Using environmental sensory data can enhance communications beam training and reduce its overhead compared to conventional methods. However, the availability of fresh sensory data during inference may be limited due to sensing constraints or sensor failures, necessitating a realistic model for multimodal sensing. This paper proposes a joint multimodal sensing and beam prediction framework that operates under a constraint on the average sensing rate, i.e., how often fresh sensory data should be obtained. The proposed method combines deep reinforcement learning, i.e., a deep Q-network (DQN), with a neural network (NN)-based beam predictor. The DQN determines the sensing decisions, while the NN predicts the best beam from the codebook. To capture the effect of limited fresh data during inference, the age of information (AoI) is incorporated into the training of both the DQN and the beam predictor. Lyapunov optimization is employed to design a reward function that enforces the average sensing constraint. Simulation results on a real-world dataset show that AoI-aware training improves top-1 and top-3 inference accuracy by 44.16% and 52.96%, respectively, under a strict sensing constraint. The performance gain, however, diminishes as the sensing constraint is relaxed.

The approximate message passing (AMP)-based data detection is a highly effective solution for terahertz (THz) multiple-input multiple-output (MIMO) communications, enabling reliable data detection at ultra-high data rates. However, in the uplink of THz MIMO systems, high channel correlation leads to performance degradation and computational inefficiencies. To address these challenges, we develop correlated probability estimation (CPE) for the standard AMP iterative data detection algorithm (AMP-IDA), achieving Bayesian-optimal (BO) bit error rate (BER) performance in highly correlated THz channels. To mitigate the significant computational complexity of CPE, we propose an effective signal-to-noise ratio (eSNR)-adjusted channel decorrelation preprocessing (ACDP) method, which leverages whitening transformation and convex optimization, mitigating the impact of row correlation without prior knowledge of correlation indices. By integrating eSNR-ACDP with the low-complexity standard AMP-IDA, we design the ACDP-AMP-IDA, which attains BER close to the BO benchmark with significantly reduced complexity. Compared to orthogonal AMP (OAMP) algorithms, ACDP-AMP-IDA outperforms standard OAMP by up to 8 dB and achieves performance comparable to OAMP with linear minimum mean square error (MMSE) while incurring only 3%–6% of its runtime. Additionally, it surpasses existing AMP-IDA-based and MMSE detectors by over 10 dB and guarantees robust convergence across various transmitter-receiver distances in uplink THz MIMO systems.

This work provides a comprehensive performance analysis of a reconfigurable intelligent surface (RIS)-assisted multiple-input multiple-output (MIMO) communication system in the presence of phase errors. We first derive the exact expressions for the first and second moments of the maximum signal-to-noise ratio at the receiver. We then use these moments to obtain highly accurate and insightful expressions for the outage probability, average symbol error probability (SEP), and ergodic rate (ER) for various phase error distributions, leveraging the moment-matching approach. Furthermore, we present closed-form asymptotic expressions for these performance metrics. The derived expressions are then leveraged to gain important insights into the impact of phase errors on the scaling laws of diversity order, coding gain, and ER. The numerical results show that the presence of phase errors can significantly degrade the system’s diversity order and coding gain. Specifically, we show that, in the absence of phase errors, the diversity order scales linearly with the number of RIS elements, while the diversity order reduces to unity in the presence of uniformly distributed phase errors. Finally, the analytical findings are demonstrated through extensive simulation results.

Infrastructure-mounted sensors can capture rich environmental information to enhance communications and facilitate beamforming in millimeter-wave systems. This work presents an efficient sensing-assisted long-term beam tracking framework that selects optimal beams from a codebook for current and multiple future time slots. We first design a large attention-enhanced neural network (NN) to fully exploit past visual observations for beam tracking. A convolutional NN extracts compact image features, while gated recurrent units with attention capture the temporal dependencies within sequences. The large NN then acts as the teacher to guide the training of a lightweight student NN via knowledge distillation. The student requires shorter input sequences yet preserves long-term beam prediction ability. Numerical results demonstrate that the teacher achieves Top-5 accuracies exceeding 93% for current and six future time slots, approaching state-of-the-art performance with a 90% reduction of model parameters. The student closely matches the teacher's performance while reducing the number of model parameters by over 1670% and cutting complexity by over 450%, despite operating with 60% shorter input sequences. This improvement significantly enhances data efficiency, reduces latency, and reduces power consumption in sensing and processing.

Millimeter wave (mmWave) channels can provide new frequency bands for communications together with improved accuracy and resolution for sensing and target localization. Those require parametric channel estimation methods capable of operating with very short coherence times in case of large Doppler spread. In this paper, we address this problem in orthogonal frequency division multiplexing (OFDM) uplink channel estimation. We first observe that it is a variation of the multidimensional harmonic retrieval (MHR) problem in which one has no direct access to the channel tensor due to the strongly time-varying channel. We then derive an improvement of the OFDM space-alternating generalized expectation-maximization (SAGE) algorithm for channel estimation. It is able to solve the problem under intense Doppler spreads and carrier frequency offset (CFO). The proposed method exploits the shift-invariance properties of the OFDM channel tensor to perform efficient coordinate descent steps. We also derive a threshold with probability of false alarm (PFA) guarantees to probe the quality of path initializations. The algorithm estimates the number of significant multipath components by progressively adding paths until the likelihood function reaches a threshold value derived from the noise distribution. We show via numerical examples that the proposed method outperforms the state-of-the-art methods both in channel tensor estimation accuracy as well as in the model order estimation performance.

We propose a novel framework to analyze the coverage performance of three-dimensional (3D) indoor terahertz (THz) communication systems and examine the impact of the location of a user equipment (UE) on such performance. Specifically, we employ Manhattan line processes to precisely characterize the deployment of wall blockages in the indoor environment. Moreover, we model locations of access points (APs) using a Poisson point process and adopt the nearest line-of-sight AP association strategy. Due to the high penetration loss caused by wall blockages, we consider that a UE, its associated AP, and interfering APs are all in the same rectangular area, i.e., a room. Based on the proposed rectangular area model, we first analyze the impact of the location of a UE on the distance to its associated AP. We then derive a new expression for the coverage probability by adopting the fluctuating two-ray distribution to accurately model the small-scale fading in THz communications. Supported by simulation results, we validate our analysis and demonstrate how the location of the UE affects its coverage probability, offering valuable insights for meeting the coverage requirements of future THz communication system deployments.

In this paper, we develop a tractable analytical framework for a three-dimensional (3D) indoor terahertz (THz) communication system to theoretically assess the impact of the pointing error on its coverage performance. Specifically, we model the locations of access points (APs) using a Poisson point process, human blockages as random cylinder processes, and wall blockages through a Boolean straight line process. A pointing error refers to beamforming gain and direction mismatch between the transmitter and receiver. We characterize it based on the inaccuracy of location estimate. We then analyze the impact of this pointing error on the received signal power and derive a tractable expression for the coverage probability, incorporating the multi-cluster fluctuating two-ray distribution to accurately model small-scale fading in THz communications. Aided by simulation results, we corroborate our analysis and demonstrate that the pointing error has a pronounced impact on the coverage probability. Specifically, we find that merely increasing the antenna array size is insufficient to improve the coverage probability and mitigate the detrimental impact of the pointing error, highlighting the necessity of advanced estimation techniques in THz communication systems.

While Cramér-Rao lower bound is an important metric in sensing functions in integrated sensing and communications (ISAC) designs, its optimization usually involves a computationally expensive solution such as semidefinite relaxation. In this paper, we aim to develop a low-complexity yet efficient algorithm for CRLB optimization. We focus on a beamforming design that maximizes the weighted sum between the communications sum rate and the sensing CRLB, subject to a transmit power constraint. Given the non-convexity of this problem, we propose a novel method that combines successive convex approximation (SCA) with a shifted generalized power iteration (SGPI) approach, termed SCA-SGPI. The SCA technique is utilized to approximate the non-convex objective function with convex surrogates, while the SGPI efficiently solves the resulting quadratic subproblems. Simulation results demonstrate that the proposed SCA-SGPI algorithm not only achieves superior tradeoff performance compared to existing method but also significantly reduces computational time, making it a promising solution for practical ISAC applications.

Joint communications and sensing (JCAS) is expected to be a crucial technology for future wireless systems. This paper investigates beamforming design for a multi-user multi-target JCAS system to ensure fairness and balance between communications and sensing performance. We jointly optimize the transmit and receive beamformers to maximize the weighted sum of the minimum communications rate and sensing mutual information. The formulated problem is highly challenging due to its non-smooth and non-convex nature. To overcome the challenges, we reformulate the problem into an equivalent but more tractable form. We first solve this problem by alternating optimization (AO) and then propose a machine learning algorithm based on the AO approach. Numerical results show that our scheme scales effectively with the number of the communications users and provides better performance with shorter run time compared to conventional optimization approaches.

We propose a 6D Bayesian-based localization framework to estimate the position and rotation angles of a mobile station (MS) within an indoor reconfigurable intelligent surface (RIS)-aided system. This framework relies on a probabilistic graphical model to represent the joint probability distribution of random variables through their conditional dependencies and employs the No-U-Turn Sampler (NUTS) to approximate the posterior distribution based on the estimated channel parameters. Our framework estimates both the position and rotation of the mobile station (MS), in the presence of channel parameter estimation errors. We derive the Cramér-Rao lower bound (CRLB) for the proposed scenario and use it to evaluate the system's position error bound (PEB) and rotation error bound (REB). We compare the system performances with and without RIS. The results demonstrate that the RIS can enhance the positioning accuracy significantly.

This work studies joint waveform design and subcarrier allocation in a multiuser multicarrier monostatic joint communications and sensing (JCAS) system. We aim to design the JCAS transmit waveform with subcarrier allocation that maximizes the energy efficiency (EE) of the multiuser communications subject to the constraints on the minimum communications and sensing signal-to-interference-plus-noise ratios (SINRs) and transmit power budget. The formulated EE design problem is a mixed-integer non-convex fractional program, which is highly challenging to solve directly. To overcome the challenges, we leverage alternating optimization (AO) and divide the original problem into two subproblems, namely, the waveform design and subcarrier allocation. For the non-convex fractional objective function in the waveform design, we propose an efficient method combining two typical techniques in fractional programming, namely, the quadratic and Dinkelbach transforms, along with successive convex approximation (SCA). For the subcarrier allocation, we leverage quadratic transform and penalty function method. Numerical results demonstrate the effectiveness of the proposed method, showing a significant improvement in energy efficiency compared to a baseline scheme.

Low-resolution analog-to-digital converters (ADCs) and hybrid beamforming have emerged as efficient solutions to reduce power consumption with satisfactory spectral efficiency (SE) in massive multiple-input multiple-output (MIMO) systems. In this paper, we investigate the performance of a hybrid receiver in massive MIMO orthogonal frequency-division multiplexing (OFDM) uplink systems with low-resolution ADCs and over-sampling. Considering both the temporal and spatial correlation of the quantization distortion (QD), we derive a closed-form approximation of the frequency-domain QD covariance matrix, which facilitates the evaluation of the system's SE. Then we jointly design the analog and digital combiners of the hybrid receiver to maximize the SE. The formulated problem is challenging due to the constant-modulus constraint of the analog combiner and its coupling with the digital one. To overcome these challenges, we transform the objective function into an equivalent but more tractable form and then iteratively update the analog and digital combiners. Numerical simulations verify the superiority of the proposed algorithm over the considered benchmarks and show the resilience of the hybrid receiver to beam squint with low-resolution ADCs. Furthermore, the proposed hybrid receiver design with oversampling can achieve significantly higher energy efficiency compared with the fully digital one.

In integrated sensing and communication (ISAC) systems, reconfigurable intelligent surface (RIS) is often able to provide a virtual line-of-sight path to overcome blockage and introduce new degrees of freedom to enhance both communications and sensing performances. In this paper, we explore the synergistic integration of massive multiple-input-multiple-output (MIMO) ISAC and RIS technologies. We first consider the uplink training phase and estimate the channels between the base station and communication users, by using the minimum mean square error method. Then, we derive the closed-form expressions for the downlink communications rate and the Cramér-Rao lower bound for the estimation of the azimuth and elevation angles associated with the sensing target. Extensive simulations verify that the derived analytical results match well with Monte Carlo simulation results. Furthermore, communication and sensing performances are significantly improved owing to the deployment of the RIS. Our analysis lay an important foundation for optimizing RIS-assisted ISAC systems.

This letter addresses the problem of estimating block sparse signal with unknown group partitions in a multiple measurement vector (MMV) setup. We propose a Bayesian framework by applying an adaptive total variation (TV) penalty on the hyper-parameter space of the sparse signal. The main contributions are two-fold. 1) We extend the TV penalty beyond the immediate neighbor, thus enabling better capture of the signal structure. 2) A dynamic framework is provided to learn the regularization weights for the TV penalty based on the statistical dependencies between the entries of tentative blocks, thus eliminating the need for fine-tuning. The superior performance of the proposed method is empirically demonstrated by extensive computer simulations with the state-of-art benchmarks. The proposed solution exhibits both excellent performance and robustness against sparsity model mismatch.

By combining the benefits of passive reflecting and active relay, the hybrid active-passive reconfigurable intelligent surfaces (RIS) (HRIS) architecture can overcome the double path loss and limited performance gains of the conventional passive RIS. In this work, we investigate the secrecy performance of a millimeter wave multiple-input-multiple-output (MIMO) network in the presence of a multiantenna eavesdropper with imperfect channel state information (CSI). To maximize the secrecy rate, we propose a joint design of the transmit beamformer and HRIS coefficients utilizing the combined tools of block coordinate ascent (BCA), stationary solution to Rayleigh quotient, Dinkelbach method, and concave-convex procedure (CCCP). The design problem encompasses both fixed and dynamic HRIS architectures. For the former, a pre-manufactured set of active elements is considered. For the latter, the set of active elements is adaptively updated, and their coefficients are optimized via a stochastic game-based algorithm. Simulation results demonstrate that the proposed HRIS schemes outperform the conventional passive RIS, particularly when high transmit power and many active coefficients are deployed at the HRIS. Additionally, a dynamic HRIS can offer 60% improvement in the secrecy rate compared to the passive RIS scheme.

In this paper, we develop a novel analytical framework for a three-dimensional (3D) indoor terahertz (THz) communication system. Our proposed model incorporates more accurate modeling of wall blockages via Manhattan line processes and precise modeling of THz fading channels via a multi-cluster fluctuating two-ray (MFTR) channel model. We also account for traditional unique features of THz, such as molecular absorption loss, user blockages, and 3D directional antenna beams. Moreover, we model locations of access points (APs) using a Poisson point process and adopt the nearest line-of-sight AP association strategy. Due to the high penetration loss caused by wall blockages, we consider that a user equipment (UE) and its associated AP and interfering APs are all in the same rectangular area, i.e., a room. Based on the proposed rectangular area model, we evaluate the impact of the UE’s location on the distance to its associated AP. We then develop a tractable method to derive a new expression for the coverage probability by examining the interference from interfering APs and considering the MFTR fading experienced by THz communications. Aided by simulation results, we validate our analysis and demonstrate that the UE’s location has a pronounced impact on its coverage probability. Additionally, we find that the optimal AP density is determined by both the UE’s location and the room size, which provides valuable insights for meeting the coverage requirements of future THz communication system deployment.

Low-resolution analog-to-digital converters (ADCs) have emerged as a promising technology for reducing power consumption and complexity in massive multiple-input multiple-output (MIMO) systems while maintaining satisfactory spectral and energy efficiencies (SE/EE). In this work, we first present the fundamental properties of optimal quantization and leverage them to derive a more accurate approximation of the covariance matrix of the quantization distortion. This theoretical finding facilitates the analysis of the system’s SE in the presence of low-resolution ADCs. Then, considering resolution-adaptive ADCs, we focus on the joint optimization of the transmit-receive beamforming and bit allocation to maximize the SE under constraints on the transmit power and the total number of active ADC bits. To solve the resulting mixed-integer problem, we first develop an efficient beamforming design for fixed ADC resolutions. Subsequently, we propose a low-complexity heuristic algorithm to iteratively optimize the ADC resolutions and beamforming matrices. Numerical results for a 64 × 64 MIMO system demonstrate that the proposed design offers 6% improvements in both SE and EE with 40% fewer active ADC bits compared with uniform bit allocation. Furthermore, it is unveiled that receiving more data streams with low-resolution ADCs can lead to higher SE and EE compared with receiving fewer data streams with high-resolution ADCs.

The beam split effect (BSE) can result in a serious loss in achievable rate in terahertz (THz) transmission. In this work, we propose a new sub-band assignment scheme in a multiuser THz communications system to address the BSE. We consider a base station employs true-time-delay hardware between radio frequency chains and uniform planar arrays (UPAs). The core idea of this hardware is that, rather than fine-tuning the UPA delays to form a single beam from each UPA, we facilitate multi-beam transmission from each UPA. We derive a novel expression for the maximum sub-band bandwidth for UPA, ensuring that the BSE is avoided within each beam. Based on this expression we design sub-band assignment across users and power allocation among sub-bands to maximize the sum-rate, relying on the principles of beam multiplexing and aggregation (BMA). Using numerical results, we demonstrate (i) the merits of our proposed sub-band assignment in contrast to the distance-aware sub-band assignment scheme, (ii) the effectiveness of BMA in comparison with the BSE, and (iii) the improved performance resulting from our proposed optimal power allocation relative to equal power allocation.

Wireless communications systems must increasingly support a multitude of machine-type communications devices, thus calling for advanced strategies for active user detection (AUD). Recent literature has investigated AUD techniques based on compressed sensing, highlighting the critical role of signal sparsity. This study examines the relationship between frequency diversity and signal sparsity in the AUD problem. Single-antenna users transmit multiple copies of non-orthogonal pilots across multiple frequency channels and the base station independently performs AUD in each channel using the orthogonal matching pursuit algorithm. We note that, although frequency diversity may improve the likelihood of successful reception of the signals, it may also damage the channel sparsity level, leading to important trade-offs. We show that a sparser signal significantly benefits AUD, surpassing the advantages brought by frequency diversity in scenarios with limited temporal resources and/or high numbers of receive antennas. Conversely, with longer pilots and fewer receive antennas, investing in frequency diversity becomes more impactful, resulting in a tenfold AUD performance improvement.

Unmanned aerial vehicle (UAV) and active reconfigurable intelligent surface (ARIS) can enhance wireless communications channels through flexible location deployment and intelligent signal reflection. On the other hand, rate-splitting multiple access (RSMA) is an efficient framework for non-orthogonal transmission and multiple access. In this paper, we explore the synergistic advantages of integrating these technologies within a unified UAV-mounted ARIS-assisted communications system employing RSMA. Specifically, we analyze the system outage probability and sum capacity considering independent and non-identically distributed Nakagami-m channels, circularly uniformly distributed random RIS phase shifts, and imperfect successive interference cancellation. We first derive the exact cumulative distribution and probability density functions of the sum single and double Nakagami-m vectors via multivariate Fox’s H-function. These allow us to derive the closed-form expressions for the outage probability, asymptotic outage probability, and sum ergodic capacity of the system. The analytical findings are then verified via extensive numerical simulations, which highlight the superior performance of UAV-ARIS RSMA compared to conventional UAV-based systems.

The employment of unmanned aerial vehicles (UAVs) aligned with multistatic sensing in integrated sensing and communication (ISAC) systems can provide remarkable performance gains in sensing, by taking advantage of the cell-free massive multiple-input multiple-output (mMIMO) architecture. Under these considerations, in this paper, the achievable sensing signal-to-noise-plus-interference ratio (SINR) of a cell-free mMIMO ISAC UAV-based network is evaluated for two different deployments of UAVs, namely, mobile and tethered. In both scenarios, a transmit precoder that jointly optimizes the sensing and communication requirements subjected to power constraints is designed. Specifically, for the scenario with mobile UAVs, beyond the transmit precoding, we also optimize the position of the transmit UAVs through particle swarm optimization (PSO). The results show that, although tethered UAVs have a more efficient power allocation, the proposed position control algorithm for the mobile UAVs can achieve a superior gain in terms of sensing SINR.

