In this paper, we address the architecture of multistandard simultaneous reception receivers and we aim to reduce the complexity of the analog front-end. To this end we propose an architecture using the double orthogonal translation technique in order to multiplex two signals received on different frequency bands. A study case concerning the simultaneous reception of 802.11g and UMTS signals is developed in this article. Theoretical and simulation results show that this type of multiplexing does not significantly influence the evolution of the signal to noise ratio of the signals.

I. INTRODUCTION

OWADAYS the market presents a real interest in the development of telecommunication networks based on radiofrequency systems. Along with the already existing ones, new standards (WiFi, WiMax or the 3G standards) allow the operators to offer new and better services in terms of speed, quality and availability. Consequently, in order to handle this important diversity of telecommunication techniques, there is a growing interest in developing new frontend architectures capable of processing several standards.

For the multistandards research domain we can distinguish two different categories of receivers: non-simultaneous receivers using switching techniques [START_REF] Evans | Development and Simulation of a Multi-standard MIMO Transceiver (Report style)[END_REF][2][3][4] [START_REF] Rampmeier | A Versatile Receiver IC Supporting WCDMA, CDMA and AMPS Cellular Handset Applications[END_REF] and simultaneous receiving receivers. The state of the art of the multistandard simultaneous reception architectures uses the front-end stack-up technique -each chain being dedicated to the reception of only one standard. Nonetheless, this architecture is characterized by some inconveniences such as the bad complexity-performance trade-off, but also the price and the physical size.

The goal of the architecture proposed in this paper, subject of a patent pending [START_REF] Burciu | Technique d'orthogonalisation permettant la réduction de l'occupation spectrale pendant le traitement simultané de deux signaux indépendants (Patent style)[END_REF], is to answer a multistandard simultaneous reception need generated by the ambient or sensor network domain, while also not being restricted to that alone. In order to answer to this need we chose to study the simultaneous reception of an 802.11g signal and a UMTS signal using only one front-end.

The structure assessed in this article implements a novel and innovating multistandard simultaneous receiving architecture using a single front-end. Moreover, the baseband signal has the same bandwidth as the one of the state of the art front-end stack-up structure. This architecture uses the double orthogonal translation technique [START_REF] Mak | Analog-Baseband Architecture and Circuits for Multistandard and Low Voltage Wireless Transceivers (Book style)[END_REF] [START_REF] Rudell | A 1.9GHz Wide-Band IF Double Conversion CMOS Integrated Receiver for Cordless Telephone Applications[END_REF] in order to multiplex the two standards signals by completely overlapping their spectrums at a intermediate frequency. After the second IQ translation the baseband signals are digitized, and then are processed by a signal processing block that separately demultiplexes the baseband component of the two standards. A key point of this structure is the orthogonal mismatches of the translation blocks, which can be meanwhile digitally mitigated by a proper signal processing [START_REF] Rudell | Frequency Translation Techniques for High-Integration High-Selectivity Multi-Standard Wireless Communication Systems[END_REF] [START_REF] Çetin | Adaptive self-calibrating image rejection receiver[END_REF] [START_REF] Traverso | Decision Directed Channel Estimation and High I/Q Imbalance Compensation in OFDM Receivers[END_REF]. In addition, the image frequency impairment is no longer a problem as each of the standards occupies the image band of the other. This paper consists of three parts. Following this introduction, section II describes the double IQ principle, along with the implantation of this technique in a novel multistandard front-end architecture, based on orthogonal multiplexing of its two input branches. The last section details the implementation of such a receiver by specifying its functionality and by presenting some significant simulation results. Finally, conclusions of this study are drawn and the follow-up to this work is provided.

II. MULTI-BAND RECEIVER USING A DOUBLE IQ STRUCTURE

A. The double IQ technique

In wireless telecommunications, the integration of IQ baseband translation structures in the receiver chain has become a common procedure. The simple IQ architecture is usually used in the receiver front-end design in order to reduce the bandwidth of baseband signals treated by the ADC.

Meanwhile, this orthogonal frequency translation technique is also used to eliminate the image frequency default during the translation steps of heterodyne front-end architectures [START_REF] Rudell | A 1.9GHz Wide-Band IF Double Conversion CMOS Integrated Receiver for Cordless Telephone Applications[END_REF], [START_REF] Rudell | Frequency Translation Techniques for High-Integration High-Selectivity Multi-Standard Wireless Communication Systems[END_REF]. The image frequency rejection technique consists in using two orthogonal frequency translations of the signal. In order to realize this double translation, three IQ translation blocks are needed. After the double orthogonal translation, a signal processing block uses the four baseband signals to eliminate the image frequency signal. This type of image rejection structure relies on the advantage of orthogonalizing the useful signal s u (t) and the signal occupying its image frequency band s Im (t). Even though the spectrums of the two signals are completely overlapped after the first frequency translation, this orthogonalization allows the baseband processing to theoretically eliminate the image frequency component while reconstructing the useful one.

This paper assesses the use of the double orthogonal translation technique to develop a multi-standard simultaneous reception front-end. In fact, the main idea refers to a technique allowing the reconstruction, in the baseband domain, of the signal from the image band. This technique relies on a signal processing parallel to that dedicated to the reconstruction of the useful signal. If the image band of the useful signal is occupied by a second useful signal, we can consider that this type of structure can simultaneously treat the two useful signals. In order to fulfill this image band condition a clever choice has to be made concerning the frequency of the local oscillator used during the first orthogonal frequency translation.

In order to realize a theoretical study of this type of multistandard reception dedicated double IQ structure, the useful components s 1 (t) and s 2 (t) of the input s(t) are considered as RF domain signals. Therefore these signals can be modeled by the following:

) 2 sin( ) ( ) 2 cos( ) ( ) ( 1 1 1 1 1 t f t Q t f t I t s π π + = , ( 1)
) 2 sin( ) ( ) 2 cos( ) ( ) ( 2 2 2 2 2 t f t Q t f t I t s π π + = , (2)
where {I k (t)+jQ k (t), k=(1;2)} are their baseband complex envelope. By taking into account this oscillator's frequency condition, the two output signals of the first IQ translation structure s I (t) and s Q (t) can be defined by:

(

where LP[.] stands for low-pass filter and where the intermediate frequency f IF = f 1 -f LO1 =f LO1 -f 2 . These equations highlight the overlapping of the useful spectrum and the image band spectrum after the intermediate frequency translation, as shown in Fig. 1.

