Mohamad Younes

Mihai Telescu

Stéphane Azou

Pascal Morel

Noël Tanguy

A very low-complexity filter lookup-table design with non-uniform spacing for SOA linearization

Nonlinearities introduced by power amplifiers in communication systems have been a subject of interest for industry and academia for a long time. On one hand the search for increased range intrinsically incites operators to boost input power. On the other hand this leads to stronger nonlinear phenomena in amplifiers which in turn increase the bit error rate. The situation led to the emergence of digital predistortion algorithms that compensate the power-amplifier's nonlinearities and improve the overall performance of the link. A rich corpus of literature exists on this topic but it mainly addresses wireless microwave networks. The case of digital predistortion specifically tailored for optical communications is much less explored. This paper focuses on predistortion via filter-lookup-tables and tackles the issue of non-uniform spacing. The work is mainly aimed at CO-OFDM systems including low-cost semiconductor optical amplifiers.

Introduction

For decades, telecommunications have been a highly competitive field of technology with actors racing for high data throughput, service versatility and flexibility and increased mobility. Orthogonal frequency-division multiplexing (OFDM) has been codified in several extant standards (ADSL, WiMAX, Wi-Fi, LTE, DVB, ...) and constitutes one of the solutions that many industry actors favor. This paper focuses on the case of Coherent Optical OFDM (CO-OFDM) and particularly on a scenario including semiconductor optical amplifiers (SOA). This scenario has been investigated in recent literature [START_REF] Schmuck | Demonstration of an SOA-assisted open metro-access infrastructure for heterogeneous services[END_REF][START_REF] Koenig | Amplification of advanced modulation formats with a semiconductor optical amplifier cascade[END_REF] and is appealing because of the SOA's large optical bandwidth, integration capability and reduced cost. However, SOAs are also known to exhibit nonlinear effects, resulting from their fast gain dynamics. In fact, if an SOA is driven by a constant bias current with a non-constant envelope input signal at its input, a large decrease of the carrier density may occur within its active region. This is explained by the fact that a higher power signal interacts with a larger number of excited electrons in the conduction band, thus resulting in depletion of carrier density and SOA gain [START_REF] Connelly | Semiconductor Optical Amplifiers[END_REF]. This translates in a variety of nonlinearities including Self-Gain and Self-Phase Modulations (SGM, SPM), Cross-Gain and Cross-Phase Modulations (XGM, XPM), Four-Wave Mixing (FWM), amplitude-phase coupling and gain compression. It should be noted that nonlinear effects are associated with all power amplifiers (PA) and several linearization techniques have been proposed in order to circumvent or at least alleviate this problem [START_REF] Kennington | High Linearity RF Ampliïň Ąers Design[END_REF]. Baseband digital predistortion (DPD) is a well-established, efficient solution in the wireless communication community [START_REF] Ghannouchi | Behavioral modeling and predistortion of wideband wireless transmitters[END_REF] and has also been considered, more recently, in optical communications [START_REF] Barros | Optical modulator optimization for orthogonal frequency-division multiplexing[END_REF][START_REF] Bao | Nonlinearity mitigation for high-speed optical OFDM transmitters using digital pre-distortion[END_REF]. The general idea is that since the PA introduces a nonlinear transformation of the data signal, one may design a specific scheme that introduces the inverse transformation and compensates this undesirable sideeffect. Predistortion schemes may feature or, on the contrary, lack memory. Memoryless systems are popular because of their simplicity [START_REF] Ghannouchi | Behavioral modeling and predistortion of wideband wireless transmitters[END_REF][START_REF] Faulkner | Adaptive linearization using predistortion. Experimental results[END_REF][START_REF] Chen | Joint Polynomial and Look-Up-Table Predistortion Power Amplifier Linearization[END_REF][START_REF] Liang | A Quadratic-Interpolated LUT-Based Digital Predistortion Technique for Cellular Power Amplifiers[END_REF][START_REF] Cavers | Amplifier linearization using a digital predistorter with fast adaptation and low memory requirements[END_REF], one of the most common solutions being simple lookup-tables mapping complex gain factors [START_REF] Liang | A Quadratic-Interpolated LUT-Based Digital Predistortion Technique for Cellular Power Amplifiers[END_REF][START_REF] Cavers | Amplifier linearization using a digital predistorter with fast adaptation and low memory requirements[END_REF][START_REF] Gersho | Principles of quantization[END_REF][START_REF] Hassani | A flexible method of LUT indexing in digital predistortion linearization of RF power amplifiers[END_REF][START_REF] Boumaiza | Adaptive Digital/RF predistortion using a nonuniform LUT indexing function with built-in dependence on the amplifier nonlinearity[END_REF][START_REF] Ba | Optimal spacing of a linearly interpolated complex-gain LUT predistorter[END_REF][START_REF] Bouajina | A robust non-Uniform LUT indexing method in digital predistortion linearization of RF power amplifiers[END_REF][START_REF] Selvadurai | A robust non-uniform indexation of a quadratically interpolated LUT predistorter for RF power amplifiers[END_REF]. In their most basic implementation lookup-tables use uniformly spaced entries. Pioneering work by Cavers [START_REF] Cavers | Amplifier linearization using a digital predistorter with fast adaptation and low memory requirements[END_REF] showed that uniformly spaced amplitude mapping usually leads to better performance than power mapping. More recent literature focused on non-uniform spacing schemes and proved that performance may be further enhanced by such approaches without increasing system complexity. In [START_REF] Cavers | Amplifier linearization using a digital predistorter with fast adaptation and low memory requirements[END_REF][START_REF] Gersho | Principles of quantization[END_REF][START_REF] Hassani | A flexible method of LUT indexing in digital predistortion linearization of RF power amplifiers[END_REF][START_REF] Boumaiza | Adaptive Digital/RF predistortion using a nonuniform LUT indexing function with built-in dependence on the amplifier nonlinearity[END_REF][START_REF] Ba | Optimal spacing of a linearly interpolated complex-gain LUT predistorter[END_REF][START_REF] Bouajina | A robust non-Uniform LUT indexing method in digital predistortion linearization of RF power amplifiers[END_REF][START_REF] Selvadurai | A robust non-uniform indexation of a quadratically interpolated LUT predistorter for RF power amplifiers[END_REF] input amplitude is fed into a companding function leading to the computation of improved LUT indexes reducing inter-modulation distortions. An analytical solution for optimal non-uniform spacing was proposed in [START_REF] Cavers | Amplifier linearization using a digital predistorter with fast adaptation and low memory requirements[END_REF]. It advocates the use of entries spaced according to an intricate law depending on the PA's gain function and on the probability density function (PDF) of the input signal. Subsequent work in [START_REF] Bouajina | A robust non-Uniform LUT indexing method in digital predistortion linearization of RF power amplifiers[END_REF] led to a more robust solution where spacing only depends on the PA's gain function. Papers [START_REF] Ba | Optimal spacing of a linearly interpolated complex-gain LUT predistorter[END_REF], [START_REF] Selvadurai | A robust non-uniform indexation of a quadratically interpolated LUT predistorter for RF power amplifiers[END_REF], [START_REF] Feng | Improved baseband digital predistortion for linearising PAs with nonlinear memory effects using linearly interpolated LUT[END_REF] also propose the interpolation of LUT entries with [START_REF] Ba | Optimal spacing of a linearly interpolated complex-gain LUT predistorter[END_REF] being remarkable in its analytic approach designed to minimize interpolation error. Filter lookup-tables (FLUT) are an improvement of the LUT concept with the potential to compensate dynamic nonlinearities. The structure of FLUTs has only been studied with uniformely spaced table entries and uniformely spaced filter codebooks [START_REF] Jardin | Filter lookup table method for Power amplifier linearization[END_REF][START_REF] Bejan | A Joint Linearization/Companding Approach for Improving a CO-OFDM Transmitter[END_REF]. In this paper we introduce non-uniform spacing solutions leading to a significant improvement in terms of error-vector magnitude (EVM). We rely on an algorithm seeking to minimize the error due to the linear interpolation between table entries. Optimal spacing results as a function of the PDF of the input signal and the inverse model of the amplifier as shown in [START_REF] Ba | Optimal spacing of a linearly interpolated complex-gain LUT predistorter[END_REF]. A better rejection of residual nonlinear distortions is thus obtained. But the main theoretical novelty is related to the filter codebook, improved according to an original method. As a general rule, with a FLUT, the magnitude range of the input signal is divided into bins and each filter in the codebook corresponds to one bin. The novel solution proposed in the present paper seeks to ensure that the number of input samples present in each interval is the same. A non-uniformly spaced codebook results leading to a further improvement in terms of EVM. Sections 2 and 3 present the theoretical principles of linearly interpolated LUT and FLUT predistorters with both conventional and original spacing solutions. Section 4 reports the numerical results showing that the newly proposed approaches perform best.

