We propose the affine filter bank modulation (AFBM) waveform for enhanced integrated sensing and communications (ISAC) in sixth generation (6G), designed by drawing on concepts from classical filter bank multicarrier modulation (FBMC) theory and recent advances in chirp-domain waveforms, particularly affine frequency division multiplexing (AFDM). Specifically, AFBM exhibits several desirable properties, with emphasis on its remarkably low peak-to-average power ratio (PAPR) and reduced out-of-band emission (OOBE) when bench-marked against the conventional AFDM waveform under doubly-dispersive (DD) channel conditions. In the communications setting, reliable symbol detection is achieved using a tailored low-complexity Gaussian belief propagation (GaBP)-based algorithm, while in the sensing setting, a range and velocity estimation approach is developed that integrates an expectation maximization (EM)-assisted probabilistic data association (PDA) framework to accurately identify surrounding targets. The highlighted performance and benefits of AFBM are validated through analytical and numerical evaluations, including conventional metrics such as ambiguity function (AF), bit error rate (BER), and root mean square error (RMSE), consolidating its position as a promising waveform for next-generation wireless systems.

International audience

Edge computing and IoT devices may have energy constraints that make it challenging to execute complex algorithms. In this paper, we propose the use of Sums of signed Powers of Two (SoPoT) as a quantization method for MultiLayer Perceptron (MLP) Neural Networks (NN) to reduce the computational burden. The so-called Matching Pursuits with Generalized Bit Planes (MPGBP) algorithm efficiently quan-tizes the coefficients of the MLP into SoPoT. It provides a clear tradeoff between the number of Signed Powers of Two (SPT) and the approximation quality, which is advantageous for computation under limited power availability. We evaluate the quantization impact in a detection problem for Multiple-Input Multiple-Output (MIMO) communication, where the Bit Error Rate (BER) for the detection using the SoPoT quantization and infinite precision are compared. The results show that the proposition reduces the model’s computational burden at the expense of performance losses, resulting in a tradeoff between computational cost and performance.

International audience

The deployment of private mobile networks is expected to revolutionize industries such as factory automation, transportation, and logistics by enhancing communication reliability and availability. In this context, Distributed MIMO (DMIMO) technology, plays a crucial role in expanding Line-of-Sight (LoS) coverage and reducing signal obstructions. While extensive research has explored D-MIMO techniques, most studies rely on stochastic channel models that may not fully capture real-world propagation effects. In this paper, we analyze downlink multi-user multiplexing in an industrial D-MIMO network, comparing deployment strategies based on spectral and energy efficiency metrics, leveraging detailed ray-tracing modeling. Our results highlight key trade-offs for network deployment. We emphasize the need for a well-balanced densification strategy to optimize the trade-off between throughput and energy consumption. Although our model provides upper-bound performance estimates due to certain simplifications, it offers valuable insights for real-world deployments.

The paper introduces the latest Multiple Input Multiple Output (MIMO) codebook specified within the 3rd Generation Partnership Project (3GPP). It relies on channel prediction and precoder compression in the Doppler domain at the UE for better up-to-date CSI provisioning at the Base Station. A channel prediction strategy in the angle-delay domain is detailed together with a novel physical layer abstraction needed for precoder selection. Monte Carlo simulations demonstrate the effectiveness of the Rel. 18 codebook with respect to the UE speed in terms of tradeoff between performance enhancement and feedback overhead increase.

Reconfigurable intelligent surfaces (RIS) is a promising device made up of many passive elements that can control the electromagnetic propagation environment by the adjustment of the phase shifts of the reflecting elements. It is considered as a potential technique to enhance coverage and capacity in future 6G communications. However, channel estimation in a RIS aided MIMO wireless communication system is considered a challenging task due to the passive nature of the RIS which implies that the individual or cascaded channels can be estimated only at the Base Station (BS) or Mobile Station (MS). To address this problem, we proposed a three-stage channel estimation approach where we estimated the channel parameters using compressive sensing (CS). Simulation results showed that the performance of the proposed algorithm is similar to the ones previously proposed as the two-staged approach, but with a lower complexity.

This work proposes a new precoded filter bank (FB) system via a two-dimensional (2D) fast Fourier transform (2D-FFT). Its structure is similar to Orthogonal Time Frequency Space (OTFS) systems, where the OFDM transmitter is changed to a filter bank multi-carrier (FBMC) one, thus obtaining a lower out-of-band emission. The complex orthogonality of the FBMC transmission is guaranteed by using pre-coding based on a discrete Fourier transform, which is also used to implement the two-dimensional fast Fourier transform. Furthermore, due to the filtering process, the proposed system obtains a good error performance using a simple equalizer in the time-frequency domain. In this sense, we also introduce a hybrid receiver in the proposed system to exploit this feature. First, the time-frequency domain equalization is performed, followed by an interference cancellation on the delay-Doppler domain. Using the delay-Doppler domain detection, the simulation results show that the proposed system obtains an error performance similar to the OTFS system. Already using the simple equalizer in the time-frequency domain, the proposed scheme presents great advantage compared to the OTFS with the same equalizer in the studied scenarios.

