This paper investigates access point (AP) cooperation cluster formation in user-centric cell-free massive MIMO (CF-mMIMO) communication systems characterized by fronthaul links with capacity restrictions. Specifically, we consider an open radio access network (O-RAN) architecture that, although it favors the deployment of ultra-dense networks, is constrained in the number of APs that can be active simultaneously. In this context, we propose an innovative framework termed pixel-based CF-mMIMO, which enables efficient control of both the AP activation and cooperation cluster formation. Recognizing the parallels with pixel-based reconfigurable antennas, the proposed framework allows dynamic reconfiguration of the network coverage map with reasonably low computational cost. The high scalability and performance of the framework are mainly supported by a learning model based on graph neural networks (GNNs) that effectively exploits the existing graph-like structures in CF-mMIMO systems. Extensive simulation experiments demonstrate that the proposed approach achieves competitive spectral efficiency (SE) in challenging scenarios with dense AP deployments and numerous user equipments (UEs).

User-centric cell-free massive MIMO (CF-mMIMO) communications rely on accurately knowing the channel state information (CSI) to perform coherent signal processing. However, achieving pilot contamination-free channel estimation is challenging due to the limited length of the coherence blocks. In this work, we combine a novel user equipment (UE) clustering method termed C-footprints and a sequential heuristic named cell-free-oriented best first pilot assignment (CF-BFPA) to find pilot allocations that effectively reduce contamination. First, the proposed clustering method groups potentially contaminating UEs based on the degree of orthogonality among the matrices of channel statistics. Subsequently, the CF-BFPA algorithm performs a greedy intra-cluster pilot assignment that ensures low interference between the most contaminating UEs. The performance of the proposed solution (C-footprints+CF-BFPA) is compared with several state-of-the-art algorithms across diverse CF-mMIMO network settings.

Forming effective access point (AP) cooperation clusters is a key challenge in user-centric cell-free massive MIMO (CF-mMIMO). Existing approaches to this task are either computationally prohibitive or overlook the complex interrelationships within communication networks. In this context, we introduce an innovative approach based on graph neural networks (GNNs). By leveraging the inherent graph structure of CF-mMIMO networks, we transform the rate maximization problem into a node classification task, enabling a competitive and robust solution. Simulation results show that the proposed method significantly outperforms conventional baselines in terms of spectral efficiency, computational complexity, and scalability.

In this paper, we introduce a novel architecture-independent technique for time-modulated arrays (TMAs) that enables simultaneous direct-antenna multi-modulation (DAMM) and beam steering. We validated the approach across a remarkably broad variety of modulation constellations, confirming its ability to implement arbitrary direct-antenna IQ modulation. This flexibility makes the technique highly suitable for Internet of Things (IoT) applications spanning scenarios that demand either robust communication or high spectral efficiency, all on a single energy-efficient, low-cost hardware platform. The proposed method operates within the available TMA bandwidth and achieves precise beam steering without relying on computationally intensive optimization algorithms. Unlike previous studies that evaluate TMAs in isolation, we design and characterize a fully integrated transmitter that combines DAMM and beam steering functionalities. The resulting TMA-based DAMM transmitter employs a reduced hardware architecture, delivering both cost and energy savings ideally matched to resource-constrained, low-latency IoT deployments.

Massive MIMO (mMIMO) communication systems are recognized as key enablers of next-generation wireless networks. However, the orthogonal pilot assignments typical of multiple-input multiple-output (MIMO) systems are not well suited to emerging use cases characterized by short channel coherence times and a high number of connected user equipments (UEs). In this work, we propose a novel approach for intra-cell pilot reuse that leverages the spatial features of correlated mMIMO channels to attain low pilot contamination while using a small number of pilot sequences. The first part of the proposed solution is a groundbreaking clustering algorithm termed K-beams, which splits the complex intra-cell pilot allocation into tractable problems without significant loss of optimality. Then, we introduce a heuristic approach called best-first pilot assignment (BFPA), designed to manage intra-cluster pilot assignments by minimizing interference among the most contaminating UEs. We evaluated the performance of our proposed solution (K-beams+BFPA) in terms of sum-normalized mean-squared error (NMSE) and sum-rate under various challenging network setups. The simulation results show that our approach is a robust alternative to more computationally demanding benchmarks.

The deployment of intelligent reflecting surfaces (IRSs) to assist multiple-input multiple-output (MIMO) communications stands as a promising step in overcoming some of the limitations of current vehicular communication systems. In this paper, we focus on the joint optimization of the IRS and the precoders in the uplink of an IRS-assisted MIMO communication established between several connected vehicles and a roadside unit (RSU). We consider a realistic imperfect channel state information (ICSI) model and propose several adjustments to the deep contextual bandit-oriented deep deterministic policy gradient (DCB-DDPG) framework to address this complex optimization problem. Simulation results show that the proposed solution is a robust alternative because it keeps learning from interactions, even when the estimation error statistics are unknown.