System model and FLUT Predistortion

Throughout this paper we consider a coherent OFDM transmitter with the PA acting as a power booster. The transmitter and receiver are standard, with the exception of one block dedicated to linearization (predistorter FLUT) [START_REF] Jardin | Filter lookup table method for Power amplifier linearization[END_REF]. Predistortion uses a simple principle. The undesirable effects introduced by a nonlinear element within the link (usually the PA) can be compensated by another nonlinear block placed upstream. The latter should idealy implement the inverse nonlinear function.

Figure 1 shows the cascaded baseband DPD (FLUT) and PA. Note that the FLUT itself is made up of two main blocks: a LUT and a filter codebook as described in [START_REF] Jardin | Filter lookup table method for Power amplifier linearization[END_REF][START_REF] Bejan | A Joint Linearization/Companding Approach for Improving a CO-OFDM Transmitter[END_REF]. The LUT, powered by the complex envelope x(n) of the input modulated signal, generates the predistorted signal xp(n) according to xp = xF j1(n) (r), where r = |x| and F j1(n) is the complex gain of the LUT for index

j 1 (n) ∈ [0, N 1 -1].
In the elementary case of a LUT-based predistortion (without the filter stage), the output of the amplifier y(n) is then y = xpG(rp), where rp = |xp| and G(rp) is the complex gain of a memoryless amplifier presented in equation 1. Ideally, the LUT is optimized to produce a simple linear gain (K) and an arbitrary constant phase shift (Φ 0 ), so that:

G(r|f (r)|)f (r) = Kexp(iΦ 0 ) (1)
where f denotes the amplifier inverse model.

If the amplifier has negligible dynamics, the standalone LUT should be sufficient to compensate the undesired nonlinear distortion it introduces. As seen later on in the paper this favorable scenario is not always realistic. The parameters of the FLUT are identified using a hybrid approach. The entries of the LUT are computed by direct learning while an indirect method is used to estimate the coefficients of the filters (see [START_REF] Jardin | Filter lookup table method for Power amplifier linearization[END_REF] for details). Functions c LU T (r) and c F IR (r), shown in figure 1, control the way in which entries are spaced within the LUT and the codebook respectively. By mathematical convention we consider that the gain K of the amplifier is normalized to the unit. We also consider that the function c(r) (c LU T (r) or c F IR (r)) is defined for the interval r ∈ [0, 1] and is monotonically increasing so that c � (r) > 0, ∀r ∈ [0, 1] over this interval. Note that the width of the resulting bins is inversely proportional to the first order derivative of the spacing function c(r) and to the number of inputs N (N 1 or N 2 ) as follows [START_REF] Ghannouchi | Behavioral modeling and predistortion of wideband wireless transmitters[END_REF][START_REF] Cavers | Amplifier linearization using a digital predistorter with fast adaptation and low memory requirements[END_REF][START_REF] Gersho | Principles of quantization[END_REF][START_REF] Hassani | A flexible method of LUT indexing in digital predistortion linearization of RF power amplifiers[END_REF][START_REF] Boumaiza | Adaptive Digital/RF predistortion using a nonuniform LUT indexing function with built-in dependence on the amplifier nonlinearity[END_REF][START_REF] Ba | Optimal spacing of a linearly interpolated complex-gain LUT predistorter[END_REF][START_REF] Bouajina | A robust non-Uniform LUT indexing method in digital predistortion linearization of RF power amplifiers[END_REF][START_REF] Jardin | Filter lookup table method for Power amplifier linearization[END_REF][START_REF] Bejan | A Joint Linearization/Companding Approach for Improving a CO-OFDM Transmitter[END_REF][START_REF] Sundstrom | Quantization analysis and design of a digital predistortion linearizer for RF power amplifiers[END_REF]:

d = 1 c � (r)N . (2)
Several techniques for linearly interpolated LUT spacing have been studied. TABLE 1 illustrates a set of conventional methods. It should be noted that A-law, originally developed for speech processing is uncommon in digital predistortion applications. It is nonetheless a viable alternative to µ-law. None of these techniques takes into account the characteristic of the PA, modulation format nor signal statistics. This is their main limitation.