Signal space diversity (SSD) introduced by Boutros and Viterbo tremendously improves the error performance over fading channels without any power or bandwidth sacrifice. Maximum benefit is obtained with full diversity (FD) multidimensional codebooks and the rather complex maximum likelihood (ML) detection. In the present work, we propose a generalized combinatorial representation and an associated low-complexity list-based detection algorithm that works for any non-full or full diversity multidimensional codebook. The algorithm includes three options of lists offering different complexity-performance trade-offs. One of the lists has random size and is guaranteed to contain the ML point, while the other two lists have fixed or limited size. Some analytical results are presented for the list sizes. The algorithm also includes a smart search to find the most probable codebook point in the list. Numerical results show that the proposed algorithm yields optimal or close-to-optimum performance with a noteworthy detection complexity reduction. Therefore, its adoption can be of practical interest, especially in applications where powerful error-correcting codes are not supported.

In this paper, we propose an uplink system which combines power-domain non-orthogonal multiple access (PD-NOMA) and sparse code multiple access (SCMA) together, in order to enable massive user connectivity. The receiver adopts successive interference cancellation (SIC) and iterative SCMA detection algorithm for decoding the superimposed signals. An optimization method of the code rate for the error-correcting channel codes is proposed by analyzing the extrinsic information transfer (EXIT) charts, so as to reach a trade-off between system convergence behavior and spectral efficiency. To evaluate the performance, a conventional SCMA system is used as comparison baseline. Numerical results show that the proposed system along with the optimization method achieves good bit error rate (BER) performance and significantly improves the spectral efficiency.

With expeditious development of wireless communications, location fingerprinting (LF) has nurtured considerable indoor location-based services (ILBSs) in the field of the Internet of Things (IoT). For most pattern-matching-based LF solutions, previous works either appeal to the simply received signal strength (RSS), which suffers from dramatic performance degradation due to sophisticated environmental dynamics, or rely on the fine-grained physical layer channel state information (CSI), whose intricate structure leads to increased computational complexity. Meanwhile, the harsh indoor environment can also breed similar radio signatures among certain predefined reference points (RPs), which may be randomly distributed in the area of interest, thus mightily tampering the location mapping accuracy. To work out these dilemmas, during the offline site survey, we first adopt autoregressive (AR) modeling entropy of CSI amplitude as location fingerprint, which shares the structural simplicity of RSS while reserving the most location-specific statistical channel information. Moreover, an additional Angle-of-Arrival (AoA) fingerprint can be accurately retrieved from the CSI phase through an enhanced subspace-based algorithm, which serves to further eliminate the error-prone RP candidates. In the online phase, by exploiting both CSI amplitude and phase information, a novel bivariate kernel regression scheme is proposed to precisely infer the target's location. Results from extensive indoor experiments validate the superior localization performance of our proposed system over previous approaches.

Previous transmission techniques for fading channels that exploit signal space diversity (SSD) are based on rotation or precoding of an original signal constellation. In this work, we follow a completely different approach to design full diversity multidimensional codebooks whose implementation has low complexity and requires little storage. The design consists of finding good permutations of the labels of points in a fixed grid. We consider both the uniform and the Gaussian-shaped grids. An ensemble average performance based on an enumerative analysis of the error events is presented, which brings some insight into the problem. Three low-complexity algorithms are then proposed to find good permutations sets. Both analytical and simulation results confirm the good performance of the proposed codebooks, which can be considered as good candidates for practical communications systems.

Forthcoming wireless systems will have to cope with heterogeneous and dense traffic. High demands in terms of bandwidth use efficiency and increased flexibility are therefore required for the related air-interface technology. In this context, Filter-Bank MultiCarrier with Offset QAM (FBMC/OQAM) waveform has been recognized as an enabling technology providing strong assets with a well-confined spectrum and enhanced spectral efficiency with respect to cyclic prefix orthogonal frequency-division multiplexing (OFDM). Nonetheless, FBMC-OQAM presents a few challenges to overcome: the intrinsic interference must be taken into consideration in the channel estimation processes and multiple antenna schemes’ design. Those challenges are induced by the reduction of the orthogonality property to the real field. In this paper, OFDM precoding/decoding is proposed as an alternative to the OQAM signaling for filter-bank-based waveforms. The OFDM precoding makes the waveforms near-complex orthogonal, which enables a straightforward adaptation to channel estimation techniques and MIMO scheme design. Two waveforms based on this precoding, namely fast Fourier transform FBMC and block-filtered OFDM, have been proposed. A common framework is proposed to study the two waveforms. This paper also investigates changing the time structure of the transmitted signal by introducing the rate factor parameter. It is shown that intrinsic interference can be greatly reduced at the price of a slight side-lobe increase.