In the second IQ frequency translation step, each of the two signals s I (t) and s Q (t) are separately multiplied by two 90° shifted sinusoids. As the frequency of the local oscillators is chosen to be f LO2 =f IF , the four output signals of this second IQ translation block are translated in the baseband domain and are given by the equations:

4 ) ( 4 ) ( )] ( ) 2 [cos( ) ( 2 1 t I t I t s t f LP t s I IF II + = = π , (5) 4 ) ( 4 ) ( )] ( ) 2 [sin( ) ( 2 1 t Q t Q t s t f LP t s I IF IQ - = = π , (6) 4 ) ( 4 ) ( )] ( ) 2 [cos( ) ( 2 1 t Q t Q t s t f LP t s Q IF QI + = = π , (7) 4 ) ( 4 ) ( )] ( ) 2 [sin( ) ( 2 1 t I t I t s t f LP t s Q IF QQ - = = π . (8)
The four output signals contain the multiplexed baseband translated information of the two RF components s 1 (t) and s 2 (t). For a mono-standard image rejection front-end architecture, only the useful component s u (t) is interesting and therefore reconstructed by a single signal processing. But both of the baseband translated information can be separately demultiplexed by two dedicated signal processing, detailed by:

)] ( ) ( [ ) ( ) ( ) ( 1 t s t s j t s t s t s IQ QI QQ II BB - + + = , (9) )] ( ) ( [ ) ( ) ( ) ( 2 t s t s j t s t s t s QI IQ QQ II BB + + - = . (10)
Each of these series of operations reconstructs one of the two components while eliminating the other. In fact, by developing ( 9) and ( 10) using ( 5), ( 6), ( 7) and ( 8), we obtain:

{s kBB (t)=I k (t)+jQ k (t),
k=(1;2)}, the same baseband characterizations as those of the RF input signals s 1 (t) and s 2 (t).

Usually, for the mono-standard image rejection architecture, only one of the treatment techniques is implemented in the analog domain so that only two signals have to be digitized instead of four in the case of a digital signal processing. But, if we wish to realize simultaneously the two dedicated signal processing, the four baseband signals have to be digitalized and then used to do the demultiplaxation step in the digital domain.

B. Theoretical consideration on the implantation of multiband double IQ architecture

All the studies presenting the integration of the double IQ technique use this method in order to cancel the image frequency default in a mono-standard reception front-end.

Here we propose implement it in a novel multistandard simultaneous reception front-end architecture (Fig. 2). The input stages of the front-end are parallelized, each branch being dedicated to the processing of only one frequency band. This way, the signal from the two different frequency bands can be separately received by a dedicated antenna, filtered and amplified by dedicated RF filters and LNAs respectively. Another key element of this structure is the power control realized in parallel for the two signals. As it will be shown below this parallel power control step allows a better rejection of the complementary standard during the digital demodulation. Once the signals are properly filtered and amplified, an addition step process the two signal in order to generate the input signal of the double IQ structure. After the double IQ frequency translation the four baseband signals are digitized and the two dedicated signal processing demultiplex the two useful signals.

As presented in the previous section, the double IQ technique allows, for ideal orthogonal mismatches conditions, a theoretically perfect rejection of the image band while reconstructing the useful signal. For the receivers using heterodyne process, the image rejection ratio is the ratio of the intermediate frequency signal level produced by the desired input signal to that produced by the image band signal. For a double IQ structure, the image rejection ratio (IRR) depends on the gain and phase mismatches between the two branches of the IQ translation structures, and especially on the mismatches of the first one as the frequency translation is generally the highest. The orthogonal mismatches are caused by design and layout defaults such as different line length between the two branches and non identical mixers, which generate phase and respectively gain mismatches [START_REF] Traverso | Decision Directed Channel Estimation and High I/Q Imbalance Compensation in OFDM Receivers[END_REF]. Supposing that the first IQ stage has a gain mismatch ∆A and a phase mismatch ∆θ, the final IRR can be modeled by the equation below [START_REF] Çetin | Adaptive self-calibrating image rejection receiver[END_REF]. [START_REF] Çetin | Adaptive self-calibrating image rejection receiver[END_REF] For a receiver implementing this kind of architecture, the image band rejection is accomplished through a combination between the front-end's input elements: antenna, external RF filter, LNA (Low Noise Amplifier) on one hand, and the image rejection technique achieved by the double-conversion configuration on the other hand. The state of the art frontend's input elements can realize an image frequency rejection of up to 40 dB depending on the choice of the intermediate frequency.

)] ( ) 2 [sin( ) ( 1 t s t f LP t s LO Q π = 2 ) 2 cos( )] ( ) ( [ 2 ) 2 sin( )] ( ) ( [ 2 1 2 1 t f t Q t Q t f t I t I IF IF π π + + - = 2 ) 2 sin( )] ( ) ( [ 2 ) 2 cos( )] ( ) ( [ 2 1 2 1 t f t Q t Q t f t I t I IF IF π π - + + = )] ( ) 2 [cos( ) ( 1 t s t f LP t s LO I π =
      ∆ ∆ + - ∆ + + ∆ ∆ + + ∆ + + = ) cos( ) A 1 ( 2 ) A 1 ( 1 ) cos( ) A 1 ( 2 ) A 1 ( 1 log 10 ) ( 2 2 θ θ dB IRR
In order to provide sufficiently high image rejection to meet, for example, the WLAN 802.11g standard, an IRR of at least 80 dB is needed. In order to achieve this 80 dB IRR it is shown [START_REF] Çetin | Adaptive self-calibrating image rejection receiver[END_REF] that only 0.01 dB gain mismatch and 0.1 degrees of phase mismatch are allowed for each of the IQ blocks-this way, the remaining 40 dB of IRR are realized using the image rejection technique.

This high degree of matching is not achievable using only good design and layout techniques, additional digital signal treatment techniques have to be employed in order to achieve this performance. One of these techniques has been developed in the digital domain using an LMS (Least mean square) algorithm [START_REF] Çetin | Adaptive self-calibrating image rejection receiver[END_REF]. The results show an image rejection ratio due to the front-end architecture reaching up to 70 dB. Therefore we can assume that the total IRR of a classical double IQ image rejection receiver reaches 110 dB. This level of image rejection allows the elimination of the external band-pass filter from the receiver's design.