3

Improved FLUT Spacing

An optimal LUT spacing

To the authors' knowledge there are no published attempts to optimize spacing in a FLUT predistorter. However, several recent advances exist in the optimization of LUT spacing and constitute a valuable resource for the present paper. One strategy is to rely on an analytical model of the PA [START_REF] Cavers | Amplifier linearization using a digital predistorter with fast adaptation and low memory requirements[END_REF][START_REF] Boumaiza | Adaptive Digital/RF predistortion using a nonuniform LUT indexing function with built-in dependence on the amplifier nonlinearity[END_REF] while another approach advocates the estimation of an inverse function [START_REF] Ba | Optimal spacing of a linearly interpolated complex-gain LUT predistorter[END_REF][START_REF] Bouajina | A robust non-Uniform LUT indexing method in digital predistortion linearization of RF power amplifiers[END_REF][START_REF] Selvadurai | A robust non-uniform indexation of a quadratically interpolated LUT predistorter for RF power amplifiers[END_REF][START_REF] Sundstrom | Quantization analysis and design of a digital predistortion linearizer for RF power amplifiers[END_REF]. In both cases

µ ln(1 + µ) 1 1 + µr A-law Ar 1 + ln(A) , r < 1 A 1 + ln(Ar) 1 + ln(A) , r ≥ 1 A A 1 + ln(A) , r < 1 A 1 1 + ln(A) 1 r , r ≥ 1 A
knowledge of the PDF of the input signal is required. Compared to the previously mentioned references, in the present paper a purely behavioral inverse model of the amplifier is adopted. This general approach requires no hypothesis regarding the physical nature of the amplifier, by modeling its AM/AM (amplitude-to-amplitude modulation) and AM/PM (amplitude-to-phase modulation) characteristics as a result of simple polynomial identification. Let the PA be considered as a purely static block at this stage and let f denote its inverse function. It is reasonable to assume that f may be expressed as a function of two distinct polynomials {f AM -AM (r), f AM -P M (r)} translating the amplitude and phase distortions respectively:

f (r) = f AM -AM (r)exp(if AM -P M (r)) (3)
with

f AM -AM (r) = L1 � l=0 β l r l , (4)
f AM -P M (r) = L2 � l=0 γ l r l (5)
and r = |x|. At this stage LUT spacing may be optimized using the method presented in [START_REF] Ba | Optimal spacing of a linearly interpolated complex-gain LUT predistorter[END_REF] which relies on a general principle of quantization theory [START_REF] Gersho | Principles of quantization[END_REF][START_REF] Sundstrom | Quantization analysis and design of a digital predistortion linearizer for RF power amplifiers[END_REF]. The lookup-table approach intrinsically introduces an approximation of r which is replaced by a finite number of bins r k with k = 0, ..., N 1 -1. Let the width of a bin be defined as d = r k+1r k . It follows that for a complex input x of magnitude r falling within the bin a bounded approximation error will be introduced so that r = r k + �r with 0 < �r < d. This error will also impact function f that will be approximated by

f (r) = f (r) + � f (6)
with � f being the resulting error. Assuming the bins are narrow enough, a good estimation of � f may be computed, hence we may

y = G(r| f (r)|) f (r)x (7)
whereas the desired output signal is y = G(r|f (r)|)f (r)x. Optimum spacing may then be defined as minimizing E(|�y| 2 ) with �y = yy. Following the procedure in [START_REF] Ba | Optimal spacing of a linearly interpolated complex-gain LUT predistorter[END_REF] the analytic expression of the derivative of the optimum spacing function c opt (r) can be obtained and written as

c � opt (r) = w 1 5 (r) � 1 0 w 1 5 (r)dr (8)
Function w(r) is defined as

w(r) = |ψ(r)| 2 p(r) (9)
where p(r) is the PDF of the input signal and ψ(r) is given by

ψ(r) = f �� (r)f * (r) + irImag(f � * (r)f �� (r)) |f (r)| 2 + rReal(f * (r)f � (r)) x (10)
Replacing c � opt in (2) yields for r = r k with k = 0...N 1 -1 a new set of bins of varying width. This result obtained by Ba et al. [START_REF] Ba | Optimal spacing of a linearly interpolated complex-gain LUT predistorter[END_REF] is of paramount importance in designing powerful predistortion linearly interpolated LUTs.

Improved filter Codebook spacing

Unfortunately for several applications, standalone LUTs make poor predistorters as one also wants to compensate for dynamic distortions. Predistorters thus have to be upgraded to include memory hence the addition of a filter codebook as described in figure 1.

Let the j 2 th FIR in the codebook be defined by its transfer function H j2 (z) and impulse response h j2 (k) which are linked by the classic relation

H j2 (z) = L-1 � k=0 h j2 (k)z -k (11)
The input-output relation of the global filter-lookup-table system is given by

x lf (n) = L-1 � k=0 h j2(n) (k)xp(n -k) (12)
Recalling that xp is the output of the LUT block and replacing its expression in (12) yields

x lf (n) = L-1 � k=0 h j2(n) (k)F j1(n-k) x(n -k) (13)
At this point, spacing, which may be considered a solved problem for the LUT, becomes an issue for the codebook. A trivial approach would be to optimize the spacing for the LUT and use the same bins for the filter codebook. In other words the index functions j 1 (n) and j 2 (n) in figure 1 could be considered identical (and N 1 = N 2 ). This solution was tested and the results are presented in the following section but the authors also propose another approach which leads to better performance and constitutes the main theoretical development in the present paper. Consider the scenario of a relatively small number of uniformly spaced bins. The probability of an input value falling within some bins may have a low probability of occurrence, as illustrated in figure 2 (PDF with a small tail on the right-hand side). Hence, during the learning stage, the estimation of filter coefficients for high values of j 2 will be based on few samples and will thus be inaccurate. This is extremely inconvenient since it is particularly at high amplitudes that nonlinear distortions are powerful. Consequently, a well chosen empirical criterion for non-uniform spacing would be the equi-distribution of samples over the different bins. The specific case of an OFDM signal is subsequently considered. It is known that the PDF in this case follows a Rayleigh law with p(r) = 2 r σ 2 exp(-

r 2 σ 2 ) (14)
In practice the cumulative probability of input magnitude exceeding a certain value is so low that the tail on the right-hand side of the Rayleigh PDF will always be clipped. Futhermore we also consider that the signal is normalized in order to be inline with previous literature. We recall that the very definition of the PDF implies � 1 0 p(r) = 1. We seek to insure that the probability for an input sample to fall within a specific bin is the same for all bins, i.e.

� qj 2 +1 qj 2 p(r) dr = 1 N 2 , with j 2 = 0, ..., N 2 -1 (15)
The spacing can then be expressed by an iterative law with the quantum for bin j 2 + 1 depending on the quantum for bin j 2 .

q j2+1 = σ � -log(- 1 N 2 + exp(- q 2 j2 σ 2 ) ), (16)
with j 2 = 1, ..., N 2 and q 0 = 0. Thus the width of the jth bin is defined by the difference

d F IR opt = q j2+1 -q j2 (17)
Using the analytical expression of the PDF to improve spacing is elegent however in practice this is not always possible. Quite often OFDM systems include companding/decompanding blocks in order to reduce peak-to-average-power-ratio (PAPR) [START_REF] Bejan | A Joint Linearization/Companding Approach for Improving a CO-OFDM Transmitter[END_REF] in which case the PDF no longer follows a Rayleigh distribution. Nonetheless, the principle of equi-distribution of samples over the various bins holds and it can be enforced empirically by studying the histogram of the input samples.

4

Performance Evaluation

Application example: CO-OFDM System

While the theoretical principles described above are general the authors tested the approach in the specific framework of a coherent optical OFDM (CO-OFDM) transmission system. The implementation of such links using semiconductor optical amplifiers (SOA) of moderate cost has attracted significant attention in recent years and appears to be promising (see [START_REF] Bejan | A Joint Linearization/Companding Approach for Improving a CO-OFDM Transmitter[END_REF], [START_REF] Khaleghi | Experimental validation of numerical simulations and performance analysis of a coherent optical-OFDM transmission system employing semiconductor optical amplifier[END_REF] and the references therein).