Location Fingerprinting (LF) is a promising localization technique that enables many commercial and industrial Location-based Services (LBS). In this paper, a Channel Impulse Response (CIR) based indoor localization system is proposed. To fully exploit the most location-specific multipath information, we first conduct a power-based time tap filtering for the received CIR measurements. Furthermore, we experimentally observe that the filtered CIR data exhibits a multivariate circularly-symmetric Gaussian feature, which hints that fingerprinting positioning can be implemented by using a more accurate probabilistic method with a less computational complexity. In the online position determination phase, the Kullback-Leibler Distance (KLD) is adopted to quantify the similarities between the received measurements of target and the fingerprint database. Afterwards, we employ a probability kernel based regression approach to accurately infer the estimated target's location. Through extensive experiments performed on CRAWDAD database, the efficiency of our proposed scheme is validated.

This paper studies the joint multiplexing, Resource-Block and power allocation problem in underlay Device-to-Device (D2D) communications. The interfering signals are assumed fully asynchronous at the device receivers and at the Base Station. Inter-Channel Interference (ICI) then generates major impairment on the expected rate gain if Orthogonal Frequency Division Multiplex (OFDM) multi-carrier modulation is used, contrary to new multi-carrier modulations such as Filter Bank Multi-Carrier (FBMC) or Fast Fourier Transform (FFT)-FBMC. The proposed centralized algorithm provides a lower bound to the maximum sum rate over D2D pairs, taking into account ICI. It increases the sum data rate compared to a Frequency Division Multiple Access (FDMA) technique and compared to two other algorithms allowing multiplexing and applying power control.

This paper studies two different multiplexing problems for Device to Device (D2D) asynchronous communications with different multi-carrier modulations. Several D2D pairs may be multiplexed on the same Resource Blocks (RB) if they are jointly able to reach a high Signal to Interference plus Noise Ratio (SINR). In asynchronous communications, inter-channel interference (ICI) is present on top of co-channel interference, and the multiplexing problem can consequently not be split per RB since RB are no longer orthogonal. In this paper, we derive the system model that takes into account ICI and then study the maximum multiplexing problem. Two heuristics based on the Frobenius and the infinity norms that lead to lower bounds on the multiplexing gain are proposed. Then, the maximum sum SINR problem is studied with the infinity norm, showing that higher rates are achieved with Filter Bank Multi-Carrier (FBMC) modulation than with Orthogonal Frequency Division Multiplex (OFDM).

We consider the downlink resource allocation for a multi-carrier-based asynchronous cognitive radio network with statistical knowledge of the channel state information of both primary and secondary systems available at the secondary base station. The problem of secondary utility maximization is investigated under primary and secondary user outage constraints and total power constraint. Analyses are provided for cases where both primary and secondary systems are utilizing either orthogonal frequency-division multiplexing or filter-bank based multi-carrier modulations. The highly complex yet challenging and non-convex joint power-subcarrier allocation problem is tackled by leveraging the principle of alternating optimization techniques. A per-carrier outage-based solution is provided for the subcarrier allocation stage. In the second stage, we introduce an upper bound to the outage constraints to ensure tractability. We propose an efficient convex approximation method to obtain near-optimal solutions of the centralized power control problem. Furthermore, to reduce computational complexity, a distributed implementation based on dual decomposition method is designed. Extensive simulation results are further provided to corroborate the validity of the theoretical findings and demonstrate the effectiveness of the proposed algorithms.

The forthcoming fifth generation of mobile technology (5G) will be designed to satisfy the demands of 2020 and beyond. 5G does not just promise a huge increase in terms of data rates and capacity but it also targets new kind of use cases like Internet of Things or vehicular communications. The currently deployed 4G technology does not provide enough network capabilities to support this wide diversity of applications which has motivated the research on alternative waveforms. In this article, a new promising modulation scheme is introduced: Block-Filtered OFDM (BF-OFDM). The proposed waveform demonstrates an excellent frequency localization and can straightforwardly be integrated with the OFDM know-how and LTE principles. Besides, the proposed waveform relies on a receiver similar to the one used in CP-OFDM. BF-OFDM systems are also scalable, which is an interesting feature in order to steer the network capabilities on demand.

In this paper, we propose a scheme that preserves the advantages of Windowed Cyclic Prefix FBMC/Circular Offset Quadrature Amplitude Modulation (WCP-COQAM) while guaranteeing the complex orthogonality. Indeed, WCP-COQAM is based on block processing and uses circular filtering in order to remove time overheads. However, like FBMC/OQAM scheme, WCP-COQAM suffers from the presence of intrinsic interference that prevents the combination with some Multiple Input Multiple Output (MIMO) techniques such as Alamouti Coding. This intrinsic interference results from the non-complex orthogonal property of WCP-COQAM and Filter Bank MultiCarrier (FBMC). In this work, we show that circular filtering makes the transmultiplexer impulse response circulant. Our proposed scheme, called COW-CFMC, exploits this circularity to restore the complex orthogonality by precoding the data symbols in each subcarrier. Thus, we show by simulation that our proposed scheme can enable Alamouti coding in a straightforward manner.