Millimeter (mmWave) beamforming is an integral component of fifth-generation (5G) and beyond radio commu-nications. 5G beamforming involves the initial beam selection procedure using a codebook with multiple radio beam directions. Conventional codebook-based alignment schemes involve exhaustive sweeping over the predefined beam directions, the number of which increases significantly with large numbers of antennas resulting in undesirable latency and communications signal overhead. In this paper, we propose a novel algebraic-based codebook using Gröbner basis polynomial solvers to reduce the signal overhead during beam alignment. We also analyze the complexity-performance tradeoff between the proposed algebraic-based codebook and the exhaustive-based beam alignment across different monomial thresholds, multiple antenna configurations and radio contextual location information. Our results show that the proposed approach reduces the beam-search overhead at an average complexity reduction ratio of 73.95% with a performance tradeoff error of 32.25%.

We consider uplink pilot allocation based on multipoint channel charting (CC) to mitigate pilot contamination in a multi-cell network with spatially correlated MIMO channels. The channel chart is created in an offline phase with full information, i.e. user channel covariance matrices are estimated at multiple base stations (BSs). In the online phase, we assume that only partial information about a user’s channel covariance is known, i.e., it is available only at the serving BS. A machine learning framework is developed to predict the CC locations in the online phase. Pilots are allocated to active users in the online phase based on weighted graph colouring. CC locations are used as proxies of user locations; similarity weights between users are constructed from CC distances. Simulation results show that the CC based approach with partial information in the online phase outperforms a solution based on full angle-of-arrival information, and performs closely to an algorithm with full covariance information. We also consider a partial information machine learning framework to predict the channel covariance matrices at other BSs, which slightly outperforms CC based approach, with the price of a larger communication overhead and computational complexity.

Millimeter-wave (mmWave) and D Band (110170 GHz) frequencies are poised to play a pivotal role in the advancement of sixth-generation (6 G) systems and beyond, owing to their ability to enhance performance metrics such as capacity, ultra-low latency, and spectral efficiency. This paper concentrates on deriving statistical insights into power, delay, and the number of paths based on measurements conducted across four distinct locations at a center frequency of 143.1 GHz. The findings underscore the suitability of various distributions in characterizing power behavior in line-of-sight (LOS) scenarios, including lognormal, Nakagami, gamma, and beta distributions, whereas the loglogistic distribution gives the optimal fit for power distribution in non-line-of-sight (NLOS) scenarios. Moreover, the exponential distribution shows to be the most appropriate model for the delay distribution in both LOS and NLOS scenarios. In terms of the number of paths, observations indicate a tendency for the highest concentration within the 10 m to 30 m distance range between the transmitter (Tx) and receiver (Rx). These insights shed light on the statistical nature of D band propagation characteristics, which are vital for informing the design and optimization of future 6 G communication systems.

This paper proposes an unmanned aerial vehicle (UAV)-based distributed sensing framework that uses orthogonal frequency-division multiplexing (OFDM) waveforms to detect the position of a ground target. The area of interest, where the target is located, is sectioned into a grid of cells, where the radar cross-section (RCS) of every cell is jointly estimated by the UAVs, and a central node acts as a fusion center by receiving all the estimations and performing information-level fusion. A periodogram is employed for local estimation at each UAV, and a digital receive beamformer is assumed. The fused RCS estimates of the grid are used to estimate the cell containing the target. To evaluate the accuracy of the proposed framework, Monte Carlo simulations are carried out to obtain the detection probability, and our results show that the proposed framework attains a notable improved accuracy over a single mono-static UAV benchmark, due to the fusion from multiple sensing UAVs.

Joint communications and sensing (JCAS) have recently emerged as a promising technology to utilize the scarce spectrum in wireless networks and to reuse the same hardware to save infrastructure costs. In practical JCAS systems, dual functional constant-modulus waveforms can avoid signal distortion in nonlinear power amplifiers. However, the designs of such wave-forms are highly complex due to the nonconvex constant-modulus constraint and high-dimensional variables, especially in wideband multicarrier systems. In this paper, we propose an efficient deep unfolding-based waveform design in a wideband large multiple-input multiple-output (MIMO) JCAS system. The deep unfolding model has a sparsely-connected structure and is trained in an unsupervised fashion. Simulation results show that the proposed deep unfolding design achieves better communications-sensing performance tradeoff while performing 98.5% faster than the conventional branch-and-bound method.

This work proposes and assesses Bayesian-based localization methods using time difference of arrival, angle of arrival, and hybrid measurements. First, a Bayesian localization model is constructed, and the Markov chain Monte Carlo method approximates the target’s three-dimensional posterior distribution. Then, the model’s performance is evaluated in a 3GPP indoor factory environment using a distributed antenna system with a centralized controller for synchronizing and merging information. The received signal strength is used to select a subset of available anchors, and the estimation accuracy is measured in terms of horizontal and vertical errors. The results show that the Bayesian framework meets the horizontal and vertical errors requirements of the 3GPP for commercial cases with over 100 measurements. The accuracy can be improved by acquiring more measurements or increasing the number of active remote radio heads. However, when information fusion is applied (hybrid model), increasing the number of active anchors decreases the estimation performance.

To enable an intelligent, programmable and multi-vendor radio access network (RAN) for 6G networks, considerable efforts have been made in standardization and development of open RAN (O-RAN). So far, however, the applicability of O-RAN in controlling and optimizing RAN functions has not been widely investigated. In this paper, we jointly optimize the flow-split distribution, congestion control and scheduling (JFCS) to enable an intelligent traffic steering application in O-RAN. Combining tools from network utility maximization and stochastic optimization, we introduce a multi-layer optimization framework that provides fast convergence, long-term utility-optimality and significant delay reduction compared to the state-of-the-art and baseline RAN approaches. Our main contributions are three-fold:$i$) we propose the novel JFCS framework to efficiently and adaptively direct traffic to appropriate radio units;$ii$) we develop low-complexity algorithms based on the reinforcement learning, inner approximation and bisection search methods to effectively solve the JFCS problem in different time scales; and$iii$) the rigorous theoretical performance results are analyzed to show that there exists a scaling factor to improve the tradeoff between delay and utility-optimization. Collectively, the insights in this work will open the door towards fully automated networks with enhanced control and flexibility. Numerical results are provided to demonstrate the effectiveness of the proposed algorithms in terms of the convergence rate, long-term utility-optimality and delay reduction.

Grant-free protocols exploiting compressed sensing multi-user detection (MUD) are appealing for solving the random access problem in massive Internet of Things (IoT) networks with sporadic device activity. Such protocols would greatly benefit from prior deterministic knowledge of the sparsity level, i.e., the instantaneous number of simultaneously active devicesK. Aiming at this, herein we introduce a framework relying on coordinated pilot transmissions (CPTs) for detectingK. Specifically, the proposed CPT mechanism includes a downlink (DL) phase for channel state information acquisition that resolves fading uncertainty in the uplink (UL) transmission phase using shared UL pilot symbols for channel compensation. We propose a signal sparsity level detector and analytically assess its accuracy when network channels are subject to Rayleigh fading. We show that the variance of the estimator increases withK, and its distribution approximates that of the sum of a Student’stand Gaussian random variable. The numerical results evince the need for carefully configuring the duration of the DL and UL phases. Indeed, we show that relatively short DL phases are preferable in highly sparse networks given the total CPT duration is fixed. Finally, we discuss and exemplify with some early results the potential of the proposed CPT framework for MUD, and highlight relevant research directions.

This paper addresses the joint user identification and channel estimation (JUICE) problem in machine-type communications under the practical spatially correlated channels model with unknown covariance matrices. Furthermore, we consider an MTC network with hierarchical user activity patterns following an event-triggered traffic mode. Therein the users are distributed over clusters with a structured sporadic activity behavior that exhibits both cluster-level and intra-cluster sparsity patterns. To solve the JUICE problem, we first leverage the concept of strong priors and propose a hierarchical-sparsity-inducing spike-and-slab prior to model the structured sparse activity pattern. Subsequently, we derive a Bayesian inference scheme by coupling the expectation propagation (EP) algorithm with the expectation maximization (EM) framework. Second, we reformulate the JUICE as a maximum a posteriori (MAP) estimation problem and propose a computationally-efficient solution based on the alternating direction method of multipliers (ADMM). More precisely, we relax the strong spike-and-slab prior with a cluster-sparsity-promoting prior based on the long-sum penalty. We then derive an ADMM algorithm that solves the MAP problem through a sequence of closed-form updates. Numerical results highlight the significant performance gains obtained by the proposed algorithms, as well as their robustness against various assumptions on the sparse activity behavior of the users.

We consider unmanned aerial vehicle (UAV)-enabled wireless systems where downlink communications between a multi-antenna UAV and multiple users are assisted by a hybrid active-passive reconfigurable intelligent surface (RIS). We aim at a fairness design of two typical UAV-enabled networks, namely the static-UAV network where the UAV is deployed at a fixed location to serve all users at the same time, and the mobile-UAV network which employs the time division multiple access protocol. In both networks, our goal is to maximize the minimum rate among users through jointly optimizing the UAV’s location/trajectory, transmit beamformer, and RIS coefficients. The resulting problems are highly nonconvex due to a strong coupling between the involved variables. We develop efficient algorithms based on block coordinate ascend and successive convex approximation to effectively solve these problems in an iterative manner. In particular, in the optimization of the mobile-UAV network, closed-form solutions to the transmit beamformer and RIS passive coefficients are derived. Numerical results show that a hybrid RIS equipped with only 4 active elements and a power budget of 0 dBm offers an improvement of 38% — 63% in minimum rate, while that achieved by a passive RIS is only about 15%, with the same total number of elements.

In this paper, we aim to enable an intelligent traffic (TS) steering application in the open radio access network (O-RAN) by jointly optimizing the flow-split distribution, congestion control and scheduling (i.e. so-called JFCS). To do so, we develop a multi-layer optimization framework based on network utility maximization and stochastic optimization methods. The proposed algorithm provides fast convergence, long-term utility-optimality and significantly low latency compared to state-of-the-art RAN approaches. In particular, our main contributions are as follows: i) we propose the novel JFCS framework to efficiently and adaptively route traffic to indented users in appropriate radio units, and ii) we develop low-complexity algorithms to effectively solve the JFCS problem in different time scales, enabling a closed-loop control of the TS in the O-RAN context. The insights presented in this work will pave the way for 0- RAN that are completely automated, offering improved control and flexibility.

We propose a new power amplifier (PA) behavioral modeling approach, to characterize and compensate for the signal quality degrading effects induced by a PA with a machine learning (ML) aided piece-wise (PW) modeling approach. Instead of using a single pruned Volterra model, we use multiple small-size pruned Volterra models by classifying the input data into different classes. For that purpose, an ML classifier model is trained by extracting some crucial features from both the input signal statistics and the PA operating point. The simulation results indicate that our approach contributes to an improved performance/complexity trade-off than a single generalized memory polynomial (GMP) model in terms of PA behavior modeling and linearization.

This paper considers joint user identification and channel estimation (JUICE) in grant-free access with a clustered user activity pattern. In particular, we address the JUICE in massive machine-type communications (mMTC) network under correlated Rayleigh fading channels with unknown channel covariance matrices. We formulate the JUICE problem as a maximum a posteriori probability (MAP) problem with properly chosen priors to incorporate the partial knowledge of the UEs’ clustered activity and the unknown covariance matrices. We derive a computationally-efficient algorithm based on alternating direction method of multipliers (ADMM) to solve the MAP problem iteratively via a sequence of closed-form updates. Numerical results highlight the significant improvements brought by the proposed approach in terms of channel estimation and activity detection performances for clustered user activity patterns.

Hybrid beamforming (HBF) is a key enabler for millimeter-wave (mmWave) communications systems, but HBF optimizations are often non-convex and of large dimension. In this paper, we propose an efficient deep unfolding-based HBF scheme, referred to as ManNet-HBF, that approximately maximizes the system spectral efficiency (SE). It first factorizes the optimal digital beamformer into analog and digital terms, and then reformulates the resultant matrix factorization problem as an equivalent maximum-likelihood problem, whose analog beamforming solution is vectorized and estimated efficiently with ManNet, a lightweight deep neural network. Numerical results verify that the proposed ManNet-HBF approach has near-optimal performance comparable to or better than conventional model-based counterparts, with very low complexity and a fast run time. For example, in a simulation with 128 transmit antennas, it attains 98.62% the SE of the Riemannian manifold scheme but 13250 times faster.

We investigate the beam squint effect in uniform planar arrays (UPAs) and propose an efficient hybrid beam-forming (HBF) design to mitigate the beam squint in multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems operating at terahertz band. We first analyze the array gain and derive the closed-form beam squint ratio that characterizes the severity of the beam squint effect on UPAs. The effect is shown to be more severe with a higher fractional bandwidth, while it can be significantly mitigated when the shape of a UPA approaches a square. We then focus on the HBF design that maximizes the system spectral efficiency. The design problem is challenging due to the frequency-flat nature and hardware constraints of the analog beamformer. We over-come the challenges by proposing an efficient decoupling design in which the digital and analog beamformers admit closed-form solutions, which facilitate practical implementations. Numerical results validate our analysis and show that the proposed HBF design is robust to beam squint, and thus, it outperforms the state-of-the-art methods in wideband massive MIMO systems.

In this paper, we present a novel method that detects appearing targets and improves their detection probability in a known static environment. Ray tracing-based channel model is used to calculate the radio signal paths using a 3D model of the environment. This preliminary information and the measured radar image are delivered to the convolutional neural network (CNN) based autoencoder (AE). The output image provides an improved representation of appearing targets, as the redundancy coming from known static environment is modeled and cancelled from the radar image. The method does not need the traditional constant false alarm rate (CFAR) threshold setting, which is an advantage, especially in heterogenous environments. The functionality of the method is tested with radar measurements in a laboratory corridor. Our results show that environmental clutter decreases significantly in the radar image and new targets are more clearly distinguished.

With the advantages of low signaling overhead and latency, grant-free random access (GFRA) becomes a promising technology for supporting massive machine-type communications (mMTCs), but poses new challenges for active user detection (AUD) and channel estimation (CE), whose performance mainly depends on the preamble detection. In this article, we design the composite preamble based on differential phase rotations by aggregating orthogonal Zadoff-Chu (ZC) sequences and multiple root ZC sequences with differential phase rotations to reduce the probability of preamble collisions, thereby improving the performance of AUD and CE. In particular, differential phase rotations extend the preamble set size so that users colliding in orthogonal sequences can be distinguished by phase rotations. In addition, it also reduces nonorthogonal interference and thus reduces CE errors. The preamble detection algorithm and CE scheme are proposed, along with the theoretical analysis of AUD and CE performance to verify the effectiveness of the designed preamble. In addition, the proposed preamble is extended to combine phase rotations with cyclic shifts to further enlarge the preamble set size with low nonorthogonality. Simulation results show that the proposed composite preamble outperforms existing preambles in terms of the probability of detection and CE accuracy.

Cellular networks that are traditionally designed for human-type communication (HTC) have the potential to provide cost effective connectivity to machine-type communication (MTC). However, MTC is characterized by unprecedented traffic in cellular networks, thus posing a challenge to its successful incorporation. In this work, we propose a unified framework for amicable coexistence of MTC and HTC. We consider a heterogeneous network where machine-type devices coexist with enhanced mobile broadband (eMBB) devices and propose transceiver techniques that promote efficient signal recovery from these devices. For this, we present an eMBB pilot and MTC data generation strategy that facilitates joint coherent decoding of eMBB data and non-coherent decoding of MTC data. Furthermore, we assess the feasibility of coexistence using receiver operating characteristics, outage probability, and normalized mean square error (NMSE). Our numerical results reveal that a harmonious coexistence of the heterogeneous services can be achieved with properly configured average signal-to-noise ratios and pilot length.

A cell-free (CF) massive multiple-input-multiple-output (mMIMO) system can provide uniform spectral efficiency (SE) with simple signal processing. On the other hand, a recently introduced technology called hybrid relay-reflecting intelligent surface (HR-RIS) can customize the physical propagation environment by simultaneously reflecting and amplifying radio waves in preferred directions. Thus, it is natural that incorporating HR-RIS into CF mMIMO can be a symbiotic convergence of these two technologies for future wireless communications. This motivates us to consider an HR-RIS-aided CF mMIMO system to utilize their combined benefits. We first model the uplink/downlink channels and derive the minimum-mean-square-error estimate of the effective channels. We then present a comprehensive analysis of SE performance of the considered system. Specifically, we derive closed-form expressions for the uplink and downlink SE. The results reveal important observations on the performance gains achieved by HR-RISs compared to conventional systems. The presented analytical results are also valid for conventional CF mMIMO systems and those aided by passive reconfigurable intelligent surfaces. Such results play an important role in designing new transmission strategies and optimizing HR-RIS-aided CF mMIMO systems. Finally, we provide extensive numerical results to verify the analytical derivations and the effectiveness of the proposed system design under various settings.

Unmanned aerial vehicles (UAVs) are the emerging vital components of millimeter wave (mmWave) wireless systems. Accurate beam alignment is essential for efficient beam based mmWave communications of UAVs with base stations (BSs). Conventional beam sweeping approaches often have large overhead due to the high mobility and autonomous operation of UAVs. Learning-based approaches greatly reduce the overhead by leveraging UAV data, like position to identify optimal beam directions. In this paper, we propose a deep Q-Network(DQN)-based framework for uplink UAV-BS beam alignment where the UAV hovers around 5G new radio (NR) BS coverage area, with varying channel conditions. The proposed learning framework uses the location information and maximize the beamforming gain upon every communication request from UAV inside the multi-location environment. We compare the proposed framework against multi-armed bandit (MAB)-based and exhaustive approaches, respectively and then analyse its training performance over different coverage area requirements, antenna configurations and channel conditions. Our results show that the proposed framework converge faster than the MAB-based approach and comparable to traditional exhaustive approach in an online manner under real-time conditions. Moreover, this approach can be further enhanced to predict the optimal beams for unvisited UAV locations inside the coverage using correlation from neighbouring grid locations.

Intelligent reflecting surfaces (IRSs) have shown huge advantages in many potential use cases and thus have been considered a promising candidate for next-generation wireless systems. In this paper, we consider an IRS-assisted multigroup multicast (IRS-MGMC) system in a multiple-input single-output (MISO) scenario, for which the related existing literature is rather limited. In particular, we aim to jointly design the transmit beamformers and IRS phase shifts to maximize the sum rate of the system under consideration. In order to obtain a numerically efficient solution to the formulated non-convex optimization problem, we propose an alternating projected gradient (APG) method where each iteration admits a closed-form and is shown to be superior to a known solution that is derived from the majorization-minimization (MM) method in terms of both achievable sum rate and required complexity, i.e., run time. In particular, we show that the complexity of the proposed APG method grows linearly with the number of IRS tiles, while that of the known solution in comparison grows with the third power of the number of IRS tiles. The numerical results reported in this paper extend our understanding on the achievable rates of large-scale IRS-assisted multigroup multicast systems.

We consider a novel hybrid active-passive reconfigurable intelligent surface (RIS)-assisted unmanned aerial vehicle (UAV) air-ground communications system. Unlike the conventional passive RIS, the hybrid RIS is equipped with a few active elements to not only reflect but also amplify the incident signals for significant performance improvement. Towards a fairness design, our goal is to maximize the minimum rate among users through jointly optimizing the location and power allocation of the UAV and the RIS reflecting/amplifying coefficients. The formulated optimization problem is nonconvex and challenging, which is efficiently solved via block coordinate descend and successive convex approximation. Our numerical results show that a hybrid RIS requires only 4 active elements and a power budget of 0 dBm to achieve an improvement of 52.08% in the minimum rate, while that achieved by a conventional passive RIS with the same total number of elements is only 18.06%.

Unmanned aerial vehicles (UAVs) are the vital components of sixth generation (6G) millimeter wave (mmWave) wireless networks. Fast and reliable beam alignment is essential for efficient beam-based mmWave communications between UAVs and the base stations (BSs). Learning-based approaches may greatly reduce the overhead by leveraging UAV data, such as position, to identify the optimal beam directions. In this paper, we propose a deep reinforcement learning (DRL)-based framework for UAV-BS beam alignment using the hierarchical deep Q-Network (hDQN) in a mmWave radio setting. We consider uplink communications where the UAV hovers around 5G new radio (NR) BS coverage area, with three dimensional (3D) beams under diverse channel conditions. A BS serves with learnt beam-pairs in an uplink manner upon every communication request from UAV inside the multi-location environment. Compared to our prior DQN-based method, the proposed hDQN framework uses the location information and the fixed spatial arrangement of the antenna elements to reduce the beam search complexity and maximize the data rates efficiently. The results show that our proposed hDQN-based framework converges faster than the DQN-based approach with an average overall training reduction of 43% and, is generic to multi-location environments across different uniform planar array (UPA) configurations and diverse channel conditions.

