This work proposes an iterative channel estimation, detection and decoding (ICEDD) scheme for the uplink of multi-user multi-antenna systems assisted by multiple reconfigurable intelligent surfaces (RIS). A novel iterative code-aided channel estimation (ICCE) technique is developed that uses low-density parity-check (LDPC) codes and iterative processing to enhance estimation accuracy while reducing pilot overhead. The core idea is to exploit encoded pilots (EP), enabling the use of both pilot and parity bits to iteratively refine channel estimates. To further improve performance, an iterative channel tracking (ICT) method is proposed that takes advantage of the temporal correlation of the channel. An analytical evaluation of the proposed estimator is provided in terms of normalized mean-squared error (NMSE), along with a study of its computational complexity and the impact of the code rate. Numerical results validate the performance of the proposed scheme in a sub-6 GHz multi-RIS scenario with non-sparse propagation, under both LOS and NLOS conditions, and different RIS architectures.

Reconfigurable Intelligent Surfaces (RIS) have emerged as a key solution to dynamically adjust wireless propagation by tuning the reflection coefficients of large arrays of passive elements. Reconfigurable Holographic Surfaces (RHS) build on the same foundation as RIS but extend it by employing holographic principles for finer-grained wave manipulation — that is, applying higher spatial control over the reflected signals for more precise beam steering. In this paper, we investigate shape-adaptive RHS deployments in a multi-user network. Rather than treating each RHS as a uniform reflecting surface, we propose a selective element activation strategy that dynamically adapts the spatial arrangement of deployed RHS regions to a subset of predefined shapes. In particular, we formulate a system throughput maximization problem that optimizes the shape of the selected RHS elements, active beamforming at the access point (AP), and passive beamforming at the RHS to enhance coverage and mitigate signal blockage. The resulting problem is non-convex and becomes even more challenging to solve as the number of RHS and users increases; to tackle this, we introduce an alternating optimization (AO) approach that efficiently finds near-optimal solutions irrespective of the number or spatial configuration of RHS. Numerical results demonstrate that shape adaptation enables more efficient resource distribution, enhancing the effectiveness of multi-RHS deployments as the network scales.

In this work, to address the fixed step-size problem of the widely linear complex-valued affine projection algorithm (WL-CAPA), we propose a sliding-window step-size (SWSS) selection scheme, which results in the SWSS-WL-CAPA. To devise this scheme, we derive the mean-square deviation (MSD) recursion of WL-CAPA and obtain the optimal step-size at each iteration based on the comparison of MSD trends of the algorithm using two different step-sizes in the sliding-window. Interestingly, the SWSS scheme removes the length limitation of the step-size sequence with iterations, which allows the proposed algorithm to achieve better steady-state behavior. Furthermore, we develop a reset mechanism for enhancing the real-time tracking capability of the algorithm for unknown systems. The efficacy of the proposed algorithm is substantiated through the execution of simulations in scenarios of system identification and stereophonic acoustic echo cancellation.

Cell-free networks outperform cellular networks in many aspects, yet their efficiency is affected by imperfect channel state information (CSI). In order to address this issue, this work presents a robust resource allocation framework designed for the downlink of user-centric cell-free massive multi-input multi-output (CF-mMIMO) networks. This framework employs a sequential resource allocation strategy with a robust user scheduling algorithm designed to maximize the sum-rate of the network and two robust power allocation algorithms aimed at minimizing the mean square error, which are developed to mitigate the effects of imperfect CSI. An analysis of the proposed robust resource allocation problems is developed along with a study of their computational cost. Simulation results demonstrate the effectiveness of the proposed robust resource allocation algorithms, showing a performance improvement of up to 30% compared to existing techniques.

In this paper, we investigate the channel estimation problem for extremely large-scale multiple-input multiple-output (XL-MIMO) systems with a hybrid analog-digital architecture, implemented within a decentralized baseband processing (DBP) framework with a star topology. Existing centralized and fully decentralized channel estimation methods face limitations due to excessive computational complexity or degraded performance. To overcome these challenges, we propose a novel two-stage channel estimation scheme that integrates local sparse reconstruction with global fusion and refinement. Specifically, in the first stage, by exploiting the sparsity of channels in the angular-delay domain, the local reconstruction task is formulated as a sparse signal recovery problem. To solve it, we develop a graph neural networks-enhanced sparse Bayesian learning (SBL-GNNs) algorithm, which effectively captures dependencies among channel coefficients, significantly improving estimation accuracy. In the second stage, the local estimates from the local processing units (LPUs) are aligned into a global angular domain for fusion at the central processing unit (CPU). Based on the aggregated observations, the channel refinement is modeled as a Bayesian denoising problem. To efficiently solve it, we devise a variational message passing algorithm that incorporates a Markov chain-based hierarchical sparse prior, effectively leveraging both the sparsity and the correlations of the channels in the global angular-delay domain. Simulation results show the effectiveness and superiority of the proposed SBL-GNNs algorithm over existing methods, demonstrating improved estimation performance and reduced computational complexity.

In this paper, we present an adaptive reweighted sparse belief propagation (AR-SBP) decoder for polar codes. The AR-SBP technique is inspired by decoders that employ the sum-product algorithm for low-density parity-check codes. In particular, the AR-SBP decoding strategy introduces reweighting of the exchanged log-likelihood-ratio in order to refine the message passing, improving the performance of the decoder and reducing the number of required iterations. An analysis of the convergence of AR-SBP is carried out along with a study of the complexity of the analyzed decoders. Numerical examples show that the AR-SBP decoder outperforms existing decoding algorithms for a reduced number of iterations, enabling low-latency applications.

User authentication in future wireless communication networks is expected to become more complicated due to their large scale and heterogeneity. Furthermore, the computational complexity of classical cryptographic approaches based on public key distribution can be a limiting factor for using in simple, low-end Internet of things (IoT) devices. This paper proposes physical layer authentication (PLA) expected to complement existing traditional approaches, e.g., in multi-factor authentication protocols. The precision and consistency of PLA is impacted because of random variations of wireless channel realizations between different time slots, which can impair authentication performance. In order to address this, a method based on error-correcting codes in the form of reconciliation is considered in this work. In particular, we adopt distributed source coding (Slepian-Wolf) reconciliation using polar codes to reconcile channel measurements spread in time. Hypothesis testing is then applied to the reconciled vectors to accept or reject the device as authenticated. Simulation results show that the proposed PLA using reconciliation outperforms prior schemes even in low signal-to-noise ratio scenarios.

To accommodate extremely high data rates, provide high reliability, improve coverage, and meet traffic demands in future wireless communication networks, novel technologies have emerged that exploit electromagnetic waves, large multiple-antenna systems, intelligent reflective surfaces, hardware innovations, new network architectures, and higher frequency bands. Considering advances in information theory and devices, fundamental questions arise for system designers on how to develop synergies between theory and practice. Current design and analysis methods are predominantly based on scalar-quantity, far-field, planar-wavefront, monochromatic, and other non-physically consistent assumptions, which can lead to significant mismatches with systems designed based on realistic propagation models.

Several approaches for estimating the direction of arrival (DOA) are traditionally developed assuming Gaussian noise, making them highly sensitive to outliers. Therefore, when confronted with impulsive noise, the performance of these methods may significantly deteriorate. In this letter, we characterize impulsive noise as Gaussian noise mixed sparse outliers. By exploiting their statistical differences, we propose an innovative DOA estimation technique within the framework of sparse signal recovery (SSR). Unlike common robust loss functions, such as$\ell _{1}$and$\ell _{p}$norms, we combine the$\ell _{2}$-norm with the Minimax Logarithmic Concave function as the loss function. Furthermore, to address the issue of grid mismatch, we utilize an alternating optimization approach to acquire the grid deviations, with the aid of rough DOA estimations and estimated outliers. Simulation results indicate that the proposed technique exhibits robustness against large outliers.

With the combination of extra-large arrays and high frequencies, near-field transmissions have become prevalent, challenging the validity of classical channel representations typically derived under the plane wavefront assumption. In this paper, we investigate the angular-domain representation of line-of-sight (LoS) extra-large MIMO (XL-MIMO) channels, considering the impact of spherical wavefront effects. First, we demonstrate the structured sparsity of LoS XL-MIMO channels in the angular domain. Leveraging this sparsity, we propose an effective spatial bandwidth channel representation method, which characterizes near-field LoS XL-MIMO channels as a superposition of multiple plane wave components, enabling us to capture the spherical wavefront effect in a low-dimensional angular channel. Subsequently, we introduce an angular-domain transceiver architecture based on this low-dimensional channel representation. This architecture could significantly facilitate the implementation of LoS XL-MIMO systems. Finally, simulation results confirm the effectiveness of the effective spatial bandwidth identification method and analyze the impact of various array geometries on the effective spatial bandwidth. Additionally, the availability of the angular-domain processing architecture is validated.

In this work, we investigate the joint visibility region (VR) detection and channel estimation (CE) problem for extremely large-scale multiple-input-multiple-output (XL-MIMO) systems considering both the spherical wavefront effect and spatial non-stationary (SnS) property. Unlike existing SnS CE methods that rely on the statistical characteristics of channels in the spatial or delay domain, we propose an approach that simultaneously exploits the antenna-domain spatial correlation and the wavenumber-domain sparsity of SnS channels. To this end, we introduce a two-stage VR detection and CE scheme. In the first stage, the belief regarding the visibility of antennas is obtained through a VR detection-oriented message passing (VRDO-MP) scheme, which fully exploits the spatial correlation among adjacent antenna elements. In the second stage, leveraging the VR information and wavenumber-domain sparsity, we accurately estimate the SnS channel employing the belief-based orthogonal matching pursuit (BB-OMP) method. Simulations show that the proposed algorithms lead to a significant enhancement in VR detection and CE accuracy as compared to existing methods, especially in low signal-to-noise ratio (SNR) scenarios.

With the combination of extra-large arrays and high frequencies, near-field transmissions have become increasingly prevalent. In this paper, we investigate the angular-domain representation of near-field line-of-sight (LoS) extra-large multiple-input-multiple-output (XL-MIMO) channels. Specifically, we first demonstrate the structured sparsity of the near-field LoS channel in the angular domain. By leveraging this sparsity property, we propose an effective spatial bandwidth channel representation method. This method characterizes near-field LoS XL-MIMO channels as a superposition of multiple plane wave components within the effective spatial band between transceiver arrays. Finally, simulation results validate the equivalence between the proposed representation and the existing antenna domain channel model and demonstrate the effects of array geometries on the effective spatial bandwidth.

Low-resolution analog-to-digital converters (ADCs) are promising for reducing energy consumption and costs of multiuser multiple-input multiple-output (MIMO) systems with many antennas. We propose low-resolution multiuser MIMO receivers where the signals are simultaneously processed by 1-bit ADCs and a comparator network, which can be interpreted as additional virtual channels with binary outputs. We distinguish the proposed comparator networks in fully and partially connected. For such receivers, we develop the low-resolution aware linear minimum mean-squared error (LRA-LMMSE) channel estimator and detector according to the Bussgang theorem. We also develop a robust detector which takes into account the channel state information (CSI) mismatch statistics. By exploiting knowledge of the channel coefficients we devise a mean-square error (MSE) greedy search and a sequential signal-to-interference-plus-noise ratio (SINR) search for optimization of partially connected networks. Numerical results show that a system with extra virtual channels can outperform a system with additional receive antennas, in terms of bit error rate (BER). Furthermore, by employing the proposed channel estimation with its error statistics, we construct a lower bound on the ergodic sum rate for a linear receiver. Simulation results confirm that the proposed approach outperforms the conventional 1-bit MIMO system in terms of BER, MSE and sum rate.

In this paper, we address two crucial challenges in the design of cell-free (CF) systems: degradation in the performance of CF systems by imperfect channel state information at the transmitter (CSIT) and high computational/signaling loads arising from the increasing number of distributed antennas and parameters to be exchanged. To mitigate the effects of imperfect CSIT, we employ rate-splitting (RS) multiple-access, which separates the messages into common and private streams. Unlike prior works, we present a clustered CF multi-user multiple-antenna framework with RS, which groups the transmit antennas in several clusters to reduce the computational and signaling loads. The proposed RS-CF system employs one common stream per cluster to exploit the network diversity. Furthermore, we propose new cluster-based linear precoders for this framework. We then devise a power allocation strategy for the common and private streams within clusters and derive closed-form expressions for the sum-rate performance of the proposed cluster-based RS-CF system. Numerical results show that the proposed clustered RS-CF system and algorithms outperform existing approaches.

A promising approach to reducing the energy consumption is to consider coarse quantization at the receiver. In this study, we investigate novel precoding techniques in space and time for bandlimited multiuser MIMO downlink channels with 1-bit quantization and oversampling at the receiver, considering zero-crossing modulation. The proposed time-instance zero-crossing modulation conveys the information into the time-instances of zero-crossings. Two design criteria for time-instance zero-crossing modulation are investigated, namely, the minimum distance to the decision threshold and the mean-square error between the received and the desired signal. The maximization of the minimum distance to the decision threshold can be formulated as a quadratically constraint quadratic program. As an alternative, an equivalent problem can be formulated based on power minimization, which reduces computational complexity. Departing from the conventional mean-square error based technique, a more sophisticated algorithm is developed, which implies active constellation extension in order to improve the performance at high SNR. The extended problem is solved with two approaches, namely by formulating the problem as a second-order cone program and by considering an alternating optimization algorithm. Numerical results show that the proposed time-instance zero-crossing precoding methods significantly improve the bit error rate compared to the state-of-the-art methods.

In this paper, the energy efficiency of a two-user downlink NOMA system aided by several ambient backscatter devices is investigated. We analyze both the tradeoff and the ratio between achievable rates versus power consumption, assuming that the backscatter devices are in fully cooperative mode. In the case of two backscatter devices, we derive a closed-form solution in terms of the optimal reflection coefficients and power allocation policy by exploiting the properties of the energy-efficiency objective and the Pareto boundary of the feasible set. For more than two backscatter devices, the problem becomes difficult and our methodology cannot be extended easily. Nevertheless, we evaluate the performance of NOMA aided by several (up to four) backscatter devices via numerical simulations. Our numerical results show that the energy efficiency of the two-user NOMA system increases with the number of cooperative backscatter devices. Moreover, in the high noise regime, the relative efficiency gain increases with the number of backscatter devices reaching up to 370 % compared to conventional NOMA.

Robust adaptive beamforming (RAB) based on interference-plus-noise covariance (INC) matrix reconstruction can experience performance degradation when model mismatch errors exist, particularly when the input signal-to-noise ratio (SNR) is large. In this work, we devise an efficient RAB technique for dealing with covariance matrix reconstruction issues. The proposed method involves INC matrix reconstruction using an idea in which the power and the steering vector of the interferences are estimated based on the power method. Furthermore, spatial match processing is computed to reconstruct the desired signal-plus-noise covariance matrix. Then, the noise components are excluded to retain the desired signal (DS) covariance matrix. A key feature of the proposed technique is to avoid eigenvalue decomposition of the INC matrix to obtain the dominant power of the interference-plus-noise region. Moreover, the INC reconstruction is carried out according to the definition of the theoretical INC matrix. Simulation results are shown and discussed to verify the effectiveness of the proposed method against existing approaches.

In this work, we investigate the sum-rate performance of multicell and cell-free massive multi-input multi-output (MIMO) systems using linear precoding and multiuser scheduling algorithms. We consider a network-centric clustering approach to reduce the computational cost of the techniques applied to cell-free systems. We then develop a greedy algorithm that considers multiple candidates for the subset of users to be scheduled and that approaches the performance of the optimal exhaustive search. We assess the proposed and existing scheduling algorithms in both multicell and cell-free networks with the same coverage area. Numerical results illustrate the sum-rate performance of the proposed scheduling algorithm against existing approaches.