Recently, fast Fourier transform filter-bank multicarrier (FFT-FBMC) modulation was proposed to enable diverse multiple-input multiple-output (MIMO) techniques with FBMC by eliminating the intrinsic interference. A per-subcarrier (PSC) channel equalizer was derived under the assumption of low frequency selectivity. However, this equalization scheme cannot be directly applied to highly frequency selective channels without performance degradation. In this letter, we analyze the received signal after demodulation without any assumption on the channel selectivity. We develop a general closed-form expression for the equivalent channel seen at the demodulator. Furthermore, we design the associate channel equalizer. Simulation results demonstrate that the developed equalizer performs as equally with the previous PSC equalizer in the case of low frequency selectivity, and outperforms the PSC equalizer in highly frequency selective channels.

This paper studies resource allocation for a multi-carrier based cognitive radio network under the assumption of statistical channel state information (CSI). To circumvent the inherent high computational complexity investigating the joint power-rate-subcarrier of the outage-constrained sum rate maximization problem, we adopt a sub-optimal strategy by solving independently the subcarrier and power-rate problem. Firstly, we propose a heuristic subcarrier allocation paradigm by utilizing an outage-based metric. Secondly, we conservatively approximate the intractable non-convex powerrate control problem and propose a sequential-based algorithm to efficiently obtain a solution to the problem. The proposed algorithm has been shown to converge to solutions that are stationary points of the original power-rate problem. Extensive simulation results are provided to demonstrate the efficiency of our proposed algorithm.

This paper studies the weighted sum rate maximization problem for device-to-device (D2D) underlay communications. Since each D2D transmitter is synchronized with its receiver, all D2D receivers get asynchronous interference from the other transmitters, and so does the Base Station (BS). This is modelled by a uniformly-distributed timing offset which generates inter-channel interferences in multi-carrier transmissions. Filter Bank based Multi-Carrier (FBMC) is then far more efficient than Orthogonal Frequency Division Multiplex (OFDM) to limit the inter-channel interference spread. In this context, D2D pairs are clustered through graph coloring in order to avoid high interference situations. Then we derive the weighted sum rate maximization algorithm with inter-channel interference and with an interference constraint at the BS. An iterative implementation of this algorithm converges to the global optimal solution, when the high Signal-to-Interference-plus-Noise Ratio (SINR) condition holds. Simulation results show that D2D users can be efficiently multiplexed, achieving high data rates while fulfilling the BS interference constraint.

We propose a further generalization of spatial modulation (SM) in a downlink multiuser MIMO system with multicast in order to improve the system's spectral and energy efficiencies. A base station equipped with multiple antennas sends three types of information to several users who are also equipped with multiple antennas. The individual information to each user is conveyed in the form of receive SM as well as through conventional QAM modulation. On top of that, common transmit SM information is conveyed to all users. To this end, specific preprocessing and postprocessing are obtained based upon the singular value decomposition technique. A simple additional processing based on phase shifts is proposed to improve detectability. Computer simulation results reveal that the proposed scheme has improved average bit-error rate (BER) over a reference scheme under the same spectral efficiency.

In this paper, we study the problem of resource allocation for an opportunistic spectrum-sharing channel under the assumption of statistical channel state information (CSI). More specifically, we assume that statistical CSI of the channels between both primary and secondary transmitters and the primary receiver is available at the secondary transmitter. We investigate the achievable rate maximization of both primary and secondary systems with successive interference cancellation (SIC) under outage, interference limit, and total power constraints. We deal with the secondary non-convex optimization problem by using the first order Taylor approximation. We propose a sequential convex approximation algorithm to solve the power allocation problem. Simulation results establish the efficiency of our proposed algorithm.

This paper addresses the problem of energy-efficiency (EE) within an underlay cognitive radio (CR) network where primary users (PUs) and secondary users (SUs) utilize two types of multicarrier modulation: orthogonal frequency-division multiplexing (OFDM) and filter bank based multicarrier (FBMC). The problem of non-cooperative SU downlink energy-efficiency is investigated using a game theoretic approach. We reformulate the EE optimization problem as a non-cooperative power allocation game (NPAG). A distributed power control algorithm is proposed to find the optimal power allocation strategy for each secondary base station on each subcarrier. The proposed non-cooperative game is demonstrated to reach a Nash-equilibrium (NE) point. Simulation analyses are then provided in order to validate the efficiency of our proposed distributed algorithm and also to highlight the advantages of using FBMC as a modulation technique if compared to OFDM.

In this paper we study the effect of the FBMC intrinsic interference on the error rate performance in the presence of a phase error. We focus our study on the symbol error rate (SER) performance in additive white Gaussian noise (AWGN) channel, and we derive the SER performance expressions for any 2m-PAM (pulse amplitude modulation) data mapping and a phase error φ. The SER performance also depends on the probability distribution of the interference term. Since the intrinsic interference is generated from the transmitted data symbols, the probability distribution of the interference is related to the data symbol mapping. We suggest to approximate the interference probability distribution to Gaussian one for the high order constellations, and simplify the analytical expression of the SER performance. The analytical expressions developed in the paper are checked and confirmed by numerical simulations. We will show in the paper that the performance curves obtained by simulation and theoretical analysis are the same for high order constellation where the approximation of the interference to the Gaussian one is suitable.