A reconfigurable intelligent surface (RIS) can be used to control the propagation of electromagnetic waves (EM). By deploying the RIS units in radio environment allows to steer the transmitted EM waves to areas that are otherwise shadowed by buildings or other geographic formations resulting in coverage enhancement. The gain offered by the RIS is due to its ability to focus the impinging signal to the desired direction. Hence, errors in the beamforming degrade the performance of a RIS-assisted link. The effect of the errors in the beamforming at the RIS are studied in this paper. Simulations show that the errors especially in the zenith angle can have significant effect on the uplink performance. With a RIS based on a 16 × 16 element reflect array errors greater than 1° start to limit the achievable throughput. After the RIS is configured based on the direction information, the user equipment must use a proper precoder to capitalize the benefit of the path via the RIS. The usability of existing precoders from the 5G NR are also studied in this paper. In the simulated use case, utilizing a RIS with 5G NR precoders resulted in 3.6 dB gain in the uplink direction.

We consider in this work a cell-free (CF) massive multiple-input-multiple-output (mMIMO) system where multiple hybrid relay-reflecting intelligent surfaces (HR-RIS) are deployed to assist communication between access points and users. We first present the signal model and derive the minimum-mean-square-error estimate of the effective channels. We then present a comprehensive analysis for the considered HR-RIS-aided CF mMIMO system, where the closed-form expression of the downlink throughput is derived. The presented analytical results are also valid for conventional CF mMIMO systems, i.e., CF mMIMO systems with and without passive reconfigurable intelligent surfaces. Finally, the analytical derivations are verified by extensive numerical results.

The hybrid relay-reflecting intelligent surface (HRRIS) has been recently introduced as an efficient solution to overcome the double path loss and limited beamforming diversity of the conventional fully passive reflecting surface. This motivates us to investigate the application of the HR-RIS in improving the secrecy capacity of millimeter wave multiple-input-multiple-output (MIMO) systems with the presence of multi-antenna eavesdropper. The joint optimization of the transmit beamformer and the relay-reflecting coefficients at RIS is tackled via alternating optimization. In the proposed solution, a closed-form expression for the optimal transmit beamformer at the transmitter is derived, and a metaheuristic solution based on particle swarm optimization is proposed to optimize the active and passive elements at the HR-RIS. The simulation results verify that under various scenarios, the HR-RIS provides significant improvement in the secrecy capacity with respect to the conventional passive reflecting surface.

While intelligent reflecting surfaces (IRSs) and non-orthogonal multiple access (NOMA) techniques have shown great potential to boost the spectral and energy efficiency for future wireless networks, unmanned aerial vehicles (UAVs) are committed for enhancing the wireless connectivity with fast and flexible deployment. In this regard, we study an integration of an IRS in UAV-enabled wireless relaying system using NOMA transmissions. We also count on the impacts of residual hardware impairments (HIs) in user devices and imperfect successive interference cancellation (SIC) in NOMA, which are inevitable in practical system implementation. We analyze the system performance by deriving the closed-form expressions of outage probability (OP) and system throughput over the line-of-sight (LoS) Rician fading channels for the aerial links. We further pursue asymptotic OP analysis to reveal useful insights on the achievable diversity order. Above all, we present a deep neural network (DNN) framework for OP prediction with a short execution time under the dynamic stochastic environment. Our results validate the theoretical proposition and accentuate the performance advantages of the proposed UAV-borne IRS relaying NOMA system.

Configuration of a reconfigurable intelligent surface (RIS) as a solution to an optimization problem for spectral or energy efficiency requires the estimation of the channels between the transmitter and the RIS and between the RIS and the receiver. The channel estimation and the RIS configuration by optimization are computationally intensive processes in RIS assisted wireless links. The approach proposed in this paper simplifies and speeds up the RIS configuration process by relaxing the demand to find the optimal solution but instead aims to reach a predetermined channel quality measured with the signal-to-noise ratio. It is also shown that when a single dominant path exists between a mobile user and the RIS and also between the RIS and a base station, the RIS configuration calculated as a solution to a spectral efficiency maximization problem and configuration based on conventional antenna array beam steering give the same result.

With massive deployment, multiple-input–multiple-output (MIMO) systems continue to take mobile communications to new heights, but the ever-increasing demands mean that there is a need to look beyond MIMO and pursue the next disruptive wireless technologies. Reconfigurable intelligent surface (RIS) is widely considered a key candidate technology block to provide the next generational leap. The first part of this article provides an updated overview of the conventional reflection-based RIS technology, which complements the existing literature to include active and semiactive RIS, and the synergies with cell-free massive MIMO (CF mMIMO). Then, we widen the scope to discuss the surface-wave-assisted RIS that represents a different design dimension in utilizing metasurface technologies. This goes beyond being a passive reflector and can use the surface as an intelligent propagation medium for superb radio propagation efficiency. The third part of this article turns the attention to the fluid antenna, a novel antenna technology that enables a diverse form of reconfigurability that can combine with RIS for ultrahigh capacity, power efficiency, and scalability. This article concludes with a discussion of the potential synergies that can be exploited between MIMO, RIS, and fluid antennas.

Reconfigurable intelligent surfaces (RISs) have been proposed as a key enabler to improve the coverage of the signals and mitigate the frequent blockages in millimeter wave (mmWave) multiple-input multiple-output (MIMO) communications. However, the channel state information (CSI) acquisition is one of the major challenges for the practical deployment of the RIS. The passive RIS without any baseband processing capabilities brings difficulty on the channel estimation (CE), since the individual channels or the cascaded one can be estimated only at base station (BS) via uplink training or mobile station (MS) via downlink training. In order to facilitate the CSI acquisition, we focus on the hybrid RIS architecture, where a small number of elements are active and able to receive and process the pilot signals at the RIS. The CE is performed in two stages by following the atomic norm minimization to recover the channel parameters, i.e., angles of departure (AoDs), angles of arrival (AoAs), and propagation path gains. Simulation results show that the proposed scheme can outperform the passive RIS CE under the same training overhead. Furthermore, we also study the theoretical performance limits in terms of mean square error (MSE) via Cramér-Rao lower bound (CRLB) analyses.

Grant-free access is considered as a key enabler for massive machine-type communications (mMTC) as it promotes energy-efficiency and small signalling overhead. Due to the sporadic user activity in mMTC, joint user identification and channel estimation (JUICE) is a main challenge. This paper addresses the JUICE in single-cell mMTC with single-antenna users and a multi-antenna base station (BS) under spatially correlated fading channels. In particular, by leveraging the sporadic user activity, we solve the JUICE in a multi measurement vector compressed sensing (CS) framework under two different cases, with and without the knowledge of prior channel distribution information (CDI) at the BS. First, for the case without prior information, we formulate the JUICE as an iterative reweighted$\ell _{2,1}$-norm minimization problem. Second, when the CDI is known to the BS, we exploit the available information and formulate the JUICE from a Bayesian estimation perspective as a maximuma posterioriprobability (MAP) estimation problem. For both JUICE formulations, we derive efficient iterative solutions based on the alternating direction method of multipliers (ADMM). The numerical experiments show that the proposed solutions achieve higher channel estimation quality and activity detection accuracy with shorter pilot sequences compared to existing algorithms.

Unmanned aerial vehicles (UAVs) are the emerging vital components of millimeter wave (mmWave) wireless systems. Accurate beam alignment is essential for efficient beam based mmWave communications of UAVs with base stations (BSs). Conventional beam sweeping approaches often have large overhead due to the high mobility and autonomous operation of UAVs. Learning-based approaches greatly reduce the overhead by leveraging UAV data, like position to identify optimal beam directions. In this paper, we propose a reinforcement learning (RL)-based framework for UAV-BS beam alignment using deep Q-Network (DQN) in a mmWave setting. We consider uplink communications where the UAV hovers around 5G new radio (NR) BS coverage area, with varying channel conditions. The proposed learning framework uses the location information to maximize data rate through the optimal beam-pairs efficiently, upon every communication request from UAV inside the multi-location environment. We compare our proposed framework against Multi-Armed Bandit (MAB) learning-based approach and the traditional exhaustive approach, respectively and also analyse the training performance of DQN-based beam alignment over different coverage area requirements and channel conditions. Our results show that the proposed DQN-based beam alignment converge faster and generic for different environmental conditions. The framework can also learn optimal beam alignment comparable to the exhaustive approach in an online manner under real-time conditions.

Joint user identification and channel estimation (JUICE) is a main challenge in grant-free massive machine-type communications (mMTC). The sparse pattern in users’ activity allows to solve the JUICE as a compressed sensing problem in a multiple measurement vector (MMV) setup. This paper addresses the JUICE under the practical spatially correlated fading channel. We formulate the JUICE as an iterative reweighted ℓ2,1-norm optimization. We develop a computationally efficient alternating direction method of multipliers (ADMM) approach to solve it. In particular, by leveraging the second-order statistics of the channels, we reformulate the JUICE problem to exploit the covariance information and we derive its ADMM-based solution. The simulation results highlight the significant improvements brought by the proposed approach in terms of channel estimation and activity detection performances.

Deep learning models usually assume that training dataset and target data have the same distribution. If this is not the case, model mismatch causes performance degradation when the model is used with the real data. With radio frequency (RF) measurements from real data traffic, the exact distribution of the measurements is unknown in many cases and model mismatch is unavoidable. This is known as concept drift, or model mis- specification in deep learning, which we are interested in for cognitive radio dynamic spectrum access predictions. In this paper, we present three concept drift detection methods and their corresponding very large scale integration (VLSI) circuits. The circuits are mapped on a Xilinx Virtex-7 field-programmable gate array (FPGA) and the resource utilization results are provided.

In this paper, we utilize tools from stochastic geometry to estimate the interference propagation via reconfigurable intelligent surface (RIS) in the millimeter wave (mmWave, 30–300 GHz) band and specifically on the D band (110–170 GHz). The RISs have been of great interest lately to maximize the channel gains in non-line-of-sight (NLOS) communication situations. We derive expressions for stochastic interference level in RIS powered systems and validate those with simulations. It will be shown that the interference levels via RIS link are rather small compared to the designed RIS link or the LOS interference as the random interference loses significant part of the RIS gain. We also analyse the validity of far field channel and antenna gains in the near field of a large array. It is shown that, while the high frequency systems require large arrays that push the far field far away from the antenna, the far field equations are very accurate up to about half way of the near field.

As a key enabler for massive machine-type communications (mMTC), spatial multiplexing relies on massive multiple-input multiple-output (mMIMO) technology to serve the massive number of user equipments (UEs). To exploit spatial multiplexing, accurate channel estimation through pilot signals is needed. In mMTC systems, it is impractical to allocate a unique orthogonal pilot sequence to each UE as it would require too long pilot sequences, degrading the spectral efficiency. This work addresses the design of channel features from correlated fading channels to assist the pilot assignment in multi-sector mMTC systems under pilot reuse of orthogonal sequences. In order to reduce pilot collisions and to enable pilot reuse, we propose to extract features from the channel covariance matrices that reflect the level of orthogonality between the UEs channels. Two features are investigated: covariance matrix distance (CMD) feature and CMD-aided channel charting (CC) feature. In terms of symbol error rate and achievable rate, the CC-based feature shows superior performance than the CMD-based feature and baseline pilot assignment algorithms.

Hybrid beamforming is considered a key enabler to realize millimeter wave (mmWave) multiple-input multiple-output (MIMO) communications due to its capability of considerably reducing the number of costly and power-hungry radio frequency chains in the transceiver. However, in mmWave MIMO orthogonal frequency-division multiplexing (MIMO-OFDM) systems, hybrid beamforming design is challenging because the analog precoder and combiner are required to be shared across the whole employed bandwidth. In this paper, we propose closed-form solutions to the problem of designing the analog precoder/combiner in a mmWave MIMO-OFDM system by maximizing the upper bound of the spectral efficiency. The closed-form solutions facilitate the design of analog beamformers while guaranteeing state-of-art performance. Numerical results show that the proposed algorithm attains a slightly improved performance with much lower computational complexity compared to the considered benchmarks.

The reconfigurable intelligent surface (RIS) plays an important role in maintaining the connectivity in millimeter wave (mmWave) MIMO systems when the direct channel between the transceivers is blocked. However, it is difficult to acquire the channel state information (CSI), which is essential for the design of RIS phase control matrix and beamforming vectors at the transceivers. In this paper, we compare the channel estimation (CE) performance and achieved spectral efficiency (SE) of the purely passive and hybrid RIS architectures. CE is done via atomic norm minimization (ANM). For the purely passive RIS, we follow a two-stage procedure to sequentially estimate the channel parameters, while for the hybrid RIS we estimate the individual channels at the RIS based on the observations from active RIS elements assuming alternating uplink and downlink training. The simulation results show that the purely passive RIS brings better CE and SE performance compared to the hybrid RIS under the same training overhead. We further consider different setups for the hybrid RIS and study the tradeoffs among them.

Reconfigurable intelligent surfaces (RIS) have the ability to steer the electromagnetic (EM) waves to a desired direction. This enables the improvement of the wireless link performance by allowing the illumination of receivers otherwise shadowed by buildings or hills. In this paper, a standards-compliant link-level simulator is developed to study the performance improvement offered by a RIS in 5G New Radio (NR) uplink operating at sub-6 GHz bands. At these frequencies the direct channel between the user and base station is rarely completely blocked, but given the stringent power restrictions of devices, the RIS is utilized for enhancing the coverage performance in the uplink direction. In the studied cases, the transmitter (TX) is close to the RIS and a line-of-sight (LoS) path between the TX and RIS is assumed. The channel between the TX and receiver (RX) is modeled as a non-line-of-sight (NLoS) channel with 5G NR clustered delay line A (CDL-A) profile. Both LoS and NLoS channels between the RIS and RX are considered. Under state-of-the-art system settings, the RIS is shown to increase the symbol error rate link performance by 6 dB. When the performance is measured with coded bit error rate, the performance gain in simulated cases is 1 dB. The coverage enhancement is measured with the throughput as a function of the distance between the TX and RX. The distance at which the maximum possible throughput can be achieved is increased about 5%. The coverage can be further extended if a lower than the maximum throughput is accepted.

In this article, we propose a novel data detection algorithm and a corresponding VLSI design for massive multiuser (MU) multiple-input-multiple-output (MIMO) wireless systems. Our algorithm uses alternating direction method of multipliers (ADMM)-based infinity-norm-constrained equalization and is called ADMIN. ADMIN is an iterative algorithm that outperforms linear detectors by a large margin when the ratio between the numbers of base-station (BS) and user antennas is small. In the first iteration, ADMIN computes the linear minimum mean-square error (MMSE) solution, which is sufficient when the ratio between the numbers of BS and user antennas is large. We develop time-shared and iterative VLSI architectures for LDL-decomposition-based soft-output ADMIN supporting 16- and 32-user systems. We present application-specific integrated circuit (ASIC) designs for 16-64 antenna base stations in 28-nm CMOS that supports up to 64 quadrature amplitude modulation (QAM). The 16-user ADMIN ASIC achieves 303 Mb/s while dissipating 85 mW. The 32-user ADMIN ASIC achieves 287 and 241 Mb/s while dissipating 121 and 135 mW for 32 and 64 BS antennas, respectively. ADMIN has also been implemented on a Xilinx Virtex-7 field-programmable gate array (FPGA) and is compared with state-of-the-art massive MIMO data detectors.

A reconfigurable intelligent surface (RIS) can shape the radio propagation environment by virtue of changing the impinging electromagnetic waves towards any desired directions, thus, breaking the general Snell's reflection law. However, the optimal control of the RIS requires perfect channel state information (CSI) of the individual channels that link the base station (BS) and the mobile station (MS) to each other via the RIS. Thereby super-resolution channel (parameter) estimation needs to be efficiently conducted at the BS or MS with CSI feedback to the RIS controller. In this paper, we adopt a two-stage channel estimation scheme for RIS-aided millimeter wave (mmWave) MIMO systems without a direct BS-MS channel, using atomic norm minimization to sequentially estimate the channel parameters, i.e., angular parameters, angle differences, and the products of propagation path gains. We evaluate the mean square error of the parameter estimates, the RIS gains, the average effective spectrum efficiency bound, and average squared distance between the designed beamforming and combining vectors and the optimal ones. The results demonstrate that the proposed scheme achieves super-resolution estimation compared to the existing benchmark schemes, thus offering promising performance in the subsequent data transmission phase.

Providing high-bandwidth and fast-speed links, wireless local area networks (WLANs) in the Terahertz (THz) band have huge potential for various bandwidth-intensive indoor applications. However, due to the specific phenomena in the THz band, including severe reflection loss, indoor blockage effects, multi-path fading, the analysis on the interference and coverage probability at a downlink is challenging. In this paper, indoor blockage effects caused by the walls and human bodies are analyzed. Next, a statistical THz channel model is proposed to characterize the THz indoor propagation. In light of these, the moment generating functions of the aggregated interference and theoretical expressions for the mean interference power are derived. As a result, the approximated coverage probability and average network throughput are derived. Extensive numerical results show that for the nearest access point (nearest-AP) user association scheme, the optimal AP density is 0.15/m2, which results in the coverage probability reaches 93% and the average network throughput is 30 Gbps/m2. In addition, by adopting a novel line-of-sight access point (LoS-AP) user association mechanism, the coverage probability and the average network throughput can be further improved by 3 percent and 2 Gbps/m2, respectively.

A reconfigurable intelligent surface (RIS) can shape the radio propagation by passively changing the directions of impinging electromagnetic waves. The optimal control of the RIS requires perfect channel state information (CSI) of all the links connecting the base station (BS) and the mobile station (MS) via the RIS. Thereby the channel (parameter) estimation at the BS/MS and the related message feedback mechanism are needed. In this paper, we adopt a two-stage channel estimation scheme for the RIS-aided millimeter wave (mmWave) MIMO channels using an iterative reweighted method to sequentially estimate the channel parameters. We evaluate the average spectrum efficiency (SE) and the RIS beamforming gain of the proposed scheme and demonstrate that it achieves high-resolution estimation with the average SE comparable to that with perfect CSI.

We consider uplink multiuser wireless communications systems, where the base station (BS) receiver is equipped with a large-scale antenna array and resolution adaptive analog-to-digital converters (ADCs). The aim is to maximize the energy efficiency (EE) at the BS subject to constraints on the users' quality-of-service. The approach is to jointly optimize both the number of quantization bits at the ADCs and the on/off modes of the radio frequency (RF) processing chains. The considered problem is a discrete nonlinear program, the optimal solution of which is difficult to find. We develop an efficient algorithm based on the discrete branch-reduce-and-bound (DBRnB) framework. It finds the globally optimal solutions to the problem. In particular, we make some modifications, which significantly improve the convergence performance. The numerical results demonstrate that optimizing jointly the number of quantization bits and on/off mode can achieve remarkable EE gains compared to only optimizing the number of quantization bits.

We consider a secure transmission including a transmitter, a receiver and an eavesdropper, each being equipped with multiple antennas. The aim is to develop a low-complexity and scalable method to find a globally optimal solution to the problem of secrecy rate maximization under a total power constraint at the transmitter. In principle, the original formulation of the problem is nonconvex. However, it can be equivalently translated into finding a saddle point of a minimax convex-concave program. An existing approach finds the saddle point using the Newton method, whose computational cost increases quickly with the number of transmit antennas, making it unsuitable for large scale antenna systems. To this end, we propose an iterative algorithm based on alternating optimization, which is guaranteed to converge to a saddle point, and thus achieves a globally optimal solution to the considered problem. In particular, each subproblem of the proposed iterative method admits a closed-form solution. We analytically show that the iteration cost of our proposed method is much cheaper than that of the known solution. As a result, numerical results demonstrate that the proposed method remarkably outperforms the existing one in terms of the overall run time.

Low latency communications is one of the key design targets in future wireless networks. We propose a queue aware algorithm to optimize resources guaranteeing low latency in multiple-input single-output (MISO) networks. Proposed system model is based on dynamic network architecture (DNA), where some terminals can be configured as temporary access points (APs) on demand when connected to the Internet. Therein, we jointly optimize the user-AP association and queue weighted sum rate of the network, subject to limitations of total transmit power of the APs and minimum delay requirements of the users. The user-AP association is viewed as finding a sparsity constrained solution to the problem of minimizing ℓq-norm of the difference between queue and service rate of users. Finally, the efficacy of the proposed algorithm in terms of network latency and its fast convergence are demonstrated using numerical experiments. Simulation results show that the proposed algorithm yields up to two-fold latency reductions compared to the state-of-the-art techniques.

