belong. DIN winegrowers are the most autonomous decision-makers, while VAL winegrowers' focus on wines that have high economic and sociocultural value and form flexible, well-connected networks. The present study not only identified winegrower networks and modes of communication but showed that technical and trade networks are levers and/or obstacles to change in viticulture systems in response to agroecological issues.

In addition to the constraints imposed by technical and trade networks, it is also necessary to examine factors such as the winegrowers' willingness and perceived ability to change their practices. These criteria were omitted from this study. Nevertheless, we acknowledge that there is no single universal recommendation for adaptive change to accommodate individual winegrowers or their profiles. Analysis of the winegrower profiles was only the first step in establishing the effectiveness of the recommendations for adaptive changes in harvest time, grape blending, variety, equipment, and trellising system. All of these parameters must be customized and optimized to increase the sustainability of viticulture systems and enable them to adapt to agroecological change. The winegrower profiles formulated here may help policymakers develop and implement strategies adapted to the geographical areas, practices, and networks of each winegrower. The large differences among the winegrower profiles make it difficult to promote recommended generic adaptations in viticulture practices in response to agroecological change. Nevertheless, the present study indicated that structures are in place to guide the adaptive sociocultural changes that winegrowers must make.

Figure 1 .

 1 Figure 1. Locations (circled) of the vineyards studied in the Loire Valley, France.

Figure 2 .

 2 Figure 2. Discriminant Factorial Analysis of the quantitative practices of the cropping system. Winegrower barycenters were projected as illustrative quantitative variables (COP in circles; DIN in triangles; NEG in squares; and VAL in diamonds) on the top. Practices of cropping systems are shown with their banner name in black (e.g. topping) on the bottom. Harvest practice is in grey, underlined and italic.

Figure 3 .

 3 Figure 3. Factor Analysis of Correspondences of qualitative practices of the cropping system. The latter are shown with their names in black (e.g. blending of grapes, tilling of the soil cover crop under the vine row). Winegrower barycenters were projected as illustrative quantitative variables (COP in circles; DIN in triangles; NEG in squares; and VAL in diamonds). Harvest practices are in grey, underlined and italic.

Table 1 .

 1 Selected practices used to perform textual network analysis by a social expert (trade network information in green and technical network information in blue).

	VAL	34.5 ha middle in MU; 13.1 ha middle in	SAN; 26 ha = large in TOU	Mixed	No		Direct sales, exportation, merchants		Individual	The winegrower and his family		High	44	High or low	Yes	Yes		Yes	Yes	Yes		50%	yes		Selling price	Children inheriting
	NEG	30.6 ha = middle in MU;	16.5 ha = small in TOU	Mixed	No		Merchants (80%) & direct	sales	Individual	The winegrower		Low	50	Low	No	No		No	Trader	No		No				Children inheriting
	DIN	45 ha = large in MU;	16.5 ha = small in TOU	Vineyard only	No		Merchants (80%) & direct	sales	Individual	Employees		High	46	Medium	Yes	Yes		No	No one	Yes		No			Selling price
	Structural elements Distinguishing variables COP	Farm Farm size (ha) 6.5 ha small in SAN; 18 ha =	middle in TOU	Cropping system Mixed	Selling & marketing Member of a co-operative Yes	network	Selling network 100% co-operative in	Touraine	Marketing network Co-operative grouped	Workers Workers in the farm The winegrower and his	family	Money invested in labour Low	Farmer's age (mean) 55	Education level High, medium, or low	Viticulture & enology Money invested in processes No	Subscription to technical journals Yes	and networks	Using extension services network No	Connected to advisors network Co-operative technician	Information/ Affiliation with business network 50%	communication yes	Public relations Affiliation with private network No	(e.g. football, school)	Owner concerned (open answer) Environmental legislation	Soil erosion

Table 2 .

 2 Selected practices used to perform DFA or FAC of the 59 fields studied in 2010 in Loire Valley (France).

			Name of the
	Statistical use	TMR position	practices	Description of practices
	DFA (quantitative	Vineyard training	YEAR	Year of planting of the vineyard
	practices)	system	TRELLIS	Number of wires (1, 2 or 3) in the trellis system used to
				support the arched vine shoots
			DENS	Planting density of the vineyard (plant/ha)
		Annual viticulture	W_PRUN_time	Winter pruning time between October and March
		practices	BUD_nb	Number of buds left after winter pruning
			TOPPING	Height of pruning/topping at the top of the vine (m)
			TOPPING_nb	Number of prunings/toppings at the top of the vine
			HARVEST	Number of days between the harvest date of the plot and
				the harvest date of the other white wine plots harvested
				on the farm (days)
	FAC (qualitative	Vineyard training	Variety	Variety of grape planted in the field. MU: Melon de
	practices)	system		Bourgogne. Sancerre and Touraine: Sauvignon blanc.
		Annual viticulture	W_PRUN_TK	Winter pruning technique (one cane Single Guyot; two
		practices		canes Double Guyot)
			interROW	Cover crop of the soil between vine rows (Fgrass: full-row
				grass cover, Hgrass: half-row grass cover, CHEM: chemical
				weeding, TILL: soil tillage)
			underROW	Cover crop of the soil under the vine row (CHEM: chemical
				weeding, TILL: soil tillage)
			CUTBUD	Cutting buds at the bottom of the vine (yes/no)
			DISBUD	Disbudding (yes/no)
			THIN_clust	Leaf thinning (yes/no)
			THIN_LEAF	Cutting some grapes to control yield (yes/no)
			THIN_LEAFt	Vine physiological period of leaf thinning (no/
				cluster_closed/harvest)
			CRUSH	Crushing vine shoots after winter pruning to return organic
				matter to the soil (yes/no)
			BLEND	Blending grapes from the fields just after harvest and before
				fermentation

Table 3 .