In comparison to the single antenna double IQ image rejection architecture, for the multiband architecture assessed here, the addition of the parallel branches' outputs generates supplementary parasitic signals that can degrade the final SNR (Signal to Noise Ratio) of the two useful signals. Each of the two antennas receives a signal made of two componentss 1 (t)+s' 2 (t) for the A 1 antenna and s' 1 (t)+s 2 (t) for the A 2 antenna, where s 1 (t) and s' 1 (t) are the same transmitted signals after two different propagation channels, as well as s 2 (t) and s' 2 (t). The parasitic components s' 2 (t) and s' 1 (t) are filtered by the input stage of each dedicated branch -antenna, RF band filter, LNA -but even when attenuated like this, these components have to be taken into account while studying the useful signals' SNR evolution.

In fact, the output signal of the adder is mainly composed of four components:

) ( ' ' ) ( ' ' ) ( ) ( ) ( 2 2 1 1 2 2 1 1 t s G t s G t s G t s G t Adder out ⋅ + ⋅ + ⋅ + ⋅ = (12)
where the coefficients G 1 , G 2 , G' 1 and G' 2 are the gains that the two input parallel branches of the receiver induce to each of the four components.

In order to evaluate the SNR evolution of the useful signal s 1BB (t) after the demultiplexing stage, the evolution of the parasitic signals s' 1 (t), s 2 (t) and s' 2 (t) compared to that of the useful signal s 1 (t) have to be taken into account:

• The s' 2 (t) signal is attenuated by the input blocks of the branch dedicated to the treatment of s 1 (t). The state of the art of the antennas, of the RF band filters and of the LNA can generate a 40 dB rejection of s' 2 (t) for an architecture such as that of Fig. 2. In addition to these 40 dB of initial rejection, the double IQ structure, along with the LMS digital processing, will achieve up to 70 dB of signal rejection from the image band of the useful signal. This means a rejection of up to 110 dB of the parasitic signal s' 2 (t).

• The s 2 (t) signal undergoes up to 70 dB of rejection compared to the useful signal s 1 (t). This rejection is generated by the double IQ structure, similar to that of s' 2 (t) as the two signals occupy the same frequency band after the addition of the two branches. In addition to this rejection, another element to be taken into account, when studying the influence of s 2 (t) on the SNR of s 1 (t), is the dedicated power control stage. In fact the worst case scenario is when s 1 (t) is at its lowest power level and the parasitic signal s 2 (t) is at its highest. This means that this is the case when s 2 (t) has its highest effect on the degradation of the useful signal. In this case, the power control will amplify s 1 (t) compared to s 2 (t) before the addition step, which means that the influence of the parasitic signal on the useful signal is decreased. The state of the art of the power controls [START_REF] Xiao | A High Dynamic Range CMOS Variable Gain Amplifier for Mobile DTV Tuner[END_REF] can provide up to 35 dB between minimum and maximum amplification. Therefore, for the worst case scenario, it can be considered that the s 2 (t) signal undergoes a 105 dB rejection compared to the useful signal s 1 (t).

• The s' 1 (t) signal, along with s 2 (t), is one of the two components of the radiofrequency signal received by the A 2 antenna. This signal doesn't undergo a rejection due to the double IQ structure as it occupies the same frequency band as the useful signal after the addition step. The only supplementary rejection that s' 1 (t) will undergo compared to the useful signal s 1 (t) is realized by the input elements of the front-end. In fact, as this signal is received by the branch dedicated to s 2 (t), the input elements will realize an attenuation of up to 40 dB. As s' 1 (t) and the useful signal s 1 (t) are not received by the same antenna, even if they are generated by the same transmitter, a phase shift and a gain shift between the two appears during the hertzian transmission. For an AWGN (Additive White Gaussian Noise) transmission channel, the phase shift between the two signals can go from 0 to 360 degrees, but the gain shift can be ignored. For this case, where the two signals s' 1 (t) and s 1 (t) have the same power level at the input of the front-end, the 40 dB of attenuation of the parasitic signal s' 1 (t) achieved before the addition step assures a 40 dB SNR of the useful signal s 1 (t) in the baseband domain after the digital signal processing. This SNR level insures a very good reception quality.

In the case of a multipath channel, where the gain shift as well as the phase shift can not be ignored, a new solution can be implemented. It consists in using a digitally controlled RF phase shifter that will cancel the phase shift between s' 1 (t) and s 1 (t) before the addition step. This way s' 1 (t) is no more a parasite, but a useful component during the digital signal processing that reconstructs the s 1 (t) signal. This solution will be developed in a future document.

Considering all this arguments concerning the additional parasitic components, it can be considered that the SNR evolution of the useful signal is the same as that of a signal treated by a classic mono-standard receiver. Therefore the single front-end multistandard simultaneous reception structure presents similar performance as a front-end stack up structure.

Meanwhile, a complexity comparison study reveals that the single front-end structure is less complex, much more compact and presents a higher on-chip integration level. The number of components is smaller because of the use of a single local oscillator for the first frequency translation compared to the two dedicated oscillators of the front-end stack-up receiver. Furthermore, the greatest advantage of the single front-end receiver is the elimination of the image rejection RF filters. In fact these external components, used to mitigate the impact of the image band signal, can not be integrated on-chip. In the proposed architecture, these components are replaced by a cheaper, on-chip and especially more flexible signal treatment. In the following section, a validation of the theoretical result will be presented.

III. IMPLEMENTATION AND PERFORMANCE

The high image rejection multistandard receiver using a double IQ front-end architecture allows the simultaneous reception of two different frequency bands. In order to validate the theoretical study, a first implementation was made and simulated using the ADS software (Advanced Design System) provided by Agilent Technologies [13]. The selection of the standards used for this implementation was influenced by their complexity and their deployment as well as by their complementarities in terms of range. These parameters, along with a direct utility of such a structure in the sensor network domain, directed our choice towards the 802.11g and the WCDMA-FDD standards. Regarding this choice, an important point that should be underlined is the implementation constraints imposed by the standards dynamics, but especially by those of the WCDMA-FDD. These dynamics constraints make this standards choice implementation the most delicate.

In order to realize a good performance comparison between the multistandard single frond-end receiver and the front-ends stack-up, the blocks used during the simulation have the same typical metrics (gain, noise figure, 1 dB compression point, third order interception point) for both cases. By taking into account all these metrics, a global characterization of the multistandard single front-end receiver is made (Table 1).