Millimeter-wave (mmWave) multiple-input multiple-output (MIMO) system for the fifth generation (5G) cellular communications can also enable single-anchor positioning and object tracking due to its large bandwidth and inherently high angular resolution. In this paper, we introduce the newly invented concept, large intelligent surface (LIS), to mmWave positioning systems, study the theoretical performance bounds (i.e., Cramér-Rao lower bounds) for positioning, and evaluate the impact of the number of LIS elements and the value of phase shifters on the position estimation accuracy compared to the conventional scheme with one direct link and one non-line-of-sight path. It is verified that better performance can be achieved with a LIS from the theoretical analyses and numerical study.

We propose a decision triggered data transmission and collection (DTDTC) protocol for condition monitoring and anomaly detection in the industrial Internet of things (IIoT). In the IIoT, the collection, processing, encoding, and transmission of the sensor readings are usually not for the reconstruction of the original data but for decision making at the fusion center. By moving the decision making process to the local end devices, the amount of data transmission can be significantly reduced, especially when normal signals with positive decisions dominate in the whole life cycle and the fusion center is only interested in collecting the abnormal data. The proposed concept combines compressive sensing, machine learning, data transmission, and joint decision making. The sensor readings are encoded and transmitted to the fusion center only when abnormal signals with negative decisions are detected. All the abnormal signals from the end devices are gathered at the fusion center for a joint decision with feedback messages forwarded to the local actuators. The advantage of such an approach lies in that it can significantly reduce the volume of data to be transmitted through wireless links. Moreover, the introduction of compressive sensing can further reduce the dimension of data tremendously. An exemplary case, i.e., diesel engine condition monitoring, is provided to validate the effectiveness and efficiency of the proposed scheme compared to the conventional ones.

The authors consider the problem of compressive sensed video recovery via iterative thresholding algorithm. Traditionally, it is assumed that some fixed sparsifying transform is applied at each iteration of the algorithm. In order to improve the recovery performance, at each iteration the thresholding could be applied for different transforms in order to obtain several estimates for each pixel. Then the resulting pixel value is computed based on obtained estimates using simple averaging. However, calculation of the estimates leads to significant increase in reconstruction complexity. Therefore, the authors propose a heuristic approach, where at each iteration only one transform is randomly selected from some set of transforms. First, they present simple examples, when block‐based 2D discrete cosine transform is used as the sparsifying transform, and show that the random selection of the block size at each iteration significantly outperforms the case when fixed block size is used. Second, building on these simple examples, they apply the proposed approach when video block‐matching and 3D filtering (VBM3D) is used for the thresholding and show that the random transform selection within VBM3D allows to improve the recovery performance as compared with the recovery based on VBM3D with fixed transform.

We investigate the coordinated multi-point noncoherent joint transmission (JT) in dense small cell networks. The goal is to design beamforming vectors for macro cell and small cell base stations (BSs) such that the weighted sum rate of the system is maximized, subject to a total transmit power at individual BSs. The optimization problem is inherently nonconvex and intractable, making it difficult to explore the full potential performance of the scheme. To this end, we first propose an algorithm to find a globally optimal solution based on the generic monotonic branch reduce and bound optimization framework. Then, for a more computationally efficient method, we adopt the inner approximation (InAp) technique to efficiently derive a locally optimal solution, which is numerically shown to achieve near-optimal performance. In addition, for decentralized networks such as those comprising of multi-access edge computing servers, we develop an algorithm based on the alternating direction method of multipliers, which distributively implements the InAp-based solution. Our main conclusion is that the noncoherent JT is a promising transmission scheme for dense small cell networks, since it can exploit the densitification gain, outperforms the coordinated beamforming, and is amenable to distributed implementation.

We study rate-distortion (RD) performance of various single-sensor compressed sensing (CS) schemes for acquiring sparse signals via quantized/encoded noisy linear measurements, motivated by low-power sensor applications. For such a quantized CS (QCS) context, the paper combines and refines our recent advances in algorithm designs and theoretical analysis. Practical symbol-by-symbol quantizer based QCS methods of different compression strategies are proposed. The compression limit of QCS - the remote RDF - is assessed through an analytical lower bound and a numerical approximation method. Simulation results compare the RD performances of different schemes.

Beamforming design for downlink coordinated multi-point (CoMP) transmission of dense small cell networks is considered. The goal is to maximize the weighted sum rate of the system subject to constraints on maximum transmit power at macro cell and small cell base stations (BSs). Here we focus on the noncoherent joint transmission technique, since it does not require tight network synchronization, and thus is more practically appealing. The considered optimization problem is intractable, thus we adopt the inner approximation (IA) to efficiently derive a locally optimal solution. Then, we develop a distributed implementation of the proposed IA based algorithm, based on the alternating direction method of multipliers (ADMM). More explicitly, in the distributed algorithm, the convex approximate subproblems obtained by the IA principles are solved by the ADMM procedure. As the result, the beamforming vectors are computed locally at the BSs. Numerical results are provided to confirm the validity of the proposed algorithms.

Phased arrays used in millimeter-wave systems challenge the concept of power amplifier (PA) linearization by digital predistortion (DPD). This is due to the shared digital path and inaccuracies in analog beamforming and other component variations. However, the group behavior of multiple parallel nonlinear branches can be expected to be more predictable due to averaging effect compared to a single branch behavior. In this paper, we use a power adaptive nonlinear model to mimic the average behavior of a single PA and utilize the probability distribution of the input power of each individual PA to approximate the expected nonlinear behavior of the array over-the-air. The approximated array response is used for the DPD training. The simulation results indicate that the proposed approach provides good linearization performance for large arrays that have varying amplitude and phase weights.

A ray tracing simulator for urban and indoor environments is introduced. The simulator uses NVIDIA graphics processing unit (GPU) accelerated CUDA parallel computing platform and programming mode and the OptiX Ray Tracing Engine. As a use case, channel characteristics for Global Navigation Satellite System (GNSS) satellites are simulated and compared with measurements in an urban area. The speedup achieved by parallel processing allows computation of multiple relevant reflections characteristic of a satellite channel.

Massive multiple-input multiple-output (MIMO) systems have been proposed to meet the user demands in terms of performance and quality of service (QoS). Due to the large number of antennas, detectors in massive MIMO are playing a crucial role in guaranteeing a satisfactory performance, while their complexity is also being increased. This paper considers several approximate algorithms to avoid direct matrix inversion, namely the Neumann method, the Gauss-Seidel (GS) method, the successive over-relaxation (SOR) method, the Jacobi method, the Richardson method, the optimized coordinate descent (OCD), and the conjugate gradients (CG) method. Also, this paper presents a comparison among the approximate matrix inversion methods and the minimum mean square error (MMSE). Simulation of 16×128, and 16×32 MIMO systems shows that a detector based on the GS method outperforms other detectors when the ratio of base station (BS) antennas to user terminal antennas, β, is small. On the other hand, the detector based on the SOR method outperforms the other approximate matrix inversion methods when β is large. In addition, this paper studies and recommends the setting values of relaxation parameter (ω) in the SOR and Richardson methods. It also provides a comparison among the approximate matrix inversion methods in the number of multiplications. Simulation results show that the Neumann method, the OCD method, and the CG method achieve the lowest number of multiplications while the CG method outperforms the Neumann and the OCD methods. This paper also shows that not every iteration improves the performance.

We consider a multi-pair amplify-and-forward relay network where the energy-constrained relays adopting the time-switching protocol harvest energy from the radio-frequency signals transmitted by the users for assisting user data transmission. Both one-way and two-way relaying techniques are investigated. Aiming at energy efficiency (EE) fairness among the user pairs, we construct an energy consumption model incorporating rate-dependent signal processing power, the dependence on output power level of power amplifiers' efficiency, and nonlinear energy harvesting (EH) circuits. Then, we formulate the max-min EE fairness problems in which the data rates, users' transmit power, relays' processing coefficient, and EH time are jointly optimized under the constraints on the quality of service and users' maximum transmit power. To achieve efficient suboptimal solutions to these nonconvex problems, we devise monotonic descent algorithms based on the inner approximation (IA) framework, which solve a second-order-cone program in each iteration. To further simplify the designs, we propose an approach combining IA and zero-forcing beamforming, which eliminates inter-pair interference and reduces the numbers of variables and required iterations. Finally, extensive numerical results are presented to validate the proposed approaches. More specifically, the results demonstrate that ignoring the realistic aspects of power consumption might degrade the performance remarkably, and jointly designing parameters involved could significantly enhance the EE.

A joint beamforming and remote radio head (RRH)-user association design for downlink of cloud radio access networks (CRANs) is considered. The aim is to maximize the system energy efficiency subject to constraints on users' quality-of-service, capacity of front haul links and transmit power. Different to the conventional power consumption models, we embrace the dependence of baseband signal processing power on the data rate, and the dynamics of the power amplifiers' efficiency. The considered problem is a mixed Boolean nonconvex program whose optimal solution is difficult to find. As our main contribution, we provide a discrete branch-reduce-and-bound (DBRnB) approach to solve the problem globally. We also make some modifications to the standard DBRnB procedure. Those remarkably improve the convergence performance. Numerical results are provided to confirm the validity of the proposed method.

In this paper, we present a novel two-path channel model for wireless terahertz (THz) systems operating in the range of 275 to 400 GHz, which accommodates both the channel particularities and the transceivers parameters. The channel particularities include the frequency selectivity, path-loss, as well as the atmospheric conditions, namely temperature, relative humidity and pressure, while the transceiver parameters, which are taken into account, are the antenna gains as well as the power of the transmitted signal. Finally, we evaluated the THz system performance in terms of average signal to noise ratio and ergodic capacity.

The main challenge in full-duplex transceiver design is the self-interference (SI). Analog SI isolation is performed at radio frequency (RF) signal by using an antenna design based on the characteristic modes theory and using active cancelation processing. The RF canceler is tuned using an automatic gain control (AGC) enhanced variable-step steepest descent algorithm while transmitting a data signal. The inclusion of a non-linear power amplifier does not affect the tuning performance, when realistic adjacent channel power ratio cases are considered. However, the changes in the SI channel can have a major impact on the full-duplex link due to need to re-tune the canceler repeatedly.

In this paper, we establish an appropriate system model for the terahertz (THz) wireless links in the range of 275 to 400 GHz, which accommodates the channel particularities and transceivers parameters. The former includes the frequency selectivity, pathloss, as well as the atmospheric conditions, namely temperature and pressure, whereas the latter are assumed to be consisted of the antenna gains as well as the power allocation of the transmitted signal. Moreover, we present analytical expressions of low computational complexity, for the evaluation of the average SNR, and capacity of the line of sight wireless THz links. These expressions are expected to be the key tools for the design of the THz link.

The terahertz band (0.1-10 THz) point-to-point links offer very large spectral resources for extremely high data rate links or an ability to share the resources among large numbers of users or devices. The latter case causes a need to consider interference in the THz band networks. The stochastic geometry is a powerful tool to estimate the network level interference and its moments in the case of random networks. This paper extends the previous works by considering bandwidth limited networks, i.e., instead of generic modeling of the interference, we apply bandwidth limited transmissions to better model realistic networks that utilize limited resources. Furthermore, we assume heterogenous network with some of the nodes utilizing directional antennas, and others using isotropic antennas. Network is further assumed to be sparse in a sense that the noise causes partial limitations in the achievable signal-to-noise-plus-interference (SINR). The mean interference and the SINR are derived and their validity is verified with computer simulations.

The main challenge in full-duplex transceiver design is the self-interference (SI). Analog SI isolation is performed at radio frequency (RF) by using an antenna design based on the characteristic modes theory and using active cancelation principle. Two different structures based on using a vector down-converter and a complex multiplier are used for analog baseband SI cancellation. Cancellers are tuned using a automatic gain control (AGC) enhanced variable-step steepest descent algorithm while transmitting a data signal to a distant node in half-duplex mode. Simulations show that the inclusion of AGC into the tuning process speeds up the convergence significantly.

This paper addresses lossy distributed source coding for acquiring correlated sparse sources via compressed sensing (CS) in wireless sensor networks. Noisy CS measurements are separately encoded at a finite rate by each sensor, followed by the joint reconstruction of the sources at the decoder. We develop a novel complexity-constrained distributed variable-rate quantized CS method, which minimizes a weighted sum between the mean square error signal reconstruction distortion and the average encoding rate. The encoding complexity of each sensor is restrained by pre-quantizing the encoder input, i.e., the CS measurements, via vector quantization. Following the entropy-constrained design, each encoder is modeled as a quantizer followed by a lossless entropy encoder, and variable-rate coding is incorporated via rate measures of an entropy bound. For a two-sensor system, necessary optimality conditions are derived, practical training algorithms are proposed, and complexity analysis is provided. Numerical results show that the proposed method achieves superior compression performance as compared with baseline methods, and lends itself to versatile setups with different performance requirements.

We investigate lossy compressed sensing (CS) of a hidden, or remote, source, where a sensor observes a sparse information source indirectly. The compressed noisy measurements are communicated to the decoder for signal reconstruction with the aim to minimize the mean square error distortion. An analytically tractable lower bound to the remote rate-distortion function (RDF), i.e., the conditional remote RDF, is derived by providing support side information to the encoder and decoder. For this setup, the best encoder separates into an estimation step and a transmission step. A variant of the Blahut-Arimoto algorithm is developed to numerically approximate the remote RDF. Furthermore, a novel entropy coding based quantized CS method is proposed. Numerical results illustrate the main rate-distortion characteristics of the lossy CS, and compare the performance of practical quantized CS methods against the proposed limits.

Dynamic network architectures (DNAs) have been developed under the assumption that some terminals can be converted into temporary access points (APs) anytime when connected to the Internet. In this paper, we consider the problem of assigning a group of users to a set of potential APs with the aim to maximize the downlink system throughput of DNA networks, subject to total transmit power and users' quality of service (QoS) constraints. In our first method, we relax the integer optimization variables to be continuous. The resulting non-convex continuous optimization problem is solved using successive convex approximation framework to arrive at a sequence of second-order cone programs (SOCPs). In the next method, the selection process is viewed as finding a sparsity constrained solution to our problem of sum rate maximization. It is demonstrated in numerical results that while the first approach has better data rates for dense networks, the sparsity oriented method has a superior speed of convergence. Moreover, for the scenarios considered, in addition to comprehensively outperforming some well-known approaches, our algorithms yield data rates close to those obtained by branch and bound method.

We consider downlink transmission in cloud radio access networks with multiple cochannel multicasting groups served by a group of remote radio heads (RRHs), which receive information from a base band unit via finite-capacity fronthaul links. Our aim is to jointly design RRH selection and beamforming vectors such that the minimum weighted data rate among users is maximized under the constraints of maximum transmit power and fronthaul capacity at each specific RRH. The problem is intractable due to the numerical difficulties of combination and nonconvex functions. Based on a semidefinite relaxation technique, bisection search, and a branch-and-bound method, we develop an upper bound, which is also the optimal solution to the original problem if the relaxation is tight. More importantly, we propose a heuristic low-complexity iterative procedure for practical applications based on the state-of-the-art sequential convex approximation. Subsequently, we modify the proposed methods for the uncertain channel state information case. To be specific, the upper bound and its suboptimal solution are altered based on the S-lemma while the low-complexity algorithm is tailored by using two different approximations of intractable robust counterpart. The validity of the proposed methods in the region of limited fronthaul capacity is confirmed by numerical results.

Non-orthogonal transmission is considered to be one of the promising techniques for improving the throughput of the existing and future wireless communication networks. We concentrate on the transmission of both independent and correlated binary sources over a non-orthogonal multiple access relay channel (MARC), which consists of two sources, one relay, and one destination. The lossy decode-and-forward (DF), developed from the conventional DF, is adopted at the relay. Two time slots are required with non-orthogonal transmission over such network setup, while three time slots are required with the conventional orthogonal transmission. We analyze the outage probability of transmission of independent binary sources over the non-orthogonal MARC based on the theorem of multiple access channel (MAC) with a helper, which combines the Slepian-Wolf rate region and the MAC capacity region. For the performance verification, we implement a practical coding-decoding chain, which is applicable to the transmission of both independent and correlated binary sources. Exclusive-OR based multi-user complete decoding is introduced at the relay node, and iterative joint decoding is utilized at the destination by taking into consideration the estimated intra-link error probability and correlation information between the sources. The practical simulation results are well matched with the theoretical analyses.

Inter-block-interference (IBI) caused in the pursuit of spectral efficiency can deteriorate channel estimation performance. For this problem, previously-proposed chained turbo estimation performs IBI cancelation by using the soft replica of the transmitted signal. The IBI cancelation technique can, however, suffer from a mean squared error (MSE) floor problem, since the soft replica is unavailable at the first turbo iteration. The IBI problem can be avoided by using channel impulse response length constraint. Nevertheless, as shown in this paper, the IBI avoidance approach is difficult to perform independently since it requires unbiased second-order statistics. This paper proposes, therefore, a new conditional ℓ1 regularized minimum mean square error channel estimation algorithm by jointly utilizing the IBI avoidance/cancelation and subspace techniques. Simulation results verify that the proposed algorithm solves the MSE floor problem, and, hence, improves the bit error rate convergence performance in realistic IBI channels including the effect of pulse shaping filters.

Downlink weighted sum rate maximizing beamformer design is considered for joint processing (JP) coordinated multi-point (CoMP) transmission. Global channel state information exchange, required by the centralized JP CoMP processing, is in many scenarios impractical due to the backhaul latency and capacity requirements. Low overhead decentralized processing enables JP even with limited backhaul capacity. The proposed best response (BR) design also takes into account the possibly non-orthogonal pilot design and noisy pilot estimation. By allowing spatially overlapping pilot sequences, the transceiver processing requires only the channel state information of locally served users. This enables more flexible pilot design and makes the proposed approach realizable in practical time correlated and noisy channel conditions. Furthermore, the proposed BR algorithm provides low signaling overhead for systems with limited backhaul capacity. Robustness for fading channel conditions and limited pilot resources is shown by numerical examples.

Degrees of freedom (DoF) is studied in the downlink of a heterogenous wireless network modeled as a two-layered interference network. The first layer of the interference network is the backhaul layer between macro base stations (MBs) and small cell base stations (SBs), which is modeled as a Wyner type linear network. The second layer is the transmission layer between SBs and mobile terminals (MTs), which is modeled as a linear Wyner LTnetwork. The SBs are assumed to be half-duplex, thus restricting the per user degrees of freedom (puDoF) in the system to 1/2. It is established that the optimal puDoF of 1/2 can be achieved in the linear network with sufficient number of antennas using only interference avoidance schemes. For the case of higher connectivity in the transmission layer, it is shown that the optimal puDoF is achieved by sending an appropriate linear combination to the SB to zero-force interference at the intended user. These results are also extended to a more realistic hexagonal cellular model.

This paper considers a multicell downlink channel in which multiple base stations (BSs) cooperatively serve users by jointly precoding shared data transported from a central processor over limited-capacity backhaul links. We jointly design the beamformers and BS-user link selection so as to maximize the sum rate subject to user-specific signal-to-interference-noise (SINR) requirements, per-BS backhaul capacity and per-BS power constraints. As existing solutions for the considered problem are suboptimal and their optimality remains unknown due to the lack of globally optimal solutions, we characterized this gap by proposing an globally optimal algorithm for the problem of interest. Specifically, the proposed method is customized from a generic framework of a branch and bound algorithm applied to discrete monotonic optimization. We show that the proposed algorithm converges after a finite number of iterations, and can serve as a benchmark for existing suboptimal solutions and those that will be developed for similar contexts in the future. In this regard, we numerically compare the proposed optimal solution to a current state-of-the-art, which show that this suboptimal method only attains 70% to 90% of the optimal performance.

The challenges with full-duplex transceiver (FD) implementation and transmission in small area radio communication systems are introduced. The main challenge in the FD transceiver design is the self-interference (SI). A three-stage SI cancellation is used for SI mitigation. Analog SI isolation is performed at radio frequency (RF) by utilizing an antenna design based on the characteristic modes theory and using active cancellation principle. Phase and attenuation values of the active cancellation signal path are tuned while transmitting a data signal to a distant node. After the tuning the SI isolation at RF is 90 dB. The remaining SI is then cancelled at baseband. For the baseband SI cancellation an estimate of the SI channel is needed. SI channel estimation is done in full-duplex mode and since the receiver has full knowledge of the transmitted signal no extra pilots are used for the SI cancellation.

