Convolutional Neural Networks (CNNs) achieve remarkable accuracy in vision tasks, yet their computational complexity challenges low-power edge deployment. In this work, we present COMET, a framework of CNN models that employ efficient hardware offset-binary coding (OBC) techniques to enable co-optimization of performance and resource utilization. The approach formulates CNN inference using OBC representations applied separately to inputs (Scheme A) and weights (Scheme B), enabling exploitation of bit-width asymmetry. The shift–accumulate operation is modified by incorporating offset-term with the pre-scaled bias. Leveraging symmetries in Schemes A and B, we introduce four look-up table (LUT) techniques—parallel, shared, split, and hybrid—and evaluate their efficiency. Building on this foundation, we develop a general matrix multiplication core using theim2coltransformation for efficient CNN acceleration. We consider LeNet-5 and All-CNN-C to demonstrate that the OBC-GEMM core efficiently supports modern workloads. Evaluation shows that COMET enables efficient FPGA deployment compared to state-of-the-art designs, with negligible accuracy loss, demonstrating its efficiency and scalability across diverse network architectures.

An undersampled autocorrelation-based spectrum sensing algorithm is proposed for orthogonal frequency division multiplexing (OFDM) signal detection in channels sampled below the Nyquist rate. The proposed algorithm reduces the complexity of autocorrelation-based detection by minimizing the number of unique multiplications required to compute the autocorrelation function, exploiting the inherent structure of the OFDM signal. This enables low-bandwidth cognitive radios to effectively detect wideband signals. The novelty of this work lies in a threshold design approach, which compensates for the performance loss due to undersampling and maintains reliable detection accuracy. Analytical and simulation results demonstrate that the proposed algorithm attains reasonable detection performance in the mid to high signal-to-noise ratio (SNR) scenarios, while also significantly reducing the false alarm probability under low SNR conditions.

Orthogonal time frequency space (OTFS) modulation has attracted significant attention recently due to its advantages over orthogonal frequency division multiplexing (OFDM) in high-mobility scenarios. The low-complexity transceiver design is of great significance for OTFS systems. In this paper, we propose time domain-based channel estimation and data detection methods for both single-input-single-output (SISO) and multi-input-multi-output (MIMO) systems. In the proposed method, the data is mapped in the delay-time domain as compared to the delay-Doppler domain in OTFS. By doing this, a few pilots are required to estimate the channel coefficients, and data detection is performed symbol by symbol. Simulation results show the robustness of the proposed method for different Doppler shifts and baseband modulation as compared to a single-tap zero forcing equalizer and message-passing algorithm.

With the increasing number of applications of Internet of Things (IoT) devices, co-channel interference is unavoidable among coexisting wireless networks, where multiple IoT devices transmit their observations to their respective destinations [access points (APs)]. In this scenario, we present a joint precoding and power allocation solution to minimize the mean squared error (MSE), while satisfying power constraints at individual IoT devices. In this regard, first, the necessary feasibility condition for the joint convexity of the optimization problem is derived, ensuring the global optimum solution. Subsequently, based on the solution, an iterative MSE algorithm is formulated and analyzed for convergence. The expressions for the MSE-based precoder is obtained via solving Karush–Kuhn–Tucker (KKT) conditions. Further analysis shows that the total resulting MSE at APs is limited by the observation signal-to-noise-ratio (SNR). It leads to the inference that in order to avoid the MSE saturation at APs at higher SNRs, the transmit power at IoT devices should be scaled proportional to and less than the observation SNR. Next, we compare the performance of our solution with two classical methods, namely, the minimum variance distortionless precoding (MVDP) and interference alignment (IA) methods, which are modified and enhanced for the given system. Simulations verify the above inference, and the global convergence of the MSE algorithm, with robustness to initializations yielding the better precoders and power allocation as compared to MVDP’s and IA’s in terms of the averaged total MSE performance.

Designing an efficient receiver for multiple users transmitting orthogonal frequency-division multiplexing signals to the base station remain a challenging interference-limited problem in 5G-new radio (5G-NR) system. This can lead to stagnation of decoding performance at higher signal-to-noise-and-interference regimes. Further, the problem is exacerbated in future critical internet-of-thing (IoT) devices operating on smaller block size due to latency constraints and IoT users moving at varying speeds introducing Doppler shift and delay spread. In this work, we propose a novel deep learning (DL)-based U-net- and Resnet-inspired receiver for multi-user uplink transmission for a 5G-NR system that replaces only the signal demapping block of the receiver chain. Compared to traditional U-net frameworks, we propose a DL receiver with upsampling in the encoder that takes complex equalized symbols as input and downsampling in the decoder to output bit-wise log-likelihood ratios for multiple users. Further, residual skip connections are introduced in the decoder to facilitate stronger connections to the upsampling blocks. Finally, the DL receiver is optimized by maximizing the optimal bit-metric decoding rate. Comparative simulations show that our proposed DL receiver outperforms traditional 5G-NR receivers by considerable margins.

In this paper, we investigate a cellular-connected unmanned aerial vehicle (UAV) network, where multiple UAVs receive messages from base stations (BSs) in the down-link, and in the meantime, BSs serve their paired ground user equipments (UEs). To effectively manage inter-cell interferences (ICIs) among UEs due to intense reuse of time-frequency resource block (RB) resource, a first p-tier based RB coordination criterion is adopted. Then, to enhance wireless transmission quality for UAVs while protecting terrestrial UEs from being interfered by ground-to-air (G2A) transmissions, a radio resource management (RRM) problem of joint dynamic RB coordination and time-varying beamforming design is formulated to minimize UAV's ergodic outage duration (EOD). To cope with conventional optimization techniques' inefficiency in solving the formulated RRM problem, a deep reinforcement learning (DRL)-aided solution is proposed, where deep double duelling Q network (D3QN) and twin delayed deep deterministic policy gradient (TD3) are invoked to deal with RB coordination in the discrete action domain and beamforming design in the continuous action regime, respectively. Numerical results illustrate the effectiveness of the proposed hybrid D3QNTD3 algorithm, compared to representative baselines.

Hearing loss is among the most serious public health problems, affecting as much as 20% of the worldwide population. Even cutting-edge multi-channel audio-only speech enhancement (SE) algorithms used in modern hearing aids face significant hurdles since they typically magnify noises while failing to boost speech understanding in crowded social environments. Recently, for the first time we proposed a novel integration of 5G cloud-radio access network, internet of things (IoT), and strong privacy algorithms to develop 5G IoT enabled hearing aid (HA) [1]. In this demonstration, we show the first-ever transceiver (PHY layer) model for cloud-based audio-visual (AV) SE, which meets the requirements for high data rate and low latency of forthcoming multi-modal HAs (such as Google glasses with integrated HAs). Even in highly noisy conditions like cafés, clubs, conferences, meetings, etc., the transceiver [2] transmits raw AV information from a hearing aid system to a cloud-based platform and obtains a clear signal. In Fig. 1, we illustrate an example of our cloud-based AV SE hearing aid demonstration. Herein, the left-side computer and Universal Software Radio Peripheral (USRP) x310 function as IoT systems (hearing aids), the right-side USRP serves as an access point or base station, and the right-side computer serves as a cloud-server for operating NN-based SE models. Please take note that the channel between the HA device and the cloud is defined as an uplink channel, whereas the channel between the access point (cloud) and the HA device is defined as a downlink channel. Given the time-varying sensitivity of the data received at HA devices, the uplink channel can therefore handle a variety of data rates. As a result, a customized long-term evolution (LTE)-based frame structure is developed for uplink transmission of data. It provides error-correction codes in the 1.4 MHz and 3 MHz bandwidths with a variety of modulations and code rates. Furthermore, the cloud access point simply supports a limited transmission rate because it only transmits audio data to the HA equipment. In order to support real-time AV SE, a modified frame structure for LTE with 1.4 MHz of bandwidth is developed. The AV SE algorithm receives cropped lip images of the target speaker and a noisy speech spectrogram, and it produces an ideal binary mask that lessens the noise-dominant regions while improving the speech-dominant areas. We use the depth-wise separable convolutions, reduced STFT window size of 32 ms, smaller STFT window shift of 8 ms, and 64 convolutions in the audio feature extraction layers of our Cochlea-Net [3] multi-modal AV SE neural network architecture to reduce processing latency. Furthermore, the visual feature extraction framework is employed. Our proposed architecture can handle streaming data frame-by-frame. Thus, the users will experience for the first time the real-world development of a physical layer transceiver that can perform AV SE in real-time under strict latency and data rate requirements. For this demonstration, we will bring two computers and two USRP x310 devices.

In this paper, physical layer security issue for a down-link wireless communication system is examined, composed of an unmanned aerial vehicle (UAV), a legitimate receiver and a passive eavesdropper. The destination is equipped with two antennas and applies the full-duplex (FD) Bob-based jamming (FD-BBJ) strategy to achieve secure transmission. Considering that practical air-to-ground (A2G) channels experience Nakagami-m fading and the FD legitimate receiver is affected by self-interference (SI), closed-form expressions of approximate ergodic achievable secrecy rate (EASR) with help of Gauss-Laguerre Quadrature (GLQ) and compact secrecy outage probability (SOP) expression are derived, respectively. To gain more insights, asymptotic secrecy performance is analysed in the case of extreme total system transmit power, via deriving closed-form expression for asymptotic EASR and compact expression for asymptotic SOP. Numerical results have verified the correctness of our theoretical analysis and proved that the FD-BBJ strategy applied in the UAV-aided wireless communication system can help achieve considerable secrecy performance gain.

Full duplex (FD) relaying can provide double spectral efficiency. Despite advanced self-interference cancellation techniques, residual self-interference (RSI) limits the performance significantly. We present an autoencoder (AE)-based block coded modulation (BCM) and differential BCM (d-BCM) for an FD amplify-and-forward (FD-AF) relay network that can tackle the deteriorating impacts of RSI. Existing works treat AE frameworks as black-box with minimal/no focus on training convergence, limiting AE’s practical deployment. Focussing on training convergence, firstly, we show that training of AE converges above minimum signal-to-noise-ratio (SNR) and below maximum RSI level, and CSI helps in faster convergence. Secondly, we establish a relationship between training hyper-parameters and AE-based BCM/d-BCM design, by showing that, for any given hyper-parameters, training of the AE has converged to its maximum potential of decoding if AE’s encoder has designed$2^{k}$codewords, with an emphasis on the minimum required training samples. To open the black-box AE, we reveal five observations in the AE-based designed codewords concerning Euclidean distance, packing density, hamming distance, and kurtosis, that resemble the desired observations of theoretical random coded modulations. By extensive simulations, we show that the proposed AE outperforms the conventional methods considerably for varying SNR, RSI, transmission rate, channel estimation errors, and small/practical block lengths.