 3 Summary of winegrower profiles according to technical and trade networks.

		NEG	COP	DIN	VAL
	Technical network	One	One	One	Several
	Trade/selling network	One	One	Several	Several
	Technical and trade network interconnections	The same	Different	Different	Different
	Likelihood of changing practices	+	++	+++	++++

Acknowledgements

This study was supported by the Angers-Nantes centre of INRAE. We thank the anonymous reviewers for their helpful critiques and suggestions. We are also grateful to Rui Catarino for reviewing the English sentences added following the review of our article, which supplemented the complete English review performed by the Taylor & Francis translation service at the time of initial submission and 'Editor du jour' for English-language editing.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Funding

This study was supported by the Angers-Nantes centre of INRAE.


Nicolas Guérin
email: nicolas.guerin@b-com.com

Malo Manini
email: malo.manini@b-com.com

Rodolphe Legouable
email: rodolphe.legouable@b-com.com

Cédric Gueguen
email: cedric.gueguen@irisa.fr

High System Capacity Pre-Scheduler for Multi-Cell Wireless Networks

Keywords: Wireless Network, Resource allocation, Pre-Scheduling, Multi-Cell, 5G

With the constant increase of throughput demands, maximizing the wireless network capacity has always been a crucial issue. Network densification becomes one of the adopted strategies to guarantee a high Quality of Experience (QoE). These new access points are included in the traditional 5G coverage scheme and offload the network in dense areas where the demand is high. A mobile may be located in the coverage area of several base stations which implies the need to extend traditional scheduling to a multicell context. This leads to the emergence of new issues concerning resource allocation strategies. When one user is suitable to receive resources from different cells, a well designed users' distribution is essential to limit unbalanced and overloaded cells. The main contribution of this paper is to propose a pre-scheduler called Multi-Cell Pre-Scheduler (MCPS) that adequately allocates user traffic between different access points, intervening before the scheduling application. Compatible with any type of simple cell existing schedulers, MCPS adapts schedulers to multi-cell context and improves their performance by delaying the system congestion and making the system more robust to unexpected traffic peaks. Performance evaluations show that the use of MCPS solution allows to increase the throughput capacity and energy efficiency while improving QoE fairness between users.

I. INTRODUCTION

With the emergence of new services (video streaming, web, IoT...) requiring more and more throughput and low latencies, guaranteeing a good QoE becomes really challenging. Indeed, network resources such as bandwidth are often scarce and need to be shared between users inside the same coverage area. In order to manage this traffic, network densification constitutes a major evolution in new network deployment. This lies in the multiplication of the number of access points with which users can establish a connection. In a 5G multicell context, we generally consider that micro cells (µgNB) are deployed in addition to the traditional 5G-based macro cell (gNB) [START_REF]2.1 scenarios, kpis, use cases and baseline system evaluation," E2E-aware Optimizations and advancements[END_REF]. These cells allow to locally unload the system in dense areas where the demand in data may be higher (shopping centers, stadiums, train stations). Access points are then divided into two categories whose characteristics are presented as follows:

• A macro cell access point provides a wide-area coverage due to their low frequencies band (<6GHz), facilitating a geographical continuous connection to users. Traditionally a macro cell base station can cover up to 10 km in rural areas and up to 1 km in dense urban area. • A micro cell access point is a low-power hotspot providing short range coverage area. Operating in high frequency bands (millimeter waves for 5G), these access points allow to deliver high data throughput thanks to large spectrum bandwidth. However these frequencies suffer from strong pathloss, shadowing and multipath fading significantly affecting the coverage area. In this context, the management of all these access points could be done by the macro cell access point. The Central Unit management (Figure 1) performs all the scheduling decisions and allocates resources to the users. This hierarchical management [START_REF] Bulakci | Ran moderation in 5g dynamic radio topology[END_REF] (also called "backhaul") optimizes the decisionmaking on how the network operates and allocates resources depending on user demands and channel quality.

In this new type of wireless network, a new problematic lies in the allocation process. Indeed, in a typical 5G multicell scenario (MIMO for instance), radio layer evolution allows a user to be located in the coverage area of several access points at the same time. This undeniably leads to the emergence of new allocation strategy issues. One of them lies in optimizing user traffic repartition between micro cell and macro cell access point. This depends on a lot of parameters such as users channel quality, services type and system load. Thereafter, the study proposes different allocation strategies when the system suffers from high traffic load and tends to be overload.