This paper considers a downlink transmission of cloud radio access network (C-RAN) in which precoded baseband signals at a common baseband unit are compressed before being forwarded to radio units (RUs) through limited fronthaul capacity links. We investigate the joint design of precoding, multivariate compression and RU-user selection which maximizes the energy efficiency of downlink C-RAN networks. The considered problem is inherently a rank-constrained mixed Boolean nonconvex program for which a globally optimal solution is difficult and computationally expensive to find. In order to derive practically appealing solutions, we invoke some useful relaxation and transformation techniques to arrive at a more tractable (but still nonconvex) continuous program. To solve the relaxation problem, we propose an iterative procedure based on DC algorithms which is provably convergent. Numerical results demonstrate the superior of the proposed solution in terms of achievable energy efficiency compared to existing schemes.

We consider a multi-carrier joint processing (JP) coordinated multi-point (CoMP) cellular system wherein the beamformers are designed by a centralized controller (CC). Those are subsequently fed back to the respective base stations (BSs) via finite capacity backhaul link. Hence, we design transmit beamformers at the CC that associate users to BSs depending on their backhaul capacity and users channel state to address this problem. Upon identifying the active links, user multiplexing and scheduling over spatial and frequency dimensions are performed in conjunction with the beamformer design for all BSs subject to the objective of minimizing the number of backlogged packets at the CC. Since the beamformers are notified to the respective BSs along with the data, the backhaul utilization must include the overhead incurred by the quantized beamformers, which has higher precision as it is used by all data symbols transmitted over a coherence block. In this paper, we assume fixed quantization levels for each complex entry in the beamformers. Therefore, we select a subset of entries for each beamformer to reduce the overhead incurred by the active antennas, which when removed has the minimal impact on the user rate. With the above mentioned constraints and requirements, the beamformer design becomes a nonconvex combinatorial problem. Therefore, we provide a solution for the proposed formulation by employing successive convex approximation technique to handle the nonconvexity and the combinatorial search involving binary variables are replaced by a linear bounds along with a sparsity inducing term in the objective. The proposed method is useful only when the backhaul usage due to the beamformers is significantly larger when compared to the overhead incurred by the active users data. Numerical results are provided to illustrate the performance.

We propose a novel data detection algorithm and a corresponding VLSI design for large multi-user (MU) multiple-input multiple-output (MIMO) wireless receiver. Our algorithm, referred to as ADMIN, performs alternating direction method of multipliers (ADMM)-based infinity norm constrained equalization. ADMIN is an iterative algorithm that outperforms linear detectors if the number of users is small compared to that of the antennas in base station (BS). ADMIN computes the linear minimum mean-square error (MMSE) solution in the first iteration. It is sufficient when the ratio between the numbers of BS antennas and users is rather large. We develop a time-shared and iterative VLSI architecture for LDL-decomposition based soft-output ADMIN. Our architecture achieves 685.71 Mb/s for linear MMSE and 212.38 Mb/s for ADMIN for a 16-user system that employs 64-QAM in a 28 nm CMOS technology.

Gradient based downlink beamforming with low computational complexity and training overhead is proposed for joint processing (JP) coordinated multi-point transmission (CoMP). Pilot contamination and estimation noise are taken into account in the pilot based transceiver training process. The proposed designs enable decentralized JP when the backhaul and computational limitations do not allow centralized processing. The impact of backhaul quantization is also considered. The stochastic gradient based designs are shown to be more robust to feedback quantization when compared to more complex methods. The trade-off between the implementation complexity and performance is established for the proposed algorithms. The results show that low complexity decentralized JP CoMP is feasible even with limited backhaul capacity.

Mean interference power and probability of outage in the THz band (0.1-10 THz) networks are studied. The frequency band has potential for enabling future short range communication systems because of the large available spectrum resources. This can enable huge data rates, or on the other hand, large numbers of users sharing the resources. The latter case is closely related to the subject of this paper on interference modeling for dense THz networks with stochastic geometry. We use it to estimate the average behavior of random networks. The literature has shown convenient closed form solutions for the mean interference power in ultrahigh frequency band (UHF, 300 MHz - 3 GHz). Those are not always readily applicable for the THz band. This is especially the case when THz band is modeled with the molecular absorption and free space path loss. Still, the mean interference power does have closed form solutions in all cases, but in some, numerical approximations have to be used. We provide the derivation and analysis of the mean interference power and the outage probability. The results are verified with computer simulations.

This paper aims at guaranteeing the achievable energy efficiency (EE) fairness in a multicell multiuser multiple-input single-output downlink system. The design objective is to maximize the minimum EE among all base stations (BSs) subject to per-BS power constraints. This results in a max-min fractional program and as such is difficult to solve in general. Our goal is to develop decentralized algorithms for the max-min EE problem based on combining the successive convex approximation (SCA) framework and the alternating direction method of multipliers (ADMMs). Specifically, leveraging the SCA principle, we iteratively approximate the nonconvex design problem by a sequence of convex programs for which two decentralized algorithms are then proposed. In the first approach, the convex program obtained at each step of the SCA procedure is solved optimally by allowing the BSs to exchange the required information until the ADMM converges. The convergence of the first method is analytically guaranteed but the amount of backhaul signaling can be noticeable in some realistic settings. To reduce the backhaul overhead, the second method performs an abstract version of the ADMM where only one variables update is carried out. Numerical results are provided to demonstrate the effectiveness of the two proposed decentralized algorithms.

Measurement results and related analysis on the diffraction properties in the THz frequency band are reported. The results are given on the bands from approximately 100 GHz to 2 THz. Measurements were made for knife-edge diffraction, and diffraction through single and double slits. The diffraction effects are of interest in modeling the non-line-of-sight (NLOS) paths in the THz band. Therefore, the measurement results have significant practical value for designing the real communication systems. The theoretical models from the literature show a very good match to the measurement results. The key finding is that wireless communication links in the THz band can be established even in the shadows of the objects through diffraction with reasonable path loss. This also indicates another path in the THz multipath environment, as the previous studies have shown the THz radiation is capable on penetration, reflections, and scattering.

We consider multicell downlink transmission where multi-antenna base stations (BSs) collaborate in transferring data to multiple cochannel multicast groups. Our aim is to design a joint transmitter selection and cooperative precoding scheme which maximizes the minimum received data rate among all receivers under constraints on the individual BS transmit power budgets as well as backhaul link capacity. The problem is naturally cast as a mixed Boolean nonconvex program whose global optimal solution is difficult to achieve. To solve this problem locally, we first use difference-convex (DC) functions to represent the Boolean variables, and further transform the problem into an equivalent but more tractable formulation. We then propose an iterative algorithm which provably converges to stationary solutions. Numerical results are provided to demonstrate the superior performance of the proposed method.

We investigate the end-to-end outage probability over a multi-source multi-relay network assuming that all the wireless links are suffering from independent block fading variations. The overall transmission is divided into two tandem hops. The first hop represents the transmission between sources and relays, while the relays transmit to the destinations in the second hop. We consider specifically a two-source two-relay single-destination network. Based on the decoding process at the relays, the data recovery at the relays can be classified into three cases based on achievable bounded rate regions. The first case where both relays cannot recover both users' data can be characterized as chief executive officer problem at the second hop. For the second case, the relays recover the data of both users, and the outage probability of the second hop can be computed by exploiting maximum ratio transmission. Finally, for the third case, only one of the users' data is recovered by one of the relays, and the outage probability of the second hop can be calculated by using the theorem of source coding with helper. Numerical results closely follow the theoretical analysis and confirm the improved performance of the multiple-antenna relaying over the single-antenna relaying.

We design rateless codes for correlated multi-source relay networks over packet erasure channels. In particular, we optimize relay combining process of distributed Luby transform (DLT) codes for a multi-source single-relay network where information data is correlated among the sources. Density evolution is used to characterize the asymptotic performance in terms of unrecovered information symbols termed as erasure rate. By exploiting the decoding graph, we derive the closed form expressions for asymptotic performance of DLT codes in a network where correlated sources transmit to a single destination via a relay. Consequently, a design framework is developed to optimize the degree distributions in terms of overhead. It is shown that the proposed DLT codes significantly outperform existing DLT coding schemes in terms of erasure and success rates. Numerical examples are provided to verify the asymptotic analysis. Numerical results also confirm the performance gain of the proposed DLT code as compared to its uncorrelated counterparts in the finite-length regime.

This paper studies the fairness of achievable energy efficiency (EE) in a multicell multiuser multiple-input single-output downlink. The objective is to maximize the minimum EE among all base stations (BSs) subject to per-BS power constraints. The resulting optimization problem is a max-min fractional program, and, thus, difficult to solve in general. Our goal is to develop a decentralized algorithm for the max-min EE problem which solves the problem locally. The idea behind the proposed method is to combine the framework of successive convex approximation (SCA) and alternative direction method of multipliers (ADMM). We transform the convex program obtained at each step of the SCA procedure into a form that lends itself to the ADMM. The resulting formulation is solved optimally by allowing the BSs to exchange the required information until the ADMM converges. In addition to further reduce the backhaul overhead, the proposed algorithm is modified to enhance the convergence speed. Numerical results are provided to demonstrate the effectiveness of the proposed algorithms.

Lossy compressed sensing (CS) of a sparse source is studied. A lower bound to the best achievable compression performance in a finite rate CS setup is established by providing support side information to the encoder and decoder. The rate-distortion problem is formulated via remote source coding and conditional rate-distortion theory. The best encoder separates into an estimation step and a rate-dependent transmission step. Numerical results illustrate the rate-distortion behavior of the scheme.

The binary chief executive officer (CEO) problem with an arbitrary number of agents is considered in this paper. A scheme which separates the reconstruction of observations and the final decision of a common source is assumed. Hence, we first derive the outer bound for the rate- distortion region by providing the converse proof of a binary multiterminal source coding problem which is the key to solve the binary CEO problem. The distortion of the binary CEO problem is then determined by the Poisson binomial process based on the using majority voting logic for the final decision. The rate-distortion behavior of the binary CEO problem is then analyzed based on the outer bound by solving a convex optimization problem. It is found that the distortion decreases until it converges to a certain level, as the sum rate and/or the number of agents increases.

Joint optimization of power allocation (PA) and relay position (RP) is investigated for a lossy-forwarding relaying in order to minimize the outage probability. We investigate adaptive PA with fixed relay position, adaptive RP with fixed PA ratio, and joint optimization of the PA and RP under total transmit power constraint. A closed-form expression of the outage probability is derived at the high signal-to-noise ratio regime. It is shown that the closed-form expression is sufficiently accurate compared to numerical calculation results. Then, the optimum PA and the optimum RP can be formulated as a convex optimization problem. It is found that the system performance with the adaptive optimum PA outperforms that with equal power allocation. The outage performance with the adaptive optimum RP outperforms that with midpoint RP. However, the joint optimization of the PA and RP is superior to the semi-adaptive optimization algorithms.

A two-node binary chief executive officer (CEO) problem is investigated. Noise-corrupted versions of a binary sequence are forwarded by two nodes to a single destination node over orthogonal additive white Gaussian noise (AWGN) channels. We first reduce the binary CEO problem to a binary multiterminal source coding problem, of which an outer bound for the rate-distortion region is derived. The distortion function is then established by evaluating the relationship between the binary CEO and multiterminal source coding problems. A lower bound approximation on the Hamming distortion (HD) is obtained by minimizing a distortion function subject to constraints obtained based on the source-channel separation theorem. Encoding/decoding algorithms using concatenated convolutional codes and a joint decoding scheme are used to verify the lower bound on the HD. It is found that the theoretical lower bounds on the HD and the computer simulation-based bit error rate performance curves have the same tendencies. The differences in the threshold signal-to-noise ratio between the theoretical lower bounds and those obtained by simulations are around 1.5 dB in AWGN channel. The theoretical lower bound on the HD in block Rayleigh fading channel is also evaluated by performing Monte Carlo simulation.

Broadband wireless channels observed at a receiver cannot fully exhibit dense nature in a low to moderate signal-to-noise ratio (SNR) regime, if the channels follow a typical propagation scenario such as Vehicular-A or Pedestrian-B. It is hence expected that ℓ1-regularized channel estimation methods can improve channel estimation performance in the broadband wireless channels. However, it is well-known that the ℓ2 multiburst (MB) channel estimation achieves the Cramér-Rao bound (CRB) asymptotically. This is because the ℓ2 MB technique formulated as a minimum-mean-square-error (MMSE) problem improves the mean squared error (MSE) performance by utilizing the subspace projection. Performance analysis shows that ℓ1-regularized channel estimation does not improve the MSE performance significantly over the ℓ2 MB technique so far as the subspace channel model assumption is correct. We demonstrate, however, a receiver with ℓ1-regularized channel estimation can improve bit error rate (BER) performance if the assumption is not always correct. For this purpose, we focus on intermittent transmission (TX) scenario which is defined as a generalized TX sequence having arbitrary length interruption between two continuous TX bursts. A receiver with the ℓ2 MB method suffers from BER deterioration in an intermittent TX scenario having abrupt channel changes. As a solution to the problem, we propose a new algorithm which is a hybrid of ℓ1-regularized least square (LS) and ℓ2 MMSE channel estimation techniques. Simulation results show that the receiver with the proposed algorithm achieves a significant BER gain over that of the ℓ2 MB technique in the intermittent TX scenario.

We consider the discrete-time intersymbol interference (ISI) channel model with additive white Gaussian noise and input symbols independently drawn from discrete alphabets. We provide a provable lower bound for the achievable rate. In contrast to available lower bounds, the proposed one does not require the input signal to be stationary. Therefore, it can be employed to analyze the information rate of ISI channels with independent non-identically distributed symbols. This analysis reveals that when the channel input is restricted to discrete input alphabets, the throughput can be improved by using non-identically distributed input symbols as opposed to Gaussian input case. This presents a new opportunity for the transmission adaptation for communication through ISI channels.

The impact of the terrestrial network interference to a satellite uplink transmission is studied in this paper. In a system, where an LTE terrestrial network is extended by a satellite communication, the terrestrial interference can become a problem. The interference levels are studied via infield measurements and link budget calculations and the interference tolerance of the satellite receiver are studied via computer simulations. The results show that in spite of the long satellite communication link, strictly power limited system and terrestrial interference the satellite link can operate on the same frequency band as the terrestrial network.

Decentralized downlink beamformer design for coherent coordinated multi-point transmission is proposed with weighted sum rate maximization system performance objective. The weighted sum rate is maximized by successive convex approximation of the corresponding weighted mean-squared error minimization problem. Decentralized beam coordination is achieved by employing a best response design, where each base station designs its own precoders in parallel assuming fixed transmission from the adjacent cells. After each beamformer update, the fixed terms are updated according to the solutions of the cooperating transmitters. The proposed design incorporates a bi-directional beamformer signaling scheme to improve the convergence properties. This scheme exploits the time division duplexing frame structure and is shown to improve the training latency of the iterative transceiver design. Furthermore, the improved transceiver convergence rate enables periodic beamformer reinitialization, which greatly improves the achieved system performance in dense networks.

Lattice reduction (LR) is a preprocessing technique for multiple-input multiple-output (MIMO) symbol detection to achieve better bit error-rate (BER) performance. In this paper, we propose a customized homogeneous multiprocessor for LR. Each individual core is based on transport triggered architecture (TTA). We propose a few modifications of the popular LR algorithm, Lenstra-Lenstra-Lovász (LLL) for high throughput. High level programming is used to implement the control path of the TTA cores and several special function units are designed to accelerate the program. The multiprocessor takes 187 cycles to reduce a single matrix for LR. The architecture is synthesized on 90 nm technology and takes 405 kgates at 210 MHz.

We consider rate constrained decentralized downlink transceiver optimization for cellular systems. Our optimization objective is the weighted sum rate, which provides generalized objective for wide class of priority and throughput management problems. The quality-of-service imposing rate constraints make the problem considerably more complex, and have been largely neglected in the past in the context of utility maximization. The decentralized design is achieved with a novel combination of successive convex approximation and alternating direction method of multipliers (ADMM). While providing more sophisticated interference management, the considered algorithm does not increase the computational complexity, when compared to other recently published rate optimizing methods. The proposed design is shown, by numerical evaluation, to provide significant improvement in the convergence properties. Furthermore, we show that the ADMM approach provides much needed stability in the time correlated channels.

Channel occupancy rate (COR) is the fraction of the time that a channel is occupied, i.e., contains signal(s) in addition to noise. Estimation of COR is important, e.g., in cognitive radio systems, which can use this information for intelligently adapting their spectrum use to the operating environment. For COR estimation, both the ability to operate with weak signals (sensitivity) and closeness of the estimate to the true COR value (accuracy) are important. In this paper, an improved COR estimation (iCOR) method is proposed enabling the use of high false alarm probabilities to improve sensitivity without the overestimation usually associated with high false alarm probabilities. The iCOR method is compared with the conventional method in terms of worst-case root-mean-square error (RMSE), which refers to the RMSE for the COR level yielding the maximum RMSE. To fairly compare different COR estimation methods, it is required that the RMSE for strong signals equals a target value and the considered methods are compared by their RMSE for weaker signals. Comprehensive theoretical analysis is performed and both exact results and approximations are derived. Experimental results verify the theoretical analysis and show significant sensitivity gains from the iCOR method (around 5 dB).

This paper considers sequential compressed acquisition and progressive reconstruction of spatially and temporally correlated sensor data streams in wireless sensor networks (WSNs) via compressed sensing (CS). We develop a sequential framework based on sliding window processing, in which the sink can efficiently reconstruct the current sensors' readings from a sequence of periodically delivered CS measurements by exploiting the joint compressibility via Kronecker sparsifying bases. Specifically, we derive a recursive CS recovery method which utilizes the estimates from the preceding decoding instants via a regularization and reweighted ℓ1-minimization to improve the reconstruction accuracy of sensor data streams while reducing the necessary communications. As beneficial features, the method produces estimates for the current sensors' readings without additional decoding delay, and, via adjusting the window size, it can dynamically trade-off between the CS recovery performance and decoding complexity. Numerical results show that our proposed method achieves higher reconstruction accuracy with a smaller number of required transmissions, and with lower decoding delay and complexity as compared to those of the state of the art CS methods.

This paper proposes a semi-analytical performance prediction method for an iterative receiver that processes signals arriving through a frequency selective multiple-input multiple-output radio channel. Earlier results provide a distribution of the signal-to-interference-and-noise ratio at the output of a frequency-domain soft-cancellation minimum mean square error equalizer, which is subsequently used to approximate the distribution of the log-likelihood ratios (LLRs) of the demapped bits. Extrinsic information transfer (EXIT) charts establish a bijective relationship between the variance of the LLRs and mutual information. We use an approximate distribution of the equalizer/demapper output's LLR variance to estimate the probability of an intersection between the decoder and equalizer graphs. The probability provides a good estimate of the frame error rate.

In this paper the up-link channel estimation problem in a multi-cell network with a Single-Input-Multiple-Output (SIMO) fading correlated channel and finite-length, nonorthogonal and contaminated pilots is considered. Our contribution is a novel channel estimation technique, referred to as the Sparse Channel Estimator (SCE), which exploits the Karhunen-Loéve Transform (KLT) sparse-representation of the channel. We show that, by simply utilizing an interference-contaminated channel sample-covariance matrix, the proposed approach outperforms Least Squares (LS)-based and Minimum Mean Squared Error (MMSE) techniques relying on the same type of assumptions.

Power-constrained mobile radio communication transmitters drive their transmit power amplifiers close to their saturation regions, which results in nonlinear intermodulation distortion that is especially harmful in multi-cluster and carrier aggregation transmission scenarios. Digital predistortion is a method for linearizing the transmitter and suppressing the most harmful spurious emissions at the transmitter power amplifier output. This paper describes a programmable implementation of a digital predistortion filter on a heterogeneous Transport Trigger Architecture (TTA) multiprocessor. The predistortion algorithm is based on a parallel Hammerstein polynomial model and the experimental results show that the proposed programmable architecture is capable of linearizing a 20 MHz LTE carrier in realtime with a power consumption that is suitable for mobile devices.

Two decentralized minimum mean-squared error downlink beamformer designs are proposed for multiple-input single-output coherent coordinated multi-point transmission. We propose a parallel beamformer design with a fast initial rate of convergence for systems with relatively few cooperative base stations (BSs). An alternating direction method of multipliers based design is provided for more complex systems with a large number of cooperating BSs. Support for data sharing among the serving BSs is assumed over limited back-haul connectivity. Channel state information (CSI) is not shared among the cooperating transmitters, and, thus, only local CSI is available at each BS via uplink pilot signaling.