In this paper, we design a first of its kind transceiver (PHY layer) prototype for cloud-based audio-visual (AV) speech enhancement (SE) complying with high data rate and low latency requirements of future multimodal hearing assistive technology. The innovative design needs to meet multiple challenging constraints including up/down link communications, delay of transmission and signal processing, and real-time AV SE models processing. The transceiver includes device detection, frame detection, frequency offset estimation, and channel estimation capabilities. We develop both uplink (hearing aid to the cloud) and downlink (cloud to hearing aid) frame structures based on the data rate and latency requirements. Due to the varying nature of uplink information (audio and lip-reading), the uplink channel supports multiple data rate frame structure, while the downlink channel has a fixed data rate frame structure. In addition, we evaluate the latency of different PHY layer blocks of the transceiver for developed frame structures using LabVIEW NXG. This can be used with software defined radio (such as Universal Software Radio Peripheral) for real-time demonstration scenarios.

This paper considers the grid formation of an unmanned aerial vehicle (UAV) swarm for maximizing the secrecy rate in the presence of an unknown eavesdropper. In particular, the UAV swarm performs coordinated beamforming onto the null space of the legitimate channel to jam the eavesdropper located at an unknown location. By nulling the channel between the legitimate receiver and the UAV swarm, we obtain an optimal trajectory and jamming power allocation for each UAV enabling wideband single ray beamforming to improve the secrecy rate. Results obtained demonstrate the effectiveness of the proposed UAV-aided jamming scheme as well as the optimal number of UAVs in the swarm necessary to observe a saturation effect in the secrecy rate. We also show the optimal radius of the unknown but constrained location of the eavesdropper.

Abstract Hybrid processing in millimeter wave (mmWave) communication has been proposed as a solution to reduce the cost and energy consumption by reducing the number of radio‐frequency (RF) chains. However, the impact of the inevitable residual transceiver hardware impairments (RTHIs), including the residual additive transceiver hardware impairments (RATHIs) and the amplified thermal noise (ATN), has not been sufficiently studied in mmWave hybrid processing. In this work, the hybrid precoder and combiner are designed, which include both digital and analog processing by taking into account the RATHIs and the ATN. In particular, a thorough study is provided to shed light on the degradation of the spectral efficiency (SE) of the practical system. The outcomes show the steady degradation of the performance by the ATN across all SNR values, which becomes increasingly critical for higher values of its variance. Furthermore, it is shown that RATHIs result in degradation of the system only in the high SNR regime. Hence, their impact in mmWave system operating at low SNRs might be negligible. Moreover, an increase concerning the number of streams differentiates the impact between the transmit and receive RATHIs with the latter having a more severe effect.

Caching popular contents in advance is an important technique to achieve low latency and reduced backhaul congestion in future wireless communication systems. In this article, a multi-cell massive multi-input-multi-output system is considered, where locations of base stations are distributed as a Poisson point process. Assuming probabilistic caching, average success probability (ASP) of the system is derived for a known content popularity (CP) profile, which in practice is time-varying and unknown in advance. Further, modeling CP variations across time as a Markov process, reinforcement Q-learning is employed to learn the optimal content placement strategy to optimize the long-term-discounted ASP and average cache refresh rate. In the Q-learning, the number of Q-updates are large and proportional to the number of states and actions. To reduce the space complexity and update requirements towards scalable Q-learning, two novel (linear and non-linear) function approximations-based Q-learning approaches are proposed, where only a constant (4 and 3 respectively) number of variables need updation, irrespective of the number of states and actions. Convergence of these approximation-based approaches are analyzed. Simulations verify that these approaches converge and successfully learn the similar best content placement, which shows the successful applicability and scalability of the proposed approximated Q-learning schemes.

In this work, we study an end-to-end deep learning (DL)based constellation design for decode-and-forward (DF) relay network. Firstly, we study both the one-way (OW) and two-way (TW) relaying by interpreting DF relay networks as stacked autoencoders, under Rayleigh fading channels, leading to a performance improvement of 0.5 dB for TWDF networks. Secondly by introducing redundant bits in transmission and reception, we design end-to-end DL-based framework similar to the differential coded modulation for OWDF and coded modulation for TWDF relay networks, under block fading Rayleigh channels and achieve performance gain of 2 dB and 1 dB over conventional method, without using the channel state information knowledge in OWDF networks.

Large number of redundant requests in wireless networks have led to the hybrid satellite-terrestrial networks, where a satellite is used for content placement at edge caches at the base stations (BSs), thereby reducing backhaul link usage. In this paper, we consider in-network caching where an unavailable content at one BS can be fetched from the nearest BS in the network, before requesting from the content server. Obtaining optimal placement incurs exponentially huge computational overhead. Recent caching solutions are not scalable for large size of content library. Therefore, we propose a low-complexity approach using an action-coded deep deterministic policy gradient (AC-DDPG) algorithm towards optimizing the long-term average network delay. The proposed approach employs continuous valued popularity profiles rather than a fixed finite set in the literature. Simulation results demonstrate the successful application of proposed approach and the improvement over the most-popular content caching method.

In this work, we focus on improving the Direction-of-Arrival (DoA) estimation of multiple targets/sources from a small number of snapshots. Estimation via the sample covariance matrix is known to perform poorly, since the true manifold structure is not revealed for a small number of samples. First, we explicitly model the sample covariance matrix that is used for the DoA estimation as a noisy version of the true one. Next, we employ a stacked denoising autoencoder (DAE) that predicts a statistically "richer" version of the sampled matrix that is subsequently used for the DoA estimation. Moreover, we consider a limited number of sensors (comparable to the number of sources) in a non-uniform linear configuration and introduce an end-to-end hybrid DoA prediction-estimation scheme. Results demonstrate significant improvement compared to the conventional approach.

In this work, we exploit the potential benefits of multi-arm bandit scheme in cooperative multiple-input multiple output (MIMO) wireless networks. In particular, we consider an online-policy for amplify-and-forward MIMO relay selection (RS), where relays are provided with uncertain channel state information (CSI). We design the RS policy as a sequential experience-driven learning algorithm with a contextual bandit (CB) approach, where the algorithm learns to select an optimal relay node using the imperfect CSI provided as a context vector and the past experience of rewards procured with current policy, with the aim of maximizing the cumulative mean reward over time. Further, with extensive simulation result, we demonstrate that proposed CB based RS policy achieves superior performance gains compared to conventional Gram-Schmidt method.

In this work, we study an end-to-end deep learning (DL) based constellation design for the amplify-and-forward (AF) relaying network. Firstly, we study the one-way (OW) and two-way (TW) AF relay networks as an autoencoder by utilizing a single channel to transmit the desired bits, whilst operating under Rayleigh fading channels. As a result of optimal constellation design via end-to-end DL-based framework, we achieve a performance gain of 4 dB and 1.2 dB at 10 dB average signal-to-noise ratio (SNR) over conventional OWAF and TWAF relay networks, respectively. Secondly, by adding redundant bits at the transmitter, we jointly design an end-to-end DL-based coding and modulation scheme for block fading Rayleigh channels. This leads to DL-based coding and modulation, and DL-based differential coding and modulation, similar to the coded modulation and differential coded modulation in conventional networks, depending upon the presence of channel state information knowledge at the receivers. Thus, we propose an end-to-end DL-based data-driven frameworks for differential coded modulation in OWAF and coded modulation in TWAF relay networks. Lastly, we show that at 20 dB average SNR, our proposed methods (DL based differential coded modulated OWAF and DL-based coded modulated TWAF) achieve a gain of 4 dB and 4.8 dB, over conventional OWAF and TWAF relay networks employing Hamming codes with same rates.

This paper investigates the hybrid precoding (HP) design for simultaneous wireless information and power transfer in a multiple-input single-output broadcast channel setup where the terminals adopt the power splitting architecture. The problem of interest is the maximization of the signal-to-interference-plus-noise-ratio and the harvested power for all terminals under a total transmit power constraint. Our focus is on the derivation of frequency- and setup-agnostic low-complexity HP methods. Two baseline approaches for the determination of the analog precoder are considered. In the first one, the phases are computed via the singular value decomposition (SVD) of the channel matrix, while in the second they are selected randomly. Then, the baseband precoder is computed by applying semidefinite relaxation (SDR) to the problem under study. Alternatively, we combine the aforementioned analog precoders with a fixed zero-forcing baseband pre-coder, in order to further reduce the computational load. Another proposed strategy focuses on the minimization of the Euclidean distance between the optimal fully-digital precoder, which is obtained via SDR, and the hybrid one. To this end, an alternating minimization algorithm that employs Gaussian smoothing to convexify the problem and utilizes stochastic gradient descent to update the phases is introduced. The performance of the proposed HP methods is comparatively evaluated versus the one achieved by the optimal fully-digital precoder via numerical simulations. The simulation results indicate that the stochastic optimization approach presents a favorable performance-complexity trade-off as well as substantial power gains.

Caching popular contents in advance is an important technique to achieve low latency and reduce the backhaul costs in future wireless communications. Considering a network with base stations distributed as a Poisson point process, optimal content placement caching probabilities are obtained to maximize the average success probability (ASP) for a known content popularity (CP) profile, which in practice is time-varying and unknown in advance. In this paper, we first propose two online prediction (OP) methods for forecasting CP viz., popularity prediction model (PPM) and Grassmannian prediction model (GPM), where the unconstrained coefficients for linear prediction are obtained by solving constrained non-negative least squares. To reduce the higher computational complexity per online round, two online learning (OL) approaches viz., weighted-follow-the-leader and weighted-follow-the-regularized-leader are proposed, inspired by the OP models. In OP, ASP difference (i.e, the gap between the ASP achieved by prediction and that by known content popularity) is bounded, while in OL, sub-linear MSE regret and linear ASP regret bounds are obtained. With MovieLens dataset, simulations verify that OP methods are better for MSE and ASP difference minimization, while the OL approaches perform well for the minimization of the MSE and ASP regrets.

To meet increasing demands of wireless multimedia communications, caching of important contents in advance is one of the key solutions. Optimal caching depends on content popularity in future which is unknown in advance. In this paper, modeling content popularity as a finite state Markov chain, reinforcement Q-learning is employed to learn optimal content placement strategy in homogeneous Poisson point process (PPP) distributed caching network. Given a set of available placement strategies, simulations show that the presented framework successfully learns and provides the best content placement to maximize the average success probability.