We consider the transmission of spatially correlated analog information in a wireless sensor network (WSN) through fading single-input and multiple-output (SIMO) multiple access channels (MACs) with low-latency requirements. A lattice-based analog joint source-channel coding (JSCC) approach is considered where vectors of consecutive source symbols are encoded at each sensor using an$n$-dimensional lattice and then transmitted to a multiantenna central node. We derive a minimum mean square error (MMSE) decoder that accounts for both the multidimensional structure of the encoding lattices and the spatial correlation. In addition, a sphere decoder is considered to simplify the required searches over the multidimensional lattices. Different lattice-based mappings are approached and the impact of their size and density on performance and latency is analyzed. Results show that, while meeting low-latency constraints, lattice-based analog JSCC provides performance gains and higher reliability with respect to the state-of-the-art JSCC schemes.

The design of zero-delay Joint Source-Channel Coding (JSCC) schemes for the transmission of correlated information over fading Multiple Access Channels (MACs) is an interesting problem for many communication scenarios like Wireless Sensor Networks (WSNs). Among the different JSCC schemes so far proposed for this scenario, Distributed Quantizer Linear Coding (DQLC) represents an appealing solution since it is able to outperform uncoded transmissions for any correlation level at high Signal-to-Noise Ratios (SNRs) with a low computational cost. In this paper, we extend the design of DQLC-based schemes for fading MACs considering sphere decoding to make the optimal Minimum Mean Squared Error (MMSE) estimation computationally affordable for an arbitrary number of transmit users. The use of sphere decoding also allows to formulate a practical algorithm for the optimization of DQLC-based systems. Finally, non-linear Kalman Filtering for the DQLC is considered to jointly exploit the temporal and spatial correlation of the source symbols. The results of computer experiments show that the proposed DQLC scheme with the Kalman Filter decoding approach clearly outperforms uncoded transmissions for medium and high SNRs.

This paper jointly addresses user scheduling and precoder/combiner design in the downlink of a wideband millimeter-wave communications system. We consider the orthogonal frequency-division multiplexing modulation to overcome the channel frequency selectivity and obtain a number of equivalent narrowband channels. Hence, the main challenge is that the analog preprocessing network is frequency flat and common to all the users at the transmitter side. Moreover, the effect of the signal bandwidth over the uniform linear array steering vectors has to be taken into account to design the hybrid precoders and combiners. The proposed algorithmic solution is based on the linear successive allocation, which greedily allocates streams to different users and computes the corresponding precoders and combiners. By taking into account the rank limitations imposed by the hardware at transmission and reception, the performance loss in terms of achievable sum rate for the hybrid approach is negligible. The numerical experiments show that the proposed method exhibits excellent performance with reasonable computational complexity.

This paper focuses on distributed linear precoding when users transmit correlated information over a fading multiple-input and multiple-output multiple access channel. The precoders are optimized in order to minimize the sum-mean square error (MSE) between the source and the estimated symbols. When sources are correlated, minimizing the sum-MSE results in a non-convex optimization problem. The precoders for an arbitrary number of users and transmit and receive antennas are thus obtained via a projected steepest-descent algorithm and a low-complexity heuristic approach. For the more restrictive case of two single-antenna users, a closed-form expression for the minimum sum-MSE precoders is derived. Moreover, for the scenario with a single receive antenna and any number of users, a solution is obtained by means of a semi-definite relaxation. Finally, we also consider precoding schemes where the precoders are decomposed into complex scalars and unit norm vectors. Simulation results show a significant improvement when source correlation is exploited at precoding, especially for low signal-to-noise ratios and when the number of receive antennas is lower than the number of transmitting nodes.

In this work we consider the problem of designing the precoders and combiners in a multiuser hybrid frequency selective millimeter wave (mmWave) system. We propose two methods to design the hybrid analog-digital filters when only an estimate of the channel is available at both the base station (BS) and the users. The first one is a projected gradient method that factorizes the all-digital solution, while the second approach is based on an alternating minimization (AM) iterative strategy. The channel is estimated using an algorithm that takes into account the hardware limitations of the hybrid architecture and leverages the sparse structure of mmWave channels. The proposed methods are evaluated in terms of achievable sum rate, outperforming the state of the art solutions. The negligible impact of the hybrid structure and the imperfect channel knowledge on the system performance are shown via numerical experiments.