We propose a novel M-estimate conjugate gradient (CG) algorithm, termed Tukey’s biweight M-estimate CG (TbMCG), for system identification in impulsive noise environments. In particular, the TbMCG algorithm can achieve a faster convergence while retaining a reduced computational complexity as compared to the recursive least-squares (RLS) algorithm. Specifically, the Tukey’s biweight M-estimate incorporates a constraint into the CG filter to tackle impulsive noise environments. Moreover, the convergence behavior of the TbMCG algorithm is analyzed. Simulation results confirm the excellent performance of the proposed TbMCG algorithm for system identification and active noise control applications.

Robust adaptive beamforming (RAB) plays a vital role in modern communications by ensuring the reception of high-quality signals. This paper proposes a deep learning approach to robust adaptive beamforming. In particular, we propose a novel RAB approach where the sample covariance matrix (SCM) is used as the input of a deep 1D Complex-Valued Convolutional Neural Network (CVCNN). The network employs complex convolutional and pooling layers, as well as a Cartesian Scaled Exponential Linear Unit activation function to directly compute the nearly-optimum weight vector through the training process and without prior knowledge about the direction of arrival of the desired signal. This means that reconstruction of the interference plus noise (IPN) covariance matrix is not required. The trained CVCNN accurately computes the nearly-optimum weight vector for data not used during training. The computed weight vector is employed to estimate the signal-to-interference plus noise ratio. Simulations show that the proposed RAB can provide performance close to that of the optimal beamformer.

A novel sparse array (SA) structure is proposed based on the maximum inter-element spacing (IES) constraint (MISC) criterion. Compared with the traditional MISC array, the proposed SA configurations, termed as improved MISC (IMISC) has significantly increased uniform degrees of freedom (uDOF) and reduced mutual coupling. In particular, the IMISC arrays are composed of six uniform linear arrays (ULAs), which can be determined by an IES set. The IES set is constrained by two parameters, namely the maximum IES and the number of sensors. The uDOF of the IMISC arrays is derived and the weight function of the IMISC arrays is analyzed as well. The proposed IMISC arrays have a great advantage in terms of uDOF against the existing SAs, while their mutual coupling remains at a low level. Simulations are carried out to demonstrate the advantages of the IMISC arrays.

This paper proposes a unified sparsity-aware robust recursive least-squares RLS (S-RRLS) algorithm for the identification of sparse systems under impulsive noise. The proposed algorithm generalizes multiple algorithms only by replacing the specified criterion of robustnessand sparsity-aware penalty. Furthermore, by jointly optimizing the forgetting factor and the sparsity penalty parameter, we develop the jointly-optimized S-RRLS (JO-S-RRLS) algorithm, which not only exhibits low misadjustment but also can track well sudden changes of a sparse system. Simulations in impulsive noise scenarios demonstrate that the proposed S-RRLS and JO-S-RRLS algorithms outperform existing techniques.

In this paper, we propose a general robust subband adaptive filtering (GR-SAF) scheme against impulsive noise by minimizing the mean square deviation under the random-walk model with individual weight uncertainty. Specifically, by choosing different scaling factors such as from the M-estimate and maximum correntropy robust criteria in the GR-SAF scheme, we can easily obtain different GR-SAF algorithms. Importantly, the proposed GR-SAF algorithm can be reduced to a variable regularization robust normalized SAF algorithm, thus having fast convergence rate and low steady-state error. Simulations in the contexts of system identification with impulsive noise and echo cancellation with double-talk have verified that the proposed GR-SAF algorithms outperforms its counterparts.

Rate splitting (RS) systems can better deal with imperfect channel state information at the transmitter (CSIT) than conventional approaches. However, this requires an appropriate power allocation that often has a high computational complexity, which might be inadequate for practical and large systems. To this end, adaptive power allocation techniques can provide good performance with low computational cost. This work presents novel robust and adaptive power allocation technique for RS-based multiuser multiple-input multiple-output (MU-MIMO) systems. In particular, we develop a robust adaptive power allocation based on stochastic gradient learning and the minimization of the mean-square error between the transmitted symbols of the RS system and the received signal. The proposed robust power allocation strategy incorporates knowledge of the variance of the channel errors to deal with imperfect CSIT and adjust power levels in the presence of uncertainty. An analysis of the convexity and stability of the proposed power allocation algorithms is provided, together with a study of their computational complexity and theoretical bounds relating the power allocation strategies. Numerical results show that the sum-rate of an RS system with adaptive power allocation outperforms RS and conventional MU-MIMO systems under imperfect CSIT.

In this work, we present a joint channel estimation, activity detection and data decoding scheme for massive machine-type communications. By including the channel and thea prioriactivity factor in the factor graph, we present the bilinear message-scheduling GAMP (BiMSGAMP), a message-passing solution that uses the channel decoder beliefs to refine the activity detection and data decoding. We include two message-scheduling strategies based on the residual belief propagation (RBP) and the activity user detection (AUD) in which messages are evaluated and scheduled in every new iteration. An analysis of the convergence of BiMSGAMP along with a study of its computational complexity is carried out. Numerical results show that BiMSGAMP outperforms state-of-the-art algorithms, highlighting the gains achieved by using the dynamic scheduling strategies and the effects of the channel decoding part in the system.

In this paper, the energy efficiency of multi-user non orthogonal multiple access (NOMA) systems in the presence of a backscatter device is investigated. The energy efficiency maximization problem is formulated as a tradeoff between the sum rate and the total power consumption and shown to be non-convex. We then derive a closed-form expression of the optimal reflection coefficient. Remarkably, the obtained expression allows the reformulation of the optimization in terms of the power allocation policy into a convex optimization problem that has recently been solved in closed form. This overall solution can then be exploited to reduce the computational complexity of Dinkelbach's algorithm for maximizing the ratio sum rate vs. total power. Simulation results show that the presence of backscatter devices significantly improve the energy efficiency of NOMA systems and reach up to 450% relative gains compared to OMA.

Rate splitting (RS) has emerged as a valuable technology for wireless communications systems due to its capability to deal with uncertainties in the channel state information at the transmitter (CSIT). RS with linear and non-linear precoders, such as the Tomlinson- Harashima (THP) precoder, have been explored in the downlink (DL) of multiuser multi antenna systems. In this work, we propose a multi-branch (MB) scheme for a RS-based multiple-antenna system, which creates patterns to order the transmitted symbols and enhances the overall sum rate performance compared to existing approaches. Analytical expressions to describe the signal-to-interference-plus- noise ratio (SINR) and compute the sum rate are derived. Simulation results show that the proposed MB-THP for RS outperforms conventional THP and MB-THP schemes.

In this work, we present an energy-efficient distributed learning framework using low-resolution ADCs and coarsely quantized signals for Internet of Things (IoT) networks. In particular, we develop a distributed quantization-aware least-mean square (DQA-LMS) algorithm that can learn parameters in an energy-efficient fashion using signals quantized with few bits while requiring a low computational cost. We also carry out a statistical analysis of the proposed DQA-LMS algorithm that includes a stability condition. Simulations assess the DQA-LMS algorithm against existing techniques for a distributed parameter estimation task where IoT devices operate in a peer-to-peer mode and demonstrate the effectiveness of the DQA-LMS algorithm.

In this paper, we propose a novel normalized subband adaptive filter algorithm suited for sparse scenarios, which combines the proportionate and sparsity-aware mechanisms. The proposed algorithm is derived based on the proximal forward-backward splitting and the soft-thresholding methods. We analyze the mean and mean square behaviors of the algorithm, which is supported by simulations. In addition, an adaptive approach for the choice of the thresholding parameter in the proximal step is also proposed based on the minimization of the mean square deviation. Simulations in the contexts of system identification and acoustic echo cancellation verify the superiority of the proposed algorithm over its counterparts.

This article introduces multiuser multiple-input multiple-output (MU-MIMO) architectures based on non-linear precoding and stream combining techniques using rate-splitting (RS), where the transmitter often has only partial knowledge of the channel state information (CSI). In contrast to existing works, we consider deployments where the receivers may be equipped with multiple antennas. This allows us to employ linear combining techniques based on the Min-Max, the maximum ratio and the minimum mean-square error criteria along with Tomlinson-Harashima precoders (THP) for RS-based MU-MIMO systems to enhance the sum-rate performance. Moreover, we incorporate the Multi-Branch (MB) concept into the RS architecture to further improve the sum-rate performance. Closed-form expressions for the signal-to-interference-plus-noise ratio and the sum-rate at the receiver end are devised through statistical analysis. Simulation results show that the proposed RS-THP schemes achieve better performance than conventional linear and THP precoders.

In this work, we present block diagonalization and power allocation algorithms for large-scale multiple-antenna systems with coarsely quantized signals. In particular, we develop Coarse Quantization-Aware Block Diagonalization${\scriptstyle \mathrm {\left ({CQA-BD}\right)}}$and Coarse Quantization-Aware Regularized Block Diagonalization${\scriptstyle \mathrm {\left ({CQA-RBD}\right)}}$precoding algorithms that employ the Bussgang decomposition and can mitigate the effects of low-resolution signals and interference. Moreover, we also devise the Coarse Quantization-Aware Most Advantageous Allocation Strategy${\scriptstyle \mathrm {\left ({CQA-MAAS}\right)}}$power allocation algorithm to improve the sum rate of precoders that operate with low-resolution signals. An analysis of the sum-rate performance is carried out along with computational complexity and power consumption studies of the proposed and existing techniques. Simulation results illustrate the performance of the proposed${\scriptstyle \mathrm {CQA-BD}}$and${\scriptstyle \mathrm {CQA-RBD}}$precoding algorithms, and the proposed${\scriptstyle \mathrm {CQA-MAAS}}$power allocation strategy against existing approaches.

In this work, large-scale multiple-antenna systems are investigated, where the base station employs a large antenna array with low-cost and low-power 1-bit analog-to-digital converters. To compensate for the performance loss caused by the coarse quantization, oversampling is applied at the receiver. Unlike existing works that use uniform oversampling, which samples the signal at a constant rate, a novel dynamic oversampling scheme is proposed. The basic idea is to perform time-varying nonuniform oversampling, which selects samples with nonuniform patterns that vary over time. We consider two system design criteria: a design that maximizes the achievable sum rate and another design that minimizes the mean square error of detected symbols. Dynamic oversampling is carried out using a dimension reduction matrix Δ, which can be computed by the generalized eigenvalue decomposition or by novel submatrix-level feature selection algorithms. Moreover, the proposed scheme is analyzed in terms of convergence, computational complexity and power consumption at the receiver. Simulations show that systems with the proposed dynamic oversampling outperform those with uniform oversampling in terms of computational cost, achievable sum rate and symbol error rate performance.

In this work, we present a cloud-driven uplink framework for multiway multiple-antenna relay systems which facilitates joint linear Minimum Mean Square Error (MMSE) symbol detection in the cloud and where users are selected to simultaneously transmit to each other aided by relays. We also investigate relay selection techniques for the proposed cloud-driven uplink framework that uses cloud-based buffers and physical-layer network coding. In particular, we develop a novel multi-way relay selection protocol based on the selection of the best link, denoted as Multi-Way Cloud-Driven Best-User-Link (MWC-Best-User-Link). We then devise a channelnorm based relay selection criterion along with the algorithm that is incorporated into the proposed MWC-Best-User-Link protocol. Simulations show that MWC-Best-User-Link outperforms previous works in terms of average delay, sum-rate and bit error rate.

Low-resolution devices are promising for systems that demand low energy consumption and low complexity as required in IoT systems. In this study, we propose a novel waveform for bandlimited channels with 1-bit quantization and oversampling at the receivers. The proposed method implies that the information is conveyed within the time instances of zero-crossings which is then utilized in combination with a Gray-coding scheme. Unlike the existing method, the proposed method does not require optimization and transmission of a dynamic codebook. The proposed approach outperforms the state-of-the-art method in terms of bit error rate.

In this paper, we propose a dynamic oversampling technique for asynchronous large-scale multiple-antenna systems with 1-bit analog-to-digital converters at the base station that is suitable for sustainable internet of things and cellular networks. To the best of our knowledge, this is the first paper to introduce a dynamic oversampling technique for such systems. The main idea is to sample the received signal at a higher rate and only few weighted samples are chosen for further signal processing. We apply the generalized eigenvalue decomposition algorithm for linearly combining the samples and performing dimension reduction. We investigate the proposed technique in terms of the Bussgang theorem based sum rate capacity. Numerical results show that with the proposed dynamic oversampling technique the system can use small number of processing samples to achieve the same sum rates as the standard uniform oversampling technique while maintaining the same power consumption.

Large‐scale multi‐user multiple‐input multiple‐output (MU‐MIMO) systems and cloud radio access networks (C‐RANs) are promising technologies for the fifth generation (5G) of wireless networks. In this context, the use of low‐resolution analogue‐to‐digital converters (ADCs) is key for energy efficiency and for complying with constrained fronthaul links. Processing signals with a few bits implies performance loss and, therefore, techniques that can compensate for quantisation distortion are fundamental. In wireless systems, an automatic gain control (AGC) precedes the ADCs to adjust the input signal level in order to reduce the impact of quantisation. In this work, the authors propose the joint optimisation of the AGC, which works in the remote radio heads (RRHs), and a low‐resolution aware (LRA) linear receive filter based on the minimum mean square error (MMSE), which works in the cloud unit (CU), for large‐scale MU‐MIMO systems with coarsely quantised signals. They develop linear and successive interference cancellation receivers based on the proposed joint AGC and LRA MMSE (AGC‐LRA‐MMSE) approach. An analysis of the achievable sum rates along with a computational complexity study is also carried out. Simulations show that the proposed AGC‐LRA‐MMSE design obtains substantial gains in bit error rates and achievable information rates over existing techniques.

In this work, the authors proposed the iterative access point (AP) selection (APS), linear minimum mean‐square error (MMSE) precoding and power allocation techniques for cell‐free massive multiple‐input multiple‐output (MIMO) systems. They considered the downlink channel with single‐antenna users and multiple‐antenna APs. They derive sum‐rate expressions for the proposed iterative APS techniques followed by MMSE precoding and optimal, adaptive, and uniform power allocation schemes. Simulations show that the proposed approach outperforms existing conjugate beamforming and zero‐forcing schemes and that performance remains excellent with APS, in the presence of perfect and imperfect channel state information.

To ensure signal receiving quality, robust adaptive beamforming (RAB) is of vital importance in modern communications. In this letter, we propose a new low-complexity RAB approach based on interference-plus-noise covariance matrix (IPNC) reconstruction and steering vector (SV) estimation. In this method, the IPNC and desired signal covariance matrices are reconstructed by estimating all interference powers as well as the desired signal power using the principle of maximum entropy power spectrum (MEPS). Numerical simulations demonstrate that the proposed method can provide superior performance to several previously proposed beamformers.

In this work, we present a cloud-driven uplink framework for multi-way multiple-antenna relay systems which aids joint symbol detection in the cloud and where users are selected to simultaneously transmit to each other aided by relays. We also investigate relay selection techniques for the proposed cloud-driven uplink framework that uses cloud-based buffers and XOR network coding. In particular, we develop a novel multi-way relay selection protocol based on the selection of the best link, denoted as Multi-Way Cloud-Driven Best-User-Link (MWC-Best-User-Link). We then devise maximum-minimum-distance and channel-norm based relay selection criteria along with algorithms that are incorporated into the proposed MWC-Best-User-Link protocol. An analysis of the proposed MWC-Best-User-Link protocol in terms of computational cost, pairwise error probability, sum-rate and average delay is carried out. Simulations show that MWC-Best-User-Link outperforms previous works in terms of sum-rate, pairwise error probability, average delay and bit error rate.