This study investigates the problem of low probability of intercept (LPI)‐based distributed multiple‐input multiple‐output (MIMO) radar waveform design against barrage jamming in signal‐dependent clutter and coloured noise. Given the priori knowledge of the extended target impulse response, signal‐dependent clutter, barrage jamming signals and coloured noise, the LPI‐based scheme for optimal radar waveform design is proposed to minimise the total power consumption of the MIMO radar system by optimising the transmitted waveforms of different transmitters with a predetermined mutual information (MI) constraint for target characterisation performance. Firstly, the MI between the received echoes from the target at each receiver and the target impulse response is derived as a practical metric to characterise the parameter estimation performance of a target. Then, the LPI‐based distributed MIMO radar waveform design strategy is developed. The resulting radar waveform optimisation problem is convex and solved analytically, whose solutions represent the optimum power allocation for each transmitter in the MIMO radar system. With the aid of numerical simulations, it is illustrated that to minimise the total transmission power, the optimal waveform should match with the target, clutter, jamming and coloured noise. In addition, it is also demonstrated that the LPI performance of the MIMO radar system can be significantly improved by employing the proposed radar waveform design scheme.

Non-orthogonal multiple access (NOMA) is one of the key technologies being evaluated for the fifth generation (5G) wireless communications. In this paper, a novel NOMA mechanism combining both the code-domain and power-domain techniques is proposed to potentially support a higher number of users. In particular, considering the code-domain NOMA method of sparse code multiple access (SCMA) as the baseline, new low data rate (LDR) users are added on top of it using power-domain NOMA. The optimization problem involving resource and power allocation in such a system is solved such that the overall achievable sum-rate is maximized. Simulation results indicate that the proposed mechanism not only supports higher number of users, but also demonstrates a higher achievable sum-rate than the original SCMA-based system.

Hybrid beamforming with phase shifters and switches has been identified as a low-cost and energy-efficient approach to harness the benefits of massive multiple-input multiple-output (MIMO) systems. In this paper, three subconnected hybrid beamforming structures with different combinations of phase shifters and switches will be considered. Firstly we assume that perfect channel state information (CSI) is available and the wireless channel follows uncorrelated Rayleigh fading model. Then, we derive the closed-form expressions of the low-complexity beamformers and their asymptotic achievable sum-rates. Based on the proposed beamformers, we develop quantized hybrid beamforming and channel estimation techniques for correlated Rayleigh fading channels. These methods rely on designing novel RF codebooks and they can be used in both CSI acquisition and data transmission phases. The proposed methods benefit from low computational complexity, low signaling overhead and robustness to estimation errors. Moreover, they are applicable to both frequency and time division duplex systems.

We propose a novel transceiver design technique to facilitate flexible spectrum sharing between a multiple-input multiple-output (MIMO) radar and a full-duplex (FD) MIMO cellular system. The optimization problem for maximizing the rate of the cellular system is formulated, subject to the constraints of individual power at the uplink users, total power at the base station, and interference power towards the MIMO radar from the cellular system so that the detection probability of the radar is not hindered. We show that the above problem can be cast as a second-order cone programming problem and the joint design of transceiver matrices can be obtained through an iterative algorithm. Numerical results show that using the spectrum shared by the radar, the FD cellular system can achieve sum rate of up to 25-30 bits/sec/Hz for a reasonable self-interference cancellation of around -70 dB. However, to facilitate this, while also maintaining a detection probability of around 0.9, the radar needs to spend an extra power of around 2-3 dB.

The fifth generation (5G) wireless communication technologies are analyzing non-orthogonal multiple access (NOMA) methods to facilitate the Internet of things (IoT) scenarios hosting a large number of users. In this paper, we consider a scenario where the network operator shares the uplink spectrum between the original long term evolution (LTE) users and new users using a code-domain NOMA technique called sparse code multiple access (SCMA). Since obtaining the optimal solution for resource allocation in such systems is exponentially complex, we propose heuristic algorithms for LTE and SCMA, with the objective of maximizing the overall achievable rate in the network. The simulation results indicate that the performance of the proposed algorithms is close to 90% of that of the optimal solution.

Here, the problem of non‐cooperative game‐theoretic distributed power control is studied in a radar network system based on low probability of intercept (LPI) subject to the signal‐to‐interference‐plus‐noise ratio (SINR) constraint and the transmit power constraint of each radar, where all the radars in the network share the same frequency band. The objective is to improve the LPI performance by reducing the transmit power caused by some radars' SINRs over the specified threshold. First, a novel LPI performance‐oriented utility function is defined as a metric to evaluate power control. Then, consider that radars in the network are self‐interested to maximise their own utilities, the distributed power control problem is formulated as a non‐cooperative game, and an iterative power control algorithm is proposed that converges quickly to the Nash equilibrium (NE) of the non‐cooperative game. Finally, the existence and uniqueness of NE are proved analytically. Numerical simulation results are provided to demonstrate that, compared with other methods, the presented algorithm not only guarantees the minimum SINR requirements of all radars but also improves the LPI performance for radar network.

In this paper, we investigate the energy harvesting (EH) technique and accordingly design transceivers for a K link multiple-input multiple-output interference channel. Each link consists of two full-duplex (FD) Internet of Things (IoT) nodes exchanging information simultaneously in a bi-directional communication channel. All the nodes suffer from interference, in particular strong self-interference and inter-node interference, due to operating in FD mode and simultaneous transmission at each link, respectively. Further, we divide the received signal at each node into two parts. While one part of the signal is used for information decoding, the other part is used for EH. We jointly design the transmit and receive beamforming vectors and receiver power splitting ratios by minimizing the total transmission power of the system, subject to both signal-to-interference-plus-noise ratio and EH threshold constraints. Furthermore, the case of multiple-input single-output interference channel is also included for the sake of comparison. We also revisit the above problems for the case when the available channel state information (CSI) at the transmitters is imperfect, where the errors of the CSI are assumed to be norm bounded. Simulation results show that the EH technique can harvest enough energy to support power consumption limited IoT devices by aiding in recharging their respective batteries.

The recent concept of beamspace multiple-input-multiple-output (MIMO) enables the millimeter-wave (mmWave) MIMO system to reduce the radio-frequency chains by utilising beam selection and yet achieve near-optimal sum-rate performance. In this work, we study the lens antenna array as the transmit antenna at the base station for mmWave multiuser MIMO (MU-MIMO) system. Due to the direction-based energy focusing property of the array, the beam selection is able to reduce to antenna selection without significant performance degradation as that using traditional antenna selection. For this mmWave MU-MIMO system, direction-based antenna selection method is proposed, where the inter-user interference, resulting from the case that the same antenna is selected for different users with high probability, is considered and solved by a re-selection based on minimization of sum-rate loss. Simulation results show that spectral and energy effectiveness of the proposed antenna selection method for the mmWave MU-MIMO using lens antenna array.

In this paper, we analyse the performance of a millimeter wave (mmWave) cloud radio access network (CRAN), where remote radio heads (RRHs) are modelled as a homogeneous Poisson point process (PPP) and blockages are randomly distributed. In contrast to the previous works on CRAN that operate below 6GHz, we consider CRAN operating in mmWave range (30-300 GHz). Since blockages have a significant impact on mmWave communications, we adopt a distance-dependent line-of-sight (LOS) probability function and model the locations of the LOS and non-line-of-sight (NLOS) RRHs as two independent non-homogeneous PPP. The outage performance and ergodic capacity of the LOS and NLOS RRHs are analysed and compared. When the RRH with the best channel is selected for transmission, the expressions of outage probability and throughput are provided. The presented results show that due to the severe path loss in NLOS links, in low transmitted power regime, the best RRH (BR) is always LOS. However, in high transmitted power regime, NLOS RRHs can be BR.

This paper presents a novel approach of direction-of-arrival (DoA) estimation for the electronically steerable parasitic array radiator (ESPAR) antennas, using only a single radio-frequency (RF) chain. Starting from the problem formulation in the Bayesian compressive sensing (BCS) framework, the CS measurements are projected onto the beamspace of the unique configuration of the ESPAR antenna. In this work, measurements collected at multiple snapshots are considered. First, we propose to solve the sparse recovery problem by the multi-task BCS [1]. Then, the DoAs are estimated by employing a noise filter on the recovered sparse signal. In this method, the number of sources need not be known a priori, and computation complexity is reduced by avoiding computing the correlation matrix of measurements unlike the traditional DoA estimation techniques. Simulations show that the proposed method can recover closely spaced sources using a small number of noisy snapshots, and it performs better with more sources than other state-of-the-art algorithms.

The secrecy outage of millimeter wave (mmWave) networks under the impact of blockages is derived. Specifically, using a network model that accounts for uncertainties both in node locations and blockages, we characterize the connection outage probability and the secrecy outage probability of mmWave networks with multiple eavesdroppers under basic factors such as density of eavesdropping nodes, antenna gain and blockage density. As a desirable side effect, certain factors such as blockages and reduced antenna gain can decrease the secrecy outage probability. This however is in contrast to general mmWave systems where it has been shown that reduced blockages and high antenna gains provide higher capacities.

In this paper three-dimensional channel estimation for passive radar using Orthogonal Frequency Division Multi-plexing(OFDM) waveforms is proposed. The passive radar has gained plenty of interests for the covert operations while the detection of the target signal is one of its issues. In modern communication systems, Single Frequency Network (SFN) has been used. This leads to the difficulty to determine the sources of the incoming signals providing that one frequency is transmitted. Instead of employing the MIMO radar with widely separated antennas, this paper uses the MIMO receiver with co-located antennas to extend two-dimensional OFDM signal in the angular domain. The channel estimates consisting of time delay, Doppler frequency and angle of arrivals are derived. Compressive sensing is applied to reduce the number of measurements of the observation matrix. The li-SVD, which is the method employing multiple time samples, has been used to reconstruct the sparse signal in comparison with a single time sample basis pursuit. The simulations show that the proposed method performs well in terms of detecting and extracting the target parameters.

The secrecy characteristics of random wireless networks considering a generalized fading model, where the communication nodes are equipped with multiple antennas is analyzed. Using stochastic geometric tools, we investigate the secrecy outage probability of such networks from the perspective of the kth best source, which has still not been well characterized in the literature. In particular, we derive the received path gain distributions of the typical destination and the eavesdropper from the k-th best source. Using these results, we derive the secrecy outage probability for two scenarios with respect to the received path gain from any best source and the k-th best source. Furthermore, we introduce a novel concept of security-region based on the k-th best source index. This is pragmatic in creating a protected communication zone for the typical destination. Tractable numerical and simulation results are presented under various assumptions of fading, path loss exponents and antenna figures.