Joint source-channel coding for discrete-time analog sources is an appealing transmission approach because of its extremely low delay and complexity. When the users access the channel orthogonally, analog transmission of correlated information over fading multiple access channels (MACs) using modulo-like mappings provides better performance than uncoded transmission. In this paper, we propose a simplified decoder for modulo mappings in possibly non-orthogonal MAC scenarios with a single-antenna users and a multiple-antenna receiver. Sphere decoding is investigated to reduce the computational complexity when the number of users is large. In addition, affordable strategies are proposed to optimize the mapping parameters according to the channel conditions and the source correlation. The obtained results show that the use of modulo mappings is suitable when the number of antennas at the receiver is larger than the number of users and for high correlation between user data.

Analog transmission of correlated information over Gaussian broadcast channels (BCs) using analog joint source- channel coding (JSCC) is addressed. This communication strategy has attractive advantages, such as low complexity, minimal delay, graceful degradation, and adaptation to time-varying environments. In this paper, we focus on the optimization of parametric continuous mappings that satisfy individual quality of service requirements over Gaussian BCs. This optimization is based on the balancing of the user distortions to ensure the feasibility of the resulting optimization problems. An analysis of the overall distortion corresponding to the considered mappings is carried out to design algorithms that determine the optimal values for the mappings parameters. Results show that the proposed analog JSCC scheme provides near optimal performance and an adequate balancing of the individual distortions.

Multimedia and data-based services experienced a nonstopping growth over the last few years. People are continuously on the move using devices to access multimedia contents or other data-based services. Due to this, railway companies are showing a great interest in deploying broadband mobile wireless networks in high-speed-trains with the aim of supporting both passenger services provisioning as well as automatic train control and signaling. Nowadays, the most widely used technology for communications between trains and the railway infrastructure is GSM for Railways (GSM-R); however, it has limited capabilities to support such advanced services. Due to its success in the mass market, Long Term Evolution (LTE) seems to be the best candidate to substitute GSM-R. In this paper, we experimentally characterize the downlink between an LTE Evolved NodeB (eNodeB) and a high-speed train in a commercial high-speed line. We consider two links: the one between the eNodeB and the antennas placed outdoors on the train roof, and the direct link between the eNodeB and a receiver inside the train. Such a characterization consists in assessing the path loss, the Signal to Noise Ratio, the K -Factor, the Power Delay Profile, the delay spread, and the Doppler Power Spectral Density.

We address the transmission of bivariate Gaussian sources using analog Joint Source Channel Coding (JSCC). The analog mappings are specifically designed to exploit the correlation between the source symbols. A parametric mapping based on sinusoidal functions is proposed and its performance is compared to that of the optimal non parametric mappings and other applicable analog JSCC mappings, and also to the theoretical bound. The obtained results show that the performance of the parametric mapping closely approaches the optimal performance, and it is very similar to that of the non parametric one.

The radio access technology for railway communications is expected to migrate from GSM for Railways (GSM-R) to a more suitable generation of communication systems for the services offered nowadays, like the fourth generation (4G) or the fifth generation (5G) ones. Moreover, taking into account the evolution of general-purpose communication standards and devices, 5G systems seem to be good candidates to substitute GSM-R as the basis technology for railway communications in the long term. Recently, considerable attention has been devoted to high-speed trains since this particular environment poses challenging problems in terms of performance simulation and measurement. In order to considerably decrease the cost and complexity of high-speed measurement campaigns, we have proposed a technique to induce effects caused by highly-time varying channels on multicarrier signals while conducting measurements at low speeds. This technique has been proved to be accurate for the cases of WiMAX and Long Term Evolution (LTE) signals, as well as the waveforms proposed for 5G systems, such as Filter Bank Multicarrier (FBMC). In this work, we employ the technique to evaluate experimentally the performance of modulation schemes proposed for 5G systems at high speeds. More specifically, we compare the performance obtained by transmitting FBMC schemes with two different prototype filters at different velocities up to 500 km/h. We also include the results obtained for Orthogonal Frequency-Division Multiplexing (OFDM) signals for comparison purposes.

Long Term Evolution (LTE) is expected to substitute the Global System for Mobile Communications (GSM) as the radio access technology for railway communications. Recently, especial attention has been devoted to high-speed trains since this particular environment poses challenging problems in terms of performance simulation and measurement. In order to severely decrease the cost and complexity of high-speed measurement campaigns, we have proposed a technique to induce effects caused by highly-time varying channels on Orthogonal Frequency-Division Multiplexing (OFDM) signals while conducting measurements at low speeds. In this work, we employ this technique to evaluate LTE transmissions in a Time-Division Duplex (TDD) fashion, which is more convenient for the railway environment due to a number of reasons (e.g., better flexibility in frequency choice and the importance of the uplink transmissions for railway control communications). We assess the performance obtained in high- speed scenarios up to 500 km/h.