A well studied topic, coordinated multipoint (CoMP) transmissions, tackles the users repartition in multi-cells context, as seen in [START_REF] Chen | Resource allocation with CoMP transmission in ultra dense cloud-based LTE small cell networks[END_REF] and [START_REF] Liu | Load Aware Joint CoMP Clustering and Inter-Cell Resource Scheduling in Heterogeneous Ultra Dense Cellular Networks[END_REF]. However, our solution takes benefit from different frequency bands for each station type. The macro cell and the micro cell work at different frequency, eliminating the interference problem. Moreover, our pre-scheduler proposal is compatible with all allocation strategies (schedulers). Four of them are briefly summarize below.

This paper aims to present MCPS pre-scheduling solution and evaluate its performance impact when applied upstream these four schedulers. MCPS computes user experienced throughput with all the access points they can establish a connection. It assigns to each user a priority index. Depending on the micro cell maximal capacity, users with the higher priority index are assigned to the micro while the rest of the users are assign to the macro. This allows to assigned to the macro cell the users who are the most beneficial to it. By dynamically arrange users between the two access points, the user traffic repartition is optimized guaranteeing a high system capacity.

The paper is organized as follows. Section II describes related work concerning scheduling schemes and multi-cells algorithms. Section III provides a detailed description of the system under study and gives an important preliminary result attesting the use of a pre-scheduler. Section IV introduces

II. RELATED WORK

A. Scheduling schemes

All the following scheduling schemes differ in their Key Performance Indicators (KPI) requirements and so in the way they operate. For example, a scheduler may focus on throughput maximization, fairness between users or energy saving.

• Round Robin (RR) [START_REF] Minelli | Scheduling impact on the performance of relay-enhanced lte-a networks[END_REF] scheduler is probably one of the simplest algorithms to study and to implement. Its performance are widely known, which make this nonopportunistic scheduler a reference. 2 (blue curve).

• MaxSNR [START_REF] Yui Wong | Multiuser ofdm with adaptive subcarrier, bit, and power allocation[END_REF] is considered as a reference in wireless resource allocation. Taking advantage of variable channel conditions and multi-user diversity, this opportunistic scheduler aims to maximize the global throughput in the system. By allocating a given resource unit (RU) to the user having the best achievable throughput on it (i.e. the best channel condition), MaxSNR scheduler firstly allocates resources to the users who is less affected by pathloss, shadowing and multipath fading. However, close users have more chance to have access to the medium. It results in a fairness deficiency between close and far users whose achievable throughput (affected by pathloss) does not allow them to have the same priority in the MaxSNR allocation process. An allocation strategy example is given on Figure 2 (red curve). • Proportional Fair (PF) [START_REF] Kim | A proportional fair scheduling for multicarrier transmission systems[END_REF][START_REF] Ge | Cdf-based scheduling algorithm for proportional throughput fairness[END_REF] scheduling provides similar benefit than MaxSNR concerning system capacity maximization but it ensures more fairness between all users. User priority depends on instantaneous SNR but is also inversely proportional to the user mean obtained throughput. Far users with lower mean throughput obtain a higher priority which allows them to get more chance to have access to the medium. So PF equally considers all users independently of their channel conditions and benefits from a better multi-user diversity. So it admits a slight system capacity gain compared to MaxSNR. • Opportunistic Energy Aware (OEA) scheduler [START_REF] Gueguen | Opportunistic Energy Aware Scheduler for Wireless Networks[END_REF][START_REF] Gueguen | Dynamic tradeoff between energy and throughput in wireless 5g networks[END_REF] objective is to minimize energy consumption exploiting active-sleep mode and channel condition together. Like previous opportunistic schemes, this will allow to reach good system capacity. OEA determines the best transmission opportunities and finds the most profitable resource mapping in terms of number of transmitted bits per Watt. This scheduler keeps a bad fairness between users.

B. Multi-cells algorithms

Using the same frequency for all access points leads to the need of advanced interference management. Coordinated multipoint (CoMP) [START_REF] Chen | Resource allocation with CoMP transmission in ultra dense cloud-based LTE small cell networks[END_REF][START_REF] Liu | Load Aware Joint CoMP Clustering and Inter-Cell Resource Scheduling in Heterogeneous Ultra Dense Cellular Networks[END_REF][START_REF] Kim | Call Blocking Probability and Effective Throughput for Call Admission Control of CoMP Joint Transmission[END_REF][START_REF] Shami | User-centric JT-CoMP clustering in a 5G cell-less architecture[END_REF] is a topic well covered in the literature and aims at covering this problem. The objective is to provide a fair throughput regarding the position of the users, where edge users may suffer from interference of nearby cells. In our context, edge users are covered by a micro-cell and does not suffer from interference thanks to the use of different frequencies. On the other hand, Ultra Dense small cell Network (UDN) reduces the opportunistic feature of schedulers due to very short Inter-Site Distance (ISD) as indicated in [START_REF] Lopez-Perez | Towards 1 Gbps/UE in Cellular Systems: Understanding Ultra-Dense Small Cell Deployments[END_REF]. One advantage of our solution is to be compatible with different types of schedulers without changing their resource allocation policy but by managing previously to the transmission the schedule between micro and macro cells.