Recently, the new concept of Licensed Shared Access/Authorized Shared Access (LSA/ASA) has emerged as a feasible commercial version of dynamic spectrum reuse based on Cognitive Radio (CR) technologies, e.g., via spectrum sensing or by exploiting geo-location information. This paper considers the problem of effective spectrum sharing between a colocated Multiple-Input-Multiple-Output (MIMO) radar that monitors the existence of a target and a wireless communications system. More specifically, the investigated scenario considers the downlink of a communications system represented by a Base Station (BS) trying to reuse the spectrum allocated for a colocated MIMO radar in order to communicate with an assigned terminal, in the vicinity of the radar system. We present an accurate model for the operation of the wireless system in the downlink, while the MIMO radar tries to maintain an acceptable detectability level of a target in the far field. The target detection problem is reformulated using a sensing approach based on energy detection, while the BS applies beamforming to null the interference created at the radar receiver. Based on the theory of Hermitian quadratic forms and with the aid of the Linearly Constrained Minimum Variance (LCMV) beamforming solution, the performance of target detection, when the MIMO radar coexists with the data transmission is quantified and numerical results show that spectral coexistence is feasible.

Nowadays, many applications require simultaneous computation of multiple independent fast Fourier transform (FFT) operations with their outputs in natural order. Therefore, this brief presents a novel pipelined FFT processor for the FFT computation of two independent data streams. The proposed architecture is based on the multipath delay commutator FFT architecture. It has an N/2-point decimation in time FFT and an N/2-point decimation in frequency FFT to process the odd and even samples of two data streams separately. The main feature of the architecture is that the bit reversal operation is performed by the architecture itself, so the outputs are generated in normal order without any dedicated bit reversal circuit. The bit reversal operation is performed by the shift registers in the FFT architecture by interleaving the data. Therefore, the proposed architecture requires a lower number of registers and has high throughput.

This paper considers a frequency domain approach for the problem of spectrum sensing based on the multitaper detector combined with singular value decomposition. We present the exact closed forms for the performance of the MTM-SVD processor over fading channels. This is achieved through formulating the decision statistic as a Hermitian form, which is shown to be a hypoexponential variable where the distribution parameters are determined by the number of discrete prolate spheriodal sequences (DPSSs), number of cooperating users and frequency resolution. Also, we formulate the nonzero eigenvalues associated with the Hermitian form, which act as the distribution parameters for both cases of the null and the alternate hypotheses. We accurately formulate the probabilities of false alarm and missed detection, where we also accurately bound the average probability of missed detection over Rayleigh channels. As a general trend, the performance of the MTM-SVD method is enhanced when increasing the number of DPSSs or increasing the number of cooperating sensing nodes. Moreover, the obtained results show that the MTM-SVD processor outperforms the case of using the Periodogram even when large sensing frames are employed.

The femtocell networks provide a promising solution to increase system capacity and improve indoor coverage. However, inter-cell interference becomes enormous in femtocells due to dense deployment and cell-size reduction. This paper proposes the interference mitigation method for the downlink of femtocells, using the electronically steerable parasitic array radiator (ESPAR) antenna at both femto-base stations (FBSs) and user terminals (UTs), which relies on a single radio-frequency (RF) chain; thus meets the demanding low-power, low-cost and small-size requirements of modern wireless terminals - FBSs and UTs. We first exploit the ESPAR antenna as a switched-beam array capable of predefining directional beampatterns accessing to different angular sectors. Then each FBS/UT dynamically selects an appropriate beampattern according to its measurement of desired direction. We also consider an optimal way to employ the ESPAR antenna by adaptively designing the beampattern steering to desired direction while placing nulls to interferers. The results show significant gains obtained by using ESPPAR antennas at both FBSs and UTs.

This paper investigates the optimization of the generalized likelihood ratio test (GLRT) eigenvalue-based spectrum sensing detector in terms of decision thresholds and sensing time. In order to guarantee the interests of primary and secondary users simultaneously, the sensing performance is assessed using the total error rate, i.e., the summation of probabilities of false alarm and missed detection. Therefore, the generalized statistical distributions of the test statistic are derived under the absence and presence of primary users, assuming an arbitrary number of receive antennas. These distributions are necessary for the analyses of the total error rate performance and the optimization. The optimization consists of two parts. Firstly, the optimal decision threshold is numerically obtained, which can minimize the total error rate under the constraints of target probabilities of false alarm and missed detection. Secondly, the optimal sensing time is obtained when a target total error rate is guaranteed, so that the spectrum sensing process can be accelerated without the loss of sensing accuracy. Furthermore, the simulation and theoretical results reveal that the chosen optimal decision thresholds benefit the primary and secondary users simultaneously and the chosen optimal sensing time improves the speed of spectrum sensing.

We present the statistical analysis of the output from three timeseries generators of the dual polarised multiple input, multiple output (MIMO) land mobile satellite (LMS). The Liolis- CTTC model [1] is the baseline for statistical modelling for the dual polarised LMS channel, leading to the development of the Enhanced Statistical Model [2] which addresses a number of issues including eliminating the unwanted high frequency components associated with low-pass filtering to impose temporal correlation on the slow variations, the introduction of Doppler effects including Doppler shaping of the fast variations, the implementation of a smooth state transition process and also the introduction of an interpolation process to sample the channel at the required sub-symbol rate for transmission. The QuaDRiGa model [3] is a geometric, ray-tracing model and the product of the ESA's extensive measurement campaign of the dual polarised LMS channel. In this paper, the timeseries of each of the three models are compared and particular focus is given to the the statistical properties of each model's output.

The adaptive beamforming algorithm-minimum variance distortionless response (MVDR) has been studied based on the electronically steerable parasitic array radiator (ESPAR) antenna. The ESPAR antenna uses a single radio-frequency (RF) front end, and its beamforming is achieved by adjusting reactance loads of parasitic elements coupled to the central active element. In the proposed beamforming method, the MVDR beamformer optimizes weights applied to outputs of beams. The optimization problem is formulated as a second-order-cone programming (SOCP) problem including a Euclidean distance metric to approximate the optimal equivalent weight vector to a feasible solution. Then the ESPAR beampattern design strategy iterates between the SOCP problem and a simple projection of reactance loads. The simulations show that the proposed MVDR beamforming method based on an ESPAR antenna gives a beam steering at the desired direction and placing nulls at the interfering directions, and it converges fast. However, when the desired source is close to the interferer, the output signal-to-interference-plus-noise ratio (SINR) degrades and where we use the interference-plus-noise sample covariance matrix to improve the beamforming performance.

In this paper, the multi-way relaying scenario is considered with M users who want to exchange their information with each other with the help of N relays (N ≫ M) among them. There are no direct transmission channels between any two users. Particularly, all users transmit their signals to all relays in the first time slot and M - 1 relays are selected later to broadcast their mixture signals during the following M - 1 time slots to all users. Compared to the transmission with the help of single relay, the multi-way relaying scenario reduces the transmit time significantly from 2M to M time slots. Random and semiorthogonal relays selections are applied. Rician fading channels are considered between the users and relays, and analytical expressions for the outage probability and ergodic sum rate for the proposed relaying protocol are developed by first characterizing the statistical property of the effective channel gain based on random relays selection. Also, the approximation of ergodic sum rate at high signal-to-noise ratio regime is derived. In addition, the diversity order of the system is investigated for both random and semiorthogonal relay selections. Meanwhile, it is shown that when the relays are randomly separated into L groups of M - 1 relays, the group with maximum average channel gain can achieve the diversity order L, which will increase when more relays considered in the scheme. Furthermore, when semiorthogonal selection (SS) algorithm is applied to select the relays with semiorthogonal channels, it is shown that the system will guarantee that all the users can decode the others information successfully. Moreover, the maximum of channel gain after semiorthogonal relays selection is investigated by using extreme value theory, and tight lower and upper bounds are derived. Simulation results demonstrate that the derived expressions are accurate.

A selective vector perturbation technique is introduced for low-power Small Cell downlink. In contrast to conventional vector perturbation (VP) where the search for perturbation vectors involves all users' symbols, here the perturbation is applied to a subset of the transmitted symbols. This therefore introduces a performance-complexity tradeoff, where the complexity is greatly reduced compared to VP by limiting the dimensions of the sphere search, at the expense of a performance penalty compared to VP. By changing the size of the subset of perturbed users, the above tradeoff can be controlled. We further propose three distinct criteria for selecting which users' symbols to perturb, each of which yields a different performance-complexity tradeoff. The presented analytical and simulation results show that the proposed is most useful in the low-power small cell scenarios where power efficiency levels improved by up to 300% compared to VP are demonstrated.

A vector perturbation (VP) scheme is proposed for the downlink of multi-user multiple input multiple output (MU-MIMO) systems with limited feedback. Instead of a computationally expensive sphere search used in conventional VP, the proposed scheme uses a MinMax Optimization to select the perturbation quantities. In addition, the proposed VP circumvents the need for receive-scaling by constraining the search of perturbing vectors to the area in the symbol constellation which is constrictive to the information symbols, i.e. the area where the distances from the decision thresholds are increased with respect to a distance threshold. Consequently, while conventional VP requires the feed-forwarding of the scaling factor to the receiver for correct detection, the proposed scheme does not require the scaling of the received symbols. This advantage is particularly pronounced in limited feedback scenarios where the scaling factors forwarded to the receiver are prone to quantization errors. As illustrated by the results, the error floor encountered in conventional VP in limited feedback scenarios is avoided in the proposed scheme.

This paper investigates optimization of the sensing threshold that minimizes the total error rate (i.e., the sum of the probabilities of false alarm and missed detection) of eigenvalue-based spectrum sensing techniques for multiple-antenna cognitive radio networks. Four techniques are investigated, which are maximum eigenvalue detection (MED), maximum minimum eigenvalue (MME) detection, energy with minimum eigenvalue (EME) detection, and the generalized likelihood ratio test (GLRT) detection. The contribution of this paper is of four parts. Firstly, we present the derivative of the matrix-variate confluent hypergeometric function, which is required for the MED case. Secondly, we derive the probabilities of false alarm for both cases MME and EME detection. Thirdly, we derive the probability of missed detection for the GLRT detector. Finally, we provide the exact expressions required to obtain the optimal sensing thresholds for all cases. The simulation results reveal that for all the investigated cases the chosen optimal sensing thresholds achieve the minimum total error rate.