The main challenge of massive machine-type communications (mMTC) is the joint activity and signal detection of devices. The mMTC scenario with many devices transmitting data intermittently at low data rates and via very short packets enables its modelling as a sparse signal processing problem. In this work, we consider a grant-free system and propose a detection and decoding scheme that jointly detects activity and signals of devices. The proposed scheme consists of a list detection technique, an l0-norm regularized activity-aware recursive least-squares algorithm, and an iterative detection and decoding (IDD) approach that exploits the device activity probability. In particular, the proposed list detection technique uses two candidate-list schemes to enhance the detection performance. We also incorporate the proposed list detection technique into an IDD scheme based on low-density parity-check codes. We derive uplink sum-rate expressions that take into account metadata collisions, interference and a variable activity probability for each user. A computational complexity analysis shows that the proposed list detector does not require a significant additional complexity over existing detectors, whereas a diversity analysis discusses its diversity order. Simulations show that the proposed scheme obtains a performance superior to existing suboptimal detectors and close to the oracle LMMSE detector.

This paper deals with the design of a sensing matrix along with a sparse recovery algorithm by utilizing the probability-based prior information for compressed sensing systems. With the knowledge of the probability for each atom of the dictionary being used, a diagonal weighted matrix is obtained and then the sensing matrix is designed by minimizing a weighted function such that the Gram of the equivalent dictionary is as close to the Gram of dictionary as possible. An analytical solution for the corresponding sensing matrix is derived that requires low computational complexity. We also exploit this prior information through the sparse recovery stage and propose a probability-driven orthogonal matching pursuit algorithm that improves the accuracy of the recovery. Simulations for synthetic data and application scenarios of video streaming are carried out to compare the performance of the proposed methods with some existing algorithms. The results reveal that the proposed compressed sensing (CS) approach outperforms existing CS systems.

In this work, a novel rank-revealing matrix decomposition algorithm termed Compressed Randomized UTV (CoR-UTV) decomposition along with a CoR-UTV variant aided by the power method technique is proposed. CoR-UTV computes an approximation to a low-rank input matrix by making use of random sampling schemes. Given a large and dense matrix of size m × n with numerical rank k, where k ≪ min{m, n}, CoR-UTV requires a few passes over the data, and runs in O(mnk) floating-point operations. Furthermore, CoR-UTV can exploit modern computational platforms and can be optimized for maximum efficiency. CoR-UTV is also applied for solving robust principal component analysis problems. Simulations show that CoR-UTV outperform existing approaches.

Large-scale multiple-antenna systems have been identified as a promising technology for the next generation of wireless systems. However, by scaling up the number of receive antennas the energy consumption will also increase. One possible solution is to use low-resolution analog-to-digital converters at the receiver. This paper considers large-scale multiple-antenna uplink systems with 1-bit analog-to-digital converters on each receive antenna. Since oversampling can partially compensate for the information loss caused by the coarse quantization, the received signals are firstly oversampled by a factor M. We then propose a low-resolution aware linear minimum mean-squared error channel estimator for 1-bit oversampled systems. Moreover, we characterize analytically the performance of the proposed channel estimator by deriving an upper bound on the Bayesian Cramér-Rao bound. Numerical results are provided to illustrate the performance of the proposed channel estimator.

In this paper, we investigate the physical layer security (PLS) of cooperative wireless-powered networks where a source transmits confidential information to a destination with the aid of wireless-powered intermediate nodes equipped with multiple antennas in the presence of a passive eavesdropper. We consider two generalized joint relay and jammer selection (GJRJS) frameworks based on the power splitting (PS) and time switching (TS) techniques, respectively. Specifically, the intermediate nodes which cannot decode the source signal successfully are selected to act as friendly jammers to transmit artificial noise, and the remaining nodes are exploited as relays to simultaneously forward the source signal through cooperative beamforming. We further propose two cooperative secure beamforming (CSB) schemes for the PS-based GJRJS (PS-GJRJS) and TS-based GJRJS (TS-GJRJS) frameworks, respectively. To be specific, we investigate the optimization of the beamforming vector of our selected relays for maximizing the secrecy rate of the source–destination transmission. A closed-form solution is derived under the assumption of available instantaneous channel state information (CSI) of the main link and statistical CSI of the wiretap link. In addition, we also illustrate that the pure relay selection (PRS) scheme is a special case of our GJRJS framework at high signal-to-noise ratios (SNRs). The numerical results show that the proposed CSB scheme achieves a higher secrecy rate than the traditional maximal ratio transmission (MRT) method for both the PS-GJRJS and TS-GJRJS frameworks. Additionally, the GJRJS framework outperforms the PRS as well as the joint best relay and jammer selection (JBRJS) methods in terms of secrecy rate.

This letter presents an adaptive learning framework for estimating structural parameters in stochastic state-based models (SSMs). SSMs are a useful modeling tool in systems biology and medicine. While models in these disciplines are traditionally hand-crafted, an automated generation based on experimental data becomes a topic of research interest. In particular, our goal is to classify measured processes using the generated models. An innovative likelihood-based adaptive learning approach capable of learning the structural parameters, i.e., the arc weights of SSMs from data and exploiting the reliability of detected inputs is presented in this letter. Its convergence behavior is analyzed and an expression for the error at steady state is derived. Simulations assess the performance of the proposed and existing algorithms for a gene regulatory network.

This paper investigates the problem of energy efficiency maximization (EEM) for the small cells that coexist with a macro cell in an underlay heterogeneous cellular network, where a macro base station and a number of small base stations transmit signals to a macro user and small users through their shared spectrum. We propose a joint power and bandwidth allocation (JPBA) scheme for the sake of maximizing the energy efficiency (EE) of small cells under the constraint of a guaranteed quality-of-service requirement for the macro cell. Considering that our formulated EEM-based JPBA (EEM-JPBA) problem is non-convex, we convert the original fractional problem into an equivalent subtractive form by adopting the Dinkelbach's method, which is addressed through the augmented Lagrange multiplier approach. Moreover, a new two-tier iterative algorithm is presented to obtain the optimal solution of our EEM-JPBA scheme. Simulation results demonstrate that the proposed two-tier iterative algorithm can quickly converge to the optimal EE solution. In addition, it is shown that the proposed EEM-JPBA scheme significantly outperforms the conventional power and bandwidth allocation methods in terms of their EE performance.

This work develops a robust diffusion recursive least squares algorithm to mitigate the performance degradation often experienced in networks of agents in the presence of impulsive noise. This algorithm minimizes an exponentially weighted least-squares cost function subject to a time-dependent constraint on the squared norm of the intermediate estimate update at each node. With the help of side information, the constraint is recursively updated in a diffusion strategy. Moreover, a control strategy for resetting the constraint is also proposed to retain good tracking capability when the estimated parameters suddenly change. Simulations show the superiority of the proposed algorithm over previously reported techniques in various impulsive noise scenarios.

Low‐density parity‐check (LDPC) codes have excellent performance for a wide range of applications at reasonable complexity. LDPC codes with short blocks avoid the high latency of codes with large block lengths, making them potential candidates for ultra reliable low‐latency applications of future wireless standards. In this work, a novel informed dynamic scheduling (IDS) strategy for decoding LDPC codes, denoted reliability‐based residual belief propagation (Rel‐RBP), is developed by exploiting the reliability of the message and the residuals of the possible updates to choose the messages to be used by the decoding algorithm. A different measure for each iteration of the IDS schemes is also presented, which underlies the high cost of those algorithms in terms of computational complexity and motivates the development of the proposed strategy. Simulations show that Rel‐RBP speeds up the decoding at reduced complexity and results in error rate performance gains over prior work.

In this work, the authors assess the performance and latency of a receiver design for multiuser multiple‐antenna systems using low‐density parity‐check (LDPC) codes with iterative detection and decoding (IDD). The proposed knowledge‐aided IDD (KA‐IDD) receiver employs a parallel interference cancellation detector with refined iterative processing and a reweighted belief propagation (BP) decoder. Additionally, two BP‐based decoding algorithms are proposed for improved performance of the decoder. The first cycle knowledge‐aided reweighted‐BP decoder takes advantage of the information about the distribution of cycles in the Tanner graph and the second expansion knowledge‐aided reweighted‐BP decoder expands the original graph into a set of subgraphs, then locally optimises the reweighting parameters of each subgraph. Novel reweighting parameter optimisation strategies are developed for decoding regular or irregular LDPC codes. The developed schemes optimise the parameters off‐line and neither of these proposed algorithms imposes extra computational complexity to the on‐line decoding procedure. Furthermore, the proposed schemes reduce the decoding latency. Simulation results show that the proposed KA‐IDD scheme and algorithms outperform prior art and require a reduced number of decoding iterations.

In this paper, we propose buffer-aided physical-layer network coding (PLNC) techniques for improving data transmission over cooperative networks. In particular, we develop buffer-aided PLNC schemes and relay pair selection algorithms for direct-sequence code-division multiple access (DS-CDMA) systems. We devise PLNC techniques based on optimal linear network coding matrices according to the maximum likelihood and minimum mean-square error design criteria in order to generate the network coded symbols that are sent to the destination. In the proposed buffer-aided PLNC schemes, relay pair selection algorithms are developed to obtain the relay pair and the packets in the buffer entries with the best performance and the associated link combinations are used for the data transmission. An analysis of the computational complexity of the proposed techniques along with their sum-rate analysis is carried out. Simulation results show that the proposed techniques significantly outperform previously reported approaches.

Analog-to-digital conversion with high resolution in amplitude has a relatively high energy consumption in communication systems. A promising alternative to reduce the energy consumption is 1-bit quantization. Considering such a receiver, we design and analyze continuous phase modulation (CPM) schemes, which are favorable because of their bandwidth efficiency and their constant envelope. In this context, oversampling with respect to the symbol duration is promising because CPM signals are not strictly bandlimited and because it reduces the loss in achievable rate caused by the quantization. The additional degrees of freedom brought by oversampling can be exploited by higher order modulation schemes. A lower bound on the achievable rate is computed based on an auxiliary channel law. In a further step, we optimize the input distribution with an optimization strategy based on a Markov source model. For a specific example, we give upper bounds on the achievable rate and present a state-machine representation for sequences which are reconstructible at the receiver. Finally, the proposed approach has the advantage of a constant envelope enabling an energy efficient transmitter design while achieving only a slightly lower 90% power containment bandwidth efficiency than existing methods with 1-bit quantization and oversampling.

Adaptive algorithms based on kernel structures have been a topic of significant research over the past few years. The main advantage is that they form a family of universal approximators, offering an elegant solution to problems with nonlinearities. Nevertheless, these methods deal with kernel expansions, creating a growing structure also known as dictionary, whose size depends on the number of new inputs. In this paper, we derive the set-membership kernel-based normalized least-mean square (SM-NKLMS) algorithm, which is capable of limiting the size of the dictionary created in stationary environments. We also derive as an extension the set-membership kernel-based affine projection (SM-KAP) algorithm. Finally, several experiments are presented to compare the proposed SM-NKLMS and SM-KAP algorithms to existing methods.

In this paper we propose novel randomized subspace methods to detect anomalies in Internet Protocol networks. Given a data matrix containing information about network traffic, the proposed approaches perform a normal-plus-anomalous matrix decomposition aided by the randomized sampling scheme and subsequently detect traffic anomalies in the anomalous subspace using a statistical test. Simulation results demonstrate improvement over the traditional principal component analysis-based subspace methods in terms of robustness to noise and detection rate.

Hardware-efficient low-complexity precoding is very important in the downlink of Massive MIMO systems for mitigating interference and optimizing performance. In this paper, we propose a correlation-driven optimized Taylor expansion (CD-OTE) precoding scheme to simplify linear minimum mean square error (MMSE) precoding. In order to simplify the hardware-expensive matrix inversion involved in the linear MMSE pre-coder, a Taylor expansion with optimized polynomial coefficients and selection of the most relevant correlation coefficients is proposed. We take into consideration the correlation between different users' channels and develop a general design criterion. Both convergence and complexity analyses are carried out. Simulation results show that the proposed CD-OTE precoder is significantly better than previously reported techniques, while requiring a similar cost.

This study deals with the issue of designing the sensing matrix for a compressed sensing (CS) system assuming that the dictionary is given. Traditionally, the measurement of small mutual coherence is considered to design the optimal sensing matrix so that the Gram of the equivalent dictionary is as close to the target Gram as possible, where the equivalent dictionary is not normalised. In other words, these algorithms are designed to solve the CS problem using an optimisation stage followed by normalisation. To achieve a global solution, a novel strategy of the sensing matrix design is proposed by using a gradient‐based method, in which the measure of real mutual coherence for the equivalent dictionary is considered. According to this approach, a minimised objective function based on alternating minimisation is also developed through searching the target Gram within a set of relaxed equiangular tight frames. Some experiments are done to compare the performance of the newly designed sensing matrix with the existing ones under the condition that the dictionary is fixed. For the simulations of synthetic data and real image, the proposed approach provides better signal reconstruction accuracy.

In this paper, we study widely linear precoding techniques to mitigate in-phase/quadrature-phase (IQ) imbalance (IQI) in the downlink of large-scale multiple-input multiple-output (MIMO) systems. We adopt a real-valued signal model, which considers the IQI at the transmitter, and then develop widely linear zero-forcing (WL-ZF), widely linear matched filter, widely linear minimum mean-squared error, and widely linear block-diagonalization (WL-BD) type precoding algorithms for both single- and multiple-antenna users. We also present a performance analysis of WL-ZF and WL-BD. It is proved that without IQI, WL-ZF has exactly the same multiplexing gain and power offset as ZF, while when IQI exists, WL-ZF achieves the same multiplexing gain as ZF with ideal IQ branches, but with a minor power loss, which is related to the system scale and the IQ parameters. We also compare the performance of WL-BD with BD. The analysis shows that with ideal IQ branches, WL-BD has the same data rate as BD, while when IQI exists, WL-BD achieves the same multiplexing gain as BD without IQ imbalance. Numerical results verify the analysis and show that the proposed widely linear type precoding methods significantly outperform their conventional counterparts with IQI and approach those with ideal IQ branches.

In this paper, a distributed compressed estimation (DCE) scheme is presented based on a distributed recursive-least squares algorithm for sparse signals and systems along with a sensing matrix design procedure based on compressive sensing techniques. The D-CE scheme consists of compression and decompression modules inspired by compressive sensing to perform distributed compressed estimation. A design procedure is developed under the DCE framework and a novel algorithm is developed to optimize the sensing matrix, which can further improve the performance of the proposed DCE and distributed adaptive algorithms. Simulations for a wireless sensor network show the advantages of the proposed scheme and algorithm in terms of convergence rate and mean square error performance.

In this work, we propose a buffer-aided distributed spacetime coding (DSTC) scheme for cooperative direct-sequence codedivision multiple access systems. We first devise a relay selection algorithm that can automatically select the optimum set of relays among both the source-relay phase and the relay-destination phase for DSTC transmission according to the signal-to-interference-plus-noise ratio (SINR) criterion. Multiple relays equipped with buffers are introduced in the network, which allows the relays to store data received from the sources and wait until the most appropriate time for transmission. Simulation results show that the proposed buffer-aided DSTC scheme and algorithm outperform prior art.