LTE is designed to support user velocities of up to 500 km/h where experiments are expensive, time-consuming and dangerous. Fortunately, such experiments can be emulated at lower velocities by time-stretching the transmit signals. This method preserves the spatial properties of the mobile radio channel but performs a spectral compression. In this paper, we propose a new set of methods that preserve the spectral properties by inserting additional subcarriers. Next, we compare the method of time-stretching to the new set of methods by applying them to LTE downlink signals. We evaluate the properties and the main drawbacks of the proposed methods by simulations using standard-compliant LTE signals. Finally, we highlight practical aspects to be considered when implementing the proposed high speed emulation techniques.

Long Term Evolution (LTE) is expected to substitute the Global System for Mobile Communications (GSM) as the radio access technology for railway communications. Recently, especial attention has been devoted to high-speed trains since this particular environment poses challenging problems in terms of performance simulation and measurement. In order to severely decrease the cost and complexity of high-speed measurement campaigns, we have proposed a technique to induce effects caused by highly-time varying channels on Orthogonal Frequency-Division Multiplexing (OFDM) signals while conducting measurements at low speeds. In this work, we evaluate this technique by comparing the results of LTE measurements at different velocities as well as by simulations. Additionally, we use this technique to show the performance of LTE for high-speed train scenarios up to 600 km/h. To accomplish this, we use both a controlled high-speed measurement setup as well as a channel model developed according to the guidelines of the ITU Radiocommunication Sector (ITU-R) for the evaluation of radio interface technologies for IMT- Advanced systems.

We address the evaluation of low-complexity analog Joint Source Channel Coding (JSCC) methods for the transmission of discrete-time analog symbols over Multiple-Input Multiple-Output (MIMO) Multiple Access Channels (MAC). Analog JSCC is employed to encode the source information at each transmitter prior to be directly input to the MAC access scheme. Three channel access methods are considered to ensure the receiver is able to recover the user information: Code Division Multiple Access (CDMA), linear MMSE access codes and opportunistic access. CDMA allows the orthogonal transmission of the user data requiring only Channel State Information (CSI) at reception. On the other hand, linear MMSE access codes exploit CSI knowledge at transmission and exhibit better performance. Finally, opportunistic access also exploits CSI at transmission and allocates all MAC resources to the user with the strongest channel. This latter access scheme exhibits the best performance in terms of sum distortion although it may lead to unfair rate distributions among users.

We address the transmission of discrete-time analog samples over a block fading Multiple Access Channel (MAC). We propose two practical access schemes to achieve the sum capacity of a MAC channel: opportunistic and Code Division Multiple Access (CDMA). The first one is optimal when the Channel State Information (CSI) from all users is available at all the transmitters. However, it can lead to an unfair distribution of the power and transmission rate for each user. On the other hand, the CDMA access scheme allows setting individual rate constraints while showing excellent performance when transmitting either uncompressed or compressed analog Gaussian samples.

We consider the problem of transmission of independent and correlated Gaussian sources over the two user Gaussian Broadcast Channel (GBC). We present a low complexity, low delay, analog joint source channel coding communication system based on extensions of existing Nested Quantization techniques. Simulations results show that the resulting performance is close to the optimal theoretical limits for several cases of interest.

LTE is expected to substitute GSM as the basis technology for railway communications. Recently, special attention has been deserved to HST as this particular environment (mainly due to the high speed condition) can severely impact wireless systems performance. Although several channel models have been derived during the few last years, most of them are not accurate enough as they are not supported by measurement campaigns. In this paper, the main requirements for HST environments are analyzed and a flexible, cost-affordable, and easily-scalable software and hardware architecture for a test bed suitable for assessing LTE at high speeds is proposed.

The aim of this work is to jointly achieve individual rate requirements and minimum total transmit power in the vector Broadcast Channel (BC). Data streams are transmitted from a multi-antenna base station to several non-cooperative single-antenna receivers having perfect Channel-State-Information (CSI). Partial CSI, e.g., obtained via feedback, is used for the design of linear transmit filters at the transmitter. Employing a duality between Multiple Access Channel (MAC) and BC rate regions and the so-called standard interference functions, we propose an algorithmic joint solution for the transmit filter design and the power allocation in this work.

ABSTRACT We propose the use of low‐rate layered serially concatenated low‐density generator matrix (SCLDGM) codes in interleave‐division multiple access (IDMA) systems to approach the multiuser capacity. We study the behavior of the soft interference cancellation (SIC) detector employed in IDMA systems and design the channel codes using EXtrinsic Information Transfer (EXIT) evolution, aiming at optimizing the system performance. Simulation results show that the designed codes approach the theoretical limits and outperform previous IDMA schemes based on Turbo‐Hadamard codes. Copyright © 2010 John Wiley & Sons, Ltd.