During this study, it will be considered that the metrics of the blocks used by the two parallel input branches are similar and therefore the performance offered by the front-end for the two standards are identical in terms of noise figure, gain and third order intercept point.

The first results (Fig. 3) represent the evolution of the two standards BER (Bit Error Rate) depending on their SNR level at the antenna. This BER evolution was observed using both the multistandard single front-end and the front-end stack-up structures as receivers. The wireless transmission channel was chosen to be AWGN while the translation blocks are considered to be ideal in terms of IQ mismatch. During the simulation of the reception of one of the standards the antenna power level of the complementary standard is set to the maximum level so that its parasitic influence is the highest.

Under these conditions the two standards BER evolutions are almost identical for both types of receivers. In fact, using the multistandard single front-end receiver allows the complete rejection of one of the standards during the digital final signal processing as the IQ mismatches are ignored for the moment. The theoretical study underlines the importance of the IQ mismatches for the performance of a receiver using a double orthogonal translation. Indeed, for this type of receiver, it is necessary to realize a good rejection of the image frequency band, which is occupied by the complementary standard. In fact, this rejection relies on two different methods: the gain control realized in the RF domain and the image band rejection realized by the IQ structure, depending on the IQ mismatches. In order to estimate the impact of the orthogonal mismatches on the evolution of the two standards BER a second set of simulations are realized. The metrics of the receiver used during these simulations are the same as those presented in Table 1, except for the gain dynamics of the AGC which take two different values of 35 dB and 40 dB. Concerning the power level of the signals at the antenna, while testing the influence of the IQ mismatches on the BER of one of the standards, the power level of the complementary standard is maximal. Meanwhile, the power level of the concerned standard is at its reference level (the minimum power level that ensures a certain service quality). For our study case, the concerned standard power level leads to a 10 -3 level of BER, when considering ideal IQ mismatch conditions.

For each standard, two normalized BER evolution are presented in Fig. 4, for an AGC gain dynamics of 35 and respectively 40 dB. Depending on the AGC dynamics the complementary signal will be attenuated by a certain amount at the input of the antenna compared to the useful signal. Another rejection step is then realized by the IQ structure, but this one is dependent of the orthogonal mismatches.

Results show that the BER performance of the receiver depends on one hand of the AGC gain dynamics and on the other hand on the orthogonal IQ mismatches. For an AGC gain dynamics varying from the state of the art 35 dB to 40 dB, the BER can triple for the same power levels and mismatch configuration. It can also be observed that, under significant orthogonal mismatches conditions, the influence of the complementary standard (at its maximum power level) on the useful one's SNR leads to a BER six times higher.

The graphs of Fig. 4 rely on simulations of the multistandard receiver architecture which does not integrate the digital signal processing (LMS) dedicated to the mitigation of the orthogonal mismatches [START_REF] Çetin | Adaptive self-calibrating image rejection receiver[END_REF]. The use of these signal processing techniques reduces the final influence of the complementary signal on the useful one's SNR. It can be considered that the final orthogonal mismatches are reduced to an equivalent level of 0.01 dB of gain mismatch and 0.1 degrees of phase mismatch, corresponding to a 70 dB rejection of the complementary signal from the image frequency band. For these levels of orthogonal mismatches, the influence of the complementary standard on the useful one can be ignored as it can be observed on the results shown in Fig. 4. Therefore the theoretical study concerning the rejection of the parasitic signals presented in section II is validated here.

IV. CONCLUSIONS

In this article, a novel multistandard simultaneous reception architecture was presented. Expected performance of its implementation has been presented for a particular study case -simultaneous reception of two signals using the 802.11g and UMTS standards. Compared to the stack-up dedicated frontends structure, this architecture uses an innovating double IQ multiplexing technique in order to use a unique front-end to receive both standards. In addition to the complexity decrease offered by the use of a single front-end, the signal processed by the analog part of the receiver presets an excellent spectral efficiency as the two standards spectrums are overlapped after the first IQ stage. Knowing that the power consumption of the analog part of the receiver is directly dependent on the bandwidth of the signal, the excellent complexity-powerperformance trade-off becomes obvious. Despite the use of a demultiplexing block in the digital domain, the power consumption of the receiver is lower compared to that of the actual state of the art. The key point of this structure is the rejection of the complementary standard during the demultiplexing stage. As a matter of fact, the rejection level depends of the orthogonal mismatches of the frequency translation blocks; a complete study of their influence has been presented.

The issues that still have to be addressed turn around the implementation of a digital processing used to mitigate the IQ impairments. Another interesting idea concerns a possible multi-antenna multistandard simultaneous reception technique using the principles of the architecture assessed in this article.

Fig. 1

 1 Fig. 1 Spectral evolution of the signals in a double IQ structure translation multiplies the input signal s(t) with two 90° shifted sinusoids, generated by a local oscillator having a frequency f LO1 =(f 1 +f 2 )/2. This choice of the oscillator frequency fulfils the image band condition: each of the two signals must occupy the image frequency band of the other before the first orthogonal frequency translation.By taking into account this oscillator's frequency condition, the two output signals of the first IQ translation structure s I (t) and s Q (t) can be defined by:

Fig. 2

 2 Fig. 2 Multiband simultaneous reception architecture using the double IQ structure

Fig. 3

 3 Fig.3802.11g and WCDMA BER evolution during multistandards simultaneous reception using two types of receivers: the classical front-end stack-up and the multistandards single front-end receiver

Fig. 4

 4 Fig. 4 802.11g and WCDMA BER evolution versus gain and phase imbalance of the IQ translation blocks. Two series are dedicated to each BER evolution for an AGC gain dynamics of 35 and respectively 40 dB

TABLE I METRICS

 I

		2
			802.11g
	normalized BER	1,2 1,4 1,6 1,8
		1
		0	0,2	0,4 PhaseIm balance(deg.) 0,6	0,8	1
						USED FOR THE SIMULATION OF THE MULTISTANDARD SINGLE
						FRONT-END RECEIVER
						Symbol	SI UNIT	VALUE
						NF	dB	6
						IIP3	dBm	-12
						Maximal Gain AGC	dB	25
						Minimal Gain AGC	dB	-10