With the knowledge of both channel and data information at the base station prior to downlink transmission, we can increase the received signal-to-noise ratio (SNR) of each user without the need to increase the transmitted power. Achievability is based on the idea of phase alignment (PA) precoding where instead of removing the destructive interference, it judiciously rotates the phases of the transmitted symbols. In this way, for each user, the received interference from the other users add up coherently, and consequently we can glean higher received SNRs at all mobile terminals. In addition, it is well-known that the regularized channel inversion (RCI) improves the performance of channel inversion (CI). In line with this and similar to the RCI precoding, in this paper we propose the idea of regularized PA (RPA) which is shown to improve the performance of original PA precoding. To do so, we first rectify the original PA precoding by deriving a closed-form expression of its scaling factor. We then use this new analysis to select an appropriate regularization factor for the proposed RPA scheme. Finally, we drive an explicit formula regarding its received SNR. Since the focus of this work is on linear precoders, we show that the proposed RPA precoding outperforms CI, RCI, and PA precoders from both symbol-error rate (SER) and sum rate perspectives. We also consider the performance of RPA under imperfect channel state information at transmit side. We show that even in this case, RPA precoding is as sensitive as other linear precoders to channel imperfections.

Interference alignment (IA) is a transmission technique for exploiting all available degrees of freedom (DoF) in the frequency- or time-selective interference channel with an arbitrary number of users, which can achieve linear capacity scaling with number of users. However, standard IA approach has limit performance in the low-to-moderate signal-to-noise ratio (SNR) region. Recently, linear combination designs have been proposed to improve performance of sum rate at finite SNR which jointly considers the interference and the signal optimization simultaneously, but is sensitive to the weight selection. Therefore, it motivates us to propose a normal-boundary intersection (NBI)-based design that jointly optimizes the rank of interference and its power with evenly spread parameters. The underlying problem is transferred into limited subproblem that can be efficiently solved. Simulation results validate the effectiveness of the proposed algorithm that achieves the Pareto optimality and provides evenly-distributed Pareto frontiers.

We propose an energy-efficient vector perturbation (VP) technique for the downlink of multiuser multiple-input-single-output (MU-MISO) systems. In contrast to conventional VP where the search for perturbation vectors involves all users' symbols, here, the perturbation is applied to a subset of the transmitted symbols. This, therefore, introduces a performance-complexity tradeoff, where the complexity is greatly reduced compared to VP by limiting the dimensions of the sphere search, at the expense of a performance penalty compared to VP. By changing the size of the subset of perturbed users, the aforementioned tradeoff can be controlled to maximize energy efficiency. We further propose three distinct criteria for selecting which users' symbols to perturb, each of which yields a different performance-complexity tradeoff. The presented analytical and simulation results show that partially perturbing the data provides a favorable tradeoff, particularly at low-power transmission where the power consumption associated with the signal processing becomes dominant. In fact, it is shown that diversity close to the one for conventional VP can be achieved at energy efficiency levels improved by up to 300% compared to VP.

This paper presents an analytical investigation on the performance of Rayleigh-product MIMO channels with linear minimum mean-square-error (MMSE) or zero-forcing (ZF) receivers. For MMSE receivers, exact closed-form expressions for the ergodic sum-rate of the system are derived. In addition, simplified expressions are obtained for the key parameters dictating the sum-rate performance of the system in the high signal-to-noise ratio (SNR) regime (i.e., high SNR slope and power offset) and low SNR regime (i.e., minimum energy per information bit required to convey any positive rate and the wideband slope). While for ZF receivers, tight closed-form upper and lower bounds for the ergodic sum-rate of the system are derived. It is analytically proven that the ZF and MMSE receivers achieve the same sum rate performance in the high SNR regime. Moreover, for both MMSE and ZF receivers, the achievable diversity-multiplexing tradeoff (DMT) of Rayleigh-product MIMO channels is characterized. The findings suggest that a larger number of scatterers will improve the the performance of Rayleigh-product MIMO channels with linear receivers, and the ZF receivers achieve the same performance as the MMSE receivers in Rayleigh-product MIMO channels in the high SNR regime. Moreover, it is demonstrated that as long as the number of the scatterers is greater than the number of receive antennas, linear receivers achieve the optimal DMT.

Vector precoding (VP) requires the feed-forwarding of the transmit scaling factor to the receiver for correct detection. This can be problematic in fast fading scenarios where the statistics of the scaling factor change frequently and in limited feedback scenarios where the feed-forwarding to the receiver is prone to quantization errors. In response to this, a new VP scheme is proposed for the downlink of multi-user multiple input multiple output (MU-MIMO) systems with limited feedback. The proposed VP circumvents the need for receive-scaling by constraining the search of perturbing vectors to the area in the symbol constellation which is constrictive to the information symbols, i.e. the area where the distances from the decision thresholds are increased with respect to a distance threshold. By doing this, the perturbation quantities need not be removed at the receiver and successful detection can be done without the use of the modulo operation and the scaling factor. In addition, instead of a computationally expensive sphere search used in conventional VP, the proposed scheme uses a MinMax Optimization to select the perturbation quantities. As illustrated by the analysis and results, the error floor encountered in conventional VP in limited feedback scenarios is avoided in the proposed scheme.

We propose a low-complexity vector precoding (VP) scheme for the downlink of multi-user multiple input multiple output (MU-MIMO) systems. Instead of performing a full sphere search to maximize the receive signal to noise ratio (SNR), the search for the perturbation vectors finishes once a threshold SNR value is reached, thus saving significant computational burden at the transmitter. This threshold is determined by the quality of service (QoS) requirements of the mobile users. To evaluate the advantages of the proposed technique compared to VP, we analytically calculate its computational complexity in terms of the volume of the associated search space. The results show that the proposed thresholded VP (TVP) offers a significantly reduced complexity compared to VP.

It has been conjectured by Høst-Madsen and Nosratinia that the K-user single-input single-output (SISO) complex Gaussian interference channels with constant channel coefficients have merely one degree of freedom (DoF) regardless of the number of users, i.e., K. Then, Cadambe and Jafar introduced the idea of interference alignment (IA) being able to achieve K/2 DoF in time-varying SISO interference channels. Moreover, their joint work with Wang settled the Høst-Madsen-Nosratinia conjecture in negative by using the idea of asymmetric complex signaling to achieve 1.2 DoF for K-user constant SISO interference channels. In this paper, a linear IA scheme for K-user constant SISO interference channels is proposed which could enable us to achieve K/4 DoF for almost all channel coefficients. This means that whenever K ≥ 5, the proposed scheme could achieve at least 1.25 DoF. The main idea of the proposed method relies on the linear IA using symbol extension by Cadambe-Jafar which is not effective for constant channels. However, we show that along with signal rotation across every two consecutive time slots to artificially build a random time-varying channel out of a constant channel, the proposed method can be directly applied to constant channels to achieve K/4 DoF.

This paper considers a K-user multiple-input multiple-output (MIMO) interference channel in which uncoordinated interference appears. Due to the uncoordinated interference, perfect interference alignment (IA) may be not attained, which indicates the interference subspaces can not be completely aligned. The rank constrained rank minimization (RCRM) framework has been recently developed to minimize the rank of the subspace spanned by interference signals with full rank constraint on the direct signal space. To solve this non-convex and intractable problem, we introduce a log-sum function as an approximation surrogate and develop a joint Frobenius norm and reweighted nuclear norm approach which jointly enhances the sum rate at low-to-moderate signal-to-noise ratio (SNR) and the achievable multiplexing gain per user in the high SNR regime. The optimum solutions are iteratively achieved with the convergence guaranteed. Simulation results are presented to validate the effectiveness of the proposed reweighted nuclear norm algorithm and its further development.

Recently, a technique entitled “Blind Interference Alignment (BIA)” was proposed, which allows interference alignment to be achieved without the knowledge of channel state information at the transmitter. The key to realizing the BIA scheme is the use of a receive antenna capable of switching among multiple beampatterns. The ESPAR antenna, which uses only a single RF front-end, is capable of forming different directional beampatterns by the use of circular permutations of the reactive loads of the passive elements. We introduce the ESPAR antenna as a potential solution for the practical implementation of the BIA for broadcast channel as well as cellular. The BIA scheme is accomplished by the transmission strategy jointly coordinating with the ESPAR beampattern switching symbol-wise. The ESPAR beampattern switching provides the necessary channel diversity for receiving users. Simulation results demonstrate that the ESPAR beam steering can be designed to improve the performance of the BIA scheme by enhancing the receive signal-to-noise ratio (SNR). Furthermore, we study the proposed BIA scheme for a simple 1-dimensional cellular setting to illustrate that the ESPAR beamforming can improve the performance of the cell-edge users through further suppressing the remaining inter-cell interference.

Recently, a technique entitled “Blind Interference Alignment (BIA)” was proposed, which allows interference to be aligned within a reduced subspace without knowledge of the channel state information at the transmitter. The key to realizing the BIA scheme is the use of a receive antenna that is capable of switching among multiple channel-states (with channel diversity) in a pre-determined way. In this paper, we exploit the ESPAR antenna, which uses only a single radio frequency chain as the receiver to provide the necessary beampattern switching for the BIA technique. Moreover, in the proposed ESPAR based BIA scheme, beam-steering of each ESPAR receiver is designed according to its position relative to the transmitter to increase the received SNR. Furthermore, we study the proposed BIA scheme by applying the ESPAR antennas to a 2-D cellular setting in order to illustrate the ESPAR beam pattern design and associated improvements in the performance of the BIA scheme. In order to keep the number of coordinating BSs at a reasonable level, we study the case where only three adjacent BSs operate the BIA scheme coordinatively. This is found to be reasonable since the other inter-cell interference is not significant due to the ESPAR beam-steering and the longer propagation distance. The simulation shows that our scheme provides comparable sum rate to that of BIA scheme operated in all cells.

Spectrum sensing for cognitive radio is studied, using an electronically steerable parasitic antenna receptor (ESPAR), which relies on a single RF front end, and therefore meets the demanding low cost, power and size requirements of modern wireless terminals. We develop a strategy whereby the angular domain is divided into sectors, that are accessed via beamforming on a time division basis, to detect signals from primary users. We study the performance of detection metrics based on energy received per beam, and also eigenvalue-based detection statistics through considering the covariance matrix across the various directional beams. The ESPAR is able to achieve over 6dBi SNR improvement due to its beamforming capability. Further, once primary users' signals have been detected, directional transmit opportunities which do not interfere with active PUs become available, and we develop an adaptive beamforming algorithm to capitalise on these to efficiently utilise the spatial domain, which numerically optimises the beampattern and antenna efficiency using a convex formulation. The resulting beampatterns give between -20dB and -30dB nulls in the primary user direction.