We propose a subspace-based Widely Linear (WL) blind channel estimation scheme based on the iterative power method for the WL constrained minimum variance Code Division Multiple Access (CDMA) receiver. The novel technique approximates the noise subspace by using a matrix power and the WL processing fully exploits the second-order non-circularity of the signal. Two adaptive recursive least squares algorithms are developed using power iterations, which completely avoid the computationally intensive singular value decomposition. Simulation results show an improved performance of the proposed algorithms in terms of convergence and complexity as compared to their linear counterparts.

This paper investigates the design of vector perturbation (VP) precoding using lattice reduction (LR) based on a multi-branch (MB) strategy for multi- user multiple-input multiple-output (MU-MIMO) systems. The MB strategy constructs a group of branches for transmitting data streams according to a pre-designed ordering scheme. For each branch, an LR-aided minimum mean square error (MMSE) VP precoder is proposed and three methods are devised for the perturbation vector design. We also develop an effective scheme to design the transmit ordering patterns with appropriate structures and a suitable selection mechanism to choose the best one. Simulation results show that the proposed MB-LR-MMSE-VP algorithm achieves a better bit error rate (BER) performance than existing VP precoding schemes.

This study proposes low‐complexity robust adaptive beamforming (RAB) techniques based on shrinkage methods. The authors first review a low‐complexity shrinkage‐based mismatch estimation batch algorithm to estimate the desired signal steering vector mismatch, in which the interference‐plus‐noise covariance matrix is also estimated by a recursive matrix shrinkage method. Then they develop low‐complexity adaptive recursive versions of stochastic gradient and conjugate gradient to update the beamforming weights, resulting in low‐cost robust adaptive algorithms. An analysis of the effect of shrinkage on the estimation procedure is developed along with a computational complexity study of the proposed and existing algorithms. Simulations are conducted in local scattering scenarios and comparisons to existing RAB techniques are provided.

This study presents distributed conjugate gradient (CG) algorithms for distributed parameter estimation and spectrum estimation over wireless sensor networks. In particular, distributed conventional CG (CCG) and modified CG (MCG) algorithms are developed with incremental and diffusion adaptive cooperation strategies. The distributed CCG and MCG algorithms have an improved performance in terms of mean square error as compared with least‐mean square‐based algorithms and a performance that is close to recursive least‐squares algorithms. In comparison with existing centralised or distributed estimation strategies, key features of the proposed algorithms are: (i) more accurate estimates and faster convergence speed can be obtained and (ii) the design of preconditioners for CG algorithms, which can improve the performance of the proposed CG algorithms is presented. Simulations show the performance of the proposed CG algorithms against previously reported techniques for distributed parameter estimation and distributed spectrum estimation applications.

This letter proposes a distributed alternating mixed discrete-continuous (DAMDC) algorithm to approach the oracle algorithm based on the diffusion strategy for parameter and spectrum estimation over sensor networks. A least mean squares (LMS) type algorithm that obtains the oracle matrix adaptively is developed and compared with the existing sparsity-aware and conventional algorithms. The proposed algorithm exhibits improved performance in terms of mean square deviation and power spectrum estimation accuracy. Numerical results show that the DAMDC algorithm achieves excellent performance.

In this work, we propose an alternating low-rank decomposition (ALRD) approach and novel subspace algorithms for direction-of-arrival (DOA) estimation. In the ALRD scheme, the decomposition matrix for rank reduction consists of a set of basis vectors. A low-rank auxiliary parameter vector is then employed to compute the output power spectrum. Alternating optimization strategies based on recursive least squares (RLS), denoted as ALRD-RLS and modified ALRD-RLS (MARLD-RLS), are devised to compute the basis vectors and the auxiliary parameter vector. Simulations for large sensor arrays with both uncorrelated and correlated sources are presented, showing that the proposed algorithms are superior to existing techniques.

Low-density parity-check (LDPC) codes are capable of achieving excellent performance and provide a useful alternative for the high-performance applications. However, at medium to high signal-to-noise ratios, an observable error floor arises from the loss of independence of messages passed under iterative graph-based decoding. In this paper, the error floor performance of the short block length codes is improved by the use of a novel candidate selection metric in code graph construction. The proposed multipath extrinsic message degree (EMD) approach avoids harmful structures in the graph by evaluating certain properties of the cycles introduced in each edge placement. We present multipath EMD-based designs for several structured LDPC codes, including quasi-cyclic and the irregular repeat accumulate codes. In addition, an extended class of the diversity-achieving codes on the challenging block fading channel is proposed and considered with the multipath EMD design. This combined approach is demonstrated to provide the gains in decoder convergence and error rate performance. A simulation study evaluates the performance of the proposed and existing state-of-the-art methods.

This work proposes adaptive buffer-aided distributed space-time coding schemes and algorithms with feedback for wireless networks equipped with buffer-aided relays. The proposed schemes employ a maximum likelihood receiver at the destination, and adjustable codes subject to a power constraint with an amplify-and-forward cooperative strategy at the relays. The adjustable codes are part of the proposed space-time coding schemes and the codes are sent back to relays after being updated at the destination via feedback channels. Each relay is equipped with a buffer and is capable of storing blocks of received symbols and forwarding the data to the destination if selected. Different antenna configurations and wireless channels, such as static block fading channels, are considered. The effects of using buffer-aided relays to improve the bit error rate (BER) performance are also studied. Adjustable relay selection and optimization algorithms that exploit the extra degrees of freedom of relays equipped with buffers are developed to improve the BER performance. We also analyze the pairwise error probability and diversity of the system when using the proposed schemes and algorithms in a cooperative network. Simulation results show that the proposed schemes and algorithms obtain performance gains over previously reported techniques.

In this paper, we propose widely-linear blockdiagonalization (BD) type precoding techniques to alleviate the impact of IQ imbalance in the downlink Massive multi-input multi-output (MIMO) systems. We first introduce a real-valued signal model and then develop widely-linear BD (WL-BD) type precoding algorithms, i.e., WL-BD, widely linear regularized BD (WL-RBD) and widely linear simplified generalized MMSE channel inversion (WL-S-GMI). We also present analysis of the sum-rate and multiplexing gain achieved by the proposed WLBD for scenarios with and without IQ imbalance. Numerical results verify the analysis and show that WL-BD type precoding methods significantly outperform their conventional counterparts with IQ imbalance and approach the ideal case.

The joint estimation and compensation of IQ imbalance (IQI) parameters at both transmitter (TX) and receiver (RX) is studied in this paper. We develop a generalized system model with a reduced number of parameters (RNP) that covers a wide range of mobile communications scenarios. We devise efficient direct least-squares (DLS) and alternating least-squares (ALS) techniques for IQI parameter estimation based on the generalized system model. For the ALS based method, we prove that the algorithm will converge to a local optimal solution of the optimization problem. Numerical results show that compared with a previously reported method, the proposed DLS-RNP achieves similar performance with a reduced computational complexity, and the proposed ALS-RNP algorithm has significantly better performance with comparable complexity, with a gain over 5 dB for QPSK and 10 dB for 64QAM in the high signal-to-noise-ratio (SNR) region.

In this paper, the authors propose a widely‐linear (WL) receiver structure for multiple access interference (MAI) and jamming signal (JS) suppression in direct‐sequence code‐division multiple‐access systems. A vector space projection (VSP) scheme is also considered to cancel the JS before detecting the desired signals. They develop a novel multiple‐candidate successive interference cancellation (MC‐SIC) scheme which processes two consecutive user symbols at one time to process the unreliable estimates and a number of selected points serve as the feedback candidates for interference cancellation, which is effective for alleviating the effect of error propagation in the successive interference cancellation (SIC) algorithm. WL signal processing is then used to enhance the performance of the receiver in non‐circular modulation scheme. By bringing together the techniques mentioned above, a novel interference suppression scheme is proposed which combines the WL MC‐SIC minimum‐mean‐squared error (MMSE) algorithm with the VSP scheme to suppress MAI and JS simultaneously. Simulations for binary phase shift keying modulation scenarios show that the proposed structure achieves a better MAI suppression performance compared with previously reported SIC MMSE receivers at lower complexity and a superior JS suppression performance.

This letter proposes a novel distributed compressed estimation scheme for sparse signals and systems based on compressive sensing techniques. The proposed scheme consists of compression and decompression modules inspired by compressive sensing to perform distributed compressed estimation. A design procedure is also presented and an algorithm is developed to optimize measurement matrices, which can further improve the performance of the proposed distributed compressed estimation scheme. Simulations for a wireless sensor network illustrate the advantages of the proposed scheme and algorithm in terms of convergence rate and mean square error performance.

In this work, we propose a novel adaptive reduced-rank receive processing strategy based on joint preprocessing, decimation and filtering (JPDF) for large-scale multiple-antenna systems. In this scheme, a reduced-rank framework is employed for linear receive processing and multiuser interference suppression based on the minimization of the symbol-error-rate (SER) cost function. We present a structure with multiple processing branches that performs a dimensionality reduction, where each branch contains a group of jointly optimized preprocessing and decimation units, followed by a linear receive filter. We then develop stochastic gradient (SG) algorithms to compute the parameters of the preprocessing and receive filters, along with a low-complexity decimation technique for both binary phase shift keying (BPSK) and M-ary quadrature amplitude modulation (QAM) symbols. In addition, an automatic parameter selection scheme is proposed to further improve the convergence performance of the proposed reduced-rank algorithms. Simulation results are presented for time-varying wireless environments and show that the proposed JPDF minimum-SER receive processing strategy and algorithms achieve a superior performance than existing methods with a reduced computational complexity.

This paper studies the impact of hardware mismatch (11M) between the base station (BS) and the user equipment (UE) in the downlink (DL) of large-scale antenna systems. Analytical expressions to predict the achievable rates are derived for different precoding methods, i.e., matched filter (MF) and regularized zero-forcing (RZF), using large system analysis techniques. Furthermore, the upper bounds on achievable rates of MF and RZF with 11M are investigated, which are related to the statistics of the circuit gains of the mismatched hardware. Moreover, we present a study of 11M calibration, where we take zero-forcing (ZF) precoding as an example to compare two 11M calibration schemes, i.e., Pre-precoding Calibration (Pre-Cal) and Post-precoding Calibration (Post-Cal). The analysis shows that Pre-Cal outperforms Post-Cal schemes. Monte-Carlo simulations are carried out, and numerical results demonstrate the correctness of the analysis.

This paper studies the impact of hardware mismatch (HM) between base station (BS) and user equipment in the downlink of large scale antenna systems. Analytical expressions to predict the achievable rates are derived for different precoding methods, i.e. matched filter (MF) and regularized zero-forcing (RZF), using large system analysis techniques. Furthermore, the asymptotic downlink signal to interference plus noise ratio (SINR) under HM is investigated, which is only related to the variances of circuit gains in most practical scenarios. Monte-Carlo simulations are carried out, and numerical results demonstrate the correctness of the analysis.

In this work, we propose an adaptive set-membership (SM) reduced-rank filtering algorithm using the constrained constant modulus (CCM) criterion for beamforming. We develop a stochastic gradient (SG) type algorithm based on the concept of SM technique for adaptive implementation. The filter weights are updated only if the bounded constraint cannot be satisfied. In addition, we also propose a scheme of time-varying bound and incorporate parameter dependence to characterize the environment for improving the tracking performance of the proposed algorithm. Simulation results show that the proposed adaptive SM reduced-rank beamforming algorithm with dynamic bounds achieves superior performance to previously reported methods at a reduced update rate.

In this work, we propose a cross-layer design strategy based on the parallel interference cancellation (PIC) detection technique and a multi-relay selection algorithm for the uplink of cooperative direct-sequence code-division multiple access (DS-CDMA) systems. We devise a low-cost greedy list-based PIC (GL-PIC) strategy with RAKE receivers as the front-end that can approach the maximum likelihood detector performance. We also present a low-complexity multi-relay selection algorithm based on greedy techniques that can approach the performance of an exhaustive search. Simulations show an excellent bit error rate performance of the proposed detection and relay selection algorithms as compared to existing techniques.

This paper describes a novel technique for promoting sparsity in the modified filtered-x algorithms required for active noise control. The proposed algorithms are based on recent techniques incorporating approximations to the l0-norm in the cost functions that are used to derive adaptive filtering algorithms. In particular, zero-attracting and reweighted zero-attracting filtered-x adaptive algorithms are developed and considered for active noise control problems. The results of simulations indicate that the proposed techniques improve the convergence of the existing modified algorithm in the case where the primary and secondary paths exhibit a degree of sparsity.

We propose an Iterative Detection and Decoding (IDD) scheme with Low Density Parity Check (LDPC) codes for Multiple Input Multiple Output (MIMO) systems in block-fading receivers and fast fading Rayleigh channels. An IDD receiver with soft information processing that exploits the code structure and the behaviour of the log likelihood ratios (LLR)'s is developed. Minimum Mean Square Error (MMSE) receivers with Successive Interference Cancellation (SIC) and with Parallel Interference Cancellation (PIC) schemes are considered. The soft a posteriori output of the decoder in a block-fading channel with Root-Check LDPC codes has allowed us to create a new strategy to improve the Bit Error Rate (BER) of a MIMO IDD scheme. The proposed strategy has resulted in up to 3dB of gain in terms of BER for block-fading channels and up to 1dB in fast fading channels.

In this study, the authors propose a cross layer design strategy that consists of a cooperative maximum likelihood detector operating in conjunction with link selection for cooperative multiple‐input multiple‐output relay networks. The system considered is a two phase relaying model, with amplify‐and forward relays. The model considers the effect of relay placement and of large scale shadowing. The authors develop a cooperative maximum likelihood detector for an arbitrary number of relays, and link selection schemes are devised for two scenarios relating to the available knowledge at the destination node, which considers relay combination, with complexity analysis. The authors also propose a cooperative list sphere decoder that processes soft information and implements an iterative detection and decoding scheme. The results of simulations of the systems are presented, with comparisons to system models used in previous literature, and show how the cooperative detector and the proposed link selection schemes affect the bit error rate performance at the destination node, for both the hard decision non‐iterative case, and the soft information iterative processing case.

Dynamic pilot allocation (DPA) for discrete Fourier transform (DFT)‐based channel estimation in multi‐input–multi‐output orthogonal frequency division multiplexing (MIMO‐OFDM) systems with spatial multiplexing can significantly improve the bit error rate performance compared with systems with uniform pilot allocation. However, the exhaustive search for optimum pilot allocation leads to very high complexity. The authors devise a multi‐input–multi‐output iterative pilot search (MIPS) algorithm applied with different MIMO‐OFDM receivers (linear, successive interference cancellation (SIC) and maximum likelihood (ML)), which significantly reduces the complexity of DPA. Exact derivations are also given based on the receivers. They also propose a novel stacked vector quantisation technique to reduce feedback burdens for DPA in MIMO‐OFDM system. Simulation results illustrate that the proposed MIPS algorithm with limited feedback can improve the performance of MIMO‐OFDM systems.

Adaptive power allocation (PA) algorithms with different criteria for a cooperative multiple‐input multiple‐output network equipped with distributed space‐time coding are proposed and evaluated. Joint constrained optimisation algorithms to determine the PA parameters and the receive filter are proposed for each transmitted symbol in each link, as well as the channel coefficients matrix. Linear receive filter and maximum‐likelihood detection are considered with amplify‐and‐forward and decode‐and‐forward cooperation strategies. In these proposed algorithms, the elements in the PA matrices are optimised at the destination node and then transmitted back to the relay nodes via a feedback channel. The effects of the feedback errors are considered. Linear minimum mean square error expressions and the PA matrices depend on each other and are updated iteratively. Stochastic gradient algorithms are developed with reduced computational complexity. Simulation results show that the proposed algorithms obtain significant performance gains as compared with existing PA schemes.