This paper addresses compressed acquisition and progressive reconstruction of spatially and temporally correlated signals in wireless sensor networks (WSNs) via compressed sensing (CS). We propose a novel method based on sliding window processing, where the sink periodically collects CS measurements of sensor samples, and then, instantaneously reconstructs current WSN samples by exploiting the spatio-temporal correlation via Kronecker sparsifying bases. By using previous estimates as prior information, the method can progressively improve the reconstruction accuracy of the signal ensemble. Furthermore, the method can control the trade-off between decoding delay and complexity. Numerical results demonstrate that the proposed method can recover WSN data samples from CS measurements with higher reconstruction accuracy, yet with lower decoding delay and complexity, as compared to the state of the art methods.

We consider the multiuser frequency allocation problem in single-input single-out put (SISO) LTE-A type uplink with carrier aggregation (CA). The increased bandwidth in LTE-A system allows orthogonal allocation of subcarriers among users. We consider both consecutive and distributed frequency allocation strategies with per user transmit power constraints and more realistic power amplifier models accounting the dependence of the amplifier efficiency on the frequency allocation. A novel binary integer programming with water-filling power allocation is proposed for consecutive frequency allocation. The system level performance is evaluated via computer simulations. The results shed light to the problem of frequency allocation with real user devices both from theoretical and practical points of view.

The paper considers coordinated multi-cell multi-user multiple-input multiple-output (MU-MIMO) transmission using orthogonal frequency division multiplexing (OFDM) technique for downlink channels. Linear beamforming and receiving are employed at BS and corresponding users, respectively. The design criterion is to minimize the number of queued packets, since the transmission is mainly guided by the backlogged packets. An indirect way to solve this problem is to associate the weights of the traditional weighted sum rate maximization scheme with the current status of the queue size, i.e., the longer queue, the higher priority. We deal with this problem directly by formulating it as a noncovex optimization problem and then applying a sequential convex approximation method to find beamformers. In particular, we propose an efficient resource allocation scheme based on jointly optimizing beamformers over the space and frequency domain. We refer to this scheme as queue minimizing (QM) joint space-frequency resource allocation (JSFRA) scheme. The proposed solutions are compared to the traditional queue weighted sum rate maximization (Q-WSRM) approaches mentioned above in terms of the rate of convergence and the backlogged packets remaining after a scheduling instant.

This paper proposes an extrinsic information transfer (EXIT)-chart based optimization technique of LDPC codes for the transmission of quantized Gauss-Markov (GM) source samples over additive white Gaussian (AWGN) noise channels. A joint source and channel (JSC) decoding technique of the proposed code is also devised. In the proposed scheme, no interleaving is performed between the source and the JSC encoder so that the decoder can well exploit the relatively low entropy of the source with memory compared to memory-less sources. At the transmitter, the quantized samples are converted to bit sequences with an injective mapping and the bit sequences are encoded using a systematic binary LDPC code. The proposed JSC decoder is a concatenation of a multi-state BCJR Markov decoder and a sum-product (SP) LDPC decoder. Decoding thresholds of the optimized codes at certain code rates are investigated for both uniform and Lloyd-Max quantizations in different numbers of bits. The decoding thresholds are close to the Gaussian code book Shannon limits for code rate Rc≤ 0.5, although the gap to the Shannon limit notably increases at the higher rates. Finally, the simulation results confirm the significant improvement of coding gain on the bit error rate (BER) performances of the optimized LDPC codes with both the quantization schemes.

One of the promising candidates for mitigating the inter-cell interference in multi-cell networks is coordinated beamforming (CB), where cells are allowed to cooperate such that the transmission from each cell takes into account the interference it would cause to the users of other cells. In this work, we study the performance of a centralized algorithm which enables beam coordination in multi-cell multi-user multiple-input multiple-output (MIMO) network. The algorithm aims at maximizing the weighted sum rate (WSR) via weighted sum mean squared error (WSMSE) minimization, taking into account the estimation noise of channel state information (CSI). The performance is studied in a seven-cell network and the numerical results show that also with inaccurate CSI, CB outperforms traditional orthogonal access (OA) scheme where the same frequency resources are not reused between cells.

This paper studies the problem of resource allocation for uplink multi-homing users in heterogeneous network where users can simultaneously transmit data to multiple radio access networks (RANs). The considered design problem is to optimally assign bandwidth and power to each user-RAN connection so as to maximize the overall energy efficiency of the network subject to QoS requirements and resource budgets. By the definition of energy efficiency which is the ratio of the aggregate throughput to the power consumption, the resulting problem is formulated as a fractional program. Then, we propose an energy efficient algorithm using the Dinkelback method that solves a series of convex problems to obtain the optimal design. Particularly, we derive closed-form expressions for the design parameters and provide useful insights into the proposed energy efficient resource allocation algorithm. Numerical results are presented to demonstrate that the proposed algorithm is superior to other resource allocation strategies in terms of energy efficiency.

This paper investigates optimal relay selection schemes for orthogonal space-time block coded multiple-input multiple-output system with non-coherent amplify-and-forward relays, where channel state information is not available at the source and relays. The source-relay and relay-destination channels undergo Rayleigh and Rician fading, respectively. Two possible relay selection schemes are proposed, and both are statistically characterised by deriving exact closed form expression for the cumulative distribution function and probability density function of the instantaneous signal-to-noise ratio (SNR) at the destination. In the first relay selection method, maximizing instantaneous SNR at the destination is considered to select the best relay. In the second scheme, maximizing relay-destination channel is considered. The derived statistical results are used to analyse the performance of the system with outage probability, average bit error rate and ergodic capacity. Finally, we compare both relay selection schemes with respect to the relay pool size and Rician factor.

This paper investigates the performance of dualhop multiple antenna amplify-and-forward relaying system of transmit beamforming (TB) and maximum ratio combining considering feedback delay and co-channel interference (CCI) at the relay node. Source-relay and relay-destination channels experience Rayleigh and Rician fading respectively. TB vector is calculated using outdated channel state information due to the feedback delay from relay-to-source, and the relay node experience CCI due to frequency reuse in the cellular network. We derive new closed form expressions for the exact cumulative distribution function of the end-to-end signal-to-interference-plus-noise ratio to statistically characterized the system. We also derive closed form expression for the outage probability, bit error rate and ergodic capacity. The system performance is investigated using the derived performance metrics to analyze the effect of Rician fading, CCI, feedback delay and number of antennas.

The self-interference (SI) channel in full duplex transceivers is investigated. The SI channel is measured using ultra wide-band antennas. Narrow-band measurement technique is used for the channel measurements so that spatial resolution of 4.3 cm is achieved. Measurements are done in a variety of locations including an anechoic chamber with different antenna orientation. Antennas are mounted on an old laptop frame. Coherence bandwidth of the SI channel is found to be varying between 1 MHz and 4 MHz, effectively making it a frequency selective channel. It is also observed that a major amount of power is transferred because of direct coupling between the antennas via the frame on which antennas are mounted.

The 15 papers in Part II of this special issue focus on the spectrum and energy efficient design of wireless communication networks.

We investigate the problem of downlink physical layer multicasting that aims at minimizing the transmit power with a massive antenna array installed at the transmitter site. We take a solution based on semidefinite relaxation (SDR) as our benchmark. It is shown that instead of working on the semidefinite program (SDP) naturally produced by the SDR, the dual counterpart of the same problem may provide a more efficient numerical implementation. Later, by using a successive convex approximation strategy, we arrive at a provably convergent iterative second-order cone programming (SOCP) solution. Our thorough numerical investigations report that the newly proposed SOCP solution offers improved power efficiency and a massively reduced computational complexity. Therefore, the SOCP solution is seen as a suitable candidate for obtaining beamformers that minimize transmit power, especially, when a very large number of antennas is used at the transmitter.

We address the problem of the downlink beamformer design for signal-to-interference-plus-noise ratio balancing in a multiuser multicell environment with imperfectly estimated channels at base stations. We first present a semidefinite program (SDP)-based approximate solution to the problem. Then, as our main contribution, by exploiting some properties of the robust counterpart of the optimization problem, we arrive at a second-order cone program (SOCP)-based approximation of the balancing problem. The advantages of the proposed SOCP-based design are twofold. First, it greatly reduces the computational complexity compared to the SDP-based method. Second, it applies to a wide range of uncertainty models. As a case study, we investigate the performance of proposed formulations when the base station is equipped with a massive antenna array. Numerical experiments are carried out to confirm that the proposed robust designs achieve favorable results in scenarios of practical interest.

This paper proposes a semianalytical performance prediction method for an iterative receiver that processes signals arriving through a frequency-selective multiple-input multiple-output radio channel. Earlier results provide a distribution of the signal-to-interference-plus-noise ratio at the output of a frequency domain soft interference cancellation minimum mean square error equalizer, which is used to approximate the distribution of the log-likelihood ratios (LLRs) of the demapped bits. Mutual information transfer charts establish a bijective relationship between the variance of the LLRs and the mutual information. We assume a 3rd Generation Partnership Project compliant turbo code, whose transfer charts are simulated in advance. By approximating the LLR variance distribution of the demapped equalizer output, we can evaluate the probability of an intersection between an equalizer chart associated with a random channel realization and a fixed decoder chart. This probability provides the frame error rate. Our proposed prediction method allows horizontally and vertically encoded spatial multiplexing schemes. The vertically encoded scheme is complemented by the inclusion of the Chase combining hybrid automatic repeat request protocol. The proposed method allows reasonable receive antenna correlation.

This paper analyzes the performance of an optimal single stream beamforming scheme for a multiple-input multiple-output (MIMO) relay network with dual-hop fixed-gain amplify-and-forward (AF) relaying. The source-relay and relay-destination channels undergo Rayleigh and Rician fading respectively. Different Rician fading scenarios are considered for relay-destination channel, depending on the rank of the Rician channel matrix. The channel state information is only available at the destination, and the destination computes the optimal transmit and receive beamforming vectors to maximize the instantaneous signal-to-noise ratio (SNR). The optimal transmit beamforming vector is sent back to the transmitter via a dedicated feedback link. We derive new analytical expressions for the cumulative distribution function, probability density function, and moments to statistically characterize the properties of the instantaneous SNR. These statistical properties are used to analyze the system performance in terms of the outage probability, average bit error rate, and the ergodic capacity. The performance analysis investigates the effects of the Rician factor, rank of the line-of-sight component, and number of antennas at the nodes on the system performance. The results reveal that the optimal single stream beamforming system provides better performance than an orthogonal space-time block coded based AF MIMO system.

We consider the single-user multiple-input multiple-output (MIMO) precoder design problem for the doubly spatially correlated partially coherent Rayleigh fading channels with discrete inputs. The objective is to design a linear precoder to adapt to the degradation caused by the imperfect channel estimation at the receiver and the transmit-receive antenna correlation. The system is partially coherent so that the MIMO channel coefficients are estimated at the receiver and its error covariance matrix is fed back to the transmitter. We utilized the cutoff rate (CR) expression, an alternative to the mutual information (MI), and propose to use it as a design criterion to design the linear precoders. A linear precoder is obtained by numerically maximizing the CR with respect to the precoder matrix with a given average power constraint. Furthermore, the precoder matrix is decomposed using singular value decomposition (SVD) into the input shaping matrix, power loading matrix, and beamforming matrix. The beamforming matrix is found to coincide with the eigenvectors of the transmit correlation matrix. The power loading and input shaping matrices are solved numerically using the difference of convex (d.c.) functions programming algorithm and optimization under the unitary constraint, respectively. A 2-block alternating optimization (AO) algorithm is proposed to solve the input shaping matrix and power loading matrix iteratively. Precoders are designed to be used in conjunction with two MIMO transmission schemes: the spatial multiplexing (SM) and space-time (ST) block transmission modes. The frame error rate (FER) and average MI are used as the performance metrics to validate the performance of the newly designed CR-precoders in comparison with the conventional no-precoding case and cutoff rate optimized partially coherent constellations (PCCs). Numerical examples show that the performance gains of the designed precoders are significant compared to the CR-PCCs and conventional codewords.

Under the opportunistic spectrum access paradigm, the shared pool of spectrum bands that the multiple autonomous cognitive radios (CRs) need to compete for is not necessarily homogeneous. The non-homogeneity in channels may lead to payoff distribution conflict among autonomous CRs, as each CR would prefer the outcome in which it selects the more desirable channels. To address this challenge, we have designed an adaptive strategy that (without explicit coordination) enables the CRs to autonomously reach an outcome that maximizes the total CR network throughput and minimizes the payoff distribution conflict among the CRs. We utilize the framework of repeated games with private monitoring to: 1) study the dynamic channel selection problem; 2) analyze the stability of the proposed strategy; and 3) investigate the impact of deviations by a selfish CR on the performance of the proposed strategy. In our model, multiple autonomous CRs are not able to observe the channel selections of other competing CRs. Rather, they get a signal from which the selections must be inferred.

We propose an efficient user admission method for multi-user regenerative relay multiple-input multiple-output (MIMO) systems with maximum sum rate system performance objective. We extend the relay system model proposed in [1] to manage multi-antenna user terminals. Our aim is to incorporate computationally efficient user admission into the conventional weighted sum rate maximization. To increase the rate of convergence and mitigate over-the-air signaling requirements, we make early predictions of the active set of spatial data streams, which effectively translates to user allocation. It is shown that the proposed method achieves close to optimal user allocation and greatly improves the performance at low signal-to-interference-plus-noise (SINR) region when compared to direct channel quality based user admission [1].

In a hybrid (integrated) satellite-terrestrial system users are provided services seamlessly via a satellite or a terrestrial network. One approach is to design the hybrid network using frequency reuse in a way that satellite spot beams do not overlap the terrestrial network coverage area for the same frequency bandwidth. However, the satellite beams are drifting over time which inevitably causes interference to the terrestrial network. In this paper, another hybrid network approach is studied in which the coverage of the satellite beam fully overlays the terrestrial network coverage. Specially, the performance of the 3GPP long term evolution (LTE) network is studied in a suburban macrocell environment with the presence of interference from overlapping satellite network. Simulation results are provided for base station (BS) intersite distance (ISD) of 3 km and 6 km assuming BS transmission power of 40 W. The bandwidth of 10 MHz was applied for the terrestrial transmission and the bandwidths of 3, 5 and 10 MHz were assumed for the satellite transmission in order to study the partially and fully overlapping cases. The terrestrial network tolerance depends strongly on the network planning issues such as the BS transmission power and the ISD of base stations. For the applied terrestrial network parameters, the system performance degradation due to satellite interference is small.

We propose a novel joint decoding technique for distributed source-channel (DSC) coded systems for transmission of correlated binary Markov sources over additive white Gaussian noise (AWGN) channels. In the proposed scheme, relatively short-length, low-density parity-check (LDPC) codes are independently used to encode the bit sequences of each source. To reconstruct the original bit sequence, a joint source-channel decoding (JSCD) technique is proposed which exploits the knowledge of both temporal and source correlations. The JSCD technique is composed of two stages, which are iteratively performed. First, a sum-product (SP) decoder is serially concatenated with a BCJR decoder, where the knowledge of source memory is utilized during local (horizontal) iterations. Then, the estimate of correlation between the sources is used to update the concatenated decoder during global (vertical) iterations. Therefore, the correlation of the sources is assumed as side information in the subsequent global iteration of each concatenated decoder. From the simulation results of frame/bit error rate (FER/BER), we note that significant gains are achieved by the proposed decoding scheme with respect to the case where the correlation knowledge is not completely utilized at the decoder.

A multirate transmission scheme to mitigate intersymbol interference (ISI) is proposed in this paper. The proposed method first decomposes the channel into two half-rate subchannels by employing successive interference cancellation (SIC). Then the channel is further decomposed into 2Mparallel channels by applying the method recursively. We demonstrate that these sub-channels tend to become memoryless as M increases. The proposed reception algorithm can be implemented in frequency domain and thus, it has a complexity in the order of linear equalizers. The proposed transmission scheme does not employ any sort of linear pre-coding at the transmitter. Therefore, it does not increase the peak-to-average power ratio at all. Furthermore, the proposed transmission method can surpass even the genie aided decision feedback equalizer in terms of reliable throughput and it can get quite close to independent identically distributed (i.i.d.) capacity of the channel which is a quite important achievement for a practical equalizer.

This paper considers an optimal single stream beamforming for a multiple-input multiple-output (MIMO) relay network with non-coherent dual-hop amplify-and-forward (AF) relaying. The source-relay and relay-destination channels undergo Rayleigh and Rician fading respectively. The channel state information is only available at the destination, and the destination computes the optimal transmit and receive beamforming vectors to maximize the instantaneous signal-to-noise ratio (SNR) at the destination. The optimal transmit beamforming vector will be sent back to the transmitter via a dedicated feedback link. We derive closed-form expressions for the cumulative density function, probability density function, and moments to statistically characterize the properties of the instantaneous SNR. These statistical properties are used to analyze the performance of the system with outage probability, average bit error rate, and ergodic capacity. The results of the performance analysis reveal that Rician factor and number of antennas at the nodes improve the system performance, and the optimal single stream beamforming system provides better error performance than an orthogonal space-time block coded based AF MIMO system.

The sum power minimization with per MS rate constraint problem in multiple-input multiple-output (MIMO) multiple access channels (MACs) is considered in this paper. The problem can be transformed into a series of convex weighted sum rate problems and iteratively solved by standard solvers. However, even with the efficient interior-point method in the latest solver, the computational effort to solve the convex problem in each iteration is still prohibitively high. We propose an efficient algorithm that utilizes the convexity of the rate region to reduce number of iterations. It consists of power bisection and optimal user weights search. The algorithm avoids the full search for the optimal user weights. By continuously constructing a convex rate polyhedron, it determines the feasibility of the current rate target at a early phase and immediately goes to the power bisection without knowing the optimal user weights. The proposed algorithm is compared with the existing methods and is shown to be significantly more efficient than the existing methods in terms of computational complexities.

We propose an energy-efficient method to enable the data collection of wireless sensor networks (WSNs) assisted by a cellular network. They use the same radio resources in an underlay manner resulting in bandwidth efficiency improvements. During the sensor data collection (SDC) period, the cellular user functions as a mobile sink node while keeping the normal cellular traffic, and multicarrier code-division multiple-access (MC-CDMA) transmission is utilized in the sensor-to-sink. Since the energy of sensor symbols from MC-CDMA transceiver is small and spread in the frequency domain, it eventually gives only rise to noise-level of each sub-carrier. After formulating the SDC procedure, we analyze the bit error probability of both the cellular traffic and sensor transmission. From the simulation results, the proposed algorithm is proved to be efficient.

Energy efficiency is an important issue for the future network since the stemming requirements of the ubiquitous access and the limited battery of the mobile devices. In this paper, we investigate energy efficient transmission for two-way relay network. Theoretically, we derive the optimal transmission data rate, which minimizes the total energy consumption per information bit. The energy efficiency region for two-way relay systems is also investigated to illustrate the effects of relay locations on the energy efficiency of the system, and the optimal relay location is also found to maximize the energy efficiency. Numerical results are presented to demonstrate the tradeoff between energy efficiency and the spectral efficiency.

The fifteen papers in this special issue are devoted to the topic of spectrum and energy efficiency of wireless and mobile communications networks.

Opportunistic resource allocation for multiple users utilizing multiple-input multiple-output (MIMO) channel in orthogonal frequency division multiple access (OFDMA) is used to attain the spectral efficiency required in future wireless communications networks. In this paper, we express analytical outage capacity results and average capacity results with optimal rate adaptation for MIMO-OFDMA systems with limited feedback. A resource block (RB)-wise SNR quantization feedback scheme and a best-M feedback method are studied for RB allocation. Feedback based transmit antenna selection (AS) is considered in the analysis. An imperfect feedback channel has a significant impact on the performance of practical feedback based communication. The proposed analysis also considers imperfect feedback in the RB allocation and AS.

We consider joint optimization of transmit power, serving base station cluster (BSC) for coherent reception, beamforming and subcarrier allocation for the uplink channel of a multicell multicarrier system. The objective is to minimize the sum transmit power subject to user-specific rate constraints. Since the problem is nonconvex and NP-hard, finding the optimal solution is challenging and not practically appealing. We propose two bit loading algorithms, in which the data rate of users is iteratively increased until the rate targets are satisfied. A joint power control, BSC selection and beamforming design problem with fixed rate allocation per subcarrier is optimally solved in every iteration. A bit switching (BSW) algorithm is further proposed in order to search for better rate allocations. The performances of the proposed algorithms are compared to upper and lower bounds achieved by the capacity-achieving scheme and equal rate allocation over subcarriers (ER), respectively. Simulation results show that proposed algorithms achieve significantly better performance than the ER scheme. The performance gains become larger as the number of users is increased.