This paper presents a hierarchical stream selection approach to deal with the interference in a heterogeneous network where different cell types are coexisting with each other to increase the sum capacity. Due to the variety of the transmit powers between the macro and small cells, interference levels are different. The proposed solution hierarchically selects the strongest streams of each cell with a contribution to the sum rate, while constructing the streams via singular value decomposition (SVD). In order to reduce the interference, the channel matrices of the remaining streams are projected orthogonally to the virtual transmit channel and virtual receive channel of the selected stream. The performance evaluations are obtained by considering different locations of small cells with respect to the macro cell. It is shown that the proposed method can dynamically select more streams in heterogeneous networks and achieve higher data rates compared to the existing algorithms.

Our main emphasis is on the use of enhanced OFDM and filter bank based multicarrier (FB-MC) waveforms for utilizing effectively the available fragmented spectrum in heterogeneous radio environments. Special attention is on the broadband-narrowband coexistence scenario of the Professional Mobile Radio (PMR) evolution. The target here is to provide broadband data services in coexistence with narrowband legacy services of the TETRA family. The core idea is a multi-mode radio platform, based on variable filter bank processing, which is able to perform modulation/detection functions simultaneously for different signal formats with adjustable center frequencies, bandwidths and subchannel spacings.

Sparsity is one of the key points in the compressed sensing (CS) theory, which provides a sub‐Nyquist sampling paradigm. Nevertheless, apart from sparsity, structures on the sparse patterns such as block structures and tree structures can also be exploited to improve the reconstruction performance and further reduce the sampling rate in CS framework. Based on the fact that the block structure is also sparse for a widely studied block sparse signal, in this study, a double‐level binary tree (DBT) hierarchical Bayesian model is proposed under the Bayesian CS (BCS) framework. The authors exploit a recovery algorithm with the proposed DBT structured model, and the block clustering in the proposed algorithm can be achieved fastly and correctly using the Markov Chain Monte Carlo method. The experimental results demonstrate that, compared with most existing CS algorithms for block sparse signals, our proposed DBT‐based BCS algorithm can obtain good recovery results with less time consuming.

When the transmitter does not have channel state information (CSI), linear precoding can be used in multicarrier systems in order to minimize the degradation of the overall bit error rate (BER) caused by deep fades appearing on subchannels. Within the multicarrier systems family, filterbank multicarrier (FBMC) systems were proposed to overcome the poor frequency localization together with the bandwidth/energy waste caused by the use of the cyclic prefix in classical Orthogonal Frequency Division Multiplexing (OFDM) systems. Previous work [1] has focused on BER performance of precoded FBMC systems with perfect equalization at the receiver. In this paper we extend our analysis taking into account the effect of the intersymbol interference (ISI) stemming from non-completely equalized subchannels in these systems, specially when the transmission channel is highly frequency selective. An analytical expression of the error performance of single-input single-output (SISO) precoded FBMC systems with linear minimum mean square error (MMSE) equalization is established taking into account the ISI resulting from imperfect equalization, which causes a loss of diversity. This expression is for uncoded transmissions and matches accurately Monte Carlo simulation results.

In the next generation of multicell networks, adaptive resource allocation techniques will play an important role to improve both quality of service and spectral efficiency. In order to employ distributed power allocation for multicell Orthogonal Frequency-Division Multiple Access (OFDMA) networks, the channel state information (CSI) belonging to all users is required to share among base stations. However, the amount of feedback increases with the number of users, base stations and subcarriers. Therefore, it is important to perform a selection at the user side for multicell networks. In this paper, we propose reduced feedback links by choosing the users based on their approximate signal to interference noise ratio (SINR). The performance of the reduced feedback links are illustrated in multicell OFDMA systems.

We address the interplay of sensing performance and the choice of an erasure correcting code used for the recovery of missing data occurring in opportunistic spectrum access when there is a collision with a primary user. The main result of this paper is the existence of an optimum functioning point on the receiver operating characteristic (ROC) curve [1], in terms of efficiency of the secondary spectrum reuse. This functioning point depends on the sensing performance and on code related parameters such as the code rate and the erasure correction capability of the code. It is a trade-off between using at best the opportunities (with a small false alarm probability PFA), the number of collisions experienced (a small non detection probability PND) and the erasure correction capability of the code, characterized by its minimum distance d, whose cost is the necessary redundancy we must introduce to perform erasure correction (i.e. the code rate).