The authors present a robust adaptive beamforming algorithm based on the worst‐case (WC) criterion and the constrained constant modulus (CCM) approach, which exploits the constant modulus property of the desired signal. Similar to the existing worst‐case beamformer with the minimum variance design, the problem can be reformulated as a second‐order cone programme and solved with interior point methods. An analysis of the optimisation problem is carried out and conditions are obtained for enforcing its convexity and for adjusting its parameters. Furthermore, low‐complexity robust adaptive beamforming algorithms based on the modified conjugate gradient and an alternating optimisation strategy are proposed. The proposed low‐complexity algorithms can compute the existing WC constrained minimum variance and the proposed WC‐CCM designs with a quadratic cost in the number of parameters. Simulations show that the proposed WC‐CCM algorithm performs better than existing robust beamforming algorithms. Moreover, the numerical results also show that the performances of the proposed low‐complexity algorithms are equivalent or better than that of existing robust algorithms, whereas the complexity is more than an order of magnitude lower.

This letter proposes a novel sparsity-aware adaptive filtering scheme and algorithms based on an alternating optimization strategy with shrinkage. The proposed scheme employs a two-stage structure that consists of an alternating optimization of a diagonally-structured matrix that speeds up the convergence and an adaptive filter with a shrinkage function that forces the coefficients with small magnitudes to zero. We devise alternating optimization least-mean square (LMS) algorithms for the proposed scheme and analyze its mean-square error. Simulations for a system identification application show that the proposed scheme and algorithms outperform in convergence and tracking existing sparsity-aware algorithms.

In this work, we propose a low-complexity robust adaptive beamforming (RAB) technique which estimates the steering vector using a Low-Complexity Shrinkage-Based Mismatch Estimation (LOCSME) algorithm. The proposed LOCSME algorithm estimates the covariance matrix of the input data and the interference-plus-noise covariance (INC) matrix by using the Oracle Approximating Shrinkage (OAS) method. LOCSME only requires prior knowledge of the angular sector in which the actual steering vector is located and the antenna array geometry. LOCSME does not require a costly optimization algorithm and does not need to know extra information from the interferers, which avoids direction finding for all interferers. Simulations show that LOCSME outperforms previously reported RAB algorithms and has a performance very close to the optimum.

We propose a reduced-rank beamformer based on the rank-$D$Joint Iterative Optimization (JIO) of the modified Widely Linear Constrained Minimum Variance (WLCMV) problem for non-circular signals. The novel WLCMV-JIO scheme takes advantage of both the Widely Linear (WL) processing and the reduced-rank concept, outperforming its linear counterpart as well as the full-rank WL beamformer. We develop an augmented recursive least squares algorithm and present an improved structured version with a much more efficient implementation. It is shown that the improved adaptive scheme achieves the best convergence performance among all the considered methods with a low computational complexity.

This paper proposes the design of robust transceivers with Tomlinson-Harashima precoding (THP) for multiple-input-multiple-output relay systems with amplify-and-forward protocols based on a multibranch (MB) strategy. The MB strategy employs successive interference cancellation on several parallel branches, which are equipped with different ordering patterns so that each branch produces transmit signals by exploiting a certain ordering pattern. For each parallel branch, the proposed robust nonlinear transceiver design consists of THP at the source along with a linear precoder at the relay and a linear minimum-mean-square-error receiver at the destination. By taking the channel uncertainties into account, the source and relay precoders are jointly optimized to minimize the mean square error. We then employ a diagonalization method along with some attributes of matrix-monotone functions to convert the optimization problem with matrix variables into an optimization problem with scalar variables. We resort to an iterative method to obtain the solution for the relay and the source precoders via Karush-Kuhn-Tucker conditions. An appropriate selection rule is developed to choose the nonlinear transceiver corresponding to the best branch for data transmission. Simulation results demonstrate that the proposed MB-THP scheme is capable of alleviating the effects of channel state information errors and improving the robustness of the system.

Tomlinson-Harashima precoding (THP) is a nonlinear processing technique employed at the transmit side which is a dual to the successive interference cancelation (SIC) detection at the receive side. Like SIC detection, the performance of THP strongly depends on the ordering of the precoded symbols. The optimal ordering algorithm, however, is impractical for multiuser MIMO (MU-MIMO) systems with multiple receive antennas due to the fact that the users are geographically distributed. In this paper, we propose a multi-branch THP (MB-THP) scheme and algorithms that employ multiple transmit processing and ordering strategies along with a selection scheme to mitigate interference in MU-MIMO systems. Two types of multi-branch THP (MB-THP) structures are proposed. The first one employs a decentralized strategy with diagonal weighted filters at the receivers of the users and the second uses a diagonal weighted filter at the transmitter. The MB-MMSE-THP algorithms are also derived based on an extended system model with the aid of an LQ decomposition, which is much simpler compared to the conventional MMSE-THP algorithms. Simulation results show that a better bit error rate (BER) performance can be achieved by the proposed MB-MMSE-THP precoder with a small computational complexity increase.

In this work, we propose a novel adaptive reduced-rank strategy based on joint interpolation, decimation and filtering (JIDF) for large multiuser multiple-input multiple-output (MIMO) systems. In this scheme, a reduced-rank framework is proposed for linear receive processing and multiuser interference suppression according to the minimization of the bit error rate (BER) cost function. We present a structure with multiple processing branches that performs dimensionality reduction, where each branch contains a group of jointly optimized interpolation and decimation units, followed by a linear receive filter. We then develop stochastic gradient (SG) algorithms to compute the parameters of the interpolation and receive filters along with a low-complexity decimation technique. Simulation results are presented for time-varying environments and show that the proposed MBER-JIDF receive processing strategy and algorithms achieve a superior performance to existing methods at a reduced complexity.

We present low complexity, quickly converging robust adaptive beamformers that combine robust Capon beamformer (RCB) methods and data-adaptive Krylov subspace dimensionality reduction techniques. We extend a recently proposed reduced-dimension RCB framework, which ensures proper combination of RCBs with any form of dimensionality reduction that can be expressed using a full-rank dimension reducing transform, providing new results useful for data-adaptive dimensionality reduction. We consider Krylov subspace methods computed with the Powers-of-R (PoR) and Conjugate Gradient (CG) techniques, illustrating how a fast CG-based algorithm can be formed by beneficially exploiting that the CG-algorithm yields a diagonal reduced-dimension covariance matrix. Our simulations show the benefits of the proposed approaches.

In this work, we propose adaptive link selection strategies for distributed estimation in diffusion-type wireless networks. We develop an exhaustive search-based link selection algorithm and a sparsity-inspired link selection algorithm that can exploit the topology of networks with poor-quality links. In the exhaustive search-based algorithm, we choose the set of neighbors that results in the smallest mean square error (MSE) for a specific node. In the sparsity-inspired link selection algorithm, a convex regularization is introduced to devise a sparsity-inspired link selection algorithm. The proposed algorithms have the ability to equip diffusion-type wireless networks and to significantly improve their performance. Simulation results illustrate that the proposed algorithms have lower MSE values, a better convergence rate and significantly improve the network performance when compared with existing methods.

A novel algorithm to construct structured LDPC codes based on graph connectivity and cycle quality is proposed. The performance of the constructed QC-LDPC and IRA codes is investigated and a performance improvement in the error floor region is demonstrated. The code constructions provided are flexible in rate and length and demonstrate that the enhanced EMD-driven PEG-based approach proposed may be applied to a number of other structured LDPC code classes, given consideration of correct constraints of the construction.

In this paper, joint partial relay selection (PRS) and power allocation methods are proposed in conjunction with cooperative maximum likelihood (ML) detectors. These methods are considered for selecting relay nodes that improve the bit error rate (BER) performance of a two-phase multiple-input multiple-output (MIMO) half-duplex decode-and-forward (DF) relay system with log-normal shadowing and power constraints, as compared to using all relays available regardless of whether the additional relays will benefit the system. Limited feedback is applied to the joint PRS and power allocation techniques within the system, with results showing the effects of the proposed relay selection methods on BER performance.

Rapidly time-varying channels introduce a signifi- cantly detrimental effect on conventional OFDM systems, which results in inter-carrier interference (ICI) and degrades the bit error rate (BER) performance, and makes channel estimation more difficult. In this paper, we propose a simple iterative receiver (MF-PIC) with multi-segmental channel estimation (MSCE) to improve the channel estimation and data detection performances over such high mobility scenarios. A matched-filter (MF) with parallel interference cancellation is employed to combat the ICI, and the symbol estimates are fed back for iterative channel estimation (MSCE). Simulation results demonstrate that the proposed receiver design can achieve a better BER performance over a wide range of normalised Doppler frequencies.

An adaptive distributed space-time coding (DSTC) scheme is proposed for two-hop cooperative MIMO networks. Linear minimum mean square error (MMSE) receive filters and adjustable matrices subject to a power constraint are considered with an amplify-and-forward (AF) cooperation strategy. In the proposed DSTC scheme, an adjustable matrix obtained by a feedback channel is employed to transform the space-time coded matrix at the relay node. Linear MMSE expressions of the adjustable code matrices based on the mean square error (MSE) and the maximum likelihood (ML) criteria are derived. The effects of the limited feedback and the feedback errors on the performance are considered. A stochastic gradient (SG) algorithm is also developed with reduced computational complexity. The simulation results show that the proposed algorithms obtain significant performance gains as compared to existing DSTC schemes.

This paper investigates a robust Tomlinson-Harashima precoding (THP) design for multiple-input multiple-output (MIMO) relay systems based on a multi-branch (MB) strategy. The proposed scheme employs a parallel MB structure at the source according to different pre-stored ordering patterns. For each parallel branch, the robust nonlinear transceiver design consists of a TH precoder at the source along with a linear precoder at the relay and a linear minimum-mean-squared-error (MMSE) receiver at the destination. By taking the channel uncertainties into account, the source and relay precoders are jointly optimised to minimise the MSE. We can finally use an iterative method to obtain the solution for the relay and the source precoders via Karush-Kuhn-Tucker (KKT) conditions. An appropriate selection rule is developed to choose the nonlinear transceiver corresponding to the best branch for data transmission. Simulation results demonstrate that the proposed MB-THP scheme outperforms existing transceiver designs with perfect and imperfect channel state information (CSI).

In this work, we propose a low-complexity adaptive transceiver algorithm for the K-pair multiple-input multiple-output (MIMO) interference channels. The proposed algorithm is based on the joint optimization of transmit and receive vectors using the constrained constant modulus (CCM) criterion. We firstly derive CCM-based expressions for the transmit and receiver vectors. Then, we develop recursive least-squares (RLS) adaptive algorithms for their efficient implementation. Unlike earlier block-based transceivers for MIMO interference channels, the proposed algorithms have low computational complexity and can track the time-varying channels and interference as changes occur in the surrounding wireless environment. In particular, simulation results show that the proposed adaptive algorithms achieve the performance of the Sum-MSE algorithm at a much reduced complexity.

In this work, we propose a minimum mean squared error (MMSE) robust base station (BS) precoding strategy based on switched relaying (SR) processing and limited transmission of side information for interference suppression in the downlink of multiuser multiple-input multiple-output (MIMO) relay systems. The BS and the MIMO relay station (RS) are both equipped with a codebook of interleaving matrices. For a given channel state information (CSI) the selection function at the BS chooses the optimum interleaving matrix from the codebook based on two optimization criteria to design the robust precoder. Prior to the payload transmission the BS sends the index corresponding to the selected interleaving matrix to the RS, where the best interleaving matrix is selected to build the optimum relay processing matrix. The entries of the codebook are randomly generated unitary matrices. Simulation results show that the performance of the proposed techniques is significantly better than prior art in the case of imperfect CSI.

In practice, LDPC codes are decoded using message passing methods. These methods offer good performance but tend to converge slowly and sometimes fail to converge and to decode the desired codewords correctly. Recently, tree-reweighted message passing methods have been modified to improve the convergence speed at little or no additional complexity cost. This paper extends this line of work and proposes a new class of locally optimized reweighting strategies, which are suitable for both regular and irregular LDPC codes. The proposed decoding algorithm first splits the factor graph into subgraphs and subsequently performs a local optimization of reweighting parameters. Simulations show that the proposed decoding algorithm significantly outperforms the standard message passing and existing reweighting techniques.

In this work, the authors propose a low‐complexity variable forgetting factor (VFF) mechanism for recursive least‐squares algorithms in interference suppression applications. The proposed VFF mechanism employs an updated component related to the time average of the error correlation to automatically adjust the forgetting factor in order to ensure fast convergence and good tracking of the interference and the channel. Convergence and tracking analyses are carried out and analytical expressions for predicting the mean‐squared error of the proposed adaptation technique are obtained. Simulation results for a direct‐sequence code‐division multiple access system are presented in non‐stationary environments and show that the proposed VFF mechanism achieves superior performance to previously reported methods at a reduced complexity.

In this study, a novel low‐complexity adaptive decision feedback (DF) detection with parallel DF (P‐DF) and P‐DF constellation constraints (P‐DFCC) is proposed for multiuser multi‐input–multi‐output (MIMO) systems. The authors propose a constrained constellation map which introduces a number of selected points served as the feedback candidates for interference cancellation. By introducing a reliability checking, a higher degree of freedom is introduced to refine the unreliable estimates. The P‐DFCC is followed by an adaptive receive filter to estimate the transmitted symbol. To reduce the complexity of computing the filters with time‐varying MIMO channels, an adaptive recursive least squares algorithm is employed in the proposed P‐DFCC scheme. An iterative detection and decoding (Turbo) scheme is considered with the proposed P‐DFCC algorithm. Simulations show that the proposed technique has a complexity comparable to the conventional P‐DF detector while it obtains a performance close to the maximum‐likelihood detector at a low to medium signal‐to‐noise ratio range. .

An adaptive distributed space-time coding (DSTC) scheme is proposed for two-hop cooperative MIMO networks. Linear minimum mean square error (MMSE) receive filters and adjustable code matrices are considered subject to a power constraint with an amplify-and-forward (AF) cooperation strategy. In the proposed adaptive DSTC scheme, an adjustable code matrix obtained by a feedback channel is employed to transform the space-time coded matrix at the relay node. The effects of the limited feedback and the feedback errors are assessed. Linear MMSE expressions are devised to compute the parameters of the adjustable code matrix and the linear receive filters. Stochastic gradient (SG) and least-squares (LS) algorithms are also developed with reduced computational complexity. An upper bound on the pairwise error probability analysis is derived and indicates the advantage of employing the adjustable code matrices at the relay nodes. An alternative optimization algorithm for the adaptive DSTC scheme is also derived in order to eliminate the need for the feedback. The algorithm provides a fully distributed scheme for the adaptive DSTC at the relay node based on the minimization of the error probability. Simulation results show that the proposed algorithms obtain significant performance gains as compared to existing DSTC schemes.

Block diagonalization (BD) based precoding techniques are well-known linear transmit strategies for multiuser MIMO (MU-MIMO) systems. By employing BD-type precoding algorithms at the transmit side, the MU-MIMO broadcast channel is decomposed into multiple independent parallel single user MIMO (SU-MIMO) channels and achieves the maximum diversity order at high data rates. The main computational complexity of BD-type precoding algorithms comes from two singular value decomposition (SVD) operations, which depend on the number of users and the dimensions of each user's channel matrix. In this work, low-complexity precoding algorithms are proposed to reduce the computational complexity and improve the performance of BD-type precoding algorithms. We devise a strategy based on a common channel inversion technique, QR decompositions, and lattice reductions to decouple the MU-MIMO channel into equivalent SU-MIMO channels. Analytical and simulation results show that the proposed precoding algorithms can achieve a comparable sum-rate performance as BD-type precoding algorithms, substantial bit error rate (BER) performance gains, and a simplified receiver structure, while requiring a much lower complexity.