Managing uncoordinated interference becomes a substantial problem for heterogeneous networks, since the unplanned interferences from the femtos cannot be coordinately aligned with that from the macro/pico base stations (BSs). Due to the uncoordinated interference, perfect interference alignment (IA) may be not attained. In order to achieve linear capacity scaling by IA, we follow the rank-constrained rank minimization (RCRM) framework which minimizes the rank of the interference subspace with full rank constraint on the direct signal space. Considering that the sum of log function can obtain low-rank solutions to linear matrix inequality (LMI) problems for positive semidefinite matrices, we introduce sum of log function as an approximation surrogate of the rank function. To minimize the concave function, we implement a Majorization-Minimization (MM) algorithm and develop a reweighted nuclear norm minimization algorithm with a weight matrix introduced. Moreover, considering the practical available signal-to-noise ratio (SNR), a mixed approach is developed to further improve the achievable sum rate in low-to-moderate SNR region. Simulation results show that the proposed algorithm considerably improves the sum rate performance and achieves the highest multiplexing gain than the recently developed IA approaches for various interference channels.

We propose a low-complexity vector perturbation (VP) precoding scheme for the downlink of multi-user multiple input multiple output (MU-MIMO) systems. While conventional VP performs a computationally intensive sphere search through multiple candidate perturbation vectors to minimize the norm of the precoded signal, the proposed precoder applies a threshold to the desired norm to reduce the number of search nodes visited by the sphere encoder. This threshold is determined by the performance requirements of the mobile users. Once the threshold is met, the search for the perturbation vectors finishes thus saving significant computational burden at the transmitter. To evaluate the advantages of the proposed technique compared to VP, we further derive the computational complexity in terms of the volume of the associated search space and the resulting numerical operations. In addition, we use a new performance-complexity metric to study the relevant tradeoff and look at the power efficiency of the system, both of which metrics can be used to optimize the user-determined threshold accordingly\color{black}. The presented analysis and results show that the proposed thresholded VP (TVP) offers a favorable tradeoff between performance and complexity where significant complexity reduction is attained while the user threshold performance is guaranteed.

We explore the performance of multiple input multiple output (MIMO) transmitters in correlated channels where increasing numbers of antenna elements are fitted in a fixed physical space. As well investigated in the literature, two main effects emerge in such a design: transmit spatial correlation and mutual antenna coupling. In contrast to the literature however, here we investigate the combined effect of reducing the distance between the antenna elements with increasing the number of elements in a fixed transmitter space. In other words, towards the implementation of large-scale MIMO transmitters in limited physical spaces, we investigate the joint effect of two contradicting phenomena: the reduction of spatial diversity due to reducing the separation between antennas and the increase in transmit diversity by increasing the number of elements. Within this context, we analytically approximate the performance of two distinct linear precoding designs. The theoretical analysis and simulations show the somewhat surprising result that for a given number of receivers the improved transmit diversity dominates the performance of practical linear precoders. Consequently, important benefits in the system sum rate can be gleaned by fitting more antenna elements in a fixed space by employing separations smaller than the wavelength of the transmit frequency.

This paper investigates the multiuser diversity introduced by opportunistic user selection in cognitive radio (CR) networks, where multiple cognitive users request to access the spectral resources of the licensed (primary) user. We investigate a simple cognitive user selection strategy aiming at maximizing the received signal-to-interference-plus-noise ratio (SINR) for a given power budget, under interference constraints to the primary. We study the statistics of the SINR at the cognitive receiver, and derive exact analytical expressions of its probability density function (PDF). We then analytically calculate the diversity gains introduced in the system due to the selection of one cognitive user amongst multiple candidates compared to the case when only one cognitive user exists and no selection occurs. Furthermore, we utilize the PDF of the SINR to predict the bit error rate (BER) of the selected cognitive user. Finally, the asymptotic behavior of the diversity gains for the low transmit power region of the primary and cognitive links, and as the number of candidate links becomes large is also investigated. All three multiaccess scenarios are investigated, namely multiple access channel (MAC), broadcast channel (BC) and parallel access channel (PAC), and the results show that the analytically derived expressions closely match simulated performance.

In this paper, we propose a class of full diversity rate one space-time block codes (STBC) satisfying the generalized orthogonal constraint (GOC). First an explicit construction of completely decoupled STBC is proposed to obtain a rate one STBC with linear decoding complexity for any number of transmit antennas. Then we propose an adaptation strategy for the codes to achieve full diversity by utilizing partial phase information of the channel obtained via a feedback link. With a few feedback bits, the proposed rate one code has full diversity while reserving the same decoding complexity as Orthogonal STBCs. Moreover, the full diversity can be still achieved even if the simple zero-forced decoding is used at the receiver.

In this paper, an improved multiple input multiple output Tomlinson-Harashima precoder (MIMO-THP) is introduced, where the transmit power loss is reduced based on optimizing the interference to be canceled. The concept behind the proposed technique builds on the fact that both the desired and interfering signals originate from the base station (BS) of the downlink system itself. Based on this observation, the proposed method influences the resulting interference, to reduce the transmission power required to cancel it, without altering the information content of the downlink message. The aim is to bring the interference closer to the replicas of the desired symbols for all users in the THP modulo-extended constellation. In this way, the power required to pre-subtract interference is decreased. Theoretical and simulation results both confirm that, by optimizing the interference to be canceled, the proposed technique offers a considerable transmit power reduction compared to conventional THP while securing an equal error rate performance.

The essential tradeoff between sensing capability and achievable throughput of the secondary network is one of the active research topics for researchers working on cognitive radio. In this paper, noise uncertainty which has a great impact on sensing methods is taken into account in the maximization of throughput using eigenvalue based spectrum sensing schemes. This issue has not been tackled in the throughput associated studies before. First, the theoretical and empirical distributions of the decision statistics and the detection performances for eigenvalue based sensing techniques are studied in the presence of noise uncertainty. The computed detection probabilities of maximum-minimum eigenvalue (MME) detector and maximum eigenvalue detector (MED) are compared with the most widely used energy detector (ED). Then, in the light of the obtained results, the throughput of the secondary network is maximized in order to find out the sensing duration for each scheme using multiple receive antennas. It is shown that, under low signal to noise ratio (SNR) regime, the designed sensing slot duration achieves the best sensing throughput tradeoff.

This work focuses on the admission of new (secondary) users in a network where existing (primary) users are interference aligned. One of the challenges in this scenario is to achieve the promised degrees of freedom for the secondary users with limited available dimensions. We design a secondary admission network to achieve interference alignment as in equivalent general peer-peer networks, even when the secondary nodes are configured with limited antenna/spatial dimensions, along with a very strict zero-interference constraint from the primary network. To provide enough dimensions for secondary users to achieve interference alignment, we utilize three effective methods: a) time extension structure; b) adaptivity to network partial connectivity; c) partial interference alignment. The presented analysis shows how the degrees of freedom (DoF) can be achieved for the secondary network in both the constant and time-variant channels.

This paper investigates the multiuser diversity introduced by opportunistic user selection in the cognitive radio parallel access channel (CR-PAC), where multiple cognitive users request to access the spectral resources of the licensed user. Assuming a simple cognitive user selection strategy based on maximizing the received signal-to-interference-plus- noise ratio (SINR), we study the statistics of the SINR at the cognitive receiver. We then use this to analytically calculate the diversity gains introduced in the system due to the selection of one cognitive user amongst multiple candidates, compared to the case when only one cognitive user exists in the network and no selection occurs. Finally, we investigate potential gains for the primary network from this user selection. The results show a close match between analytical expressions and simulation results, while a tight lower bound for the multiuser gain is derived in closed form.

In this paper, we study the eigenvalue-based spectrum sensing techniques for multiple-antenna cognitive radio networks. First, we study the extreme eigenvalue distributions of a complex Wishart matrix and then, in contrast to the asymptotic analysis reported in the literature, we derive the exact distribution of the test statistics of (i) maximum eigenvalue detector (MED) (ii) maximum-minimum eigenvalue (MME) detector and (iii) energy with minimum eigenvalue (EME) detector for finite number of samples (n) and finite number of antennas (m). These distributions are represented by complex hypergeometric functions of matrix argument, which can be expressed in terms of complex zonal polynomials. We also describe the method to compute these complex hypergeometric functions. Based on these exact distribution of the test statistics we find the exact decision thresholds as a function of the desired probability of false-alarms for MED, MME and EME. Simulation results show superior performance compared to the decision thresholds obtained from asymptotic (i.e, n,m → ∞) distributions.

This paper presents an analytical investigation on the performance of optimal beamforming scheme in multiple-input multiple-output dual-hop amplify-and-forward systems. We first derive a set of new closed-form expressions for the statistical properties of the resultant channel matrix, including the cumulative distribution function and probability density function of the maximum eigenvalue, as well as their first order asymptotic expansions. These analytical expressions are then applied to derive some important performance metrics of the system, i.e., outage probability, diversity order, array gain and ergodic capacity. Numerical simulations are carried out to validate the correctness of the analytical results.

This paper presents a method to significantly reduce the preprocessing complexity of the sphere decoder (SD) in frequency-selective channels. The method consists of calculating an approximate QR decomposition (AQRD) of the channel matrix, making use of its special Toeptliz and block-Topelitz structure in single and multiple-antenna frequency-selective channels, respectively. The AQRD obtains the QR decomposition of a small submatrix of the channel matrix and extends that result to the rest of the matrix, resulting in a considerable complexity reduction compared to the original full QR decomposition (FQRD). Simulation results show that, despite the lower complexity of the AQRD, it causes only a small bit error rate (BER) performance degradation in the SD.

In this paper, we consider a cognitive relay assisted multiple-input multiple-output (MIMO) communication scenario through Rayleigh fading channel where multiple primary and secondary users wish to communicate with two different receivers in the presence of a cognitive relay. We assume that secondary users transmit simultaneously with the primary users over the same channel instead of waiting for an idle channel which is traditional for a cognitive radio. We consider both the cases when cognitive relay is present or not and derive the expressions for the mutual information received at the primary and secondary receivers. We also present the closed form expressions for the outage probabilities and complementary cumulative distribution functions (CCDFs) of mutual information received at the primary and secondary receivers in the absence of cognitive relay. Our results show that with the help of a cognitive relay, not only both the primary and secondary users are able to communicate with their intended receivers compensating interferences created at their receivers, but also the outage performance of primary and secondary users are improved due to the additional diversity obtained via cognitive relaying.