III. SYSTEM DESCRIPTION

A. Deployment scenario

In the studied deployment scenarii, the entire bandwidth is split into several frequency slots also called "subcarriers". We assume that each subcarrier has independent channel state value [START_REF] Andrews | Providing quality of service over a shared wireless link[END_REF]. Depending on the numerology used, time slot duration and subcarrier sizes of this granulated time/frequency grid may differ. The packets issued from the backhaul network are buffered in the access point which schedules the downlink transmission. The radio resources are further divided in the time domain in frames. Each frame is itself divided in Time Slots (TS) of constant duration. The Time slot duration is an integer multiple of the OFDM symbol duration. The number of subcarriers is chosen so that the width of each sub-frequency band is less than the coherence bandwidth of the channel. Moreover, the frame duration is fixed to a value much smaller than the coherence time of the channel. We consider two access points with available Resource Units (RUs). Each Resource Unit is characterized by a {frequency ; time} pair and represents an unbreakable element which is entirely made available to a user (Figure 3). All results are obtained using a discrete events simulator written in C++.

We assume a simple deployment scenario by considering a unique micro cell access point whose coverage area (represented in red on figures) is fully included in the macro cell one (represented in blue). We also assume that a user achievable throughput is linked to the power he receives and is limited due to his distance from the access point and multipath fading [START_REF] Parsons | The Mobile Radio Propagation Channel[END_REF]. The potential number of bits that can be transmitted on a given RU is given by the following equation 1 corresponding to the upper bound:

q k,n ≤     log 2   1 + 3P × T s × 1 d k 3.5 × α 2 k,n 2N 0 erf c -1 BER target,k 2 2         (1)
where P is the transmit power, N 0 is the noise spectral density, T s the OFDM symbol duration, d k the distance between the user k and the access point, α k,n represents the flat fading experienced by user k on subcarrier n. In the following, α k,n is Rayleigh distributed with an expectation equal to unity. The potential number of bit that a user can transmit on a RU will fluctuate around this value over the time. In the rest of the paper we consider that all users get the same BER target of 10 -3 . We assume each base station has a full knowledge of the channel for each user.

We also consider an admission control that denies the connection between a user and an access point if its received power and therefore its achievable throughput is below a given limit. This situation may happen when a user is far from a base station and need too much RUs to guarantee a sufficient quality of experience. Each of the two access points made available its own RUs that are modulated on two different frequency bands which do not interfere together. In the rest of the paper we also consider that users covered by the micro cell are also covered by the macro cell.

B. Preliminary result

The main objective of this subsection is to establish an important preliminary result necessary to fix the implementation of the pre-scheduler. In a typical 5G multicell scenario in which a user may be in the coverage area of several base stations, the main problematic is to find the optimal way to share the traffic between all access points. This lies in giving a priority order to each access point in the allocation process. This priority order allows to always select the best access point to establish a connection. Three different states can describe this multicell scenario: • State 1: The system is not overloaded, both access points can provide RUs. • State 2: The system is in the process of congestion. One of the two access points can't provide RUs anymore. Two different subcases are presented: (a) The micro cell is congested, the macro cell is not. (b) The macro cell is congested, the micro cell is not.

• State 3: The system is totally congested, no more access points can provide RUs.

As the load in a system increases, it goes from State 1 to State 2 (2(a) or (2(b)) and finally to State 3. Two allocation strategies acting during State 1 are studied. These two strategies are called "MiLP" and "MaLP" for respectively "Micro Load Priority" and "Macro Load Priority". MiLP solution prioritizes State 2(a) by allocating users to the micro in priority (if they are in its coverage area) while MaLP solution favours State 2(b) by allocating users to the macro in priority. In other words, with MiLP strategy, traditional schedulers (RR, MaxSNR,...) firstly allocate users with the micro cell access point. When this one overloads, macro cell deals with users which couldn't be assigned to the micro (due to overloading) but also with users outside micro coverage. With MaLP solution, traditional schedulers firstly allocate users with the macro cell access and then to micro cell when the first one overloads. An important result lies in finding the best allocation strategy allowing the system to be more robust to the system congestion keeping user QoE at its highest level.

In order to evaluate the performance of the two allocation strategies we decide to simulate a simple user deployment scenario. We consider a random user deployment in which some users are inside the micro cell coverage and some users outside. Simulation results were averaged over 100 different user position patterns. One of them is presented on Figure 4.

We also consider a variable traffic. Each user generates a traffic following an exponential distribution which fluctuates around an expectancy value common to all users. During a simulation, system load increases by adding users 2 by 2. Base stations characteristics (frequency, power) are given in table I. 5 gives the performance results in terms of global system mean packet delay (5(a)) and QoE (5(b)) for the four SoTA schedulers. Delay indicator represents the time duration between a packet creation in the backhaul network and its reception by the user equipment. A packet is considered out of delay if the time constraint imposed by the user service (streaming video, web,...) is not respected. QoE measures the user experience and is linked to the number of packets out of delay. At the end of each communication, we determine the ratio between the number of packets out of delay and the total number of packets sent to the user.