35 dB AGC 802.11g 40 dB AGC WCDMA 35 dB AGC WCDMA 40 dB AGC


1-Introduction

In today wireless system, the principle of combining OFDM (Orthogonal Frequency Division Multiplexing) scheme and multi-antenna processing (MIMO or SIMO architectures) is often use and offer an important increase of achievable performances. Another objective is to propose more and more integrated systems, and to reduce the space and the cost of the wireless designs. In this way, homodyne down conversion structure appears to be very seductive. Unfortunately, multiantenna systems and complexity reducing are really contradictive: increasing the number of multibranches leads to RF components duplication and increase in space occupation. In order to decrease systems cost, the dirty RF concept [START_REF] Fettweis | Dirty RF, a new Paradigm[END_REF] which aims at identifying constraints that can be relaxed on hardware component is an interesting solution. However, OFDM technique is really sensitive on RF impairments [START_REF] Woo | Combined Effects of RF impairments in the Future IEEE 802.11n WLAN Systems[END_REF] and numerical processing have to be used in order to estimate and correct nonidelallities of the hardware stage to minimize the decreasing of performances transmission due to dirty RF. Especially in the case of the use of an homodyne structure, when RF impairments are much more critical, as compared with super-heterodyne receivers [START_REF] Brandolini | Toward Multi-Standard Mobile Terminals -Fully Integrate Receivers Requirements and Architecture[END_REF]. Three most critical RF impairments have been studied in the special case of OFDM receiver: phase noise [START_REF] Amrmanda | Understanding the effects of Phase Noise in Orthogonal Frequency Division Multiplexing (OFDM)[END_REF][START_REF] Corvaja | Phase Noise Spectral Limits in OFDM Systems[END_REF] and frequency offset [START_REF] Pollet | BER Sensitivity of OFDM Systems to Carrier Frequency Offset and Wiener Phase Noise[END_REF][START_REF] Ma | Effect of frequency offset on BER of OFDM and Single Carrier Systems[END_REF] due to local oscillator non idealities and IQ imbalance due to mismatch between the both I and Q branches especially critical in an homodyne structure [START_REF] Windish | Performance Degradation due to IQ imbalance in Multi-Carrier Direct Conversion Receivers: A Theoretical Analysis[END_REF][START_REF] Liu | Impact of IQ imbalance on QPSK OFDM QAM detection[END_REF]. For each considered impairments numerical processing ensuring an impairments defaults exist, but error can still occure and the use of these algorithm increase the numerical complexity of the developed receiver. In this context, the aim of this work is to point out the potential of naturally compensating the RF impairment presented before jointly with the fading effects by the usage of a classical SIMO antenna processing algorithm, thus without increasing numerical complexity in the special context of the often used 802.11g WLAN OFDM transmission [START_REF]Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) specifications: Further Higher Data Rate Extension in the 2.4 GHz Band[END_REF]. In this current article, main of the presented results are obtained by taking into account most as possible realistic working condition by the use of a global system evaluation scheme based on Agilent Technologies equipments [START_REF] Morlat | A Global System Evaluation Scheme for Multiple Antennas Adaptive Receiver[END_REF]. As presented in [START_REF] Morlat | A Global System Evaluation Scheme for Multiple Antennas Adaptive Receiver[END_REF], realistic consideration of the characteristics of transmission channel, the antenna coupling and also the channel correlation would indeed allow a fast and effective design of multiantenna wireless systems in order to obtain an important design cycle reducing.

The first part presents the used global system level approach and the associated tools with some theoretical, simulated and measured results in order to validate the developed testbed radio plateform. In the second part, the three presented RF impairments are considered one by one in a Zero-IF down conversion structure and their effect on OFDM transmission performances are detailed. In the last part, after a short description of the SIMO processing that is used, the natural compensation of each RF impairment it is possible to obtained taking advantage of space diversity is presented. Results given in this part are obtained by taking into account different measured propagation channels (AWGN or fading channels) and a complete 802.11g structure.

2-Description and validation of the global simulation scheme

This part of the work presents the global simulation scheme developed in order to efficiently simulate and measure all part of a SIMO (Single Input Multiple Output) 802.11g transmission chain from the global system level down to the particular model of RF components or fading channels. [START_REF] Morlat | A Global System Evaluation Scheme for Multiple Antennas Adaptive Receiver[END_REF] presents the testbed radio platform developed using Agilent Technologies equipments: the ADS software and the measurement hardware (two arbitrary waveform generators -ESG 4438Cand a vector spectrum analyzer with two RF inputs -VSA 89641). Due to the capabilities of the developed platform, realistic 2x2 MIMO transmissions, extending measurements up to 6 GHz, with a received bandwidth analysis of 36 MHz could be experimented (Fig. 1). In order to validate the developed testbed platform, the first tests were done for an uncoded 36 Mbps 802.11g SISO transmission, under an AWGN propagation channel simulating a perfect Zero-IF base band conversion stage. For such a data rate, a 16-QAM modulation scheme is used. The theoretical Symbol Error Probability s P for a M-QAM is computed considering two independent M PAM modulations on both I-and Q-channels:

( ) 2 1 1 M s P P - - = (1) with :
( )

        -       - = b M M M Q M P γ 1 log 3 1 1 2 2 (2)
where b γ denotes the energy per bit to noise ratio (Eb/No) and Q the Gaussian function. The relationship between the Signal to Noise Ratio (SNR), the transmission data rate, the coding rate c R , the signal bandwidth (BW) and b γ is given by:

b c BW R rate data SNR γ ⋅ ⋅ = _ (3)
Where BW = 20 MHz, and c R =3/4 in the case of a 802.11g transmission.

In the end, the theoretical Bit Error Probability (BER) is:

( )

M P BER s 2 log = (4)
Fig. 2

3-RF impairment SISO effects

Notations

Three RF impairments at the receiver side are taken into account. Our work focuses on the effect of frequency offset, the local oscillator (LO) phase noise, the I-Q imbalance (both gain and phase). Fig. 3 presents the structure of a Zero-IF converter including the RF impairments considered in our study. To ease readability, the low pass filter stage and the ADC are intentionally omitted even though they are also responsible for I-Q imbalance. θ (rad) and α (V) refer to phase and gain imbalance respectively; ∆f (Hz) is the mismatch between the carrier frequency f c of the emitter and the receiver; φ is the phase noise due to local oscillator imperfection. The gain imbalance G is given in dB by:

      - + = α α 1 1 log 20 G (5)
The frequency offset value is often expressed in parts per million: x(t), y(t) and z(t) (to be introduced later) denote the complex emitted signal, the received signal after channel propagation without RF impairments, and the baseband signal after RF impairment degradation in the temporal domain, respectively. h(t) and n(t) refer to the temporal channel impulse response and the additive white Gaussian noise, respectively. The same notation using capital letters refers to the transposed signals in the frequency domain. ⊗ is the convolution operator, and *

( ) c ppm f Hz f f ∆ = ∆ (6)
s RF (t) π/2 + θ/2 cos(2π(f c -∆f)t +φ(t)) I(t) Q(t) base band processing 1+α/2 1-α/2 π/2 -θ/2
denotes complex conjugation. M is the number of sub-carrier used in the OFDM system (M=64 for a 802.11g transmission).