Filter-bank multicarrier (FBMC) transmission system was proposed as an alternative approach to orthogonal frequency division multiplexing (OFDM) system since it has a higher spectral efficiency. One of the characteristics of FBMC is that the demodulated transmitted symbols are accompanied by interference terms caused by the neighboring transmitted data in time-frequency domain. The presence of this interference is an issue for some multiple-input multiple-output (MIMO) schemes and until today their combination with FBMC remains an open problem. We can cite, among these techniques, the Alamouti scheme and the maximum likelihood detection (MLD) with spatial multiplexing (SM). In this paper, we shall propose a new FBMC scheme and transmission strategy in order to avoid this interference term. This proposed scheme (called FFT-FBMC) transforms the FBMC system into an equivalent system formulated as OFDM regardless of some residual interference. Thus, any OFDM transmission technique can be performed straightforwardly to the proposed FBMC scheme with a corresponding complexity growth compared to the classical FBMC. First, we will develop the FFT-FBMC in the case of single-input single-output (SISO) configuration. Then, we extend its application to SM-MIMO configuration with MLD and Alamouti coding scheme. Simulation results show that FFT-FBMC can almost reach the OFDM performance, but it remains slightly outperformed by OFDM.

Interference at the radio receiver is a key source of degradation in quality of service of wireless communication systems. In this paper, we address the impact of imperfect inter-cell synchronization on the performance of orthogonal frequency division multiplexing (OFDM) and filter bank based multicarrier (FBMC) multi-cellular networks. Based on computing the moment generating functions of the asynchronous interference power, a useful analytical method for computing the average error rates is derived considering the frequency correlation fading between adjacent interfering subcarriers.

The growing demand for wireless communication makes it important to determine the capacity limits of the different multicarrier systems. In this paper, we address the impact of imperfect inter-cell synchronization on the average capacity of orthogonal frequency division multiplexing (OFDM) and filter bank based multicarrier (FBMC) multi-cellular networks. Based on computing the moment generating functions of the asynchronous interference power, a simple new expression for the exact evaluation of the average capacity is derived considering the frequency correlation fading between adjacent interfering subcarriers.

This paper proposes a general method for bi-directional relaying with three nodes over four time intervals, composed of alternate MAC phases and broadcast phases. Different nodes transmit in MAC phases, and the data stored in broadcast phases allows decoding of all information. The physical layer network coding strategy for this relaying protocol is a lattice-based coding scheme combined with power control, so that the relay receives an integer linear combination of the nodes codewords. This strategy is analyzed for MIMO with 2 receive and transmit antennas. Analytical expressions of the exchange rate and transmit powers under high SNR approximation are provided, and numerically validated with respect to the general SNR mode. The proposed method is then compared with Amplify-and-Forward. It is shown that lattice-based coding provides a gain of 4.5 dB in average SNR on the achievable exchange rate, and is totally fair, contrary to Amplify-and-Forward.

Multicarrier modulation and especially CP-OFDM is widely used nowadays in several radio communications. However, Filter Bank Multicarrier (FBMC) is a potential alternative to CP-OFDM since it does not require cyclic prefix and thus it has a higher spectral efficiency. One of the characteristics of FBMC is that it induces intrinsic interference among the transmitted data. The presence of this interference complicates the application of the MLD (Maximum Likelihood Detection) in the MIMO Spatial Data Multiplexing configuration which is known to improve the capacity. In this paper, we are interested in the encoded data case and we propose two receiver schemes. Both schemes are based on interference estimation and cancellation. The first one uses MMSE equalization in order to get an estimation of the intrinsic interference. The second one improves the interference estimation by exploiting the decided symbols around the considered time-frequency position. The performance of both approaches are assessed by simulation, and we show that the second approach exhibits either the same performance or better than OFDM

This paper provides a theoretical performance evaluation of the downlink of asynchronous orthogonal frequency division multiplexing (OFDM) and filter bank based multicarrier (FBMC) cellular radio communication systems. An accurate derivation, for the interference caused by the timing synchronization errors in the neighboring cells, is developed. The multipath effects on the interfering and desired signal are also considered. Based on computing the moment generating functions of the interference power, exact expressions are derived for average error rates of OFDM and FBMC systems considering the frequency correlation fading in the case of block subcarrier assignment scheme.

In this work, a new hybrid transmit processing technique based on switched interleaving and chip-wise precoding is proposed to suppress the multiuser interference (MUI) for downlink multi-carrier code division multiple access (MC-CDMA) multiple antenna systems. A set of possible chip-interleavers are constructed and prestored at both the base station (BS) and mobile stations (MSs), which are also equipped with another codebook of quantized downlink channel state information (CSI). Each MS quantizes its own downlink CSI and feeds back the index to the BS by a low-rate feedback channel, then the selection function at the BS determines the optimum interleaver based on all users' quantized CSIs to transmit signals. Simulation results show that the performance of the proposed techniques is significantly better than prior art.

In this work we propose novel decision feedback (DF) detection algorithms with error propagation mitigation capabilities for multi-input multi-output (MIMO) spatial multiplexing systems based on multiple processing branches. The novel strategies for detection exploit different patterns, orderings and constraints for the design of the feedforward and feedback filters. We present constrained minimum mean-squared error (MMSE) filters designed with constraints on the shape and magnitude of the feedback filters for the multi-branch MIMO receivers and show that the proposed MMSE design does not require a significant additional complexity over the single-branch MMSE design. The proposed multi-branch MMSE DF detectors are compared with several existing detectors and are shown to achieve a performance close to the optimal maximum likelihood detector while requiring significantly lower complexity.