Recently, the use of analog joint source-channel coding for the transmission of data samples at high rates over AWGN channels has been proposed in the literature. Simulation results have shown that this analog scheme performs close to the theoretical limits for several source distributions and transmission rates. In this paper we study the performance of such a system in comparison with optimized capacity approaching digital Bit Interleaved Coded Modulation (BICM) schemes. We show that in practical situations the analog transmission performs better than the digital scheme with a much lower encoding and decoding complexity when considering Gaussian and Laplacian source distributions.

Recently, analog joint source-channel coding has been proposed as a means of achieving near-optimum performance for high data rates with a very low complexity. However, no experimental evaluation showing the practical feasibility of this scheme has been performed to date. In this paper, we describe a software-defined radio implementation of an analog joint source-channel coded wireless transmission system. Experimental evaluation carried out in an indoor environment making use of a wireless testbed show that the performance perfectly matches that originally reported by simulations in additive white Gaussian noise channels for signal-to-noise ratio values below 20 dB.

We study the ability of low-rate Serially-Concatenated Low Density Generator Matrix (SCLDGM) codes to approach theoretical limits. Although two layer SCLDGM codes approach capacity for medium rates (0.25 <; R <; 0.9), they do not perform adequately for lower rates. In this case, substantial performance improvements are obtained using three layers.

Abstract We analyze Multiple‐Input Multiple‐Output (MIMO) coded modulation systems where either Bit‐Interleaved Coded Modulation (BICM) with spatial multiplexing or concatenation of channel coding and Space‐Time Block Codes (STBCs) is used at transmission, assuming iterative Turbo‐like decoding at reception. We optimize Serially‐Concatenated Low‐Density Generator Matrix (SCLDGM) codes (a subclass of LDPC codes) for each system configuration, with the goal of assessing its ability to approach the capacity limits in either ergodic or quasi‐static channels. Our focus is on three relevant STBCs: the Orthogonal Space‐Time Block Codes (OSTBCs) for two transmit antennas (i.e., the Alamouti code), which enables optimum detection with low complexity; the Golden code, which provides a capacity increase with respect to the input constellation; and Linear Dispersion (LD) codes, which enable practical detection in asymmetrical antenna configurations (i.e., more transmit than receive antennas) for cases in which optimum detection is infeasible. We conclude that BICM without concatenation with STBCs is in general the best option, except for Alamouti‐coded 2×1 and Golden‐coded 2×2 MIMO systems. Copyright © 2009 John Wiley & Sons, Ltd.

This paper describes a multicore Software Defined Radio (SDR) architecture devised to implement a fully reconfigurable downlink for WiMAX transceivers. The proposed architecture is made up of Commercial-Off-The-Shelf (COTS) modules available in the market and includes a DSP, three different models of FPGAs, DACs and ADCs. We show that the architecture is capable of supporting all the functionalities of the downlink sub frame of the Orthogonal Frequency Division Multiple Access (OFDMA) WiMAX physical layer, including Partial Usage of Sub carriers (PUSC) symbol structure and Forward Error Correction (FEC). The primary advantage of the design is the full reconfigurability at different levels: bandwidth, size of the FFT, modulation, code rate, etc. without modifying or restarting the system. We show that the five downlink profiles defined by the WiMAX Forum can be successfully implemented with the proposed architecture.

This paper has two major contributions. First, we present measured frequency selective wireless channels and show that there is only a small difference between mutual information and capacity. For single-antenna channels the maximum difference is about 0.15 bit/s/Hz, but typical differences are much smaller. These differences are increased with the number of transmit and receive antennas, achieving a maximum of about 0.4 bit/s/Hz for 2×2 MIMO channels and about 1.8 bit/s/Hz for 4×4 MIMO channels. The second contribution is a simple two-parameter channel model for frequency selective wireless channels, exhibiting the identical behavior in terms of capacity and mutual information than the measured channels. Due to its simplicity, this so-called poor-rich model is especially suitable for fast simulations and for analytical research in the field of information theory.

In this work, we provide a new throughput-based antenna selection criterion that considers the entire system design rather than only the channel state information. The performance evaluation was carried out in a measurement campaign in a common outdoor urban scenario. We present our results in terms of physical layer throughput over transmit power. In addition to diversity gains, we also see gains due to the selection of differently polarized antennas. In terms of SNR, the results show a gain of several decibels depending on the number of antennas selected.