In this paper, we propose exact decision thresholds for “blindly combined energy detection (BCED)” in the case of multiple receiver collaborative spectrum sensing. As opposed to the decisions thresholds estimation based on an asymptotic analysis in the sense of large samples and/or large collaborative antennas presented in the literature, the proposed mathematical formulation can be used to calculate exact thresholds for finite number of samples and collaborative antennas. The proposed formulation is based on our recent progress in the exact eigenvalue distributions of complex Wishart matrices with finite sizes. Moreover, the proposed thresholds valid for both correlated and uncorrelated Gaussian noise cases. Finally, we show that the probability of detection performance with the proposed exact decision thresholds performs better than the performance achieved with the decision thresholds calculated based on the asymptotic analysis, thus validate the importance of this work.

In this letter, we propose a lattice-based full diversity design for rate-one quasi-orthogonal space time block codes (QSTBC) to obtain an improved diversity product for eight transmit antennas where the information bits are mapped into 4-D lattice points instead of the common modulation constellations. Particularly, the diversity product of the proposed code is directly determined by the minimum Euclidean distance of the used lattice and can be improved by using the lattice packing. We show analytically and by using simulation results that the proposed code achieves a larger diversity product than the rate-one QSTBCs reported previously.

This letter derives mathematical expressions for the received signal-to-interference-plus-noise ratio (SINR) of uplink Single Carrier (SC) Frequency Division Multiple Access (FDMA) multiuser MIMO systems. An improved frequency domain receiver algorithm is derived for the studied systems, and is shown to be significantly superior to the conventional linear MMSE based receiver in terms of SINR and bit error rate (BER) performance.

Recently, full rate and full diversity two-group (2Gp) and four-group (4Gp) decodable space-time block codes (STBC) derived from quasi-orthogonal STBC (QSTBC) and designed under diversity product maximization criterion have been proposed. In this paper, we derive an upper bound of diversity product for those STBCs and discover that the diversity product of the current 2Gp-QSTBC and 4Gp-QSTBC has the potential to approach the upper bound for 8 transmit antennas. To this end, we propose an improved design of 2Gp and 4Gp STBC with increased diversity product for 8 transmit antennas by allowing sufficient number of dimensions for constellation rotation. The diversity product of the proposed two-group decodable STBC achieves the derived upper bound.

In this paper, we propose a novel iterative receiver strategy for uncoded multiple-input, multiple-output (MIMO) systems employing improper signal constellations. The proposed scheme is shown to achieve superior performance and faster convergence without the loss of spectrum efficiency compared to the conventional iterative receivers. The superiority of this novel approach over conventional solutions is verified by both simulation and analytical results.

We consider a cognitive relay assisted multiple-input multiple-output (MIMO) communication scenario where multiple secondary users wish to communicate with the secondary receiver through Rayleigh fading channels in the presence of a primary user. We assume that secondary users transmits simultaneously with the primary user over the same channel instead of waiting for an idle channel which is traditional for a cognitive radio. We derive the expressions for mutual information received at the primary and secondary receivers in both the cases when cognitive relay is present or not. We also present the closed form expressions for the outage probabilities and complementary cumulative distribution functions (CCDFs) of mutual information received at the primary and secondary receivers in the absence of cognitive relay. Our results show that in the presence of cognitive relay, not only both the primary and multiple secondary users are able to communicate with intended receivers compensating interferences created at their receivers, but also the outage performance of primary and secondary users are improved due to the additional diversity obtained via cognitive relaying.

In this paper, we present a low complexity maximum likelihood (ML) detection based on the sphere decoder (SD) for distributed time-reversal space-time block code (D-TR-STBC) with frequency selective fading links. Unlike direct transmission, the relay-assisted transmission results in higher number of taps for the resultant end-end channel. The complexity of Viterbi algorithm (VA) grows exponentially with the channel memory and the signal modulation order. Hence makes it prohibitive for the above scenario. On the other hand, the complexity of SD is a low-degree polynomial in the block length and does not vary significantly with the channel memory and the modulation order over the signal to noise ratio (SNR) range of interest. This offers a significant computational reduction over VA specifically for relay networks that provide higher diversity. To corroborate our claims, we have shown the simulation results comparing the average complexities of SD and VA for various system settings. A further reduction in the average complexity of SD is achieved for D-TR-STBC with multiple relays and with relay selection.

This paper considers multiuser MIMO CDMA systems with high rate space-time linear dispersion codes (LDC) and orthogonal space-time block codes (O-STBC) in time-varying Rayleigh fading MIMO channels. We propose a multi-function process integrating multi-user detection, space-time decoding and symbol demodulation, which can be coupled with soft channel decoding to improve the system performance in an iterative fashion. We show that the space-time coded CDMA systems approach the single-user bound with only two iterations, and full diversity LDCs enable the systems to utilize the time diversity inherent in fast fading channels. The space-time coded CDMA systems are also compared to the MIMO CDMA system based on spatial multiplexing, some recommendations are made on how to design a practical MIMO CDMA system based on the comparative studies.

This paper investigates the coding gain achieved between the best relay and the relay subset selection in a two-hop wireless network with relay-assisted transmission. Both best relay and relay subset selection achieves the maximum diversity, but the later becomes a better alternative in certain scenarios. First in low mobility scenarios, when the channel fading remains static for longer duration and this could result in an unfair usage of the same (best) relay terminal for an extended period of time. Second, when there is a power constraint which limits the maximum transmit power at the relay terminals. We have presented a simple technique for calculating the pairwise error probability (PEP) upper bound for distributed time-reversal space-time block code (D-TR-STBC) with best relay and relay subset selection, which offers an easy way of quantifying the coding gain achieved. Results from the numerical simulation of error probabilities are given to corroborate our analysis.

In this paper, we apply the Jacobi iterative algorithm to combat intersymbol interference caused by frequency selective channels. An analytical bound of the proposed equalizer is analyzed in order to gain an insight into its asymptotic performance. Due to the error propagation problem, the potential of this algorithm is not reached in an uncoded system. However, its extension to a coded system with the application of the turbo processing principle results in a new turbo equalization algorithm which demonstrates comparable performance with reduced complexity compared to some existing filter based turbo equalization schemes.

In this paper, we design and analyse a distributed time-reversal space-time block code (D-TR-STBC) that can achieve significant power gain and optimum diversity order in a relay-assisted transmission for single-carrier frequency-selective channels. The idea behind distributed space-time block coding (D-STBC) is to have the relays cooperate in such a way that the signal at the destination is a space-time code, so as to obtain full diversity. In contrast to the literature, we show that the orthogonality of D-TR-STBC can be preserved at the destination by including the signals received via the direct link (from the source to the destination) at the first half of the signalling interval along with the space-time block coded signal received at the second half of the signalling interval. The pairwise error probability (PEP) derivation shows that the proposed scheme achieves the optimum diversity order. Moreover, the symbol error rate (SER) performance of the proposed scheme and competing scheme using different equalization techniques and signal to noise ratios (SNRs) in the source to relay link shows that the proposed protocol outperforms the other by an SNR margin of 2 – 5dB.

In recent years, due to the low complexity nature of the turbo processing and excellent bit-error-rate (BER) performance, designing turbo-like receivers for frequency-selective MIMO channels has been of great research interest. The performance gain in turbo decoders is due to an extrinsic information transfer (EXIT) process between the detection and the decoding stages as compare to a traditional system that treats these processes in isolation. However, the challenge faced with these iterative receivers is the understanding of their performance and convergence behaviour. In this paper, we study an iterative maximum a posteriori (MAP) receiver for MIMO orthogonal frequency division multiplexing (OFDM) channels and its convergence behaviour. We analyze the performance of the proposed transceiver system with Gray and anti-Gray mapping using EXIT chart and study the effects of various settings of transmit and receive antenna on the turbo cliff in BER performance.

Channel estimation for multiple-input, multiple-output (MIMO) systems is studied in this paper. In particular, we present a simplified MIMO channel estimator based on orthogonal design. The performance of the proposed scheme is theoretically analyzed and compared to that of the optimum maximum likelihood estimator. The effect of non-orthogonality of the training sequences is investigated. Some modifications of the proposed estimator with sample stacking and averaging are introduced to further improve the estimation performance. This simplified scheme is evaluated in the context of the WiMAX MIMO systems in terms of mean square error for the channel estimation and bit error rate for the space-time turbo equalization. Both analytical and simulation results indicate that despite of its low computational complexity, this simplified estimator leads to minimum variance unbiased estimation and achieves identical performance to that of the maximum likelihood estimator.

In this letter, we propose a modified Hough transform (HT) algorithm for radar detection by shifting the parameter space cells as well as exploiting the phase information of signal. The proposed modified HT brings (1) significant improvement of radar target detection at low signal-to-noise ratios and (2) complexity reduction of HT implementation in radar detection applications compared to the conventional HT-based methods. Simulation results are presented showing the performance gain obtained by the proposed modified HT over the conventional methods.

Recently, a single-symbol decodable transmit strategy based on preprocessing at the transmitter has been introduced to decouple the quasi-orthogonal space-time block codes (QOSTBC) with reduced complexity at the receiver . Unfortunately, it does not achieve full diversity, thus suffering from significant performance loss. To tackle this problem, we propose a full diversity scheme with four transmit antennas in this letter. The proposed code is based on a class of restricted full-rank single-symbol decodable design (RFSDD) and has many similar characteristics as the coordinate interleaved orthogonal designs (CIODs), but with a lower peak-to-average ratio (PAR).

In this letter, the performance bound of the IEEE 802.16d channel is examined analytically in order to gain an insight into its theoretical potential. Different design strategies, such as orthogonal frequency division multiplexing (OFDM) and single-carrier frequency-domain equalization (SC-FDE), time-domain decision feedback equalization (DFE), and sphere decoder (SD) techniques are discussed and compared to the theoretical bound.

A low complexity transmit diversity scheme is derived in this paper in order to overcome the prohibitive complexity imposed by the maximum likelihood detection for the systems with space-time block code (STBC) over frequency selective channels. By taking advantage of multipath propagation and exploiting temporal diversity gain, the proposed turbo equalization algorithm significantly improves the system performance compared to the original Alamouti algorithm as well as the conventional minimum mean square error (MMSE) detection scheme.

Multiple-input, multiple-output (MIMO) communication is an effective scheme to improve wireless communication performance of multiuser applications. However, reliable communication in multiuser systems is affected by the presence of both multi-access interference (MAI) and inter-symbol interference (ISI) in multi-path channels. In this paper, we therefore investigate a transceiver design for a wideband multiuser-MiMO communication system, where the co-channel users are equipped with multiple transmit and multiple receive antennas. In particular, we propose a two-step interference cancellation scheme with an error correction coding technique for the receiver of a multiuser uplink system. The scheme employs orthogonal frequency division multiplexing (OFDM) modulation and space-time block codes (STBC). The receiver performs as a soft output multiuser detector based on minimum mean-squared error (MMSE) interference suppression at the first stage, and then, MAI cancellation is implemented with a bank of single-user channel decoders. The paper also includes computer simulations which help to improve the understanding of specific issues involved in the design of multiuser STBC-OFDM systems, and confirm the utility of the proposed approach.