Cognitive radio (CR) is proposed to automatically detect and exploit unused spectrum while avoiding harmful interference to the incumbent system. In this paper, we emphasize the channel capacity comparison of a CR network using two types of multicarrier communications: conventional Orthogonal Frequency Division Multiplexing (OFDM) with Cyclic Prefix (CP) and Filter Bank based MultiCarrier (FBMC) modulations. We use a resource allocation algorithm in which subcarrier assignment and power allocation are carried out sequentially. By taking the impact of Inter-Cell Interference (ICI) resulting from timing offset into account, the maximization of total information rates is formulated under an uplink scenario with pathloss and Rayleigh fading, subject to maximum power constraint as well as mutual interference constraint between primary user (PU) and secondary user (SU). Final simulation results show that FBMC can achieve higher channel capacity than OFDM because of the low spectral leakage of its prototype filter.

This paper addresses the effect of timing synchronization errors for two different multicarrier techniques CPOFDM and FBMC. Such errors degrade the performance of the reference receiver by causing multi user interference. This interference occurs when the signals from different transmitters arrive at a given receiver with arbitrary timing misalignments, leading to the destruction of orthogonality between subcarriers. In this paper, we propose a model of interference that provides a useful computational tool in order to analyze the performance of an OFDM/FBMC receiver in frequency selective fading environment. Finally, simulation results confirmed the accuracy of the proposed model.

Multi-carrier modulation and especially CP-OFDM is widely used nowadays in several radio communications. However, FBMC is a potential alternative to CP-OFDM since it does not require cyclic prefix, and thus, it has a higher spectral efficiency. One of the characteristics of FBMC is that the received data are accompanied by a two dimensional inter-symbol interference term (2D ISI), which complicates MIMO techniques when combined with optimum detection. In this paper we consider the association of FBMC with the multiple-input multiple-output (MIMO) technique focusing on the spatial data multiplexing (SDM), and we investigate the use of Maximum Likelihood (ML) detection. We propose modified FBMC schemes based on inserting null data among QAM symbols to reduce, in this way, the overlapping of the symbols.

In SDMA-OFDMA wireless communication systems, the feedback load increases with the number of users, subcarriers and antennas in the cell. In this paper, we propose two efficient reduced feedback algorithms by selecting the clusters at the user side. For each cluster, we select the users according to their norm and their orthogonality. We evaluate the performance of the user selection algorithms considering the quantization effect. We also design a specific codebook design to quantize CSI for the proposed criterion.

In this paper we propose a novel decision feedback (DF) detection strategy for multi-input multi-output (MIMO) spatial multiplexing systems based on multiple processing branches. The proposed detection structure employs multiple cancellation branches with appropriate transformations that modify the initial design. The novel structures for detection exploit different patterns and orderings for the modification of the original feedforward and feedback filters design. We present minimum mean-squared error (MMSE) estimators for the multi-branch structure and the design of MIMO receivers and show that the proposed MMSE design does not require a significant additional complexity. The proposed multi-branch MMSE DF detectors are compared with several existing detectors in the literature via computer simulations and are shown to close the gap between MMSE detectors and the optimal maximum likelihood detector.

The future wireless communication is expected to be able to improve the efficiency of spectrum usage. To solve the challenge of spectrum shortage, an innovative opportunistic spectrum access strategy, called cognitive radio (CR) has been proposed. Conventional orthogonal frequency division multiplexing (OFDM) has already been suggested as a physical layer candidate for CR system. Herein another potential candidate for CR, OFDM offset quadrature amplitude modulation (OFDM/OQAM) is introduced, and its spectral efficiency for coded multicarrier transmission is compared with cyclic prefix based OFDM (CP-OFDM) and raised cosine windowed OFDM (RC-OFDM) in CR context. Simulated results of spectral efficiency comparison (SEC) for different multicarrier systems are interpreted by theoretically analyzing the out-of-band radiation of their prototype pulses shaping. Both theoretic analysis and experimental results can show that OFDM/OQAM is a more natural candidate than CP-OFDM and RC-OFDM for CR networks application.

Abstract The future wireless communication is expected to be able to improve the efficiency of spectrum usage. To solve the challenge of spectrum shortage, an innovative opportunistic spectrum access strategy, called cognitive radio (CR) has been proposed. In the concept of CR, secondary users or CR users, are allowed to transmit and receive date by detecting the portions of spectra where/when primary users are inactive provided that secondary transmissions cause no harmful interference to the licensed systems. Conventional orthogonal frequency division multiplexing (OFDM) has also been suggested as a physical layer candidate for CR system. In this paper, another potential candidate for CR, OFDM/offset quadrature amplitude modulation (OQAM) is introduced and compared with cyclic prefix based‐OFDM (CP‐OFDM) and raised cosine windowed‐OFDM (RC‐OFDM) in CR context, in which including spectral efficiency comparison (SEC) for uncoded transmission and coded transmission. SEC is investigated by balancing the tradeoff between the interference level caused by CR user to licensed user (LU) and the throughput of CR user. Simulated results of SEC for different multicarrier systems are interpreted by theoretically analyzing the out‐of‐band radiation of their prototype pulses shaping. Both theoretic analysis and experimental results can show that OFDM/OQAM is a more natural candidate than CP‐OFDM and RC‐OFDM for CR networks application. Copyright © 2008 John Wiley & Sons, Ltd.