- In the present study, a commercial 750 µm SOA (INPHENIX-IPSAD1501) is considered. The CO-OFDM link was simulated using a hybrid Matlab-ADS implementation that has already been validated by experiments [START_REF] Khaleghi | Experimental validation of numerical simulations and performance analysis of a coherent optical-OFDM transmission system employing semiconductor optical amplifier[END_REF]. The modulation format is quadrature phase shift keying (QPSK) with 128 subcarriers and 3 GHz bandwidth, resulting in a 5 Gb/s transmission. The SOA is powered by a bias current of 200 mA, resulting in a gain of 19 dB and a noise factor of 7 dB for a laser wavelength of 1540 nm. It should be emphasized that FWM may be considered as the prominent nonlinear effect in SOAs if multicarrier modulation are employed, with the nonlinear distortion depending both on the subcarrier spacing and on injected optical power. The FWM products become weaker compared to the main signal when the frequency interval increases. Note that the scenario proposed here can be considered challenging, from the linearization point of view, once the optical power injected into the SOA exceeds -21 dBm (saturated region).

Non-uniform spacing methods

The authors conducted extensive testing of various FLUT configurations listed in TABLE 2 with linearly interpolated LUT(s). The most basic variants use uniform amplitude spacing (FLUT 1 ) and uniform power spacing (FLUT 2 ). Conventional non-uniform (NU) spacing (µ-law, A-law) was used for (FLUT 3 ) and (FLUT 4 ). The following configurations use the advanced spacing algorithms with linear interpolation described in this paper. FLUT 7 features an optimal LUT block (computed according to the method developed in [START_REF] Ba | Optimal spacing of a linearly interpolated complex-gain LUT predistorter[END_REF] and summarized in section 3.1) coupled with a uniformly spaced codebook. FLUT 6 uses a codebook which follows the same spacing as the optimal LUT; note, however, that in this case optimality holds for the LUT block only and that the whole predistorter does not minimize the residual nonlinear distortion at the output of the amplifier. FLUT 8 features optimal LUT spacing and the new, improved codebook spacing method (see section 3.2). Furthermore a scenario where both the LUT and the codebook follow the sample equidistribution principle, described in section 3.2, was also considered for comparison purposes (FLUT 5 ).

Comparative results for these predistorters are collected in figure 3a and3b. Average power at the input of the amplifier varies from -21 dBm to -15 dBm. A very compact FLUT has been considered in order to fit the needs of high optical throughput (N 1 = 6; N 2 = 6 and L = 3). The FLUT was identified using a learning sequence of 2 11 QPSK symbols with average input power P ref = -18 dBm. NU spacing via µ-law and A-law (see TABLE 1 for the expression of the laws) were implemented for µ = 100 and A = 150 respectively. Trial and error seems to indicate that these values perform best. Results for basic OFDM (without predistortion) are also plotted for referencing purposes. We note that while conventional NU spacing algorithms outperform uniform spacing optimizing the LUT leads to a significant reduction in EVM. Mixing LUT optimization and an improved codebook brings further gain as one notes that FLUT 8 performs best.

Influence of the Identification Power (P ref )

Figures 3a and 3b correspond to scenario where the FLUT parameters are identified for a fixed value of the input power and a power sweep is conducted to see how the various predistorters perform in a given range around this value. Another scenario involves reidentifying FLUT parameters for various values of the average input power. This is not unrealistic, in practice an FPGA implementation of the system could reasonably allow switching between various sets of parameters. The scenario is tested with P ref varying from -23 dBm up to -13 dBm. The improvement in terms of EVM (Gain EVM ) for five FLUT variants is plotted in figure 3c. Note that, quite predictably, as the input power goes beyond a certain value saturation becomes such that predistorters no-longer manage to cope with the nonlinearities and Gain EVM decreases sharply, with an EVM eventually exceeding the FEC * limit (assumed to correspond to an uncoded BER † of 10 -3 [START_REF] Schmogrow | Error vector magnitude as a performance measure for advanced modulation formats[END_REF]). However in the realistic power range featured in figure 3c FLUT 8 clearly outperforms other techniques. The effectiveness of this predistorter can also be assessed with the AM-AM and AM-PM characteristics, illustrated in figure 4, or in frequency domain where a reduced distortion can be observed both in-band and out-of-band (Fig. 5). At the considered input power P ref = -17 dBm, FLUT 8 clearly outperforms FLUT 1 with performance still meeting the FEC limit.

Influence of the Henry Factor

Further investigation focused on the influence of amplitude-phase coupling in the SOA. The latter can be quantified by the Henry factor and has significant impact on the nonlinear behavior of the amplifier [START_REF] Khaleghi | Experimental validation of numerical simulations and performance analysis of a coherent optical-OFDM transmission system employing semiconductor optical amplifier[END_REF]. All previous results in the paper were obtained for a Henry factor α H of 5. In the new scenario it varies from 1.52 to 7.6 with a step of 0.76. FLUT parameters are re-identified for each value * Forward error correction † Bit-error rate of α H . The results are collected in figure 3d. FLUT 8 , once again, outperforms other variants.

FLUT Size

The size of the LUT and of the codebook are key parameters of the predistorter as they impact system complexity. In figures 3e-3f the performance of the various FLUT variants is tested varying N 1 and N 2 from 1 to 20 and 1 to 150 with N 1 = N 2 and keeping the same memory depth (L = 3). Beyond N 1 = N 2 = 4 the FLUT 8 outperforms other variants. It should be noted that for unrealistically high values of N 1 and N 2 (> 120) all variants tend to yield similar performance which is expected (figure 3f). Nonetheless, in a realistic scenario FLUT 8 offers a good performance/complexity trade-off.

Conclusion

The topic of digital predistortion has attracted significant attention in recent years. In the present paper the authors provided a comprehensive study for FLUTs testing several variants. A new method of non-uniform spacing was developed for the FIR codebook leading to a significant improvement in performance. It should be noted that the solution is particularly well adapted to optical systems. Its simplicity is well suited for optical data rates and no feedback loop is required. Indeed, in microwave systems one may be tempted to implement more complex, adaptive solutions featuring online computation that are simply not feasible in optical communications. Thus, a well designed FLUT appears all the more interesting.

Fig. 1 :

 1 Fig. 1: Complex gain FLUT predistortion followed by PA.

Fig. 2 :

 2 Fig. 2: PDF as a function of the input amplitude of the codebook based on classic and proposed spacing (N 2 = 6); case of an OFDM signal with Nsc = 128 subcarriers, clipped and then normalized.

  Effect of FLUT input size, for P ref = -18 dBm.

Fig. 3 :

 3 Fig. 3: Standard and alternative spacing methods.

IETFig. 4 : 8 Fig. 5 :

 485 Fig.4: AM-AM (left) and AM-PM (right) characteristics for an optical power of -17 dBm at SOA input for original system, compared to linearized systems F LU T 1 , F LU T 8 with size N 1 = N 2 = 6.