Mean delay result shows that by applying MiLP strategy, the mean delay experienced by users is more robust to the system load increase. For example, with MaxSNR scheduler, about 35 users are needed to reach a system delay of 20ms with MaLP solution and up to 56 for MiLP strategy (Figure 5(a)). In other words MiLP deployment allows to accept 60% more users while guaranteeing a QoE beyond 90% (Figure 5(b)).

By keeping the MiLP solution, the system can improve the probabilities that all users are in the radio coverage to an access point which is not overloaded. Indeed, if all microcell users are firstly assigned to the macro cell leading to its congestion (MaLP solution), a user outside the micro cell coverage can no longer receive RU from it (State 2b). It's a sub-optimal solution knowing that several users are inside micro coverage (which is not overloaded) but assigned to the macro cell. These same users should be assigned to the micro cell releasing the macro bandwidth to users that are outside the micro coverage. This result constitutes an important preliminary result before applying the MCPS prescheduler (introduced in the next section IV). In the rest of the document, MCPS pre-scheduler is implemented by considering MiLP strategy which allows to be more robust to traffic congestion.

IV. MULTI-CELL PRE-SCHEDULER (MCPS)

MCPS pre-scheduler objective is to avoid the system congestion by solving an allocation optimization problem when the micro cell tends to be overloaded. This situation may occur when too many users are located in the micro cell coverage area causing a high traffic peak demand leading to its congestion. In this case some traffic (i.e. some users) need to be moved to the macro cell. MCPS pre-scheduler allows to adequately choose the users located in the micro cell coverage area which are assigned to the macro cell in the allocation process since some users are more profitable to be assigned to the macro cell. MCPS allows to effectively choose these users by allocating to each one a priority index. The higher a user index is, the higher its probability to be allocated by the micro cell is. The main objective is to keep in the micro cell the more profitable users and then exclude the more profitable users for the macro (considering throughput). MCPS pre-scheduler, whose main objective is to increase system capacity, is based on users experienced throughput calculation and is described in subsection IV-A.

A. Algorithm description

MCPS pre-scheduler is based on the calculation of the users experienced throughput with the access points they can establish a connection. To each mobile k, a M CP S k is associated whose value is updated at each allocation decision instant. M CP S k is calculated following equation [START_REF] Bulakci | Ran moderation in 5g dynamic radio topology[END_REF].

M CP S k = D micro k -D macro k , (2)
where D micro k and D macro k represent the mean user k experienced throughput on a RU respectively with the micro cell and the macro cell access points. So M CP S k values is mainly linked to users location (pathloss) and the implemented scheduler.

All users are then classified according to their M CP S value with an index between 1 and k max (where k max is the number of users in the micro base station coverage and then concerned by M CP S). The user with higher M CP S value gets index 1 and the user with lower M CP S value gets index k max . A positive (respectively negative) M CP S k value reflects the interest of connecting user k to the micro cell (respectively macro cell) access point with which its experienced throughput is higher. Unfortunately, because of micro cell limited capacity, all users with a positive M CP S value could not necessarily be allocated by the micro cell. Users with higher M CP S value get higher index priority. The second step is then to determine the maximal number of users the micro cell can manage. This limit is calculated as follows:

Limit [%] = 100 × D micro × N micro RB kmax k=1 BO k (t) , (3)
where:

• D micro [bit/RU] is the mean spectral efficiency of the micro cell and is measured from the previous allocations.

• N micro RB [RU]
is the number of resource blocks made available by the micro cell access point and is constant.

• kmax k=1 BO k (t)
[bit] corresponds to the Buffer Occupancy of user k at time t. The BO if the number of bit that has to be transmitted to a given user. k max is the total number of users in the coverage area of the micro cell base station. This limit corresponds to the ratio between the number of RUs the micro cell can provide and the total traffic (bits) In other words, this limit value gives the ratio of RUs the micro can handle until its congestion which corresponds to a bandwidth usage ratio equal to 100%. According to this limit ratio of users, the micro cell manages the percentage of the k max users that have the higher M CP S value letting the macro cell the other users with lower M CP S. By doing so, the system allows the macro cell to allocate the users which are the most beneficial to it. So the system dynamically manages all the users by adequately arrange them between the two access points in order to increase the global system capacity. Once the MCPS has made this pre-allocation by assigning each user to an access point, traditional schedulers (RR, MaxSNR,...) take over their own assigned users in both cells. This prescheduling process is summarized by the Figure 7 with its flow chart.

B. MCPS example

In order to well understand MCPS operation, this subsection is dedicated to give an example presented on Figure 8(a). This example considers a group of 10 users all covered by both micro and macro cell access points. They are therefore concerned by MCPS pre-scheduling. In this implementation, M CP S value (fourth column) is calculated from experienced throughput respectively in micro (second column) and in macro (third column). In this example, micro cell that has shows the importance to sort users in order to maximize the mean system throughput. The upper table represents the previous situation 8(a) for which users are classified (MCPS applied). In this case system throughput reachs 21.8 MBit/sec. Bottom table of Figure 8(a) considers that MCPS is not applied and therefore users are not classified. So the excess traffic is randomly chosen. In this case, the mean global throughput reaches 19.5 MBit/sec. It shows that MCPS users sort efficiently assigns a given user to the access point allowing him to reach a good throughput.