The purpose of this paper is to analyze Orthogonal Frequency Division Multiplexing carrying Offset QAM (OFDM/OQAM) signal detection using cyclostationary signatures, which are artificially embedded into digital modulated signals. The spectral correlation characterization of OFDM/OQAM signal can be described by a special Linear Periodic Time-Variant (LPTV) system. Using this description we have derived the explicit theoretical formulas of Cyclic Autocorrelation Function (CAF) and Spectral Correlation Function (SCF) for OQAM signal. A low-complexity cyclostationary signature detector can be utilized for OQAM signal detection, and experimental results show the advantages of this signature detector.

We consider a multiple input single output antenna system with a feedback link. While most of the previous works have considered perfect feedback channels, in this paper, we evaluate the impact of a noisy feedback channel on the quality and the performance of the transmission. We compare the finite rate feedback scheme with the analog feedback schemes where the channel parameters are transmitted without quantization over the uplink channel. Two analog feedback schemes are considered : the feedback of the channel vector and the feedback of the normalized channel vector. We provide bounds on the performance and give numerical results. We have shown that at low to medium uplink SNR the analog feedback of the normalized channel vector outperforms the analog feedback of the channel vector. Depending on the range of uplink SNR, quantized feedback can perform poorly compared to analog feedback.

In this paper we consider the iterative decoding of channels with strong phase noise. We propose to estimate the phase using a message passing algorithm based on particle filtering. Instead of estimating the phase directly we use the whole estimated probability density function for the channel decoder. Three versions of phase estimators are derived. The simulation results are given for binary LDPC codes. We compare the proposed iterative data and phase estimators with another solution where the forward backward algorithm is performed over the trellis obtained from the discretization of the phase. The results show that the proposed algorithm achieves a good compromise between performances and complexity

We evaluate the impact of multiple transmit antennas on the performance of multicast OFDM systems by proposing a suboptimal low complexity algorithm. Associated with a powerful erasure code, it is possible to increase the data rate of multicast OFDM systems by selecting for each subcarrier to the users with a good channel condition. We show that the resource allocation which includes the precoding vector selection, subcarrier allocation and bit loading is a difficult optimization problem. We propose a suboptimal algorithm to solve this problem by avoiding the optimization to reduce complexity. When the users are not symmetrically distributed around the base station, we add a fair scheduler to guarantee that each user receives the same amount of data. We present simulation results where we compare the proposed multicast systems with the classical multicast OFDM systems. When the users are symmetrically distributed around the base station, the gain is rather small, whereas when the users are non-symmetrically distributed, the proposed algorithm outperforms OFDM systems with subcarrier allocation.

To achieve near-capacity in systems employing multiple antennas, an iterative detection scheme must be used. A full a posteriori probability (APP) MIMO detector is associated with a convolutional code or a turbo code. However, the APP inner detector presents quickly a prohibitive complexity and schemes known as list sphere decoders have been developed to reduce this complexity. In this paper, we provide a new reduced-complexity MIMO list detector based on the particle filtering (PF) methods. This detector can be seen as a tree-search detector, which uses the a priori information to explore the paths of the tree. We show that the average number of paths analyzed by the PF detector decreases at each iteration. Moreover, it is adapted according to the signal-to-noise ratio. We compare the performance and the complexity of the PF detector and the list sphere decoder.

For iterative decoding of multiple antenna systems concatenated with an outer error correcting code, it is important to use an a posteriori probability detector for the MIMO detection to achieve near capacity performance. To avoid full APP detection, we propose a reduced complexity detector based on breadth first algorithms. Although these algorithms are sub-optimal, we show that they can provide a good list of candidates for the APP calculation. Furthermore, by exploiting the a priori information delivered from the outer decoder, it is possible to decrease the MIMO detector complexity at each iteration. Using simulation results, we will compare the performance of the proposed detectors with the list sphere detector.

For a given computational complexity, the Viterbi algorithm applied on the discrete representation of the state space provided by a standard particle filtering, outperforms the particle filtering. However, the computational complexity of the Viterbi algorithm is still high. We propose to use the M and T algorithms in order to reduce the computational complexity of the Viterbi algorithm and we show that these algorithms enable a reduction of the number of particles by up to 20%, practically without loss of performance with respect to the Viterbi algorithm.

Instead of reduced-state decision-feedback sequence estimation (DFSE) equalization which is the state-of-the-art solution for the complexity reduction of the full-state Viterbi decoder over a multipath fading channel, we propose a new reduced complexity maximum likelihood sequence detector (MLSD) based on the particle filtering technique. The computational complexity of this new detector is adapted according to the signal-to-noise ratio. Compared to the DFSE detector the particle detector offers a better trade-off between performance and computational complexity.