Most of the presented performances are given in relative BER (i.e. ratio between BER with imperfection and BER with perfect RF conditions). Reference BER is about 5.10 -3 . This is obtained for a perfect simulated Zero-IF down converter. To have a precise estimation of relative BER, 3000 measurements or simulated frames of 1600 bit are performed.

Phase noise effect

Phase noise effect can be separated into a multiplicative and an additive part in single-input single output OFDM systems. Due to phase noise impairments φ(t), the temporal received signal is defined by

( ) ( ) ( ) [ ] ( ) ( ) t n e t x t h t z t j + ⋅ ⊗ = ϕ (7)
After removing the cyclic prefix and applying the DFT on the remaining samples, the demodulated carrier amplitudes

( ) k Z m with k the sub-carrier index ( 1 0 - < < M k
) of the m th OFDM symbol are

given by [START_REF] Petrovic | Properties of the Intercarrier Interference due to phase noise in OFDM[END_REF] ( )

( ) ( ) { ( ) ( ) ( ) k N S l H l X S k X k H k Z m ICI M k l l k l m CPE m m m m + + = ∑ - ≠ = - 4 4 4 3 4 4 4 2 1 1 , 0 , 0 , (8)
The term i m S , corresponds to DFT of one realization of ( ) n j e ϕ during the m th OFDM symbol:

( ) ( ) ∑ - = + - = 1 0 / 2 , 1 M n n M i n j i m e M S ϕ π (9)
The received sample in frequency domain [START_REF] Armstrong | Analysis of new and existing methods of reducing intercarrier interference due to carrier frequency offset in OFDM[END_REF] exhibits two terms due to phase noise. All phase noise correction schemes are based on Common Phase Error (CPE) estimation and mitigation, and are relatively efficient in WLAN OFDM systems for small phase noise working conditions. However, under high phase noise, the Inter-Carrier Interference (ICI) term dominates over the CPE. In this case, the phase noise suppression becomes really difficult [START_REF] Pollet | BER Sensitivity of OFDM Systems to Carrier Frequency Offset and Wiener Phase Noise[END_REF] and the performances requirement may not be guaranteed due to the loss of orthogonality between sub-carriers.

The most common way of characterizing oscillator's phase noise is its Power Spectral Density (PSD), S φ (f), where f is the frequency offset to the carrier frequency. The phase noise effect is commonly characterized as a Wiener process corresponding to a PSD slope of -20dB/dec. In this case, the critical parameter of the oscillator quality is the single sideband -3 dB bandwidth of the Lorentzian power density β. The phase noise variance is hence computed as T πβ σ ϕ 4 2

=

, with T being the sample time [START_REF] Pollet | BER Sensitivity of OFDM Systems to Carrier Frequency Offset and Wiener Phase Noise[END_REF]. [START_REF] Amrmanda | Understanding the effects of Phase Noise in Orthogonal Frequency Division Multiplexing (OFDM)[END_REF] gives the variance expression for a model more general than the classical Wiener process, closer to the measurements and which studies the influence of different phase noise spectrum slopes on the system performance:

( )df f S ∫ ∞ ∞ - = ϕ ϕ σ 2 (10)
When no phase noise suppression algorithm is applied, [START_REF] Amrmanda | Understanding the effects of Phase Noise in Orthogonal Frequency Division Multiplexing (OFDM)[END_REF] shows that the Energy per Symbol Ratio degradation D (in dB) caused by phase noise defined by any PSD spectrum -assuming phase noise variance is small (σ 2 <<1) -is not dependent on the number of sub-carriers, and is given by: ( )

s D γ σ 2 1 log 10 + = ( 11
)
s γ denotes the initial Energy per Symbol Ratio without phase noise impairments. We observe relatively good matching between the results obtained with the developed ADS 802.11g transmission scheme and the analytical results. However, it is important to note that in many WLAN OFDM receivers, CPE correction is applied (see [START_REF] Morlat | On Relaxing constraints on Multibranches RF Front-End for a SIMO OFDM -A Global System Evaluation Scheme[END_REF] for example). As CPE can easily be suppressed, only ICI distribution has to be considered to compute SNR penalty. The properties of the ICI term were previously studied by several authors. In many cases, only a Wiener process is considered and the ICI term is assumed to be complex Gaussian distributed. This approximation is valid only for small phase noise process [START_REF] Petrovic | Properties of the Intercarrier Interference due to phase noise in OFDM[END_REF]. As explained in [START_REF] Corvaja | Phase Noise Spectral Limits in OFDM Systems[END_REF], it is important to take into account different slopes values in the phase noise spectrum to provide applicable models for hardware design. Our developed test-bed answers this need, but it is not the aim of this work.