For the separation of the signals for multiple users in the vector broadcast channel (BC), channel state information (CSI) is necessary at the transmitter. Since the transmitter has no access to this information in many cases, the CSI must be fed back from the receivers to the transmitter. Before the feedback, the receivers estimate the CSI and apply a rank reduction possible due to the channel correlations. We propose a joint optimization of the estimation, the rank reduction, and the codebook used for the feedback. Interestingly, the estimator and the rank reduction resulting from this monolithic formulation are independent of the used codebook which can be computed with the generalized Lloyd algorithm. Applying the proposed feedback design to a system with multi-user precoding based on CSI feedback shows the clear superiority of the optimized codebook compared to previous designs.

In this paper we investigate the design of a robust MMSE linear precoding Multiuser Multiple Input Single Output (MU MISO) system with limited feedback that exploits multiple feedback vectors to improve the quality of the available CSI at transmission. We explain how to appropriately exploit this additional past channel information to design the channel estimator, rank basis reduction and the quantizer parameters.

We propose a distributed multilayer architecture that provides user access to a MIMO testbed at the convenient abstraction level for researchers. This novel architecture releases the user from the need of low-level programming, making the MIMO testbed accessible through high level software (i.e. Matlab or similar). The layers of this architecture are highly decoupled among them, allowing us to run each layer on a different PC. Also, each layer permits independent extension or customisation as needed, for example to implement new testbed features such as a feedback channel or to deploy multiuser MIMO scenarios.

Abstract In this paper we employ a 2 × 2 Multiple‐Input Multiple‐Output (MIMO) hardware platform to evaluate, in realistic indoor scenarios, the performance of different space‐time block coded (STBC) transmissions at 2.4 GHz. In particular, we focus on the Alamouti orthogonal scheme considering two types of channel state information (CSI) estimation: a conventional pilot‐aided supervised technique and a recently proposed blind method based on second‐order statistics (SOS). For comparison purposes, we also evaluate the performance of a Differential (non‐coherent) space‐time block coding (DSTBC). DSTBC schemes have the advantage of not requiring CSI estimation but they incur in a 3 dB loss in performance. The hardware MIMO platform is based on high‐performance signal acquisition and generation boards, each one equipped with a 1 GB memory module that allows the transmission of extremely large data frames. Upconversion to RF is performed by two RF vector signal generators whereas downconversion is carried out with two custom circuits designed from commercial components. All the baseband signal processing is implemented off‐line in MATLAB®, making the MIMO testbed very flexible and easily reconfigurable. Using this platform we compare the performance of the described methods in line‐of‐sight (LOS) and non‐line‐of‐sight (NLOS) indoor scenarios. Copyright © 2007 John Wiley & Sons, Ltd.

Design of MIMO capacity-approaching codes relies on matching the extrinsic information transfer (EXIT) functions of the code and the detector. We focus on list sphere detection (LSD) because it presents the best performance/complexity tradeoff. We obtain the EXIT functions of the most representative LSDs: maximum likelihood (ML) and maximum a posteriori (MAP). We then utilize these EXIT functions to design good IRA and SCLDGM codes that perform close to the capacity limits.

Low density generator matrix (LDGM) codes are a particular class of low density parity check (LDPC) codes with very low encoding complexity. Single LDGM codes present high error-floors, which can be substantially reduced with the serial concatenation of two LDGM (SCLDGM) codes. We propose a technique to obtain good SCLDGM codes using extrinsic information transfer (EXIT) functions in a novel way. Although the optimization is performed for AWGN channels with binary signaling, the resulting codes are also optimal for AWGN and perfectly-interleaved Rayleigh fading channels with non-binary signaling and perfect CSI at reception, provided that Gray mapping is utilized. Optimized regular and irregular SCLDGM codes outperform heuristically-designed LDGM codes existing in the literature, and have a performance similar to or better than that of irregular repeat accumulate (IRA) codes.

We investigate the ability of serially-concatenated low density generator matrix (SCLDGM) codes to approach the capacity of ergodic multiple-input multiple-output (MIMO) channels. First, we explain how to design good SCLDGM codes using extrinsic information transfer (EXIT) analysis and optimum detection. However, optimum detection becomes unfeasible in many cases of practical interest where the number of transmitting antennas and/or the number of bits per constellation symbol is large. In this case, we resort to list sphere detection (LSD), considering two versions: maximum likelihood (ML) and maximum a posteriori (MAP). Again, we use EXIT analysis to obtain good SCLDGM codes for these specific LSD versions. We provide examples of optimized rate 1/2 SCLDGM codes and show that they perform within 1 dB from the theoretical capacity limit for a large number of antenna configurations and modulation formats. Finally, we discuss how to iteratively improve channel estimation using the soft information computed during the decoding process. We also show how the complexity of channel estimation is reduced by utilizing the candidate list calculated by the LSD.