We consider precoder design for maximizing the weighted sum rate (WSR) of successive zero-forcing dirty paper coding (SZF-DPC). For this problem, the existing precoder designs often assume a sum power constraint (SPC) and rely on the singular value decomposition (SVD). The SVD-based designs are known to be optimal but require high complexity. We first propose a low-complexity optimal precoder design for SZF-DPC under SPC, using the QR decomposition. Then, we propose an efficient numerical algorithm to find the optimal precoders subject to per-antenna power constraints (PAPCs). To this end, the precoder design for PAPCs is formulated as an optimization problem with a rank constraint on the covariance matrices. A well-known approach to solve this problem is to relax the rank constraints and solve the relaxed problem. Interestingly, for SZF-DPC, we are able to prove that the rank relaxation is tight. Consequently, the optimal precoder design for PAPCs is computed by solving the relaxed problem, for which we propose a customized interior-point method that exhibits a superlinear convergence rate. Two suboptimal precoder designs are also presented and compared to the optimal ones. We also show that the proposed numerical method is applicable for finding the optimal precoders for block diagonalization scheme.

The space-time (ST) constellation design problem and the related bit mapping schemes for multiple-input multipleoutput (MIMO) systems in spatially correlated Rayleigh fading channels with imperfect channel estimation at the receiver are considered. We assume that the channel estimation error covariance matrix, as well as transmit and receive correlation matrices are perfectly known both at the transmitter and at the receiver. We derive the cutoff rate (CR) and an upper bound (UB) on the average bit error probability (ABEP) expression for spatially correlated channels and propose them as the constellation design criteria subject to a given average transmit power constraint. Additionally, to use the resulting constellations together with forward error correction (FEC) codes requires efficient bit mapping schemes. Because these constellations lack geometrical symmetry in general, the Gray mapping is not always possible in the majority of the constellations obtained. Moreover, different mapping schemes may lead to highly different bit error rate (BER) performances. Thus, an efficient bit mapping scheme called the modified binary switching algorithm (MBSA) is proposed. It minimizes an upper bound on the ABEP (UB-ABEP) and is used to find the bit mapping schemes. Numerical examples show that the designed constellation and its optimized bit mapping together with turbo codes outperform the conventional constellations with respect to the frame error rate (FER).

An iterative receiver for a multiple-input-multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) system is considered to jointly decode the transmitted bits and estimate the channel state. The receiver consists of the a posteriori probability (APP) algorithm, the repeat-accumulate (RA) decoder, and the least-squares (LS) channel estimator. An obvious problem, with more than two blocks in an iterative receiver, is to find the optimal activation schedule of the different blocks. This paper proposes to use extrinsic information transfer (EXIT) charts to characterize the behavior of the receiver blocks and find out the optimal activation schedule for them. A semi-analytical expression of the EXIT function is derived for the decision directed LS channel estimator. An algorithm is proposed to generate the EXIT function of the APP algorithm as a function of channel estimate mutual information (MI). Surface fitting is used to get closed form expressions for the EXIT functions of the APP algorithm and the RA decoder. Trellis search based algorithms are shown to find the convergence with the lowest possible complexity using the EXIT charts. With the proposed concept, the activation scheduling can be adapted to prevailing channel circumstances and unnecessary iterations will be avoided.

We consider a distributed total transmit power minimization in a multi-hop single-sink data gathering wireless sensor network by jointly optimizing the resource allocation and the routing with given source rates. An inherent coupling in optimal routing and resource allocation is taken into account via cross-layer optimization to increase the energy efficiency of the network. Instead of distributing the solution process horizontally by commonly used dual decomposition, we apply consensus optimization in conjunction with the alternating direction method of multipliers (ADMM). By duplicating flow variables, the problem decomposes into node specific subproblems with local variables. These variables are iteratively driven into consensus via the ADMM. Numerical examples show that the proposed algorithm converges significantly faster as compared to the state of the art methods based on the dual decomposition. Additionally, the algorithm is appealing for practical implementation due to its low local communication overhead, robust operation in slightly changing channel conditions and scalability to large networks.

We consider the beamformer design for multiple-input multiple-output (MISO) broadcast channels (MISO BCs) using zero-forcing dirty paper coding (ZF-DPC). Assuming a sum power constraint (SPC), most previously proposed beamformer designs are based on the QR decomposition (QRD), which is a natural choice to satisfy the ZF constraints. However, the optimality of the QRD-based design for ZF-DPC has remained unknown. In this paper, first, we analytically establish that the QRD-based design is indeed optimal for any performance measure under a SPC. Then, we propose an optimal beamformer design method for ZF-DPC with per-antenna power constraints (PAPCs), using a convex optimization framework. The beamformer design is first formulated as a rank-1-constrained optimization problem. Exploiting the special structure of the ZF-DPC scheme, we prove that the rank constraint can be relaxed and still provide the same solution. In addition, we propose a fast converging algorithm to the beamformer design problem, under the duality framework between the BCs and multiple access channels (MACs). More specifically, we show that a BC with ZF-DPC has the dual MAC with ZF-based successive interference cancellation (ZF-SIC). In this way, the beamformer design for ZF-DPC is transformed into a power allocation problem for ZF-SIC, which can be solved more efficiently.

This paper studies limited feedback unitary precoding for MIMO spatial multiplexing systems with QR-SIC receiver and equal power allocation. An optimal codebook design criterion, which can design the optimal codebooks for various fading and spatial correlated channels with arbitrary antenna configurations, is proposed. However, due to the fact that the closed-form of the probability density function (p.d.f.) of the optimal precoding matrix is hard to obtain, the optimal codebook design criterion is difficult to be applied to the practical systems. Thus, a suboptimal codebook design criterion is given, which can be implemented in practice and is also applicable to any scenario. Sequentially, an iterative codebook design algorithm that converges to an optimum codebook is given. Simulation results demonstrate that the proposed scheme achieves better performance than the 3GPP R10 LTE-A codebooks.

We consider the weighted throughput maximization with general convex transmit power constraints in multi-cell multi-user multiple-input multiple-output system. The problem formulation is separated into receive beamformer and transmit precoder design problems. The non-convex precoder design problem is reformulated as a difference of convex functions program and solved with successive convex approximation. The convex approximation of the precoder design problem can be further formulated as a second-order cone program. Distributed and iterative solution via Karush-Kuhn-Tucker conditions for the precoder design with sum transmit power constraints is also proposed. It is shown that the rate of convergence can be significantly improved with the proposed algorithm while achieving comparable sum rate when compared to other recently published methods. This is an import factor in practical solutions as this increases the achievable sum rate with respect to required signaling iterations.

This paper proposes a fast distributed optimization algorithm for total transmit power minimization in single-sink data gathering wireless sensor networks. Many of the existing decentralized optimization algorithms addressing cross-layer design over the physical and network layer are based on dual decomposition. Our design includes joint power and routing optimization with given source rates by using consensus mechanism in conjunction with alternating direction method of multipliers (ADMM). Thus, the problem is decoupled across the nodes via introducing local copies of the variables, which are then iteratively driven into consensus with the ADMM. By the numerical experiments, the proposed distributed algorithm is shown to converge significantly faster to near optimal solutions with a small amount of local variable exchange as compared to the existing methods based on the dual decomposition.

Programmable MIMO-OFDM detector design, benchmarking and comparison of implementations have been considered in this paper. We emphasize the significance of co-optimizing the algorithm, software and hardware together in order to reach demanding energy, latency and area restrictions introduced in current standards. We compare energy consumption of the detection algorithms based on the theoretical complexities in function of signal-to-noise ratio. Applying co-optimizing we show how a carefully designed programmable architecture can achieve the 3G long term evolution (LTE) detection rate requirements with a reasonable energy consumption.

Regenerative relaying is an efficient method of increasing the achievable capacity and cell coverage. We propose a weighted mean squared error (WMSE) minimization based throughput maximizing precoder design subject to separate sum transmit power constraints for the base station (BS) and relay station (RS). The proposed algorithm is implemented in a distributed manner, thus, further improving the efficiency. We assume a system consisting of a single multi-antenna BS and multi-antenna RS, both of which are serving multiple single-antenna users. We consider a two-stage transmission strategy, in which, at the first stage, BS transmits to the associated users and the RS. At the second stage, the BS and RS transmit to their associated users. It is demonstrated that, with appropriate over-the-air (OTA) signaling, the system performance can be significantly improved.

We consider the beamformer design for zero-forcing dirty paper coding (ZF-DPC), a suboptimal transmission technique for MISO broadcast channels (MISO BCs). Beamformers for ZF-DPC are designed to maximize a performance measure, subject to some power constraints and zero-interference constraints. For the sum rate maximization problem under a total power constraint, the existing beamformer designs in the literature are based on the QR decomposition (QRD), which is used to satisfy the ZF constraints. However, the optimality of the QRD-based design is still unknown. First, we prove that the QRD-based design is indeed optimal for ZF-DPC for any performance measure under a sum power constraint. For the per-antenna power constraints, the QRD-based designs become suboptimal, and we propose an optimal design, using a convex optimization framework. Low-complexity suboptimal designs are also presented.

This paper considers the precoder designs for successive zero-forcing dirty paper coding (SZF-DPC), a suboptimal transmission technique for MIMO broadcast channels (MIMO BCs). Existing precoder designs for SZF-DPC often consider a sum power constraint. In this paper, we address the precoder design for SZF-DPC with per-antenna power constraints (PAPCs), which has not been well studied. First, we formulate the precoder design as a rank-constrained optimization problem, which is generally difficult to handle. To solve this problem, we follow a relaxation approach, and prove that the optimal solution of the relaxed problem is also optimal for the original problem. Considering the relaxed problem, we propose a numerically efficient algorithm to find the optimal solution, which exhibits a fast convergence rate. Suboptimal precoder designs, with lower computational complexity, are also presented, and compared with the optimal ones in terms of achievable sum rate and computational complexity.

Successive zero-forcing dirty paper coding (SZF-DPC) is a simplified alternative to DPC for MIMO broadcast channels (MIMO BCs). In the SZF-DPC scheme, the noncausally-known interference is canceled by DPC, while the residual interference is suppressed by the ZF technique. Due to the ZF constraints, the precoders are constrained to lie in the null space of a matrix. For the sum rate maximization problem under a sum power constraint, the existing precoder designs naturally rely on the singular value decomposition (SVD). The SVD-based design is optimal but needs high computational complexity. Herein, we propose two low-complexity optimal precoder designs for SZF-DPC, all based on the QR decomposition (QRD), which requires lower complexity than SVD. The first design method is an iterative algorithm to find an orthonormal basis of the null space of a matrix that has a recursive structure. The second proposed method, which will be shown to require the lowest complexity, results from applying a single QRD to the matrix comprising all users' channel matrices. We analytically and numerically show that the two proposed precoder designs are optimal.

Space-time (ST) constellation design problem and bit mapping schemes for multiple-input multiple-output (MIMO) systems in correlated Rayleigh fading channels with imperfect channel estimation at the receiver are considered. The channel coefficients are assumed to be correlated in space and uncorrelated in time from one coherence interval to another after which they change to another independent realization according to the spatial correlation model. We further assume the channel error estimation variance, as well as transmit and receive correlation matrices are perfectly known both at the transmitter and at the receiver. We derive the cutoff rate (CR) and an upper bound on the average bit error probability (BEP) expression for spatially correlated channels and propose them as the constellation design criteria subject to average power constraint. Additionally, to use the resulting constellations together with forward error correction (FEC) codes requires efficient bit mapping schemes. Because these constellations lack geometrical symmetry in general the Gray mapping is not always possible. Moreover, different mapping schemes may lead to different error rate performances. Thus, an efficient bit mapping scheme called binary switching algorithm (BSA) is proposed to use in order to find the optimal bit mappings. The Monte Carlo simulation results show that the designed constellation and its optimized bit mapping together with turbo codes outperform the earlier ones and the conventional constellations.

We propose a decentralized algorithm and corresponding low overhead signaling concepts of effective channel state information (CSI) for weighted sum rate (WSR) maximization via linear downlink transmit-receive beamforming in multi-cell multi-user MIMO systems operating in the time division duplex (TDD) mode. The proposed iterative processing consists of optimization steps that are run locally by base stations (BS), and facilitated by a combination of over-the-air uplink pilot signaling and scalar backhaul information exchange. In the uplink pilot signaling phase, each multi-antenna user terminal (UT) transmits a single precoded pilot beam. In case the UT was actively receiving, the uplink beam reveals its effective channel to all the coordinating BSs. On the other hand, if the UT was not receiving, the beam indicates its strongest effective vector channel to the serving BS. The processing strategy follows the changes of the time-varying channel and performs spatial user scheduling dynamically. According to the results, the strategy converges on the average in static channels, and in time-varying channels it effectively tracks the changes.

In two-hop relay systems, time division duplex (TDD) is usually adopted to support asymmetric traffic and offer channel reciprocity. When a relay is in the amplify-and-forward (AF) mode, the reciprocity is only provided within each hop. The equivalent channel between a base station (BS) and a mobile station (MS) cannot be reciprocal due to the operation at the relay. In some cases, the BS needs the whole downlink channel information to do pre-processing. However, the needed information could just be acquired through feedback in the conventional methods. In this paper, a gain matrix design method is proposed to maintain the whole link reciprocity and avoid large amount of feedback. Simulation results show that the mean square error (MSE) between whole downlink and uplink channel in proposed method is much smaller than that in the conventional gain matrix design schemes. Besides, the proposed method can maintain complete link reciprocity without performance loss.

We propose a joint bit and power loading, beamforming, multiple base station set (MBS) selection and subcarrier allocation algorithm for a resource allocation problem in a uplink multicell coordinated multi-basestation reception system. The objective is to minimize the total transmit power with rate constraint per mobile station (MS). The resources that we are considering include power, subcarrier, beamforming and the MBS set. The algorithm iteratively loads bits to the best link and subcarrier for the current rate increment and updates the signal-to-interference-plus-noise ratio (SINR) targets of all MSs on that particular subcarrier. Given a fixed rate target per subcarriers, the algorithm gives the optimal solution, i.e, optimal power vector, beamforming vector and MBS set. The iteration continues until all MSs reach their rate targets. We also propose a simplified scheme of such an algorithm. The simulation results show that the proposed algorithms perform significantly better than the simple approach without considering subcarrier allocation, i.e., equal rate allocation over the frequency spectrum, especially in a strong interference limited scenario.

A new space-time block code (STBC) is presented for distributed 4 × 2 multiple-input multiple-output (MIMO) systems. A 2 × 2 full-rate full-diversity (FR-FD) low complexity space-time code, presented by Sezginer and Sari, is employed for two distributed links. Alamouti STBC is applied over the two links to obtain a double layer space-time block code (DLSTBC) providing full diversity in a distributed 4 × 2 MIMO system. The proposed technique is simulated in Rayleigh channel and compared to several known STBC techniques. The performance results show that proposed STBC offers almost the same performance as a three-dimensional space-time-space (3D-STS) code presented by Nasser et al., while the receiver decoding complexity with constellation size M is decreased from M8to M6.

The problem of maximizing weighted sum rates in the downlink of a multicell environment is of considerable interest. Unfortunately, this problem is known to be NP-hard. For the case of multi-antenna base stations and single antenna mobile terminals, we devise a low complexity, fast and provably convergent algorithm that locally optimizes the weighted sum rate in the downlink of the system. In particular, we derive an iterative second-order cone program formulation of the weighted sum rate maximization problem. The algorithm converges to a local optimum within a few iterations. Superior performance of the proposed approach is established by numerically comparing it to other known solutions.

Space-time (ST) constellations for multiple-input multiple-output (MIMO) systems have usually been designed assuming either perfect or no channel state information (CSI) at the receiver. Practical systems are often partially coherent, i.e., have imperfect receiver CSI. In this paper, we consider the partially coherent ST code and constellation design for MIMO systems in Rayleigh fading channels while keeping the communication resource (i.e., power and rate) allocation to the training symbols minimum. As a result receiver acquires an imperfect CSI estimate, which is Gaussian random variable with zero mean and a non-zero variance, while the transmitter has perfect knowledge of the CSI estimation variance available via feedback. We propose to use the cutoff rate (CR) with respect to signal points as the design criterion. The CR expression is first derived. The CR maximization problem is formulated and numerical optimization algorithms are proposed. Constraints on the peak-to-average power ratio (PAPR) are also introduced. Numerical examples show that the designed constellations outperform earlier ones with respect to the PAPR, mutual information between the channel input and output as well as symbol error rate (SER) especially at low or medium signal-to-noise ratio (SNR) regime.

Relays are used in the current standards to enhance the cell throughput and coverage. Often, the relays are evaluated without considering the overhead involved in providing the relay with the data prior to the transmission. In this paper, we evaluate the system performance by considering the duplexing loss with over- the-air transmission (OTA) for relay with decode and forward as the relaying protocol. We demonstrate that the benefit of relays can be even detrimental if the in-band signaling needs are not properly accounted for. We assume a single-cell system extended with a single multi-antenna relay station serving single-antenna terminals. The system design objective is to allocate the users between the networks nodes and design their precoders such that the total transmit power is minimized while satisfying given user rate constraints.

In this paper, we investigate the opportunities offered by floating-point arithmetics in enabling an assembly and intrinsics free high-level language based development. We compare the characteristics of floating- and fixed-point arithmetics by simulating a MIMO-OFDM soft output detector in a 3G LTE link level simulator. The hardware complexity and energy dissipation are analyzed by implementing three programmable processors supporting 32- and 12-bit floating-point and 16-bit fixed-point arithmetics. The processors are based on the transport triggered architecture (TTA) that has a very low programmability overhead. The analysis shows that at the same goodput rate a floating-point implementation can achieve a lower gate count and better power efficiency than a fixed-point design.

A three-dimensional (3D) multi-cell multi-user system model along with spatial array processing are presented and studied in this paper. Most of the radio propagation in the literature has been studied and modeled in a two dimensional plane. The 3D propagation channel modeling has drawn attentions recently. This is due to the fact that, in some scenarios, the assumption of small elevation domain angular spread does not hold. With multiple antenna elements displaced in a 3D space, e.g., uniform linear array (ULA) or uniform planar array (UPA), the base station (BS) is capable of forming beams in vertical domain. This is particularly interesting for the interference limited scenario, since the 3D array processing provides another degree of freedom to transmit signal and eliminate the interference. Using the 3D multicell channel model, the impact of elevation domain angular spread and antenna analog beam pattern on the base station antenna design, digital beamforming and power control are studied.

We propose decentralized algorithms and corresponding channel state information (CSI) signaling concepts for weighted sum rate (WSR) maximization via linear downlink transmit-receive processing in multi-cell multi-user MIMO systems operating in the time division duplex (TDD) mode. Decentralized processing relies on a combination of over-the-air pilot signaling and scalar information exchange between the base stations. Each base station (BS) computes the transmit precoders to serve its own user terminals locally. Similarly, the terminals compute their receive filters locally and signal their effective downlink channels to the BSs via precoded uplink sounding. As a result, the transmit strategies of the neighboring cells adapt to each other over time. WSR maximization results in the desirable property that spatial scheduling, or user and beam selection, is carried out implicitly. Thus, only a subset of the potential transmit beams are allocated with non-zero transmit powers.

Soft-output detection of a multiple-input-multiple-output (MIMO) signal pose a significant challenge in future wireless systems. In this paper, we introduce a soft-output modified metric first (MMF)-LSD algorithm for MIMO detection. We design a scalable architecture and address a method to decrease memory requirements. We provide implementation results for a spatial multiplexing (SM) system with four transmitted streams and with 16- and 64-quadrature amplitude modulation (QAM) on a 0.18-μ m CMOS application specific integrated circuit (ASIC) technology. The MFF-LSD implementation is more efficient than the depth first (DF)-LSD in the crucial low signal-to-noise rate (SNR) region and the detection rate of the 64-QAM implementation is 39.2 Mbps@26 db with 48.2 kGEs complexity.