Table 1

 1 Standard spacing functions with their derivatives.

	Spacing scheme	Spacing function c(r)	Derivative of the spacing function
	Amplitude [11-17]	r	1
	Power [11, 16, 17]	r 2	2r
	µ-law [11]	ln(1 + µr) ln(1 + µ)

  It follows that the true output signal of the amplifier can be analytically written as

IET Research Journals, pp. 1-6 2 c � The Institution of Engineering and Technology 2015 consider it known.

Table 2

 2 Spacing methods used in the paper. The LUT optimal spacing refers to the method described in[START_REF] Ba | Optimal spacing of a linearly interpolated complex-gain LUT predistorter[END_REF].

	Spacing scheme	LUT spacing	Filter Codebook spacing
	F LU T 1	uniform (amplitude) uniform (amplitude)
	F LU T 2	uniform (power)	uniform (power)
	F LU T 3	non uniform (µ-law)	non uniform (µ-law)
	F LU T 4	non uniform (A-law)	non uniform (A-law)
	F LU T 5	follows codebook	improved
	F LU T 6	optimal	follows LUT
	F LU T 7	optimal	uniform (amplitude)
	F LU T 8	optimal	improved

OPT-2019-0044.R1revisedchangesblue.pdf M ainDocument IET Review Copy Only


Florence Charue-Duboc

Research role in defining customer needs on innovative projects

Background

The development of innovative products has long been driven by technological advances. Scientific progress achieved by research centres has fostered the planning of new products and brought about significant improvements in existing ones. New product development has thus been guided by research, with the focus on new fields which reflect acquired expertise, new findings reported in the literature, and topics studied by academic research teams. This "technology-push" strategy has undeniably made it possible to market many innovative products, as is shown by the technological progress accomplished since the turn of the century. Some developments, however, have not achieved the expected market success, and these setbacks have been attributed to flawed knowledge of the market.

In the mid-1970s, two corporate trends became apparent, the first of which was the rise in the power of marketing. As marketing skills grew, internal market analysis and responsiveness to customer expectations and segmentation grew accordingly, and centres of expertise were restructured into divisions or departments. Marketing experts, initially called upon to support product sales, were subsequently asked to define target-products for development. The second such trend was the rationalization of research operations, aimed at imposing budget limitations, often determined by a percentage of "normal" revenues for the industrial sector involved. The question then arose as to how projects should be prioritized in terms of their inherent advantages and disadvantages, and the urgency of demand for them. The by-word for the past fifteen years for research management has thus been "market-pull". The customer makes purchasing decisions, which means the customer is king, and his or her needs must therefore be understood. Understanding customer needs became therefore the task of marketing experts, and the point of departure for establishing research programs and formulating goals and priorities. This was a total reversal of the process and rationale on which new research projects had previously been based.

With ten years' experience behind us, we can now assess this new model in terms of both its positive and its negative aspects. On the positive side, it has clearly fostered a learning process, strengthening the customer rationale for product development. It has proved to be broadly applicable and effective for innovations of an incremental type, and for industries operating in proximity to the final market. On the negative side, in today's innovation-based competition between corporations, adaptive innovations, although necessary, are no longer enough to ensure a strong position in a given market (Tushman & Andersen 1986, Cohendet and Llerena 1990, D'Aveni 1994, Lynn, Morone & Paulson 1996, Chakhravarthy 1997). In addition, to satisfy new customer needs and develop the innovations they need, corporations which manufacture mass-market products are increasingly turning to sourcing sectors (Iansiti & Clark, 1994, Kesseler 1998). Finally, product-targeting errors cannot be corrected, as competitors do not give innovators a second chance. They learn rapidly from the failures of others, and are quick to offer new products of their own.

The punctuated equilibrium (Romanelli & Tushmann 1994) which alternates long periods of incremental innovation and breakthrough related to radical innovations does not seem to be appropriate to describe today's race between firms. Recent works focus on a new model of organization favouring continuous change which means the development of important innovations at a sustained rythm (Utterback 1994, Dougherty & Hardy, 1996, Brown & Eisenhardt 1997).Their focus is to characterize these kinds of organization as a whole, and they insist for example, on project portfolios and how to articulate today's developments and competencies with future innovations. Our contribution belongs to this research stream. This process has also been studied for radical innovations in Science Sociology (Ackrich, Callon Latour 1988). They pinpoint the role of various actors surrounding the innovation and the importance of their involvement in the success. What are the implications within a firm of such theoretical results ? I focus on the process of product targeting and the early stages of design. I elaborate on the notion of the low cost probe mentioned by Brown & Eisenhardt 1997 and the probe and learn process of Lynn Morone and Paulson 1996, in defining the role and competencies of research and marketing and the way they coordinate in those very uncertain situations.

What would the ideal development-design model for breakthrough innovations be, especially at the level of industrial sourcing? This is the broad question I have attempted to give some answers to based on research conducted at a major French chemicals corporation, for which this question is particularly relevant. This is because the specialized chemicals industry is subject to a product-differentiation strategy and most of its products are sold to processors. Finally, product-innovation and product-function innovations raise fundamental research questions. The ability to transform technical innovations into marketable products is therefore of key importance.

In the first section, I describe the limitations of the "marketpull" model, the impact of which is particularly strong because it focuses on innovations in basic materials processed by intermediate manufacturers before reaching the end-user. In the second section, I formulate a design-model which takes these limitations into account, pinpointing one specific aspect: the coordinating mechanisms which facilitate the simultaneous acquisition of expertise concerning customer needs and markets, as well as product accessibility. In conclusion, I have touched on other aspects of the model which require clarification, so that this coordination can be implemented effectively: the internal organization of expertise; contracts and incentives for the various players; and the involvement of customers or partners.

The limitation of the "market pull" model

First of all, I present a brief review of several defining elements in the market-pull model before going on to list the main problems involved in its implementation.

The market-pull model assumes an initial definition of the products, services, or functions to be developed; they are defined on the basis of an analysis of markets, customers, and customer needs and preferences. This model thus assumes the presence of a player with the appropriate expertise: the marketing expert. Marketing experts are expected to formulate the product-target before the initiation of the research operation required to fulfill it. The relationship between marketing and research is "contractual" in nature. Respective responsibilities are clearly delineated in a manner resembling the propertydeveloper/contractor relationship which is characteristic of project-management models, particularly in the construction field. Lastly, the respective players in this relationship mobilize bodies of disparate expertise. Although in practice the implementation of this model does not always involve roles as contrasted and "compartmentalized" as this, a critique of the ìideal-model" appeared to us to offer greater scope for analysis.

Marketing-expert techniques and expertise

However, first let us return to the marketing experts, who are expected to use their analytic techniques to formulate specifications for the customers' dream product. The basic techniques used are market analysis and product positioning.