In this work we study the ability of regular serially concatenated low-density generator matrix (SCLDGM) codes to approach the capacity of MIMO channels with two transmit antennas. We focus on two schemes: spatial multiplexing in a BICM fashion and concatenation with the Alamouti code. We show that good SCLDGM codes designed for SISO channels also approach MIMO capacity in the Alamouti concatenated scheme. These codes, however, do not perform adequately in spatial multiplexing and, in this case, have to be specifically designed for each antenna configuration and modulation format. Decoding complexity in spatial multiplexing is larger but the performance is in general better because the Alamouti code reduces the MIMO channel capacity when there is more than one receiving antenna

We propose a bit interleaved coded modulation with iterative decoding (BICM-ID) scheme for MIMO systems using systematic linear codes with low-density generator matrix (LDGM). We compare the performance of the proposed scheme with the (constrained input) channel capacity and show its ability to perform close to the theoretical limit. The main advantage of the proposed structure relies on its low encoding and decoding complexity.

We present a novel turbo equalization structure for space-time coded systems. It is based on performing channel equalization via a decision feedback equalizer (DFE) designed using a novel maximum likelihood criterion. The resulting optimization problem is solved using the space-alternating generalized EM (SAGE) algorithm. This results in expressions for the forward and backward filters of the DFE that take large benefit from the more and more refined estimations of the transmitted symbols iteratively obtained by the outer decoder.

This paper addresses the problem of equalizing Multiple-Input Multiple-Output (MIMO) channels in Space-Time Coded (STC) systems. We propose to apply the Maximum Likelihood (ML) principle in order to compute the coefficients of a multidimensional Decision-Feedback Equalizer (DFE) that generalizes the classical DFE structure to the MIMO case. The equalizer coefficients are numerically computed by means of the Space Alternating Generalized Expectation-maximization (SAGE) algorithm. This algorithm is known to present local convergence problems due to the existence of multiple maxima in the likelihood function. To avoid this limitation, we suggest to incorporate a small number of a priori known symbols into the transmitted data. Since the aim of these symbols is not to improve performance but just to avoid misconvergence, we term the resulting equalizer as semiblind.

In this paper we show that a convolutive mixture of statistically independent signals (sources) can be separated in the frequency-domain using only two frequency bins. The idea deals with finding a 2 × 2 invertible matrix which relates the frequency-domain sources and the temporal sources. This matrix exists when the Fourier transform of the observations is computed using zero-padding and sliding windows with delay of one point. We also propose a blind approach where a well-known algorithm is used and the amplitude/permutation indeterminacy is removed using a strategy based on the statistically independence of the temporal sources.

In this paper we investigate how to increase the capacity of GSM radio interfaces in subway tunnel environments by means of antenna arrays and Space-Time Coding (STC) techniques. We first consider space-time equalization techniques that only account for the noise and the Intersymbol Interference (ISI). Then, we show how to improve the performance by considering of a ST encoder at transmission and an iterative ST decoding algorithm at reception.

We propose a novel approach to estimating multiple-input-multiple-output (MIMO) channels in space-time coded systems. The channel is assumed to introduce dispersion in both the spatial and temporal dimensions. The channel estimator is obtained by applying the maximum likelihood principle, not only over a known pilot sequence, as in classical least-squares approaches, but also over all the symbols (both known and unknown) in a data frame. Since this results in an optimization problem without closed-form solution, we utilize an iterative method, the expectation-maximization (EM) algorithm, to calculate the solution. The resulting channel estimator is particularly suitable to be used in a turbo equalizing structure because it benefits from the a posteriori probabilities about the transmitted symbols computed at each decoding iteration of the turbo process.

Abstract In this paper, we investigate how to increase the capacity of GSM (Global System for Mobile communications) radio interfaces in subway tunnel environments by means of antenna arrays and Space–Time (ST) receivers. We address the modeling, both theoretically and experimentally, of the multiple‐antenna wireless channels encountered in subway tunnels. We demonstrate that propagation is conveniently modeled by a flat‐fading channel, but there exist strong spatial correlations among channel components that decrease capacity and affect receivers' performance. Different space–time GSM receiving strategies have also been investigated. We first consider ST equalization techniques that only account for the noise and the controlled Intersymbol Interference (ISI) introduced by the modulation format employed in GSM. Then, we analyze the performance of receivers that incorporate ST coding capabilities and show the superior performance of iterative MAP (Maximum a Posteriori) receivers that interchange soft information among the equalizer and the ST decoder. All receivers are evaluated with experimental channels measured in the subway of Paris. Copyright © 2002 John Wiley & Sons, Ltd.