Note that the MCPS limit (i.e. the number of users considered by the micro cell) is closely linked to the generated traffic at the decision-making instant. Each user produces its own variable traffic and so get a weighting that depends on what he generates. At a given time t, some users may generate high traffic peak while some others very stable one. This impacts the MCPS limit value and so the number of users assigned to the micro cell. Figure 8(c) gives an example of two scenarii impacted by different traffic peaks. Scenario A is a visualization of the table issued from Figure 8(a) In scenario B, user 7 with a high MCPS value produces a high traffic peak that occupy a large portion of the microcell bandwidth. This impacts the rest of the micro cell in bandwidth availability. The result is that the number of users assigned to the micro cell may decrease. This is the case in Scenario B for which only 5 users are assigned to the micro cell. The traffic generated by the k max users being totally independent to the micro cell capacity, micro cell limit is rarely fixed between two distinct users. This is the case in Scenario A with user 10 and scenario B with user 9. Their generated traffic allows to achieve 100 + % of the total micro cell bandwidth. MCPS considers that, if the micro cell bandwidth usage ratio is less than 100%, it assigned to it one more user by slightly exceed its maximal capacity. In that case, the micro cell capacity is fully exploited allowing to achieve better performance due to higher multi-user diversity in the micro cell. Then, the % overflowed traffic is transferred to the macro cell.

C. Discussion concerning MCPS value

As explained in previous subsection IV-A, MCPS is linked to the user throughput and so closely depends on their channel condition. Experienced throughput is also linked to the implemented scheduler and on the way it favors users compared to some others. According to the channel conditions, path loss affects the user achievable throughput. If we consider a Gaussian channel without fading attenuation, α k,n = 1 in equation ( 1) so M CP S value can be written as:

M CP S = log 2     1 + η P micro d 3.5 kmicro 1 + η P macro d 3.5 kmacro     , ( 4
)
where η is a constant which is common to all users and equal to

3 × T s 2N0 erf c -1 BER target,k 2
2 .

In this case, M CP S mainly depends on base station radiating power and user location. Figure 9 shows that M CP S value can take value from 12 (red part close to the micro cell base station) to -6 (blue part close to the macro cell base station). Circles of same M CP S values are notable around micro cell base station. This phenomenon is closely linked to the distance as shown in equation 1. Theorically locations where M CP S = 0 represent the neutral user locations where there is no advantage to assigned the user to the micro or to the macro. We can easily show that M CP S = 0 under the condition that P micro × d 3.5 kmacro = P macro × d 3.5 kmicro . This shows that assigning a user to an access point depends on its

MCPS value in Gaussian channel

x coordinates (km) y coordinates (km) Figure 9: MCPS location, on base stations power and also on relative position of the two access points. Some users deployments bring more benefits to MCPS pre-scheduler than some others. Indeed, given the presence of only two base stations, it is clear that MCPS value admits a symmetry behaviour along the axis of the two access points.

V. PERFORMANCE EVALUATION

A. Simulation setup and studied KPIs

This section aims to study the performance of the four SoTA schedulers, introduced in first section, when they are applied or not downstream to the M CP S pre-scheduler. CoMP solutions are not considered in this part, due to architecture difference. All simulation parameters are described in section III-A and summarized in table II. Table II: Base stations parameters

In the two following deployment scenarios, six different Key Performance Indicators (KPI) are studied to judge the efficiency of M CP S:

• The bandwidth usage ratio corresponds to the ratio between the number of RUs used and the total number of RUs made available by the access point. A ratio of 100 % underlines a congested system. • The spectral efficiency is the quantity of transmitted information on a RU. Depending on the scheduler and its ability to use the Channel State Information (CSI) knowledge, the mean spectral efficiency may differ. • The user capacity limit with QoE guarantee indicates the number of users the system can manage while guaranteeing a global system mean delay less than the application delay threshold.

• The system energy efficiency is a key indicator allowing to know how much a scheduler may allow a user to save energy which is essential regarding the need to improve the device battery lifetime and to reduce the carbon impact. • The throughput represents the total number of distributed to the users. It grows according to the number of connected users in the system and stabilized at the maximum system capacity. • The QoE fairness index indicates the system ability to ensure fairness regarding users requirements. This one is calculated according the following Raj Jain method [START_REF] Shi | Fairness in wireless networks:issues, measures and challenges[END_REF]:

J (user 1 , user 2 , ..., user n ) = n k=0 x k 2 n n k=0 (x k ) 2 , (5)
where x k represents the evaluated parameter, x k = QOE k in our case. As in the section III-B, QoE is determined with the mean ratio between the number of packets out of delay and the total number of packets. In the next two subsections V-B and V-C, two users deployment scenarii and their respective obtained performance Performance results are obtained using discrete event simulations. The six KPIs are studied according the load in the system and more precisely following the number of users connected to the system. Application delay threshold is fixed to 100 ms in the following. For each discrete point, simulation results are averaged over a communication duration of 1000 Frames.