During the early project phases, knowledge about the product is minimal. Will this additive for paint be totally biodegradable? Solvent-free? Here, as in any development process, there are imponderables. How can traditional focusgroup and survey techniques be used when no product-samples are available to quantify preferences and spot threshold effects? This is the first problem encountered when the marketing approach is used during the early project phases. It requires the development of new investigative methods, on the basis of which expertise regarding preference can be effectively built.

The second problem is that knowledge about the market is also minimal. Depending on the future properties of the product, it may or may not be useful for certain applications (as an additive for "do-it-yourself" varnishes, for instance; or, conversely, for industrial paint). Here again, market segmentation will be difficult as long as uncertainties concerning the product persist.

The third problem is to extrapolate over one, two, or several years. Let us assume that precise product and product-market profiles have been defined; the market survey then generates a sales forecast based on an analysis of today' products. By the time they will be introduced onto the market, many factors will all have been subject to change. Meanwhile, other innovations will have been introduced; new standards may have been established; the market may have been fragmented by these other innovations, etc.

Making use of marketing techniques without additional precautions and adjustments is thus particularly risky. J. M. Gaillard (1997) even goes so far as to claim that if the goal is to kill an innovation, there is no better way to do so than to conduct a market survey. He suggests doing the opposite: i.e. constructing a methodology specifically suited to these highly uncertain early phases. He bases his argument on many personal interviews and on the meticulous examination of a qualitative analysis drawn from the findings. I shall return to these findings in the second section.

Customer expertise

A second category of problems has to do with customers and their own expertise.

Breakthrough innovations are only rarely identified by asking even enlightened customers about their potential needs. Who would ever have imagined buying a glue that doesn't stay glued? And yet, this is the very property that everyone appreciates today. It was up to 3M (or, more modestly, to a researcher) to invent a possible use by consumers. Here, the issue is to invent (or crystallize) diffuse, latent, implicit needs that could be satisfied; and to construct, in cooperation with customers (by integrating them into the validation loop) a usage-value drawn from the opinions they express. This is not merely a matter of "harvesting" existing information which needs only to be tabulated.

The problems do not differ significantly even when the product under development is similar to existing products. The views of some customers will be of no use whatsoever if the product is not ready to be handled and tested. In the case of a varnish-development project, for example, the product's dryingtime following application did not emerge as a crucial parameter until very late. The craftsmen at whom this product was being targeted simply assumed they would be able to apply a second coat the day after applying the first one, and did not bother mentioning this criterion. This demonstrates the vulnerability of evaluations made before the product has been tested. Some customers, on the other hand, will formulate extremely rigid technical specifications. Chemicals-industry customers, from detergent manufacturers to auto-makers, tend to present highly detailed specifications. In instances such as this, specifications based on customer expertise can also act as a brake on innovation, not because the criteria have not been thoroughly identified at the inception of the project, but because the desired objective involves too many demands. Specifications of this kind are frequently based on a technical solution to be improved. For example, when an auto-maker presents dashboard specifications independently to a plastics supplier, there is a good chance that the resulting component will be too heavy and too costly. To produce a lighter, less costly component, several points in the specifications need to be changed, and defining the margin for maneuver within which profits can be earned by both parties assumes a continuous mutual-learning process. As a consequence we can observe the formation of design partnerships

In general, customers' expertise about their own potential needs and preferences is patchy. Some customers make assumptions which they have difficulty expressing. The formulation of purchasing specifications fills in some of the gaps, but can place limitations on innovation. Meanwhile, other gaps will appear. Customers will only be able to perceive the value of an unfamiliar property, or pinpoint their preferences, by evaluating the product. It is therefore advantageous to create a market expertise which customers do not necessarily possess. It is even reasonable to claim that this type of expertise yields a competitive advantage.

Customer-system complexity

A third problem category encountered in the market analysis of products under development is the diversity of relevant opinion-sources.

For example, which opinion source should be given priority when the customer is a corporation (as is often the case in the chemicals industry)? The Purchasing Department, the R&D Department, the project teams, or the strategists? This question is particularly important, as the issue here is not to market a product, but to anticipate what will satisfy customers several months or even years ahead.

Customer-contacts can be made internally by various players: sales personnel, researchers (through technical assistance), and plant workers (through complaints). How can these various ways of approaching the customer be combined? Furthermore, a direct customer is not necessarily in the best position to evaluate usage-relevance for the end-consumer, as there may be several intermediate stages between them. Consultants may also play a decisive role. While a direct customer might consider a specific function to be superfluous, the customer's own client might consider it crucial. A good example is a product designed to eradicate salmonella at poultry slaughterhouses. The direct customer (the slaughterhouse) might show little interest in a substance that would increase costs of a product (poultry) for which the profit margin is already very low. However, the final consumer (the purchaser of poultry in a supermarket) might be willing to pay a premium for added product-safety. If innovations are to be successful, these different rationales must be taken into consideration.

Furthermore, strategic considerations are integral to the relationships among intermediaries in a given industry. The -8 -introduction of an innovation provides an opportunity to redefine the profit margins and prerogatives of each intermediary. In the auto industry, the situation is relatively stable, but this is not the case for all industries, and particularly chemicals. The structure of the chemicals industry is complex: a customer for one type of product might be a supplier of another type, and a competitor for yet a third. To a complexity which reflects the large number of players involved is added a strategic complexity which is increasing due to current trends towards developing alliances, joint ventures, and licensing agreements.

This means that the market-testing of innovations may have an undesired repercussion: the triggering of competition.

Then again, customer interest (or lack of it) in an innovation may reflect rationales only remotely connected with the technical evaluation of the proposed product. For example, a customer might give serious attention to innovations offered by various suppliers, not because it is looking around for a new supplier, but simply because it wants to shake up its old ones. Alternatively, a customer might indicate lack of interest in a new product solely to discourage its development by a competitor of one of its own corporation's divisions, which is trailing the field. Customers might also be reluctant to show interest in an innovation due to the testing expenses involved, and to the risk that a new product still experiencing teething problems might negatively affect their brand image while at the same time providing no guarantee of long-term market penetration.

When the multiplicity of opinion sources represented by the customer-system is taken into account, the question arises of how divergent viewpoints should be analysed and rated.

The demand dynamic

The current competitive context is characterized by market versatility, rapid introduction of product/process innovations, and accelerated change in the regulations governing environmental protection (Cohendet Llerena 1990). All of this takes place in a price-war climate. Thus stability of demand is an extremely strong assumption, rarely verified,. Examples abound of market-demand instability and the speed with which market demand shifts relative to product-development timeframes. The impact of this problem is accentuated by the fact that product/process and expertise development require a longer time-frame than that allowed by the pace of change in the market and the competitive environment.

The various limitations described above have an even greater impact on lengthy development projects (lasting several years), and breakthrough innovations usually fall within this category. Problems arise in connection with anticipating shifts in the market; with the difference between the productfunctions being developed and those customers are familiar with; with the time required before a testable product can be introduced, etc. When the innovation being developed targets industrial suppliers, the difficulty in anticipating market demands increases in proportion to the strategic complexity of the industry and the interrelationships among its various players.