In this work, decision feedback (DF) detection algorithms based on multiple processing branches for multi-input multi-output (MIMO) spatial multiplexing systems are proposed. The proposed detector employs multiple cancellation branches with receive filters that are obtained from a common matrix inverse and achieves a performance close to the maximum likelihood detector (MLD). Constrained minimum mean-squared error (MMSE) receive filters designed with constraints on the shape and magnitude of the feedback filters for the multi-branch MMSE DF (MB-MMSE-DF) receivers are presented. An adaptive implementation of the proposed MB-MMSE-DF detector is developed along with a recursive least squares-type algorithm for estimating the parameters of the receive filters when the channel is time-varying. A soft-output version of the MB-MMSE-DF detector is also proposed as a component of an iterative detection and decoding receiver structure. A computational complexity analysis shows that the MB-MMSE-DF detector does not require a significant additional complexity over the conventional MMSE-DF detector, whereas a diversity analysis discusses the diversity order achieved by the MB-MMSE-DF detector. Simulation results show that the MB-MMSE-DF detector achieves a performance superior to existing suboptimal detectors and close to the MLD, while requiring significantly lower complexity.

In this work, we propose a low-complexity variable forgetting factor (VFF) mechanism for blind adaptive constrained constant modulus (CCM) recursive least square (RLS) algorithms applied to linear interference suppression in direct-sequence code division multiple access (DS-CDMA) systems. The proposed VFF mechanism employs an updated component relating to the time average of the constant modulus (CM) cost function to automatically adjust the forgetting factor in order to ensure good tracking of the interference and the channel. Analytical expressions for predicting the mean-squared error of the proposed adaptation technique are obtained. Simulation results show that the proposed VFF mechanism achieves superior performance to existing methods at a reduced complexity.

A novel construction for quasi-cyclic (QC) regular and irregular low-density parity-check (LDPC) codes based on a modification of the QC Progressive Edge Growth (PEG) algorithm is presented. Edge placement of the PEG-based algorithm is enhanced by use of the sum-product algorithm in the design of the parity-check matrix. The proposed algorithm is highly flexible in block length and rate, in particular when compared with algebraic constructions. The codes constructed by the proposed methods are tested in the AWGN channel and performance improvements are achieved. The proposed QC-LDPC codes provide an inherent trade-off between code performance and encoding/decoding complexity.

An adaptive iterative decision multi-feedback detection algorithm with constellation constraints is proposed for multiuser multi-cantenna systems. An enhanced detection and interference cancellation is performed by introducing multiple constellation points as decision candidates. A complexity reduction strategy is developed to avoid redundant processing with reliable decisions along with an adaptive recursive least squares algorithm for time-varying channels. An iterative detection and decoding scheme is also considered with the proposed detection algorithm. Simulations show that the proposed technique has a complexity as low as the conventional decision feedback detector while it obtains a performance close to the maximum likelihood detector.

In this work a low-complexity strategy for accelerating the convergence of convex combinations of adaptive filters is proposed. The idea is based on an instantaneous transfer of coefficients from a fast adaptive filter to a slow adaptive filter, which is performed according to a pre-defined window length. A theoretical model that is capable of predicting the excess mean squared error (EMSE) of the proposed strategy is also presented. Simulation results illustrate the good performance of the proposed strategy and the effectiveness of the proposed model to predict the EMSE.

In this paper, we consider a multihop wireless sensor network (WSN) with multiple relay nodes for each hop where the amplify-and-forward (AF) scheme is employed. We present a strategy to jointly design the linear receiver and the power allocation parameters via an alternating optimization approach that maximizes the sum-rate of the WSN. We derive constrained maximum sum-rate (MSR) expressions along with an algorithm to compute the linear receiver and the power allocation parameters with the optimal complex amplification coefficients for each relay node. Computer simulations show good performance of our proposed methods in terms of sum-rate compared to the method with equal power allocation.

By employing the regularized block diagonalization (RBD) preprocessing technique, the multi-user multi-input multi-output (MU-MIMO) broadcast channel is decomposed into multiple parallel independent single user multi-input multi-output (SU-MIMO) channels and achieves the maximum diversity order at high data rates. The computational complexity of RBD, however, is relatively high due to two singular value decomposition (SVD) operations. In this paper, a low-complexity lattice reduction aided RBD is proposed. The first SVD is replaced by a QR decomposition, and the orthogonalization procedure provided by the second SVD is substituted by a lattice reduction whose complexity is mainly contributed by a QR decomposition. Simulation results show that the proposed algorithm can achieve almost the same sum-rate as RBD while offering a lower complexity and substantial BER gains with perfect as well as imperfect channel state information at the transmit side.

This work presents joint iterative power allocation and interference suppression algorithms for spread spectrum networks, which employ multiple hops and the amplify-and-forward cooperation strategy for both the uplink and the downlink. The authors propose a joint constrained optimisation framework that considers the allocation of power levels across the relays subject to individual and global power constraints and the design of linear receivers for interference suppression. The authors derive constrained linear minimum mean-squared error (MMSE) expressions for the parameter vectors that determine the optimal power levels across the relays and the linear receivers. In order to solve the proposed optimisation problems, the authors develop cost-effective algorithms for adaptive joint power allocation, and estimation of the parameters of the receiver and the channels. An analysis of the optimisation problem is carried out and shows that the problem can have its convexity enforced by an appropriate choice of the power constraint parameter, which allows the algorithms to avoid problems with local minima. A study of the complexity and the requirements for feedback channels of the proposed algorithms is also included for completeness. Simulation results show that the proposed algorithms obtain significant gains in performance and capacity over existing non-cooperative and cooperative schemes.

In this work, a constrained adaptive filtering strategy based on conjugate gradient (CG) and set-membership techniques is presented for adaptive beamforming. A constraint on the magnitude of the array output is imposed to derive an adaptive algorithm that performs data-selective updates when calculating the beamformer's parameters. A linearly constrained minimum variance optimisation problem is consider with the bounded constraint based on this strategy and propose a CG-type algorithm for implementation. The proposed algorithm has data-selective updates, a variable forgetting factor and performs one iteration per update to reduce the computational complexity. The updated parameters construct a space of feasible solutions that enforce the constraints. The authors also introduce two time-varying bounding schemes to measure the quality of the parameters that could be included in the parameter space. A comprehensive complexity and performance analysis between the proposed and existing algorithms are provided. Simulations are performed to show the enhanced convergence and tracking performance of the proposed algorithm as compared with existing techniques.

This study proposes novel sparsity-aware space–time adaptive processing (SA-STAP) algorithms with L1-norm regularisation for airborne phased-array radar applications. The proposed SA-STAP algorithms suppose that a number of samples of the full-rank STAP datacube are not meaningful for processing and the optimal full-rank STAP filter weight vector is sparse, or nearly sparse. The core idea of the proposed method is imposing a sparse regularisation (L1-norm type) to the minimum variance STAP cost function. Under some reasonable assumptions, the authors firstly propose an L1-based sample matrix inversion to compute the optimal filter weight vector. However, it is impractical because of its matrix inversion, which requires a high computational cost when using a large phased-array antenna. In order to compute the STAP parameters in a cost-effective way, the authors devise low-complexity algorithms based on conjugate gradient techniques. A computational complexity comparison with the existing algorithms and an analysis of the proposed algorithms are conducted. Simulation results with both simulated and the Mountain-Top data demonstrate that fast signal-to-interference-plus-noise-ratio convergence and good performance of the proposed algorithms are achieved.

This work presents generalized low-rank signal decompositions with the aid of switching techniques and adaptive algorithms, which do not require eigen-decompositions, for space-time adaptive processing. A generalized scheme is proposed to compute low-rank signal decompositions by imposing suitable constraints on the filtering and by performing iterations between the computed subspace and the low-rank filter. An alternating optimization strategy based on recursive least squares algorithms is presented along with switching and iterations to cost-effectively compute the bases of the decomposition and the low-rank filter. An application to space-time interference suppression in DS-CDMA systems is considered. Simulations show that the proposed scheme and algorithms obtain significant gains in performance over previously reported low-rank schemes.

In this work, we propose adaptive frequency-domain biased estimation algorithms with mechanisms to automatically adjust the shrink age factors. The proposed estimation algorithms improve the performance of the conventional least squares (LS) estimator in terms of mean-squared error (MSE), while requiring a very modest in crease in complexity. An extension of the Cramer-Rao Lower Bound (CRLB) is computed in order to serve as a performance benchmark for the MSE performance of the biased estimators. We consider an application of the proposed algorithms to single-carrier frequency domain equalization (SC-FDE) of direct-sequence ultra-wideband (DS-UWB) systems, in which the channel estimation is performed by the proposed algorithms. The simulation results show that the proposed algorithms significantly outperform existing methods.

We propose a non-data-aided adaptive beamforming algorithm based on Widely Linear (WL) processing techniques and the Auxiliary Vector Filtering (AVF) algorithm for non-circular signals, where only the steering vector of the desired user is known. The proposed Widely Linear Auxiliary Vector Filtering (WL-AVF) algorithm recursively updates the filter weights by a sequence of auxiliary vectors that are designed according to the Widely Linearly Constrained Minimum Variance (WLCMV) criterion. It takes full advantage of the second-order statistics of the non-circular data, achieving a higher maximum signal-to-interference-plus-noise ratio (SINR) than the linear AVF. Key properties of the proposed WL-AVF are analyzed. Simulation results show that the WL-AVF beamforming algorithm performs the best among the existing adaptive algorithms.

A novel construction for irregular low density parity check (LDPC) codes based on a modification of the Progressive Edge Growth (PEG) algorithm is presented. The edge placement procedure of the PEG algorithm is enhanced by the use of the Sum-Product decoder in the design of the parity-check matrix. The proposed algorithm, like the PEG algorithm, is highly flexible in block length and rate. The codes constructed by the methods presented are tested in the AWGN channel and significant performance improvements are achieved. The flexibility of the proposed decoder optimisation operation in its application to PEG-based construction methods is then demonstrated by its use in modifying the Improved PEG (IPEG) extension to the PEG algorithm to achieve further performance gains.

A novel linear blind reduced-rank receiver based on the Joint Iterative Optimization (JIO) and the constrained constant modulus (CCM) design criterion is proposed for interference suppression in direct-sequence ultra-wideband (DS-UWB) systems. The proposed receiver consists of a projection matrix that performs dimensionality reduction and a reduced-rank filter that produces the output. The columns of the projection matrix and the reduced-rank filter are updated jointly at each time instant or iteration to minimize the CM cost function subject to a constraint. Recursive least-squares (RLS) algorithms are developed for the adaptive implementation. Simulation results show that the proposed scheme has excellent performance in suppressing the inter-symbol interference (ISI) and multiple access interference (MAI) with a low complexity.

In this paper, we investigate a dynamic pilot allocation algorithm for Discrete Fourier Transform (DFT)-based channel estimation in MIMO-OFDM systems, which employs feedback to adapt pilot locations to mobile channels for different receivers (ZF-linear, ZF-SIC, MMSE-linear, MMSE-SIC). The pilot allocation is dynamically controlled by feedback of the pilot allocation index so to optimize SER performance. Furthermore, mathematical expressions of the instantaneous signal to interference and noise ratio (SINR) in the presence of channel estimation error for different receivers are derived to obtain the error probabilities of data subcarriers through an approximate error probability function for MPSK modulation. The pilot locations are based on these error probabilities to avoid data symbols transmitted over these subcarriers with high error probabilities instead of pilots over them. Simulation results are provided to illustrate the performance gains achieved with these receivers by this dynamic pilot allocation.

In this paper, a new successive interference cancellation (SIC) strategy for multiple-input multiple output (MIMO) spatial multiplexing systems is developed to combat the error propagation (EP) in decision feedback systems. The proposed scheme employs a parallel multi-branch (MB) structure. Each branch employs a SIC with multi-feedback (MF) strategy to detects the signals according to their respective ordering pattern. The MF-SIC scheme considers the feedback diversity by using a number of selected constellation points as the feedback set if a previous decision is considered unreliable. The shadow area constraint (SAC) is proposed to reduce the computational complexity by avoiding redundant MF processing with reliable decisions. The MB-MF-SIC achieves a higher detection diversity by selecting the branch which yields the signal estimates with the best performance according to the maximum likelihood rule. The simulation results show that the MB-MF-SIC scheme successfully mitigates the EP and approaches the ML performance while requiring lower complexity than sphere decoders.

In this study, the authors propose a novel successive interference cancellation (SIC) strategy for multiple-input multiple-output spatial multiplexing systems based on a structure with multiple interference cancellation branches. The proposed multi-branch SIC (MB-SIC) structure employs multiple SIC schemes in parallel and each branch detects the signal according to its respective ordering pattern. By selecting the branch which yields the estimates with the best performance according to the selection rule, the MB-SIC detector, therefore, achieves higher detection diversity. The authors consider three selection rules for the proposed detector, namely, the maximum likelihood (ML), the minimum mean square error and the constant modulus criteria. An efficient adaptive receiver is developed to update the filter weight vectors and estimate the channel using the recursive least squares algorithm. Furthermore a bit error probability performance analysis is carried out. The simulation results reveal that the authors' scheme successfully mitigates the error propagation and approaches the performance of the optimal ML detector, while requiring a significantly lower complexity than the ML and sphere decoder detectors.

In this paper we propose a novel switched-interleaving algorithm based on limited feedback for both uplink and downlink DS-CDMA systems. The proposed switched chip-interleaving DS-CDMA scheme requires the cooperation between the transmitter and the receiver, and a feedback channel sending the index of the interleaver to be used. The transmit chip-interleaver is chosen by the receiver from a codebook of interleaving matrices known to both the receiver and the transmitter and the codebook index is sent back using a limited number of bits. In order to design the codebook, we consider a number of different chip patterns by using random interleavers, block interleavers and a proposed frequently selected patterns method (FSP). The best interleaving patterns are chosen by the selection functions of the received signal to interference plus noise ratio (SINR) for both downlink and uplink systems. Since the selection function needs to determine the best interleaver based on the channel state information, it is necessary to predict reliably the channel state information for typical delay values. We present symbol-based and block-based linear minimum mean squared error (MMSE) receivers for interference suppression. Simulation results show that our proposed algorithm achieves significantly better performance than the conventional DS-CDMA (C-CDMA) systems and the existing chip-interleaving, linear precoding and adaptive spreading techniques.

In this paper, a low-complexity multiple feedback successive interference cancellation (MF-SIC) strategy is proposed for the uplink of multiuser multiple-input multiple-output (MU-MIMO) systems. In the proposed MF-SIC algorithm with shadow area constraints (SAC), an enhanced interference cancellation is achieved by introducing {constellation points as the candidates} to combat the error propagation in decision feedback loops. We also combine the MF-SIC with multi-branch (MB) processing, which achieves a higher detection diversity order. For coded systems, a low-complexity soft-input soft-output (SISO) iterative (turbo) detector is proposed based on the MF and the MB-MF interference suppression techniques. The computational complexity of the MF-SIC is comparable to the conventional SIC algorithm since very little additional complexity is required. Simulation results show that the algorithms significantly outperform the conventional SIC scheme and approach the optimal detector.