B. Scenario 1: Proof of concept deployment

In order to clearly underline the MCPS pre-scheduler behavior, we first study the simple users deployment scenario presented on Figure 10. This lies in considering two groups of users located at the same distance from the micro cell base station (100m). One group is far (400m) and the other one close (200m) from the macro cell base station. A random position variation is assigned to each user in its group. These horizontal and vertical variations ∆ x and ∆ y follow a uniform distribution on [-20m,20m]. This allows users to be assigned to uncorrelated fading allowing to have a better multi-user diversity. So opportunistic schedulers and MCPS can operate by always having a wide range of choices. We also consider that each user generates a realistic variable traffic [START_REF] Baey | Modeling mpeg4 video traffic based on a customization of the dbmap[END_REF][START_REF] Tamimi | Modeling and prediction of high defninition video traffic: A real-world case study[END_REF].

First results are exposed on Figure 12 and present the performance of the studied deployment scenario for the four SoTA schedulers when they are applied (full line), or not (dashed line) downstream to our MCPS pre-scheduling solution. In order to well understand the system behaviour facing the increase of the traffic load, bandwidth usage ratio graph (Figure 12(a)) gives the result both for micro and macro cells. We consider that the full system (micro+macro) is totally congested when both cells admit a bandwidth usage ratio of 100%. Before starting any technical explanation, it is important to remember that MiLP allocation strategy has been implemented: users are firstly assigned to micro cell, if possible, before the macro cell.

Concerning the bandwidth usage ratio, results (Figure 12(a)) highlights that the global system resists to a higher load (higher number of users) when the MCPS pre-scheduler is applied. With MCPS, schedulers are able to absorb more traffic and are more robust to unexpected traffic peaks. For example MCPS allows Round Robin to accept 24 users in the system without MCPS and 32 with MCPS. This lead to accept 33.3% more users with MCPS. In the same way MCPS allows the system to deal with 17.4% more users with MaxSNR. Figure 12(b) is linked to this result and shows the number of users considered by schedulers in the micro cell when applied with MCPS (and in dashed line without MCPS). For very low traffic loads the total numbers of users considered by the micro cell is equal to the total number of users in the system. When traffic load increase, the MCPS attributes the most beneficial users respectively to the micro and macro cell. Maintaining the maximum number of user manageable by the micro, curve flat appears when micro cell reach maximum capacity and macro begins to manage the overloaded traffic. The asymptotic behavior testifies of the maximal number of users the micro cell can manage depending of the scheduler.

Bandwidth usage behaviour is closely linked to the spectral efficiency one. By applying MCPS pre-scheduler, a significant decrease of the spectral efficiency for opportunistic schedulers (MaxSNR, PF, OEA) is notable in the micro cell (Figure 12(c)). This loss in the micro cell is mainly due to this type of scheduler that need a high multi-users diversity in order to always choose the best user allowing to offer the highest system capacity. By unloading some users to the macro cell (case (b) on Figure 11), MCPS pre-scheduler restrains the optimal operation of schedulers when selecting the best user. This degradation is perceptible from a given number of users ( 14 for OEA, 16 MaxSNR and PF), that underlines when MCPS begins to operates. Indeed, below this number of users, congestion probabilities are negligible and MCPS is inactive. Round Robin which is a non opportunistic scheduler admits a slight gain in the micro by applying MCPS. Round Robin does not take into account channel conditions and user diversity. By applying MCPS with Round Robin, the loss of multi-user diversity has therefore no consequence on spectral efficiency. The impact of MCPS is even positive on spectral efficiency. Indeed, according to Figure 11(b) when the micro cell saturates, close users from the macro access point (and the farthest to the micro) are the first assigned to the macro (MCPS behaviour). Users staying in the micro cell admit a higher mean achievable throughput due to better path loss, spectral efficiency for RR increases in the micro cell.

However, this micro cell spectral efficiency loss is largely compensated by a gain in the macro cell (Figure 12(d)). As soon as the load exceeds a given threshold specific to each scheduler, MCPS detects the micro cell congestion risk and adequately share the most beneficial users between micro and macro cell macro cell. This threshold is reached when the micro cell has managed the number of users corresponding to its maximal capacity (Figure 12(b)). Macro cell spectral efficiency behaviour can be divided into four parts:

• For very low loads (<10 users), system is widely underloaded and macro spectral efficiency does not exist since all users are allocated by the micro cell (Figure11(a)). • When the micro cell overloads, first users that are assigned to the macro cell are the one with good throughput (close users -low MCPS). Spectral efficiency in the macro increases (Figure11(b)). The spectral efficiency gain with MCPS is linked to a better management of users. Macro cell receives the more profitable users to it. • When the micro reached its maximal users capacity, the macro cell has to manage far users, which are less advantageous in terms of throughput. This slowdown is noticeable on Figure 12(d) from 20 to 32 users for RR, 30 to 42 users for OEA, and from 40 to 50 users for MaxSNR and PF. The curve with MCPS solution tends to get closer from the curve without MCPS solution (compared to Figure11(d)). But the increase still persists due to a better users-cells repartition. • For very high loads this gap seems to be maintained or even increased. With MCPS, opportunistic schedulers resume their normal operation reaching good performance due to the supplementary of multi-users diversity (Figure11(e)). A significant throughput gain with MCPS Figure 12(e) aggregates previous results and shows the global reachable throughput by each solution in the whole system. It underlines that all schedulers increase their system capacity limit with MCPS.