What kind of model can account for all these market characteristics, and for the problems involved in the market analysis of breakthrough-innovation development?

Towards an integrated product/market model design

Demand instability, the inability of traditional marketing techniques to identify unexpressed needs, and the strategic complexity associated with multiple players all point to the desirability of placing the process of target-market construction in a time-frame that parallels that of the technical productdesign process. In this way, the product's technical feasibility, its user's needs, and the price the latter is willing to pay can be explored in a parallel, rather than sequential fashion. Two learning processes occur; two bodies of expertise are constructed on the basis of two interdependent "objects." This is the guiding principle underlying the integrated-model design, and it is borne out by other theoretical work.

For example, research which analysed innovation processes, conducted at the Centre de Sociologie de l'Innovation M. Ackrich, M. Callon, and B. Latour (1988) criticizes the "linear" model which postulates a sequence from basic research to the market, successively passing through applied research, product development, and production. This work underscores the importance of achieving the involvement of the various players throughout the innovation process: future users, financiers, consultants, and production-process developers. The authors stress the importance of negotiating the technical objective with all the parties involved.

Theoretical work by Simon (1969) demonstrates that the definition of the problem cannot be divorced from the formulation of the solution. Design is a dual process which results from the combined definition of the problem and the solution. Research by C. Midler (1993) on project management has highlighted the time and cost advantages accruing from simultaneous consideration of the project's target and its technical solution (product/process).

The evolution in project management for the case studied demonstrates the emergence of this design construct, which focusses on both the relevant target and the potential products.

However, in the "market-pull" model, marketing/research coordination follows a linear sequence; and, in fact, this is also the case for the "technology-push" model. The output of the former becomes the operational input of the latter. The only difference between them is the order in which the departments intervene, and their hierarchical position in relation to each other: the one which establishes the constraints is considered to be the one in the dominant position. Coordination is greatly simplified by another point: concentration of the market interface on a single player, the marketing expert. Information thus travels in a linear manner between the firm's various departments: the players involved with internal production have as their (internal) customer a profit-centre marketing expert who oversees the interface with the post-production customer, represented by a purchasing function which, in turn, communicates with production/development functions, and so on, all the way to the final consumer. According to this rationale, marketing-department expertise is oriented primarily towards familiarity with customers, commercial negotiations with them, and prices.

Under the new model being proposed, what are the coordination modalities which will enable researchers to do their work despite the fact that no one has asked them any questions; and what will enable those in the process of formulating the questions (the market experts) to trust that their colleagues are working on solutions to a problem they have not yet fully defined?

As the process advances, coordination objectives and modalities will be specified which facilitate the parallel and symmetrical deployment of both the research and the market explorations.

A common point of departure for parallel explorations

The point of departure for an innovation project is a pathway to be explored: a silicon mastic for tools so they can be cleansed with water for example. Initial formulation of the objective is common to both the research and the marketing players, and serves as the launch-pad for parallel exploration.

The researchers will posit various technical solutions. Experiments which prove technically unfeasible are easily eliminated. Often, however, initial findings are not decisive. One approach will present certain advantages, another approach different ones. Choosing which one to concentrate on cannot be done by the researchers alone; an evaluation must also be made by the marketing experts.

In parallel, market analysis and technical exploration go forward together. The market analysis builds an initial assessment of the project's potential advantages and disadvantages. It draws in a highly traditional way on a collection of sales-volume data for the type of product with which the new development will be positioned, and for various existing products and their prices. When marketing experts are expected not only to provide volume and price forecasts, but also to supply a detailed definition of the target market; to orient development on the basis of initial research findings, and to establish compromises between functionalities, they must be able to establish a basis for providing answers to a number of information in order to orient or re-orient the research program; and this, in turn, means a strategy of broad-based exploration. This approach is very different from the one aimed at finding as quickly as possible a product consistent with specifications fixed at the project's inception, and then conducting feasibility tests for the most promising candidate.

The marketing experts' task is to identify the firms which might be contacted for product-evaluation, and the markets in which they might be representative. Marketing experts also select the players to be involved: direct customers, customers of customers, consultants, etc. For one of the projects studied (for example), it proved possible to use the same technical concept for the development of water-based matte paint, wood varnishes, and industrial paints. For these respective applications, potential-customer lists were drawn up, and the partnership agreement covering concept evaluation was focussed in each instance on a highly specialized segment of the market.

From this stage onwards, the importance of developing the marketing and research approaches in tandem is obvious. To acquire increased knowledge of the market, it is necessary to be in a position to offer test samples in order to plan partnerships; to have access to comparative evaluations of the product relative to the competition; and to learn what the customer criteria are.

Customer evaluations: an opportunity to diversify market interfaces

Data culled from customer evaluations are an important source of information, also providing an opportunity to diversify contacts with customers. The outstanding feature of the relationship with a customer who has evaluated a sample is that much can be learned from it, but no one knows exactly what. To be sure, some questions can be raised at the outset. However, a major advantage of this type of interaction is the opportunity it provides for pinpointing important factors of which the project team may have been unaware. A risk that comes to light is far less "serious" than one that remains in the dark.

Players involved in different departments of a given firm are not always equal in their ability to recognize key points which have been omitted from the development process. Marketing experts, exercising fairly broad judgment, will be particularly sensitive to project evaluations bearing on sales volume and price. Researchers will be sensitive to evaluations bearing on technical points: for example, criteria fixed by them which turn out to be of minor importance; or criteria originally deemed of minor importance which turn out to be major. All the researchers interviewed who had had direct contacts with customers used the opportunity to fine-tune their objectives for the product under development. "When I heard the comments and questions raised by the test for evaluating biodegradability, I realized there were technical problems which needed to be investigated, and that the problem was not just a negotiating one, as had been assumed by the project's marketing supervisor." "In the beginning we were trying to make a paint with no solvent. However, it turned out this wasn't a key criterion for the customer. The product's benefits were assessed on the basis of another criterion altogether." Considering the exposure to potential information loss when a single intermediary from a single field -marketing, for example-is used, this demonstrates the importance of keeping the door open to contacts between customers and a broad spectrum of project designers.

An integrated product/market model design is one in which marketing experts no longer serve as the sole intermediaries for controlling the market interface. However, they are responsible for conducting the market-analysis process, and thus for training project players in methodology. They can explicate tacit areas of expertise and the hypotheses underlying their choices and expectations, and fine-tune project criteria. It is clear that implementation of the integrated product/market model will involve elaborating new types of marketing expertise. Researchers, for their part, must become involved in customer interaction -a situation with which they are not necessarily familiar and must attempt to gather a maximum of information for subsequent stages in the development process. One critical point has to do with problems of confidentiality, which are particularly acute in the case of complex and relatively unstable industries; or of firms that are customers for a product but suppliers through another branch of their operations; or of firms which might also be competitors, through holding-companies or subsidiaries. It is risky to foster contacts between a customer and a researcher possessing the kind of scientific, technical, or economic information a practiced interviewer might seek to acquire. This is why careful preparation of direct customer contacts is needed.