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 paper, a reduced-rank knowledge-aided technique for MIMO radar space-time adaptive processing (STAP) design is proposed. We focus on the advantage of MIMO radars in achieving better spatial resolution by employing the colocated antennas. The scheme is based on knowledge-aided constrained joint iterative optimization of adaptive filters (KAC-JIOAF) and takes advantage of the a priori covariance matrix by employing additional linear constraints in the design. A recursive least squares (RLS) implementation is derived to reduce the computational complexity. We evaluate the algorithm in terms of signal-to-interference-plus-noise ratio (SINR) and probability of detection PD performance and compare it with the state-of-the-art reduced-rank algorithms. Simulations show that the proposed algorithm outperforms existing reduced-rank algorithms.

Knowledge-aided space-time adaptive processing (KA-STAP) algorithms, which incorporate a priori knowledge into radar signal processing methods, have the potential to substantially enhance detection performance while combating heterogeneous clutter effects. In this paper, we develop a KA-STAP algorithm to estimate the inverse interference covariance matrix rather than the covariance matrix itself, by combining the inverse of the covariance known a priori, R0-1, and the inverse sample covariance matrix estimate R̂-1. The computational load is greatly reduced due to the avoidance of the matrix inversion operation. We also develop a cost-effective algorithm based on the minimum variance (MV) criterion for computing the mixing parameter that performs a convex combination of R0-1and R̂-1. Simulations show the potential of our proposed algorithm, which obtain substantial performance improvements over prior art.

This work presents joint iterative power allocation and interference suppression algorithms for spread spectrum networks with multiple relays and the amplify and forward cooperation strategy. A joint constrained optimization framework that considers the allocation of power levels across the relays subject to individual and global power constraints and the design of linear receivers for interference suppression is proposed. Constrained minimum mean-squared error (MMSE) expressions for the parameter vectors that determine the optimal power levels across the relays and the parameters of the linear receivers are derived. In order to solve the proposed optimization problems efficiently, stochastic gradient (SG) algorithms for adaptive joint iterative power allocation, and receiver and channel parameter estimation are developed. The results of simulations show that the proposed algorithms obtain significant gains in performance and capacity over existing cooperative and non-cooperative schemes.

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.

This paper proposes an auxiliary vector filtering (AVF) algorithm based on a constrained constant modulus (CCM) design for robust adaptive beamforming. This scheme provides an efficient way to deal with filters with a large number of elements. The proposed beamformer decomposes the adaptive filter into a constrained (reference vector filters) and an unconstrained (auxiliary vector filters) components. The weight vector is iterated by subtracting the scaling auxiliary vector from the reference vector. The scalar factor and the auxiliary vector depend on each other and are jointly calculated according to the CCM criterion. The proposed robust AVF algorithm provides an iterative exchange of information between the scalar factor and the auxiliary vector and thus leads to a fast convergence and an improved steady-state performance over the existing techniques. Simulations are performed to show the performance and the robustness of the proposed scheme and algorithm in several scenarios.

In this paper, we propose a reduced-rank direction of arrival (DOA) estimation algorithm based on joint and iterative subspace optimization (JISO) with grid search . The reduced-rank scheme includes a rank reduction matrix and an auxiliary reduced-rank parameter vector. They are jointly and iteratively optimized with a recursive least squares algorithm (RLS) to calculate the output power spectrum. The proposed JISO-RLS DOA estimation algorithm provides an efficient way to iteratively estimate the rank reduction matrix and the auxiliary reduced-rank vector. It is suitable for DOA estimation with large arrays and can be extended to arbitrary array geometries. It exhibits an advantage over MUSIC and ESPRIT when many sources exist in the system. A spatial smoothing (SS) technique is employed for dealing with highly correlated sources. Simulation results show that the JISO-RLS has a better performance than existing Capon and subspace-based DOA estimation methods.

This paper presents a novel reduced-rank implementation of the set-membership (SM) Bounding Ellipsoid Adaptive Constrained Least Squares (BEACON) algorithm using the method of joint iterative optimization (JIO) of adaptive filters. The sparsely updating estimation error SM framework is applied to the adaption of the rank reduction matrix and the symbol estimation filter that operates in the reduced-rank signal subspace. A derivation of the scheme, based on least squares (LS) optimization, is given along with an intuitive geometric interpretation of the update procedure. The proposed JIO-BEACON algorithm along with other established reduced-rank algorithms are applied to interference suppression in a multiuser DS-CDMA system. The JIO-BEACON algorithm is shown to exceed the performance of the standard LS JIO and other existing algorithms in terms of convergence and steady state error whilst achieving significant complexity savings.

This work presents joint iterative power allocation and interference suppression algorithms for DS-CDMA networks which employ multiple relays and the amplify and forward cooperation strategy. We propose a joint constrained optimization framework that considers the allocation of power levels across the relays subject to individual and global power constraints and the design of linear receivers for interference suppression. We derive constrained minimum mean-squared error (MMSE) expressions for the parameter vectors that determine the optimal power levels across the relays and the parameters of the linear receivers. In order to solve the proposed optimization problems efficiently, we develop recursive least squares (RLS) algorithms for adaptive joint iterative power allocation, and receiver and channel parameter estimation. Simulation results show that the proposed algorithms obtain significant gains in performance and capacity over existing schemes.

In this work, we propose a conjugate gradient (CG) based structured channel estimation (SCE) scheme for single-carrier frequency domain equalization (SC-FDE) in multiuser direct-sequence ultra-wideband (DS-UWB) systems. The minimum mean square error (MMSE) linear detection strategy is used and a cyclic prefix is employed. We perform the adaptive channel estimation in the frequency domain and implement the despreading in the time domain after the FDE. In this scheme, the linear MMSE detection requires the knowledge of the number of users and the noise variance. For this purpose, we propose algorithms for estimating these parameters. A CG adaptive algorithm is then developed for the SCE scheme. With lower complexity than the recursive least squares (RLS) algorithm and better performance than the Least mean squares (LMS) algorithm, the SCE-CG achieves a better tradeoff between the complexity and the performance.

We consider a two-stage framework for linear interference mitigation, in which a transformation performs dimensionality reduction followed by a reduced-rank filter. A generic reduced-rank scheme that jointly optimizes the transformation and the reduced-rank filter by using the minimum mean squared error (MMSE) criterion is investigated. Then, we impose constraints on the design of the transformation and propose the switched approximations of adaptive basis functions (SAABF) scheme, in which the transformation is chosen instantaneously from a set of mapping matrices and adaptive basis functions. Least-mean squares (LMS) algorithms and model-order selection algorithms are also proposed. A complexity analysis shows that the proposed scheme is significantly simpler than the existing reduced-rank schemes. Simulations show remarkable interference mitigation performance in direct-sequence ultra-wideband (DS-UWB) systems.

In this paper, we consider a general cooperative wireless sensor network (WSN) and the problem of channel estimation. We develop a matrix-based set-membership normalized least mean squares (SM-NLMS) algorithm for the estimation of the complex channel parameters in order to reduce the computational complexity significantly and extend the lifetime of the WSN by reducing its power consumption. The proposed SM-NLMS channel estimation method requires the setting of a bound for appropriate performance. However, an inappropriate and fixed error bound will result in overbounding and underbounding problems which degrade the performance significantly. Therefore, we present and incorporate an error bound function into the SM-NLMS channel estimation method which can adjust the error bound automatically with the update of the channel estimates. Computer simulations show good performance of our proposed algorithms in terms of convergence speed and steady state, reduced complexity and robustness to the time-varying environment and different signal-to-noise ratio (SNR) values.

Here, the authors propose two adaptive detection schemes based on single-carrier frequency-domain equalisation (SC‐FDE) for multiuser direct-sequence ultra-wideband systems, which are termed structured channel estimation (SCE) and direct adaptation (DA). Both schemes use the minimum mean square error (MMSE) linear detection strategy and employ a cyclic prefix. In the SCE scheme, adaptive channel estimation is performed in the frequency domain and the despreading is implemented in the time domain after the FDE. In this scheme, the MMSE detection requires the knowledge of the number of users and the noise variance. For this purpose, simple algorithms are proposed for estimating these parameters. In the DA scheme, the interference suppression task is fulfilled with only one adaptive filter in the frequency domain and a new signal expression is adopted to simplify the design of such a filter. Least mean squares, recursive least squares and conjugate gradient adaptive algorithms are then developed for both schemes. A complexity analysis compares the computational complexity of the proposed algorithms and schemes, and simulation results for the downlink illustrate their performance.

We propose blind adaptive multi-input multi-output (MIMO) linear receivers for DS-CDMA systems using multiple transmit antennas and space-time block codes (STBC) in multipath channels. A space-time code-constrained constant modulus (CCM) design criterion based on constrained optimization techniques is considered and recursive least squares (RLS) adaptive algorithms are developed for estimating the parameters of the linear receivers. A blind space-time channel estimation method for MIMO DS-CDMA systems with STBC based on a subspace approach is also proposed along with an efficient RLS algorithm. Simulations for a downlink scenario assess the proposed algorithms in several situations against existing methods.

In this paper we propose a novel switched interleaving algorithm based on limited feedback for downlink DS-CDMA systems. The proposed switched chip-interleaving DS-CDMA scheme requires the cooperation among the transmitter, the receiver and a feedback channel sending the index of the interleaver to be used. The transmit chip-inter leaver is chosen by the receiver from a codebook of interleaving matrices known to both the receiver and the transmitter and we send back the codebook index using a limited number of bits. In order to design the codebook, we consider patterns such as the block interleavers, and a selection function is designed to maximize the received signal to interference plus noise ratio (SINR). We present block-based and symbol-based linear minimum mean squared error (MMSE) receivers for interference suppression. Simulation results show that our proposed algorithm achieves significantly better performance than the conventional CDMA systems and the existing chip-interleaving schemes.

This paper presents joint power allocation and interference mitigation techniques for the downlink of CDMA systems which employ multiple relays and the amplify and forward cooperation strategy. We propose a joint constrained optimization framework that considers the allocation of power levels across the relays subject to individual power constraints and the design of linear receivers for interference suppression. We derive constrained minimum mean-squared error (MMSE) expressions for the parameter vectors that determine the optimal power levels across the relays and the linear receivers. In order to solve the proposed optimization problem efficiently, we develop a joint adaptive power allocation and interference suppression stochastic gradient (SG) algorithm. The proposed SG algorithm mitigates the interference by adjusting the power levels across the relays and estimating the parameters of the linear receiver. An SG channel estimation algorithm is also derived to determine the coefficients of the channels across the base station, the relays and the destination terminal. The results of simulations show that the proposed methods obtain significant gains in performance and capacity over non-cooperative systems and cooperative schemes with equal power allocation.

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.

In this paper we propose a novel transmission scheme based on switched interleaving and limited feedback for downlink DS-CDMA systems. The proposed switched interleaving DS-CDMA scheme (SI-CDMA) requires the cooperation among the transmitter, the receiver and a feedback channel sending the index of the interleaver to be used. The transmit chip-interleaver is chosen by the receiver from a codebook of interleaving matrices known to both the receiver and the transmitter and the codebook index is sent back from the feedback channel using a limited number of bits. In order to design the codebook, we consider patterns such as the block interleavers, and a selection function is designed to maximize the received signal to interference plus noise ratio (SINR). We present a block-based linear minimum mean squared error (MMSE) receiver for interference suppression. Simulation results show that our proposed algorithm achieves significantly better performance than the conventional CDMA systems and the existing chip-interleaving schemes.

In this paper we propose a novel successive interference cancellation (SIC) strategy for multiple-input multiple-output (MIMO) spatial multiplexing systems based on multiple interference cancellation branches. The proposed detection structure employs SICs on several parallel branches which are equipped with different ordering patterns so that each branch produces a symbol estimate vector by exploiting a certain ordering pattern. The novel detector, therefore, achieves higher detection diversity by selecting the branch which yields the estimates with the best performance according to the selection rule. We consider three selection rules for the proposed detector, namely, maximum likelihood (ML), minimum mean square error (MMSE), constant modulus (CM) criteria. The simulation results reveal that our scheme successfully mitigates the error propagation and approaches the performance of the optimal ML detector, while requiring a significantly lower complexity than the ML detector.

This paper presents novel adaptive reduced-rank filtering algorithms based on joint iterative optimization of adaptive filters. The novel scheme consists of a joint iterative optimization of a bank of full-rank adaptive filters that constitute the projection matrix and an adaptive reduced-rank filter that operates at the output of the bank of filters. We describe least squares (LS) expressions for the design of the projection matrix and the reduced-rank filter and recursive least squares (RLS) adaptive algorithms for its computationally efficient implementation. Simulations for a space-time interference suppression in a CDMA system application show that the proposed scheme outperforms in convergence and tracking the state-of-the-art reduced-rank schemes at about the same complexity.

This work proposes a low-complexity blind adaptive reduced-rank method (BARC) for symbol estimation using an adaptive decimation and interpolation scheme based on diversity-combining and the constant modulus criterion for interference suppression. The proposed approach employs an iterative procedure to jointly optimize the interpolation, decimation and estimation tasks for blind reduced-rank parameter estimation. We describe joint iterative estimators based on the constrained constant modulus (CCM) criterion, introduce alternative decimation structures, including the optimal decimation scheme, and develop low-complexity stochastic gradient adaptive algorithms for the proposed structure. Simulations for a CDMA interference suppression application show an excellent performance and substantial gains over prior art.

In this paper, two low-complexity adaptive step size algorithms are investigated for blind adaptive beamforming. Both of them are used in a stochastic gradient (SG) algorithm, which employs the constrained constant modulus (CCM) criterion as the design approach. A brief analysis is given for illustrating their properties. Simulations are performed to compare the performances of the novel algorithms with other well-known methods. Results indicate that the proposed algorithms achieve superior performance, better convergence behavior and lower computational complexity in both stationary and non-stationary environments.

This paper presents a novel adaptive reduced-rank multi-input-multi-output (MIMO) linear equalization structure based on joint iterative optimization of adaptive filters. The proposed reduced-rank linear equalization structure consists of a joint iterative optimization of two equalization stages, namely, a projection matrix that performs dimensionality reduction and a reduced-rank linear equalization filter that retrieves the desired transmitted symbol. The novel linear reduced-rank structure is responsible for cancelling the inter- antenna interference caused by the associated data streams and exploiting the available degrees of freedom at the antenna-array receiver. We describe least squares (LS) expressions for the design of the projection matrix and the reduced-rank filter along with computationally efficient recursive least squares (RLS) adaptive estimation algorithms. Simulations for a MIMO linear equalization application show that the proposed scheme outperforms the state-of-the-art reduced-rank and the conventional estimation algorithms at about the same complexity.

In this paper, we propose a novel and simple approximate maximum likelihood detector (A-ML-D) for single input and single output (SISO) systems over frequency-selective fading channels based on Tomlinson-Harashima pre-equalizer. By assuming full channel state information (CSI) at the transmitter side, the pre-equalizer can remove some inter-symbol interference (ISI) at the transmitter. At the receiver, we implement a Gaussian approximation, a pre-whitening filter and a matched filter to realize a set of parallel SISO schemes, and thus, facilitate single symbol detection. The proposed scheme can obtain full multi-path diversity and achieve near-optimal performance with a complexity lower than linear MMSE and MMSE-DFE. Analytical symbol error rate (SER) is derived to further justify the proposed detector.

This paper presents a novel adaptive reduced- rank multi-input-multi-output (MIMO) decision feedback equalization structure based on joint iterative optimization of adaptive estimators. The novel reduced-rank equalization structure consists of a joint iterative optimization of two equalization stages, namely, a projection matrix that performs dimensionality reduction and a reduced-rank estimator that retrieves the desired transmitted symbol. The proposed reduced- rank structure is followed by a decision feedback scheme that is responsible for cancelling the inter-antenna interference caused by the associated data streams. We describe least squares (LS) expressions for the design of the projection matrix and the reduced-rank estimator along with computationally efficient recursive least squares (RLS) adaptive estimation algorithms. Simulations for a MIMO equalization application show that the proposed scheme outperforms the state-of-the-art reduced-rank and the conventional estimation algorithms at about the same complexity.