Frequency offset

The complex baseband received signal affected by frequency carrier mismatch between the receiver and the emitter's local oscillator is:

( ) ( ) ( ) [ ] ( ) t n e t x t h t z t j f + ⋅ ⊗ = ∆ -π 2 (12)
We define the normalized frequency offset ε as the ratio between the carrier frequency offset in Hz and the adjacent OFDM sub-carrier spacing. In the expression below, BW is the occupied signal bandwidth (recalling that BW = 20 MHz for 802.11g transmissions): [START_REF] Armstrong | Analysis of new and existing methods of reducing intercarrier interference due to carrier frequency offset in OFDM[END_REF] gives the expression of the sampled signal for the sub-carrier k (k=0,…, M-1) after the receiver fast Fourier transform processing:

BW M f ⋅ ∆ = ε (13)
( ) ( ) ( ) { ( ) ( ) ) ( 1 , 0 0 k N l X l H S S k X k H k Z ICI M k l l k l CPE + + = ∑ - ≠ = - 4 4 3 4 4 2 1 (14)
Where the sequence k l S -is given by:

( ) [ ] ( ) ( )       + -       - -       + - + - = ε π ε π ε π l k N j k l e k l M M k l S 1 1 sin sin (15)
For a quick evaluation, [START_REF] Pollet | BER Sensitivity of OFDM Systems to Carrier Frequency Offset and Wiener Phase Noise[END_REF] gives a well-known good approximation of the Energy Per Symbol

I-Q imbalance

The imbalance can be modelled either symmetrical or asymmetrical (these are equivalent representations). In the symmetrical method which is used here [START_REF] Tubbax | Compensation of IQ imbalance in OFDM systems[END_REF], each arm (I and Q) experiences half of the phase and amplitude errors. It has been shown that multi-carrier signals are affected by a mutual inter-carrier interference between each pair of symmetric sub-carriers [START_REF] Liu | Impact of IQ imbalance on QPSK OFDM QAM detection[END_REF].

The perfect baseband received signal after channel propagation is given by: ( ) ( ) ( ) ( )

t n t x t h t y + ⊗ = (17)
[10] gives the expression of the imperfect baseband signal due to I-Q imbalance

( ) ( ) ( ) * t y t y t z ⋅ + ⋅ = λ µ (18)
Injecting ( 18) in (17), one can show that

( ) ( ) ( ) ( ) [ ] ( ) ( ) ( ) [ ] * t n t x t h t n t x t h t z + ⊗ ⋅ + + ⊗ ⋅ = λ µ (19)
where µ and λ depend on the I-Q imbalance:

             -       =       +       = 2 sin 2 cos 2 sin 2 cos θ θ α λ θ α θ µ j j ( 20
)
In the frequency domain, for each data sub-carrier k of the OFDM signal (k ∈ [-26, 26]), the received signal is given by [START_REF] Liu | Impact of IQ imbalance on QPSK OFDM QAM detection[END_REF] shows that the error ∆ between the estimated received symbol and the emitted symbol for each sub-carrier k due to the I-Q imbalance is [START_REF] Zareian | Analytical BER Performance of M-QAM-OFDM Systems in the Presence of IQ imbalance[END_REF] gives the analytical BER expression for M-QAM OFDM systems in the presence of I-Q imbalance, based on constellation degradation. Fig. 6 presents, in the case of AWGN transmission, uncoded 16 QAM-OFDM BER performances by using the presented radio communication platform. Simulated and measured data are compared with theoretical results. In this figure, phase imbalance and gain imbalance are respectively equal to θ=5° and α=0.6 dB.

( ) ( ) ( ) ( ) ( ) ( ) ( ) * * * k N k N k X k H k X k H k Z - + + - - + = λ µ λ µ (21)
( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) * * * 1 1 k N k H k N k H k X k H k H k - + + - - = ∆ µ λ µ λ (22)

4-RF impairments mitigation using SIMO processing

The SIMO 802.11g architecture

Spatial diversity is very often considered in today radio receiver ensuring BER performances improving by the use of SIMO processing. Instead of using only one antenna at the receiver side, N antennas are used to take advantage of the several versions of the same emitted signal, combining these N signals and increasing the SNR of the received signal.

In our case, a Minimum Mean Square Error (MMSE) approach is used as the optimization criterion using a Sample Matrix Inversion (SMI) technique [START_REF] Gupta | SMI adaptive antenna arrays for weak interfering signals[END_REF] to estimate the optimal complex weight to apply on each received branch. These coefficients are computed using the knowledge of the two long preambles (corresponding to two OFDM symbols) at the beginning of each 802.11g frame in the frequency domain. Considering a constant propagation channel response during the complete frame duration, the SMI processing ensures a very good trade-off between BER performance and computation complexity.

In our equipment, the two incident signals are recorded simultaneously by the VSA, and the baseband signals are re-injected in the ADS simulated multi-antenna structure. Simulated and measured performances of 1x2 SIMO structures for different propagation channels and in realistic working conditions are presented in [START_REF] Morlat | Measured Performances of a Multistandard SIMO receiver[END_REF]. In the next part of this work, we present the effect on RF impairments due to a non-ideal Zero-IF baseband converter. Even if adapted numerical processings already exist to decrease and correct RF impairments, some errors can still occur. That is why it seems to be an interesting study to detail the natural compensation due to SMI properties it is possible to reach. Furthermore, this kind of study corresponds well on the context of a global system performances presentation.

Phase Noise Mitigation

As detailed in section 3, local oscillator phase noise impacts M-QAM OFDM transmission significantly. Even if CPE can be removed, residual errors due to ICI still exist. Furthermore, the design of precise local oscillators ensuring a decreasing phase noise variance is expensive. That is why we present, in this part, the achievable performances in a SIMO configuration for different propagation channels including phase noise impairment. The considered phase noise PSD is modelled as follow: Fig. 7 gives the relative BER performances obtained by simulation considering a complete 802.11g transmission scheme: both phase tracking ensuring CPE suppression and FEC decoding are applied.

AWGN and frequency selective channels with fading are used to simulate the wireless propagation.

The parameter β takes different values between 1 kHz and 30 kHz, i.e. between 3.2.10 -3 and 9.6.10 -2 the OFDM sub-carrier spacing of 312.5 kHz.

As showed in Fig. 9, SIMO processing does not mitigate phase noise impairments significantly, especially under the AWGN case. This result can be explained by the fact that the optimal complex coefficients applied on each arm of the SIMO receiver are estimated with only the 128 samples of the 802.11g frame preamble (2 OFDM symbols). However, as reported in ( 8) and ( 9) the phase noise contribution is not constant during a whole frame, and takes different values at each new OFDM symbol; the SIMO weights become obsolete. This observation could be mitigated if the transmission is under a frequency selective channel. In this case, the multi-antenna receiver is able to take advantage of spatial diversity and ( )

l k S k H
-can take small values on each received branch, ensuring a decreasing of the ICI term.

Frequency Offset Mitigation

In a common 802.11g receiver, digital stages of frequency offset estimation and correction are implemented [START_REF] Gil Jimenez | Design and Implementation of Synchronization and AGC for OFDM-based WLAN Receivers[END_REF]. However, residual frequency offset can occur, and introduce errors in the propagation channel estimation. SIMO performance evaluation in a realistic 802.11g structure seems to be an interesting choice and is detailed in the next part.