The construction of internal expertise on usage properties

Although direct contacts provide a wealth of information, they are no substitute for more carefully-calibrated demand evaluations. This is because they are extremely time-consuming -a problem for researchers who need to stay in their laboratory and focus on their experimentation to keep projects moving along. Further, customers are reluctant to make large numbers of evaluations for products under development with no guarantee that they will ever be marketed. Customers may be very interested in the benefits promised by a new product, and still consider that they have no reason to participate in its development.

"Prototypes," which are crucial milestones in the development process, will therefore be small in number. This is why it is important to identify customer criteria at the earliest possible phase in the process; to do so selectively for compromises that are difficult to establish definitively; and to arrange for taking a number of "soundings" during the life of the project in order to ensure that its foundations do not prove totally flawed in the light of subsequent developments.

The way to accomplish the above is to acquire internal evaluation techniques reflecting customer criteria. The task of the "application" research laboratories is to formulate repeatable tests for measuring product usage. The task of the researchers is to acquire a body of expertise. The fit between "application" research laboratory evaluations and customer perceptions is of course crucial. Here again, the importance of coordination between research and marketing for selecting definitive product criteria and standardized measurements becomes apparent. In addition, considering the volatility of customer preference, it is also necessary to conduct regular assessments of the tests formulated internally in relation to direct customer evaluations.

The acquisition of new research expertise

The exploration of technical solutions for improving a product feature, or for selecting products which must present very different properties for users, infers a body of scientific expertise which can link a property of measured application with physical/chemical properties. This is known as "applicability". Here again, the implementation of an integrated product/market model design system fosters the acquisition of expertise which will accelerate the process. At the firm studied, several applicability laboratories were established.

Deploying a parallel research/marketing exploration thus clarifies the contribution of both fields to the definition of the product target, while strengthening the specific expertise developed by each respective field in order to contribute to the process. I have also shown that this type of model has significant implications for both researchers and marketing experts: participation in new situations (at the customers' for researchers; orientation of the on-going research process for marketing experts); different types of intermediary production (prototypes, sampling strategy); the acquisition of new expertise (bearing on anticipation and partnership for marketing experts; with applications and applicability for researchers); and more frequent interaction between different departments, requiring the construction of intersubjectivity between protagonists.

Some consequences of the implementation of an integrated model Beyond the emergence of coordination modalities facilitating simultaneous and convergent explorations, the implementation of this type of model raises other questions deserving of mention.

The first question has to do with project-portfolio management. If there is no business plan at the inception of the project, on what basis can the decision to launch a new project be made? How should a project be ranked in relation to other developments? Does the existence of much more reliable market forecasts for more advanced projects, or for projects reflecting a rationale of incremental innovation, threaten to stand in the way of breakthrough innovations? One strategy consists of managing breakthrough-innovation and long-term projects separately. But the prioritization of projects in this category remains a persistent problem: the development of a broad-based exploration strategy depends on the capacity to terminate fairly rapidly projects which do not generate conclusive findings during the early stages, and to transfer the teams to other projects.

The second question has to do with departmental organization. The proposed model infers the continuation of players dealing with the theme being explored and of those in contacts opened with customers. At the same time, it emphasizes the emergence and development of new expertise. How can these two rationales -specialization and projectdedicated players -be articulated? This point infers an internal organization by specialty facilitating the mobilization of a fragmented collective competence. It assumes the formulation of a profile defining players possessing the skills needed to do this, and of facilities supporting them within this dynamic.

A third question has to do with individual motivation. The linear "market-pull" model clearly establishes responsibilities facilitating the attribution of success or failure to specific players, and the corresponding remuneration. In the proposed model, this kind of responsibility attribution is not possible; but individual interest could be developed around the realization of a shared objective. Ultimately, individual commitment and internal contract could be based on the schedule enabling marketing experts to define, in stages, a precise product target, a market share, and a price. Meanwhile, the research team would base its contract and responsibilities on the strategy of exploration and of the intermediary stages at which testable "products" would be delivered to the marketing-expert partners.

A fourth question has to do with the implication of customers. Partnership strategies have repeatedly been evoked as a way to implicate customers in the design process. The development of this type of cooperation is not spontaneous, and it raises questions as to what share of the final marketable product's value-added will be attributed to each of the various intermediaries involved in its development.

In this chapter, some limitations of the market pull model are underlined especially for innovation. The main weakness of such a model for developing innovative products is the continuous evolution of customer demand and the complexity of the customer system preferences. An alternative model is elaborated where the exploration of the market and the exploration of the technical possibilities for product are two parallel and interconnected processes.

Different conditions to implement such a model are developed.

A continuous coordination between marketing experts and the research team has to be structured, as the specifications of the target product remain partial and moving until late in the project. It is based on the definition of testing strategy combining sets of prototypes and sets of customer types and items to be evaluated.

This coordination mean has implications on the activity of the marketing experts. They have to conduct the market analysis not anymore sole in contact with clients. But they have also to structure the data collected by researcher as well. The -19 -researchers' activity differs also from what it used to be. They have to propose prototypes in order to define what can be done and what product the customer would prefer and not only to find the product that fits perfectly with the specifications previously defined.

Finally, new knowledge has to be developed to speed up this targeting process: in research departments, scientific expertise linking functional properties of the product with chemical of physical properties of the product or of the process.

These seem to be key points to consider in implementing the integrated product/market model design and overcome the limitation of the market pull model for innovative product development.

diverse questions: What causes customers to prefer one product over another? Does the product under development meet all customer demands? Is it highly effective for one category subtype but less so for another?, etc.

The above criteria assume that marketing experts will be able to embark rapidly on a broader analysis than usual: a comprehensive identification of all customer-system players, including the customer's own customers, the consultants, and the other customers of varying types (not just the largest ones); and the formulation of a methodology designed to provide an understanding of usages and preferences. Depending on the age of the operation for which the new application is intended, this knowledge may already exist; or, inversely, the market may be totally unexplored. The speed with which the project will meet the relevant target is often directly contingent on this factor.

Formulation of testing strategy: a mean of coordination

There are two goals at this stage: first, to extend knowledge of the market; and, second, to pinpoint the research effort. Considering the difficulty customers have in explaining their needs, one strategy consists in facilitating the expression of tacit needs by offering different products and collecting data concerning customer reactions to them. Marketing/research coordination operates through the adoption of a testing strategy which of course will be constructed in a linear manner. It assumes that agreement can be achieved on questions such as: Why test 2 or 3 products? What information do we expect to gain? How do we select customers for product testing? What are the major imponderables, the main factors which might cast doubt on the products?

The researchers' task is generated by the explorations carried out during the first stage, and therefore consists in identifying the different "prototypes" which will enhance understanding of the product diversity to which they might lead. The first consequence for research departments working under this integrated approach is that they must deliver prototypes quickly, organizing a research program enabling them to do so. The second consequence has to do with selection of the products to be supplied. The objective is to acquire a maximum amount of