We design and analyze spectrally efficient half-duplex cooperative diversity protocols using multiple relays (say JV), which combat the multi-path fading by retransmitting the bit-interleaved coded information transmitted by a source to destination. In the first proposed protocol, the relay with the best mutual information relay path (source-relay-destination) will relay the source information - thus termed as best-relay protocol. In the second proposed protocol, called N-relay cyclic protocol, each relay will transmit only a portion of the signal transmitted by the source. We derive the probability density functions of the mutual information for both of these protocols and using these densities, we analyze the outage probability and outage capacity performances. The results show that the proposed protocols achieve the maximum diversity order N + 1. The simulation results using turbo code are given and results show the superior performance of the best-relay protocol.

We design and analyze spectrally efficient half-duplex cooperative diversity protocols using multiple relays (say JV), which combat the multi-path fading by retransmitting the bit-interleaved coded information transmitted by a source to destination. In the first proposed protocol, the relay with the best mutual information relay path (source-relay-destination) will relay the source information - thus termed as best-relay protocol. In the second proposed protocol, called N-relay cyclic protocol, each relay will transmit only a portion of the signal transmitted by the source. We derive the probability density functions of the mutual information for both of these protocols and using these densities, we analyze the outage probability and outage capacity performances. The results show that the proposed protocols achieve the maximum diversity order N + 1. The simulation results using turbo code are given and results show the superior performance of the best-relay protocol.

The application of turbo principle in designing receivers for multiple-input multiple-output (MIMO) wireless systems not only achieves practical complexity receiver systems but also near optimal performances for many of the next generation systems. In particular, our recent research has shown that significant performance can be achieved by using a low complexity iterative soft interference cancellation minimum mean-squared error (SIC-MMSE) equalizer in various coded MIMO wireless channels including frequency-selective MIMO channels (S. Ahmed et al., 2006). This performance gain is due to an extrinsic information exchange process between the equalization and the channel decoding stages compared to a traditional system that treats these processes in isolation. However, the challenge faced with these iterative receivers is the understanding of their convergence behaviour. In this paper, to better understand the convergence behaviour of the proposed iterative receiver, we study the notion of extrinsic information transfer (EXIT) characteristics. Using simulations, we derive the extrinsic information trajectory on the EXIT chart at various Eb/N0ranges to confirm the convergence of the proposed equalizer.

We investigate the performance of multirate layered space-time coded MIMO systems with successive decoding and interference cancellation (SDIC) receivers in quasi-static Rayleigh fading channels. The proposed framework can be viewed as a class of diagonal layered space-time coded system with each of the layers is encoded independently with different rates subject to equal per-layer outage probabilities. We derive the probability density functions of the per-layer mutual informations, which can be used to estimate the per-layer rates. Using these densities we show that the proposed transceiver increases the outage capacity. We also present simulation results illustrating the outage capacity performance for a variety of transmit and receive antenna combinations and the associated near optimal per-layer rates of input signals. In particular we show that for sufficiently large numbers of transmit and receive antennas, the system can achieve near capacity in quasi-static fading environments. Based on these results, multirate codes are designed using punctured turbo codes and simulation results show significant gains in packet error-rate (PER) performances compared to that of V-BLAST architectures with lower receiver complexities.

In this paper, we propose a practical iterative transceiver design for multiple-input multiple-output (MIMO) frequency-selective wireless channels, which is able to realize a significant portion of the capacity promised by information theory. At the transmitter end, we exploit the idea of space-time bit-interleaved coded modulation (ST-BICM) scheme by using turbo or convolutional codes. This encoding scheme is highly effective if used in conjunction with receiver employing iterative detection and decoding. At the receiver end, we propose a novel multi-antenna detection scheme, which equalizes the intersymbol interferences (ISI) and soft estimate the transmitted symbols. These symbols are then input to a sub-optimal turbo-like receiver that performs iterative decoding of the ST-BICM codes in an iterative and, most important, simple fashion. The simulation results show that the proposed so called turbo-MIMO transceiver error performance improves with the number of iterations of the decoding algorithm. This performance improvement will enhance the capacity and quality of the wireless link

We study a multirate layered architecture for multiple-input multiple-output (MIMO) quasi-static Rayleigh fading channels, which uses a diagonal space-time interleaved (DSTI) coded modulation and a low complexity linear successive decoding receiver. The key challenge involved with this architecture is estimating the per-layer rates. We derive the probability density functions of per-layer mutual information, which are used to estimate the per-layer rates that maximize the information rate subject to an upper bound on the outage probability. Based on these rates, we design punctured turbo codes and the simulation results show significant gains in bit error rate (BER) performances. In comparison with the BLAST architecture, the proposed multirate system has more practical virtues while achieving near capacity performance for sufficiently large diversity orders

Orthogonal frequency division multiplexing (OFDM) requires an expensive linear amplifier at the transmitter due to its high peak-to-average power ratio (PAPR). Single carrier with cyclic prefix (SC-CP) is a closely related transmission scheme that possesses most of the benefits of OFDM but does not have the PAPR problem. Although in a multipath environment, SC-CP is very robust to frequency-selective fading, it is sensitive to the time-selective fading characteristics of the wireless channel that disturbs the orthogonality of the channel matrix (CM) and increases the computational complexity of the receiver. In this paper, we propose a time-domain low-complexity iterative algorithm to compensate for the effects of time selectivity of the channel that exploits the sparsity present in the channel convolution matrix. Simulation results show the superior performance of the proposed algorithm over the standard linear minimum mean-square error (L-MMSE) equalizer for SC-CP.

We propose a multirate diagonal space-time interleaved (DSTI) coded modulation system with a low complexity linear successive decoding and interference cancellation receiver for quasi-static Rayleigh fading multiple-input multiple-output (MIMO) channels. In comparison with the traditional BLAST architecture, the proposed multirate DSTI system has more practical virtues while achieving near capacity performance for sufficiently large multiple antennas. The key findings of this paper are: (i) the derivation of the probability density functions of per-layer mutual informations, which can be used to estimate per-layer rates, and (ii) the design of multirate DSTI system using punctured turbo codes. Simulation results show significant gains in packet error-rate (PER) performances.

We address the problem of designing multirate codes for a multiple-input and multiple-output (MIMO) system by restricting the receiver to be a successive decoding and interference cancellation type, when each of the input signals is encoded independently. It is assumed that the receiver knows the instantaneous fading channel states but the transmitter does not have access to them. We design a multirate coded MIMO system using punctured turbo codes and demonstrate the efficiency of the proposed scheme using simulation results. In particular, the simulation results show performance within 2 dB of MIMO channel capacity.

Theoretical investigations of spatially correlated multitransmit and multireceive (MTMR) links show that not only independently and identically distributed links, but also spatially correlated links can offer linear capacity growth with increasing number of transmit and receive antennas. We explore the suitability of the turbo-BLAST architecture in correlated Rayleigh-fading MTMR environments. In particular, for an MTMR system with a large number of receive antennas, a near optimal performance can be achieved by the turbo-BLAST architecture in spatially and temporarily correlated Rayleigh-fading environments. The performance of turbo-BLAST, in terms of both bit-error rate and spectral efficiency, is analyzed empirically in indoors and correlated outdoor environments.

TURBO-BLAST is a novel multi-transmit multi-receive (MTMR) antenna scheme for high-throughput wireless communications. It exploits a novel space-time coding scheme based on the independent block forward error correction (FEC) codes and space-time interleaving, and a near-optimal iterative decoder, for decoding a new generation of space-time codes. The proposed iterative decoder has two decoding stages: a soft interference cancelation detector and a set of soft-in soft-out decoders. We focus on designing a robust parallel interference cancelation scheme that jointly estimates the soft interference and the linear beamformer weights to minimize the mean-square error (MMSE) between the true and estimated signals. Using simulation results, we show that the proposed scheme outperforms the previously proposed soft interference cancelation receivers based on the maximum ratio combining (MRC) principle.

We propose a simplified Diagonal-BLAST (D-BLAST) architecture with parallel soft interference cancellation receiver based on the Turbo-BLAST (T-BLAST) architecture. In the T-BLAST architecture, the inter-substream coding is designed by a combination of random space-time interleaving and independent block encoding of each substream, using the same forward-error correction (FEC) code. We show that for the T-BLAST architecture, by using a systematic space-interleaving design that layers each substream diagonally across the antennas, a simplified diagonal inter-substream coding can be achieved without undue implementation complexity. The proposed diagonal inter-substream coding also facilitates the use of an iterative parallel interference cancellation receiver for decoding the simultaneously transmitted data, thereby achieving more capacity compared to the achievable capacity of traditional BLAST (Bell Labs Layered Space Time) architectures using sequential interference cancellation receivers. In this paper, we also present simulation results on fading channels, which confirm these findings.

We propose TURBO-BLAST, a novel multi-transmit, multi-receive antenna scheme, for high data rate wireless communications based on the Bell-Labs Layered Space Time (BLAST) architecture. In the TURBO-BLAST (T-BLAST) scheme, the incoming substreams use the same forward error correction (FEC) code, but they are interleaved differently using randomly generated inter-substream permuters. The transmitter structure leads to an iterative ("Turbo-like") receiver for decoding the simultaneously transmitted data. For the receiver, we consider the design and performance evaluation of an iterative and parallel soft interference-cancelation scheme followed by maximum-ratio combining (MRC) since it is simple and effective.

A new theory is developed for the feature spaces of hyperbolic tangent used as an activation kernel for non-linear support vector machines. The theory developed herein is based on the distinct features of hyperbolic geometry, which leads to an interesting geometrical interpretation of the higher-dimensional feature spaces of neural networks using hyperbolic tangent as the activation function. The new theory is used to explain the separability of hyperbolic tangent kernels where we show that the separability is possible only for a certain class of hyperbolic kernels. Simulation results are given supporting the separability theory.

In this paper a new measurement approach is presented for reconstructing the 3-D shape of electrical discharges using binocular camera images. Eventually, the orientation of the channel sections and the location of the discharge strike points were found. This paper also addresses a computationally inexpensive matching algorithm to match the feature points of electrical discharges, without knowing the point to point correspondence. By minimizing the sum of squared disparity differences (SSDD) of neighboring features, the maximum possibility for the correspondence was obtained. The SSDD of neighboring features were also used as a priori knowledge of finding the correct pairs of left and right images. The experimental results are presented demonstrating the ability of recovering shape of the electrical discharges using the presented approach.