In addition to increase the overall capacity of the system (bandwidth usage ratio and spectral efficiency previously studied), some additional KPI are improved by applying MCPS pre-scheduler (fairness, energy saving). For example Figure 12(f) shows that the global system energy consumption are improved with MCPS. Indeed, MCPS effectively assigned users with the access point they can achieve a good throughput and therefore they are able to finish their connection quicker. Users are then in an active mode for a shorter time and then consume less.

Figure 12(h) also shows the MCPS ability to keep QoE fairness between users as the load increases. Considering MaxSNR scheduler at 40 users in the system, the fairness index reaches 60% and 72% respectively without and with MCPS. This gain is perceptible for the four schedulers. When users are assigned to base station, they share the access point with other users whose throughput difference is not significant. More users obtain a good QoE satisfying their application requirements (12(g)).

These first simulation results constitute a proof of concept testifying the performance bring by MCPS pre-scheduling. A more realistic deployment is studied in the next subsection V-C.

C. Scenario 2: Realistic user deployment

In order to confirm the previous performance results bring by MCPS pre-scheduling, we simulate a more realistic users deployment scenario. This deployment is inspired by 3GPP deployment specifications [START_REF]3rd Generation Partnership Project, 5G; Service requirements for next generation new services and markets, 3GPP TS 22.261[END_REF] and is represented on Figure 14. We consider an ultra-dense deployment scenario for which a user group is distributed around an access point. This access point allows to unload the macro cell in this dense area where the demand is high. Typically, we can consider a stadium deployment (Football stadium, concert arena). A stadium is typically a place where the traffic is high (video uploading,...) and where a micro cell hotspot deployment makes sense. This deployment represents a concrete case for which MCPS pre-scheduler can bring a benefit compared to a traditional solution. This realistic deployment considers a hotspot located in the center of the stadium (Figure 14(a)) providing high data rate broadband coverage to the users located around it. We evaluate the MCPS performance by studying the same KPIs than previous users deployment. In order to get reliable KPIs, simulation results were average over 100 different users deployment patterns. The traffic generated by users and base stations characteristics, remains the same as previous studies. Results are presented on Figure 13.

According to Figure 13 this scenario confirms MCPS prescheduler performance. Results concerning bandwidth usage ratio show that the system can allocate 9.6% more users for MaxSNR and 16% more for RR when applying MCPS. We can also note the same spectral efficiency trends, meaning a loss in the micro cell compensated by a gain in the macro. User QoE (Figure 13(g)) guarantees and QoE fairness (13(h)) are also improved.

Figure 13 (b) shows that the numbers of users considered by the micro cell is always lower than the total number of users in the system. This shows that some users are located outside the micro cell base station coverage who necessarily have to be allocated by the macro cell access point. We also note the same asymptotic behavior testifying the micro cell maximal capacity and so the number of users the micro cell can manage. The two scenarios previously studied show the benefit brings by MCPS pre-scheduler in a multicell scenario.

VI. CONCLUSION

In the literature many research efforts have been done focusing on scheduling optimization in a simple deployment scenario with one cell. However these scheduling techniques are often not optimized to a multicell scenario. This paper proposed a new pre-scheduling solution called "MCPS" for multicell wireless networks. Taking into account users achievable throughput, MCPS effectively share the traffic load between the different access points increasing the global system capacity. Performance results have shown the benefits bring by this new approach allowing the system to be more robust to the congestion and traffic peaks. Future work will focus on improving the MCPS solution towards a service-oriented pre-scheduler taking into account users service parameters allowing to better adapt the allocation process to users needs (generated traffics, service time constraints,...).

Figure 1 :

 1 Figure 1: Typical 5G multicell scenario

Figure 2 :

 2 Figure 2: RR ( ) and MaxSNR ( ) allocation strategies

Figure 3 :

 3 Figure 3: 5G frame structure in TDD mode

Figure 4 :

 4 Figure 4: Random user position deployment

  Quality of Experience (QoE) indicator

Figure 5 :Figure 6 :

 56 Figure 5: MiLP/MaLP performance results comparison

Figure 7 :

 7 Figure 7: MCPS pre-scheduler algorithm flow chart

( c )

 c Figure 8: MCPS example

Figure 10 : 10 (

 1010 Figure 10: Grouped position deployment

::

  Users allocated by macro cell I Users allocated by micro cell Considering a micro cell maximal capacity of 17 users

Figure 11 :

 11 Figure 11: MCPS behaviour with grouped users deployment

Figure 12 :

 12 Figure 12: Performance results for scenario 1

Figure 13 :

 13 Figure 13: Performance results for scenario 2

Figure 14 :

 14 Figure 14: Realistic scenario representation

Table I :

 I Base stations characteristicsFigure

	Micro cell BS	Macro cell BS
	Frequency	Power	Frequency	Power
	26.6 GHz	33dBm	4GHz	44 dBm