Fig. 8 presents the BER relative performances for different frequency offset values, in the range of 0 to 55 kHz (i.e. 0 to 23 ppm). These results were obtained by treating measured signal in the case of an NLOS with fading transmission. Even though the frequency carrier offset takes an important value of 50 kHz, the impact of this impairment that is obvious for a SISO transmission is really well mitigated using diversity.

Even if the analytical impact of frequency offset and phase noise on the received complex samples are almost the same, we observe that SIMO processing ensures larger mitigation of frequency offset than of phase noise impairment. This is due to the fact that the ICI term is constant in the case of a transmission impaired by frequency offset. Hence, this constant error is taken into account by the SIMO processing and complex weights computed by MMSE algorithm are still optimal during the entire frame (from a frequency offset mitigation point of view).

I-Q mismatch mitigation

At first, SMI gain related to simultaneous measured signals is presented in Fig. 9 for an I-Q phase imbalance in the range of 0° to 10°. The solid line curves represent BER performance for an AWGN propagation channel. This clearly shows that the SMI algorithm efficiently compensates RF impairments impact, even for a large phase imbalance of 10°, and that SIMO processing allows a very good rejection of the jamming effect due to phase imbalance. In the case of a wireless link under a frequency selective and fading channel, performance degradation and their mitigation with the SMI processing are presented with dotted lines on the same figure. SISO performances under AWGN and NLOS working conditions are fairly close, which seems contradict (22). A possible explanation is related to the noise level at the receiver input. In the present case, the reference BER value of 5.10 -3 is relative high, so the contribution of the first term in (22) is not significant. Furthermore, the I-Q impairments mitigation with the SMI processing is less important for frequency selective working conditions than for a transmission under an AWGN channel. This could be explained by ( 22) when reminding that the same complex weights are applied to all the sub-carriers. The mitigation difference clearly appears for phase imbalance of up to 8°, which is an important mismatch. Following the same approach, I-Q gain imbalance influence was studied. Fig. 10 presents the mitigation which can be reached using diversity for different I-Q gain imbalance values from 0 to 1.2 dB. Similar conclusions can be drawn for I-Q gain imbalance mitigation with SMI processing.

Even if the mitigation with the SMI algorithm is less important in multi-path propagation channels than in AWGN conditions, excellent performance can be observed for I-Q gain imbalance values up to 1 dB.

5-Conclusion

We present a comprehensive study of a multi-antenna OFDM homodyne receiver, based on theory, simulation and measurements with a reduced design cycle results. The aim of this approach is to get a realistic view of a complex system's performance without requiring many stages of prototyping.

This study highlights the key points of the RF element design for this kind of receiver.

The multi-antenna approaches, as software radio principles, call to reduce strains on the quality of components to achieve an acceptable cost-performance ratio. We prove that the conventional multiantenna treatment used can significantly reduce damage caused by the most common RF impairments: frequency offset, local oscillator phase noise and IQ imbalance. The multiplication of RF branches can be made cheaply if we consider this natural impairments compensation, without ever having to resort to specific digital processing. The result, however, is that particular attention should be paid to the problems of phase noise, by the means of a strong constraint on the selected components, or by the addition of a dedicated digital processing.

As future work, we plan to assess the same constraints and possible compensations as part of a multi-standard broadband architecture, making it possible to obtain a feasible receiver for applications such as cognitive or opportunistic radio. More results concerning this work are presented in [START_REF] Morlat | On Relaxing constraints on Multibranches RF Front-End for a SIMO OFDM -A Global System Evaluation Scheme[END_REF].

6-References

Figure 1 -

 1 Figure 1 -The 2x2 MIMO connected solution using the interaction between ADS software and Agilent Technologies equipments. Emitter part, channel propagation and receiver part can be studied and modelled.

Figure 3 -

 3 Figure 3 -Zero-IF converter with RF impairments (local oscillator phase noise, frequency offset and I-Q imbalance).

Fig. 4 Figure 4 -

 44 Fig. 4 shows a comparison between simulated and theoretical BER performance for an uncoded 16-QAM OFDM 802.11g transmission in AWGN propagation condition. These results were obtained considering a phase noise with a PSD spectrum for positive frequencies modelled as follows, and which correspond to a phase noise variance of σ 2 = 0.015 rad 2 : -60 dBc/Hz at 1 kHz -90 dBc/Hz at 100 kHz -110 dBc/Hz at > 1 MHz

Fig. 5 compares

 5 Fig. 5 compares BER performances of an uncoded 16-QAM OFDM transmission under an AWGN propagation channel, taking into account a frequency offset equal to 8.3 ppm (∆ f = 20 kHz). The fact that the different curves match validates our structures and measurement methods.

Figure 5 -

 5 Figure 5 -Frequency offset impact on AWGN SISO BER performances (theoretical, simulated and measured data). The frequency offset value is 20 kHz.

Figure 6 -

 6 Figure 6-I-Q imbalance impact on AWGN SISO BER performances (theoretical, simulated and measured data). The phase imbalance value is 5° and the gain imbalance is equal to 0.6 dB.

-

  60 dBc/Hz at β Hz -80 dBc/Hz at β x10Hz -100 dBc/Hz at > β x100 Hz This corresponds to a Wiener phase noise exhibiting a -20dB/dec slope with β the PSD spectrum bandwidth of the phase noise. With β increasing, SNR degrades and hardware components get cheaper.

Figure 7 -

 7 Figure 7 -Phase noise mitigation in AWGN and NLOS simulated conditions. Results are given in relative BER versus local oscillator phase noise characteristics (β)

Figure 8 -

 8 Figure 8 -Relative BER versus frequency offset in NLOS working conditions. SISO and 1x2 SIMO performances are given.

Figure 9 -Figure 10 -

 910 Figure 9 -Relative BER versus I-Q phase imbalance in AWGN and NLOS measured working conditions. SISO and 1x2 SIMO performances are given.

Figure 2 -Uncoded 802.11g AWGN BER performances. Theoretical, simulated and measured data are plotted versus SNR.

	1,0E+00
	1,0E-01
	1,0E-02
	BER
	1,0E-03
	1,0E-04		theory
			simulation
			measure
	1,0E-05
	4	6	8	10	12	14	16	18	20
					SNR -dB