This paper addresses the problem of interference suppression in direct sequence code division multiple access (DS CDMA) systems. We propose a novel semiblind decision feedback (DF) receiver based on the maximum likelihood (ML) principle that simultaneously exploits the transmission of training sequences and the statistical information concerning the unknown transmitted symbols. The space alternating generalized expectation maximization (SAGE) algorithm allows an efficient iterative implementation of the receiver. Computer simulations show that the resulting multiuser detector attains practically the same performance as the theoretical DF minimum mean square error (MMSE) receiver.

This paper investigates new algorithms for blind source separation that use cumulants instead of nonlinearities matched to the probability distribution of the sources. It is demonstrated that separation is a saddle point of a cumulant-based entropy cost function. To determine this point we propose two quasi-Newton algorithms whose convergence is isotropic and does not depend on the sources distribution. Moreover, convergence properties remain the same when there is Gaussian noise in the mixture.

This paper addresses the problem of blind and semiblind channel estimation and detection in multiuser communications. The proposed receivers are optimum in the sense that they make full use of the available statistical information to compute the maximum likelihood (ML) estimate of the multiuser channel using the expectation maximization (EM) algorithm and perform ML detection of the transmitted symbols. Computer simulations show that both proposed demodulators attain optimum performance in code division multiple access (CDMA) time dispersive channels. The semiblind approach, in addition, is highly robust to the local misconvergence problems of the EM algorithm.

This paper presents a new blind channel identification method for downlink multicarrier CDMA (MC-CDMA) systems. This technique uses subspace decomposition for channel identification and exploits the orthogonality property between the noise subspace and the received user codes to obtain a channel identification algorithm. It is shown that in order to increase the number of simultaneous users, and to still have a good estimation of the channel, a modification of the MC-CDMA system has to be considered by using codes larger than the spreading gain. The performance of this algorithm is analyzed by deriving a theoretical expression of the mean square error (MSE) estimation. This expression is compared with numerical results of some computer simulations to illustrate the validity of the analysis. The paper also introduces a linear detector for this environment.

In this paper we present an iterative inversion (II) approach to blind source separation (BSS). It consists of a quasi-Newton method for the resolution of an estimating equation obtained from the implicit inversion of a robust estimate of the mixing system. The resulting learning rule includes several existing algorithms for BSS as particular cases giving them a novel and unified interpretation.It also provides a justification of the Cardoso and Laheld step size normalization. The II method is first presented for instantaneous mixtures and then extended to the problem of blind separation of convolutive mixtures. Finally, we derive the necessary and sufficient asymptotic stability conditions for both the instantaneous and convolutive methods to converge.

In this paper we present several Gauss-Newton algorithms for blind source separation of convolutive mixtures. The algorithms can be interpreted as generalizations of two previous approaches due to Van Gerven and Van Compernolle (1995) and Nguyen-Thi and Jutten (1995). Since they are of the Gauss-Newton type, they exhibit a fast rate of convergence. Also, we present a stability analysis for two sources and instantaneous mixtures where we show that the algorithms cannot converge to non-separating solutions.

We investigate a constant modulus based criterion for the blind suppression of multiple access interference (MAI) in multicarrier code division multiple access (MC CDMA) systems. The method, termed linearly constrained constant modulus (LCCM), presents the same performance as minimum mean square error (MMSE) receivers with the only requirement of a rough estimate of the desired user code and timing. Therefore, it overcomes the main limitation of existing blind approaches based on the linearly constrained minimum variance (LCMV) criterion which are tightly dependent on this estimate.

This paper explains how fractional cyclic moments of CPFSK signals can be used in adaptive beamforming. It is shown that CPFSK signals have non-zero fractional cyclic moments because they generate spectral lines when they are raised to the inverse of its modulation index, which is generally a fractional number. To exploit this property, an optimization criterion is proposed to compute the antenna coefficients that maximize the output SINR. The resulting technique is blind because it does not need to know the transmitted symbols: only the carrier frequency, the symbol rate and the modulation index is required.

This paper addresses the problem of separating an unknown linear mixture of signals. A new algorithm is presented to adapt the coefficients of the linear combiners that perform the signal separation. The method is based on the well-known Shalvi and Weinstein (1990) criteria for blind equalization to which a cross-term has been added to prevent the same signal be extracted by several linear combiners simultaneously. The unconstrained criterion stationary points are analysed, the particular choices of the criteria are shown, a stochastic gradient algorithm is derived to compute the optimum separating parameters, and simulation results are presented.

This paper describes a method to incorporate linear constraints into an adaptive beamformer based on cyclostationary signal properties, previously proposed by the authors (see Proc. ICASSP'93, vol.IV, p. 284-287, Minneapolis, MN, April, 1993). The constraints allow an efficient exploitation of the spatial information available from the environment. This knowledge is used to prevent the beamformer from being captured by interferences showing the same cyclostationary properties as the desired signal and to control the sidelobe level of the radiation pattern.>