In this paper we propose iterative turbo minimum mean squared error (MMSE) successive parallel arbitrated decision feedback (DF) receivers for direct sequence code division multiple access (DS-CDMA) systems. We describe the MMSE design criterion for DF multiuser detectors along with successive, parallel and iterative interference cancellation structures. A novel efficient turbo DF structure that employs soft-decision successive cancellation with parallel arbitrated branches and a near-optimal low complexity user ordering algorithm are presented. The proposed iterative turbo DF receiver structure and the ordering algorithm are then combined with iterative cascaded DF stages for mitigating the deleterious effects of error propagation for uncoded systems. Simulations for an uplink scenario show substantial gains for the proposed iterative turbo DF detectors over the best known methods.

A space–time adaptive reduced-rank processor for interference mitigation in DS-CDMA systems is proposed based on interpolated finite impulse response filters with time-varying interpolators. The proposed space–time processor allows a significant reduction in the number of estimation elements, thereby increasing the convergence and tracking performance of the estimation algorithms. In order to compute the parameters of the proposed space–time processor, a least squares design is presented and computationally efficient recursive least-squares (RLS) algorithms are developed for estimating the parameters of both reduced-rank receiver and interpolator. A linear and successive interference cancellation space–time receivers based on the proposed reduced-rank processor for mitigating multi-access and intersymbol interference in an uplink scenario are proposed. An analysis of the convergence properties of the proposed space–time processor is carried out and analytical expressions are derived for predicting the mean squared error performance of the proposed RLS algorithm. Simulation results show that the proposed reduced-rank space–time processor and RLS algorithms outperform existing techniques at lower complexity.

Blind adaptive and iterative interference cancellation (IC) receiver structures for direct sequence code division multiple access systems in multipath channels are proposed. A code-constrained constant modulus design criterion based on constrained optimisation techniques and adaptive algorithms for receiver and channel parameter estimation are described for successive IC (SIC) and parallel IC (PIC) detectors and a new hybrid IC (HIC) scheme in scenarios subject to multipath fading. The proposed HIC structure combines the strengths of linear, SIC and PIC receivers and is shown to outperform the conventional linear, SIC and PIC structures. A novel iterative detection approach that generates different cancellation orders and selects the most likely symbol estimate on the basis of the instantaneous minimum constant modulus criterion is also proposed and combined with the new HIC structure to further enhance performance. Simulation results for an uplink scenario assess the algorithms, the proposed blind adaptive IC detectors against existing receivers and evaluate the effects of error propagationof the new cancellation techniques.

This paper presents a new approach to efficient acoustic echo cancellation (AEC) based on reduced-rank adaptive filtering equipped with selective-decimation and adaptive interpolation. We propose a novel structure of an AEC scheme that jointly optimizes an interpolation filter, a decimation unit, and a reduced-rank filter. With a practical choice of parameters in AEC, the total computational complexity of the proposed reduced-rank scheme with the normalized least mean square (NLMS) algorithm is approximately half of that of the full-rank NLMS algorithm. We discuss the convergence properties of the proposed scheme and present a convergence condition. First, we examine the performance of the proposed scheme in a single-talk situation with an error-minimization criterion adopted in the decimation selection. Second, we investigate the potential of the proposed scheme in a double-talk situation by employing an ideal decimation selection. In addition to mean squared error (MSE) and power spectrum analysis of the echo estimation error, subjective assessments based on absolute category rating are performed, and the results demonstrate that the proposed structure provides significant improvements compared to the full-rank NLMS algorithm.

In this paper we propose minimum mean squared error (MMSE) iterative successive parallel arbitrated decision feedback (DF) receivers for direct sequence code division multiple access (DS-CDMA) systems. We describe the MMSE design criterion for DF multiuser detectors along with successive, parallel and iterative interference cancellation structures. A novel efficient DF structure that employs successive cancellation with parallel arbitrated branches and a near-optimal low complexity user ordering algorithm are presented. The proposed DF receiver structure and the ordering algorithm are then combined with iterative cascaded DF stages for mitigating the deleterious effects of error propagation for convolutionally encoded systems with both Viterbi and turbo decoding as well as for uncoded schemes. We mathematically study the relations between the MMSE achieved by the analyzed DF structures, including the novel scheme, with imperfect and perfect feedback. Simulation results for an uplink scenario assess the new iterative DF detectors against linear receivers and evaluate the effects of error propagation of the new cancellation methods against existing ones.

A space-time adaptive decision feedback (DF) receiver using recurrent neural networks (RNNs) is proposed for joint equalization and interference suppression in direct-sequence code-division multiple-access (DS-CDMA) systems equipped with antenna arrays. The proposed receiver structure employs dynamically driven RNNs in the feedforward section for equalization and multiaccess interference (MAI) suppression and a finite impulse response (FIR) linear filter in the feedback section for performing interference cancellation. A data selective gradient algorithm, based upon the set-membership (SM) design framework, is proposed for the estimation of the coefficients of RNN structures and is applied to the estimation of the parameters of the proposed neural receiver structure. Simulation results show that the proposed techniques achieve significant performance gains over existing schemes.

In this paper we present a blind interference suppression technique for DS-CDMA systems based on a reduced-rank decomposition of a code-constrained constant modulus design criterion. We describe a least-squares (LS) type design criterion for blind linear detectors using the constrained optimization of the constant modulus cost function subject to code constraints. Based on this design approach and using the Lanczos algorithm, a multistage decomposition in the Krylov subspace is devised for blind reduced-rank parameter estimation. A computationally efficient blind reduced-rank LS type algorithm is also developed and compared with existing methods. Numerical results show that the proposed full-rank and reduced-rank techniques outperform existing methods for the linear suppression of multi-access and intersymbol interference in DS-CDMA systems.

This work proposes a low-complexity reduced-rank method for general parameter estimation using an adaptive decimation and interpolation scheme based on diversity-combining. The new approach employs an iterative procedure to jointly optimize the interpolation, decimation and estimation tasks for reduced-rank parameter estimation. We describe joint iterative minimum mean-squared error (MMSE) design filters, propose alternative decimation structures, including the optimal decimation scheme, and develop low-complexity adaptive algorithms for the proposed structure. Simulations for a block equalization application in doubly-selective channels show the remarkable potential of the proposed scheme.

An adaptive low-complexity space-time reduced-rank processor is proposed for interference suppression in asynchronous DS-CDMA systems based on a diversity-combined decimation and interpolation method. The novel design approach for the processor employs an iterative procedure to jointly optimize the interpolation, decimation and estimation tasks for reduced-rank parameter estimation. We describe joint iterative least squares (LS) design parameter estimators, propose alternative decimation structures, including the optimal decimation scheme, and develop low complexity adaptive recursive least squares (RLS) algorithms for the proposed structure. Linear space-time receivers with antenna arrays based on the proposed reduced-rank processor are presented and investigated for mitigating multi-access interference (MAI) and intersymbol interference (ISI) in an asynchronous DS-CDMA system uplink scenario.

Most research on differential MIMO is based on space-time block codes, aiming to achieve maximum transmit diversity and thus make the transmission more robust by the aid of the special orthogonal or quasi-orthogonal code structures. However, a differential scheme based on a spatial multiplexing approach such as the Bell-Labs layered space time (BLAST) wireless architecture would be likely to provide a much greater capacity. To this end, in this paper, we derive a simple differential modulation scheme based on BLAST for any number of transmit antennas and receive antennas. A special symbol mapping method is developed to avoid amplitude variation of the transmitted signals, which can also improve the system performance. This differential scheme can significantly reduce the system complexity, since it avoids the need for channel estimation. Moreover, an improved sub-optimal detection algorithm based on a Gaussian approximation is applied to greatly reduce computational complexity at the receiver, otherwise prohibitive, with only very slight performance loss.

The realistic performance of a multi-input multi-output (MIMO) communication system depends strongly on the spatial correlation properties introduced by clustering in the propagation environment. Simulating realistic correlated channels is essential to predict the performance of real MIMO systems. Since the modeling method of the correlated channels suggested by the Third Generation Partnership Project (3GPP) channel model can result in considerable implementation complexity for large networks, this paper presents a computationally efficient method to approximately calculate the spatial correlation matrix for channel models such as the 3GPP channel model, which are based on clusters of scatterers. This proposed approximation method is on the basis of using the Taylor series expansion to the steering vectors for uniform linear arrays (ULAs) and a moderate angle spread of the cluster. The approximation method is evaluated in terms of the mean square error (MSE) of the approximated correlation matrix, and by the cumulative distribution function (CDF) of the mutual information of the MIMO channel. This shows that the proposed approximation method is close for angle spread of the cluster within 10deg, with high efficiency and low complexity.

This letter proposes a novel adaptive reduced-rank filtering scheme based on joint iterative optimization of adaptive filters. The novel scheme consists of a joint iterative optimization of a bank of full-rank adaptive filters that forms the projection matrix and an adaptive reduced-rank filter that operates at the output of the bank of filters. We describe minimum mean-squared error (MMSE) expressions for the design of the projection matrix and the reduced-rank filter and low-complexity normalized least-mean squares (NLMS) adaptive algorithms for its efficient implementation. Simulations for an interference suppression application show that the proposed scheme outperforms in convergence and tracking the state-of-the-art reduced-rank schemes at significantly lower complexity.

This work presents blind constrained adaptive filtering algorithms based on the set-membership concept and incorporates time-varying bounds for CDMA interference suppression. Constrained constant modulus (CCM) and constrained minimum variance (CMV) gradient type algorithms designed in accordance with the specifications of the set-membership filtering concept are proposed. Furthermore, the important issue of bound specification is addressed in a new framework that takes into account parameter estimation dependency and multi-access (MAI) and inter-symbol interference (ISI) for multiuser communications. Simulations show that the new algorithms are capable of outperforming previously reported techniques with a smaller number of parameter updates and a reduced risk of overbounding or underbounding

This work presents low complexity blind constrained data-reusing adaptive filtering algorithms based on the minimum variance and constant modulus cost functions. Constrained minimum variance (CMV) and constrained constant modulus (CCM) affine projection type algorithms are developed and investigated in a CDMA interference suppression scenario. Computer simulations are used to analyze the proposed techniques and compare them with existing stochastic gradient (SG) and recursive least-squares (RLS) type techniques. The results show that the new algorithms outperform previously reported SG techniques with small additional computational requirements and achieve a performance very close to RLS algorithms at greatly reduced complexity

This work presents set-membership adaptive algorithms based on time-varying error bounds. The important issue of error bound specification is addressed in a new framework that takes into account parameter estimation dependency, multi-access (MAI) and intersymbol interference (ISI) for DS-CDMA communications. An algorithm for tracking and estimating the interference power is presented and incorporated into the time-varying error bound. Computer simulations show that the algorithms are capable of outperforming previously reported techniques with a smaller number of parameter updates and a reduced risk of overbounding or under bounding

A space-time adaptive reduced-rank processor for interference suppression in DS-CDMA systems is proposed based on interpolated FIR filters. The interpolated minimum mean squared error (MMSE) solution is described for a novel scheme where the interpolator is rendered adaptive and adaptive algorithms are developed for estimating the parameters of both reduced-rank receiver and interpolator. Linear and successive interference cancellation (SIC) space-time receivers with the proposed structure are investigated for mitigating multi-access interference (MAI) and intersymbol interference (ISI) in an uplink scenario. An analysis of the convergence properties of the proposed structure is carried out and simulations for typical scenarios are performed, showing the superiority of the method against previously reported ones

In this paper we examine blind adaptive and iterative decision feedback (DF) receivers for direct sequence code division multiple access (DS-CDMA) systems in frequency selective channels. Code-constrained minimum variance (CMV) and constant modulus (CCM) design criteria for DF receivers based on constrained optimization techniques are investigated for scenarios subject to multipath. Computationally efficient blind adaptive recursive least squares (RLS) algorithms are developed for estimating the parameters of DF detectors along with successive, parallel and iterative DF structures. A new successive parallel arbitrated DF scheme is presented and combined with iterative techniques for use with cascaded DF stages for mitigating the deleterious effects of error propagation. Simulations for an uplink scenario assess the new blind algorithms, DF structures and the effects of error propagation of the new techniques

In this letter, we propose a broadly applicable reduced-rank filtering approach with adaptive interpolated finite impulse response (FIR) filters in which the interpolator is rendered adaptive. We describe the interpolated minimum mean squared error (MMSE) solution and propose normalized least mean squares (NLMS) and affine-projection (AP) algorithms for both the filter and the interpolator. The resulting filtering structures are considered for equalization and echo cancellation applications. Simulation results showing significant improvements are presented for different scenarios.

Blind adaptive reduced-rank receivers based on interpolated finite impulse response (FIR) filters with adaptive interpolators for direct sequence code division multiple access (DS-CDMA) systems are proposed and examined in multipath channels. The interpolated constrained minimum variance (CMV) solutions for both receiver and interpolator are described for mitigating multiple access interference (MAI) and multiple-path propagation effects in a downlink scenario. Computationally efficient blind adaptive algorithms for both receiver and interpolator based upon the minimum variance (MV) performance criterion are then developed with appropriate constraints to mitigate MAI, intersymbol interference (ISI) and jointly estimate the channel.

We propose reduced-rank receivers based on adaptive interpolated finite impulse response (FIR) filters and adaptive interpolators for direct sequence code division multiple access (DS-CDMA) systems, and examine the novel structures in multipath fading channels. We describe the interpolated minimum mean squared error (MMSE) and the interpolated least squares (LS) for both receiver and interpolator to mitigate multiple access interference (MAI) and multiple-path propagation effects in a downlink scenario. Based on the interpolated MMSE and LS solutions, we present normalised least mean squares (NLMS) and recursive least squares (RLS) algorithms for both receiver and interpolator filters. Simulation experiments show that the proposed structures achieve a superior convergence performance to previously reported reduced-rank techniques and to the full-rank receiver.

In this work we propose the blind adaptive decision feedback (DF) receivers for direct sequence code division multiple access (DS-CDMA) systems in frequency selective channels. Closed-form expressions for the design of blind DF DS-CDMA receivers are presented. Blind adaptive stochastic gradient (SG) and recursive least-squares (RLS) type algorithms are developed for use with the constrained minimum variance (CMV) and constrained constant modulus (CCM) receivers along with successive and parallel DF structures. Numerical results show significant performance gains for the proposed blind DF receivers.

We investigate the use of adaptive minimum bit error rate (MBER) gradient-Newton algorithms in the design of linear multiuser receivers (MUD) for DS-CDMA systems. The proposed algorithms approximate the bit error rate (BER) from training data using linear multiuser detection structures. We carry out a comparative analysis of linear MUDs employing minimum mean squared error (MMSE), previously reported MBER and the proposed MBER algorithms. Computer simulation experiments show that the MBER gradient-Newton approaches outperform other analysed algorithms and can operate with shorter training sequences.

We investigate the use of an approximate minimum bit error rate (MBER) approach to multiuser detection using recurrent neural networks (RNN). We examine a stochastic gradient adaptive algorithm for approximating the MBER from training data using RNN structures. A comparative analysis of linear and neural multiuser receivers (MUD), employing minimum mean squared error (MMSE) and approximate MBER (AMBER) adaptive algorithms is carried out. Computer simulation experiments show that the neural MUD operating with a criterion similar to the AMBER algorithm outperforms neural receivers using the MMSE criterion via gradient-type algorithms and linear receivers with MMSE and MBER techniques.