This paper addresses the problem of power allocation for single carrier point-to-point multiple input multiple output (MIMO) systems with iterative frequency-domain (FD) soft cancellation (SC) minimum mean squared error (MMSE) equalization. Two novel heuristic power allocation methods are proposed. The proposed methods explicitly take into account the convergence properties of the iterative equalizer while transmission power is minimized. The proposed heuristic schemes are based on, convergence constraint power allocation (CCPA), technique that decouples the spatial interference between streams using singular value decomposition (SVD), and minimize the transmission power while achieving the target mutual information for each stream after iterations at the receiver side. The proposed heuristic transmission schemes are inspired by well-known greedy algorithm resulting in a simple and efficient solutions to the power allocation problem. Numerical results show that the proposed heuristic schemes can achieve close to optimal performance in the terms of equalizer convergence as well as the total transmission power.

Relays can be used between the source and the destination to improve network coverage or reliability. In the relay based distributed space-time block-coded (DSTBC) single-carrier (SC) transmission scheme, asynchronism in time and frequency causes degradation in system performance because of the interblock interference, non-coherent combining and multiuser interference. The simultaneous presence of timing offset and carrier frequency offset also destroy the orthogonal structure of DSTBC. In this paper, we study the combined effect of timing offset and carrier frequency offset and propose a two stage equalization technique for this system at the destination terminal. Technique is based on interference cancellation and cyclic prefix reconstruction. The proposed equalization technique maintains the orthogonal structure of DSTBC and allows the use of low complexity one-tap frequency-domain equalizer. The technique significantly alleviates the effect of time offsets and frequency offsets at the destination without increasing the complexity of the receiver or disturbing the 3rd generation partnership project-long term evolution (3GPP LTE) uplink frame structure or the data rate over frequency selective channels.

An iterative receiver for a multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) system is considered to jointly decode the transmitted bits and estimate the channel state. The receiver consists of the a posteriori probability (APP) algorithm, the repeat-accumulate (RA) decoder, and the least-squares (LS) channel estimator. An obvious problem, with more than two blocks in an iterative receiver, is to find the optimal activation schedule of the different blocks. This paper proposes to use extrinsic information transfer (EXIT) charts to characterize the behavior of the receiver blocks and find out the optimal activation schedule for them. An analytical expression of the EXIT function is derived for the LS channel estimator. An algorithm is proposed to generate the EXIT function of the APP algorithm as a function of channel estimate mutual information (MI). Surface fitting is used to get closed form expressions for the EXIT functions of the APP algorithm and the RA decoder. A trellis search based algorithm is shown to find the convergence with the lowest possible complexity using the EXIT charts. With the proposed concept, the activation scheduling can be adapted to prevailing channel circumstances and unnecessary iterations will be avoided.

We consider multi-antenna systems utilizing groupwise layered space-time coding. Simple, yet accurate approximations for the probability density function (PDF) of the signalto- interference-plus-noise ratio (SINR) at the output of a linear zero-forcing (ZF) and minimum mean square error (MMSE) data estimators are presented. It is shown that the derived PDFs are exact in the limit of large number of receive antennas. We also demonstrate via numerical examples that the approximations are accurate already for relatively small numbers of receive antennas. Closed form expressions in terms of some well-known special functions for the uncoded bit error probability as well as frame error probability (FEP) of turbo-like coded system with iterative sum-product based decoder are derived. For comparison, we also give analytical lower bounds for the FEP of a coded multiantenna system utilizing conventional spatial multiplexing and linear ZF or MMSE based estimator at the receiver.

This paper studies the resource allocation for orthogonal frequency division multiple access (OFDMA) in a fixed amplify-and-forward (AF) relay link. We express closed-form average capacity results for the system when frequency allocation is based on limited and imperfect feedback information and multiple antennae are assumed at the relay node (RN) and the user equipment (UE). Feedback strategies based on the best-M method and resource block (RB)-wise one bit quantization are analyzed and compared. Capacity expressions are presented for space-time block coding (STBC) and transmit antenna selection (AS) schemes. The capacity results with imperfect feedback path are of particular interest. The results illustrate that promising tradeoff between the system performance, feedback overhead, and robustness against feedback errors can be achieved by using AS and limited feedback in frequency allocation.

A list sphere detector (LSD) is an enhancement of a sphere detector (SD) that can be used to approximate the optimal MAP detector. In this paper, we introduce a novel architecture for the increasing radius (IR)-LSD algorithm, which is based on the Dijkstra's algorithm. The parallelism possibilities are introduced in the presented architecture, which is also scalable for different multiple-input multiple-output (MIMO) systems. The novel architecture is implemented on a Virtex-IV field programmable gate array (FPGA) chip using high-level ANSI C++ language based Catapult C Synthesis tool from Mentor Graphics. The used word lengths, the latency of the design, and the required resources are presented and analyzed for 4 times 4 MIMO system with 16- quadrature amplitude modulation (QAM). The detector implementation achieves a maximum throughput of 12.1Mbps at high signal-to-noise ratio (SNR).

We propose an algorithm to reject co-channel interference in 3GPP long term evolution (LTE) multiple input multiple output orthogonal frequency division multiplexing (MIMO- OFDM) systems. An improved channel estimation method and interference rejection algorithm are discussed. The interference information is measured on the pilot sub-carriers which are sent regularly in time and frequency. An improved model averaged interference mitigation method parameterizes the interference and noise spatial-covariance-matrix on each carrier as a combination of several low-rank models, each associated with probability soft information. A reduced complexity a posteriori receiver is derived based on the low-rank spatial covariance model. Parameterizing the interference and noise covariance matrix helps in adapting receiver for different interference scenarios. Simulation results are provided to demonstrate the efficacy of the model averaged interference rejection method applied to MIMO-OFDM system.

This paper proposes a novel linear precoder design technique for single carrier single-user multiple input multiple output (MIMO) systems with frequency-domain (FD) soft cancellation (SC) minimum mean squared error (MMSE) iterative equalization where the convergence properties of the equalizer are taken into account. The proposed precoder design technique, convergence constrained precoding (CCP), minimizes the transmission power while it achieves the target mutual information for each stream after the iterations at the receiver side. We show that the optimality criterion for the proposed design can be formulated as a convex optimization problem. The results demonstrate that our proposed technique outperforms the existing linear precoding techniques by ensuring the convergence with a reduced transmission power. Furthermore, we show that with CCP we can adjust transmission according to convergence properties of the iterative equalizer in a more flexible way than, e.g., minimum sum mean squared error (MinSumMSE) and maximum information rate (MaxRate) precoding.

The performance of the uplink (UL) and downlink (DL) of IEEE 802.16e-2005 or Worldwide Inter-Operability Microwave Access (WiMAX) standard is studied via extensive computer simulations in this paper. The channel estimation is introduced and its impact on the system performance is evaluated under different channel conditions. The main contribution is to address the impact of the hybrid automatic repeat request (HARQ) protocol on the WiMAX system performance. For DL, the schemes include single-input single-output (SISO), Alamouti space-time block coding (STBC), and spatial multiplexing, i.e., vertical coded 2times2 multiple-input multiple-output (MIMO). For UL, the transmission schemes include SISO and 1times2 SIMO. It is observed that the HARQ protocol can improve the system performance dramatically, typically 2 ~ 3.5 dB gain in SNR from the first retransmission. The retransmission gain is also found to be relatively independent of the number of receive antennas.

A decision directed (DD) channel estimation is considered for a multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system to improve the spectral efficiency. Unlike in the pilot based channel estimation, the least-squares (LS) channel estimator operating in the DD mode for MIMO-OFDM requires a matrix inversion. The size of the matrix to be inverted depends on the number of transmit antennas and the length of the channel impulse response. The frequency domain (FD) space-alternating generalized expectation-maximization (SAGE) channel estimator calculates the LS estimate iteratively avoiding the matrix inversion with a constant envelope modulation. The drawback with the FDSAGE channel estimator is the required matrix inversion with a non-constant envelope modulation. The size of the matrix to be inverted depends on the length of the channel impulse response. However, it is considerably less complex than the LS channel estimator in the DD mode. In this paper, a time domain (TD) SAGE channel estimator is derived to avoid the matrix inversion in DD channel estimation for MIMO-OFDM systems when using non-constant envelope modulation. The derived TDSAGE channel estimator is shown to offer the same performance as the FD-SAGE channel estimator with reduced complexity.

Coordinated linear transmitter-receiver processing by block diagonalization (BD) with beam selection is a straightforward method to utilize all degrees of freedom available in multiuser multiple-input multiple-output (MIMO) networks in order to increase system capacity. By applying channel state information (CSI) in the transmitter, the BD criterion offers zero- forcing between the downlink data streams of different users. This paper proposes practical uplink MIMO schemes for time division duplex (TDD) systems to co-exist with downlink TX-RX zero-forcing so that the locally available CSI of the BD channel is used by the terminals in the uplink transmission. It is shown that the preceded pilot symbols are sufficient in both uplink and downlink to satisfy the needs of both transmission and reception. The achievable rate of the system is evaluated in conjunction with linear receivers in a time-varying fading channel and with channel estimation. According to the results the proposed uplink transmission strategy provides increased rates compared to user selection as well as non-precoded transmission, without being sensitive to CSI uncertainty.

Relays are used between the source and the destination to improve network coverage and reliability in cooperative relay system. Imperfect time delay synchronization at the receiver arises because of time difference in propagation of the signals from the source and the relay to the receiver. The performance degrades due to interference from the previous block and the defective channel matrix. In this paper, we study the effect of time delay asynchronism and propose to use interblock interference cancellation and cyclic prefix reconstruction techniques on amplify and forward relay based distributed space-time block coding for single carrier-frequency domain equalization system, which removes the unwanted interference part and reconstructs the defective channel matrix to circulant one. The proposed method significantly reduces the effect of time delay asynchronism at the receiver without disturbing the 3rd generation partnership project-long term evolution (3GPP LTE) uplink frame structure, data rate and increasing the complexity over frequency selective channels.

Opportunistic resource allocation in orthogonal frequency division multiple access (OFDMA) with limited feedback improves the performance of wireless networks and it has been considered in several studies. However, the performances of reduced feedback schemes have not been characterized analytically. In this paper, the analysis of the resource allocation in downlink OFDMA with limited feedback is presented. OFDMA system with a practical resource fair round robin allocation and erroneous feedback channel is considered. The performances of the resource allocation based on three feedback methods are analyzed and compared. A resource-wise signal-to-noise ratio (SNR) quantization based feedback method is considered and two feedback strategies are based on the best-M method, where the M best resource blocks are informed to the transmitter. Analytical bit error probability (BEP) expressions are derived for binary phase-shift keying (BPSK) modulation for each feedback strategy. Furthermore, an asymptotic BEP performance for large SNR values is derived for the best-M feedback schemes. The outage capacity results are derived for the considered feedback schemes to illustrate performance with slow link adaptation. Numerical results based on the analysis are compared to those obtained by computer simulations to validate the correctness of the proposed analysis.

Opportunistic user allocation and multiple antenna transmission provide significant performance enhancement in future wireless communication systems. In this paper, theoretical outage capacity analysis of user allocation in downlink orthogonal frequency division multiple access (OFDMA) with multiple antenna transmission and limited feedback is presented. Several multiple antenna transmission schemes are considered in OFDMA system, where resources are allocated according to the resource-wise signal-to-noise ratio (SNR) quantization. Performance results show that the resource-wise one bit feedback from few uncorrelated resources provides significant allocation gain. Numerical results based on the analysis are compared to those obtained by computer simulations to validate the correctness of the proposed analysis.

Opportunistic user allocation based on limited feedback information has been shown to offer significant enhancement for wireless multiple access communications. In this paper, theoretical analysis of user allocation in downlink orthogonal frequency division multiple access (OFDMA) with limited feedback is presented. The performances of resource allocation based on two feedback methods are analyzed and compared. We consider feedback based on the resource-wise SNR quantization and the ordered best-M feedback strategy, where the M best resources are informed to the transmitter. Exact bit error probability (BEP) expressions and analytical outage capacity calculus are presented for the both feedback strategies. Results indicate that significant allocation gain can be achieved with very limited feedback in wideband OFDMA systems. To achieve this allocation gain, we combine multiple resource selection and feedback strategies.

Theoretical outage capacity analysis for resource allocation in a downlink orthogonal frequency division multiple access (OFDMA) with limited feedback is presented. OFDMA system with practical resource fair round robin scheduler and erroneous feedback channel is considered. Two feedback methods are analyzed and compared. In the set best-M feedback method, the feedback word indicates the group of the M best resource units. The other feedback method is based on the resource-wise signal-to-noise ratio (SNR) quantization. Results indicate that the set best-M feedback scheme is attractive due to the low feedback overhead and significant allocation gain when few resource units are used and M les 3. The allocation based on the resource-wise one bit feedback outperforms the set best-M schemes especially with erroneous feedback at the cost of the larger feedback overhead.

The uplink-downlink SINR duality theorem is a key tool which simplify substantially the problem of joint design of the linear transmit and receive beamformers in multiple-input multiple-output (MIMO) downlink channels. The theorem has been proved previously under the assumption that the cross- coupling matrix between the users is primitive or, alternatively, by postulating the nonnegativity of the resolvent. By using the Lagrange duality theory, we first give an alternative proof which holds for arbitrary cross-coupling matrices. The proof does not only extend the result to a larger set of practical applications, but it also reveal more insight on the sum power minimization problem under a set of minimum SINR requirements for data streams. As a practical application, we apply the uplink-downlink SINR duality to derive a general method for MIMO downlink linear transceiver optimization according to different system performance criteria, including weighted sum rate maximization, weighted sum mean square error minimization, and minimum SINR maximization. The proposed method can handle multiple antennas at the BS and at the mobile user with single and/or multiple data streams per scheduled user. The numerical simulations show that the sum rate achieved by the sum rate maximization algorithm is within 0.5-1.5 bits/second/Hz close to the sum capacity. When compared to the traditional zero forcing based solutions, the proposed method provides more than 4 dB SNR gain and up to 3.5 bits/sec/Hz better spectral efficiency.

A localization algorithm based on double-thresholding (LAD) has been recently proposed for localizing concentrated signal(s) in the frequency or time domain. The LAD method blindly detects and separates concentrated signals and estimates their characteristics. This letter presents a theoretical performance analysis of the LAD method and an enhancement of the LAD method: the LAD with adjacent cluster combining (ACC). The good detection performance of the LAD ACC method is confirmed both theoretically and numerically.

Unitary space-time constellation design is considered for noncoherent multiple-antenna communications, where neither the transmitter nor the receiver knows the fading coefficients of the channel. By employing the Clarke's subdifferential theorem of the sum of the kappa largest singular values of a unitary matrix, we present a numerical optimization procedure for finding unitary space-time signal constellations of any dimension. The Chernoff bound of the pairwise error probability is used directly as a design criterion. The constellations are found by performing gradient descent search on a family ldquosurrogaterdquo functions that converge to the maximum pairwise error probability. The complexity of the search procedure increases with the dimension and the size of the constellation, but it can be considered to be acceptable for an off-line design procedure. Since the designed constellations are unstructured, and, thus, require an exhaustive search over all codewords in decoding, their main practical value is to serve as constellation design performance benchmarks. We compare the performance of the new constellations to that of some other well-known constellations. Computer simulation results illustrate typically about 0.4-3.5 dB performance gains.

Cooperative processing of transmitted signal from several multiple-input multiple-output (MIMO) base stations (BS) is considered for users located within a soft handover (SHO) region. The downlink resource allocation problem with different BS power constraints is studied for the orthogonal frequency division multiplexing system with adaptive MIMO transmission. Joint design of the linear transmit and receive beamformers in a MIMO multiuser transmission subject to per BS power constraints is considered. A solution for the weighted sum rate maximization problem is proposed. The proposed algorithm is shown to provide a very efficient solution despite of the fact that the global optimality cannot be guaranteed due to the non-convexity of the optimization problem. Moreover, efficient resource allocation method based on zero forcing transmission is provided. The impact of the size of a SHO region, the overhead from the increased resource utilization, and different inter-cell interference distributions due to the SHO are evaluated by system level simulations. Although the overhead from the SHO processing can be significant, it can be mitigated by using space division multiple access for users having an identical SHO active set composition. The users located at the SHO region may enjoy from greatly increased transmission rates. This translates to significant overall system level gains from the cooperative SHO processing.

A list sphere detector (LSD) can be used to approximate the optimal maximum a posteriori (MAP) detection. The total complexity of the LSD algorithms is relative to the number of visited nodes in the search tree. We compare the differences between real and complex signal model in the LSD algorithm implementation and study its impact on the complexity and performance with different search strategies. In hardware implementation, the number of visited nodes needs to be bounded in order to determine the complexity and the latency of the implementation. Thus, we study the performance of LSD algorithms with a limited number of nodes in the search. We show that the algorithms with real signal model are less complex compared to the complex signal model, and that the performance may suffer significantly with limited search depending on the search strategy.

Joint design of linear multiuser multiple-input multiple-output (MIMO) transceiver subject to different quality of service constraints per user and with per antenna or antenna group power constraints is considered. A solution for finding a maximum weighted common rate achievable for each scheduled user with minimum rate requirements per user is proposed. The proposed joint transceiver optimization algorithms are compared to corresponding optimal nonlinear transmission methods as well as to zero forcing transmission solutions. The proposed algorithms provide efficient solutions for difficult non-convex transceiver optimization problems.

We consider a single-cell multiple-input multiple-output (MIMO) downlink channel where linear transmission and reception strategy is employed. The base station (BS) transmitter is equipped with a scheduler using a simple opportunistic beamforming strategy, which associates an intended user for each of the transmitted data streams. For the case when the channel of the scheduled users is available at the BS, we propose a general method for joint design of the transmit and the receive beamformers according to different optimization criteria. The proposed method can handle multiple antennas at the BS and at the mobile user with single and/or multiple data streams per scheduled user. By exploiting the uplink-downlink SINR duality, we decompose the original optimization problem as a series of simpler optimization problems which can be efficiently solved by using standard convex optimization tools. The simulations show that the algorithms converge fast to a solution, which can be a local optimum, but is still efficient. Only one iteration of the proposed method is enough to substantially outperform the zero forcing based solution.

Joint cooperative processing of transmitted signal from several multiple-input multiple-output (MIMO) base station (BS) antenna heads is considered for users located within a soft handover (SHO) region. Downlink resource allocation problem with different BS power constraints is studied. The mathematical framework for the SHO based MIMO system is derived and the joint design of linear transmit and receive beamformers in a MIMO multiuser transmission according to weighted sum rate maximization criterion and subject to per BS power constraints is considered. The proposed algorithm is shown to provide very efficient solutions despite of the fact that there is no guarantee of achieving the global optimum due to the non-convexity of the problem. Moreover, practical and efficient resource allocation method based on generalized zero forcing transmission is provided.

We present an asymptotically (in number of receive antennas) exact probability density function for the signal-to-noise-plus-interference ratio (SINR) of the output of a zero-forcing (ZF) estimator in a multi-antenna system utilizing groupwise layered space-time (GLST) transmission. We show that the approximation is extremely accurate also for finite and relatively small numbers of receive antennas. Closed form expression for the uncoded bit error probability (BEP) is given in the case of binary or quadrature phase shift keying (BPSK/QPSK). Frame error probability (FEP) lower bound for turbo-like codes and iterative sum-product based decoder is presented.

In this paper, a joint detection algorithm is proposed for multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) channels. In MIMO OFDM channels, if enough cyclic prefix (CP) is used, intersymbol interference is completely cancelled and consequently spatial multiplexing interference (SMI) is the main limiting factor in performance. The proposed algorithm is based on a new soft parallel interference cancellation (SPIC) technique where, in each iteration, a part of SMI proportional to the probability of correctness of estimation in last iteration is cancelled. This probability is calculated approximately assuming the correct estimation for other sub-streams in last iteration. The proposed algorithm approaches the optimum performance in only a few iterations. The proposed algorithm is simulated for 4 states 1/2 turbo code and 8times12, 12times12 and 4times4 MIMO OFDM channels for different values of the Eb/N0. Bit error rate (BER) and frame error rate (FER) are considered as comparison criteria and it is shown that its performance is good and very close to interference free (lower bound) case.

A list sphere detector (LSD) based on Dijkstra's algorithm, namely increasing radius (IR) - LSD, is introduced for multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. The complexity and the performance of the IR-LSD is compared to a breadth first and depth first search based LSDs. The required list sizes for considered LSDs are determined for a 4times4 system with quadrature amplitude modulations (QAMs). The complexity of the algorithms is relative to the number of visited nodes in the search tree structure. Thus, the algorithm complexities are compared by studying and comparing the number of visited nodes per received symbol vector by the algorithm via computer simulations. The IR-LSD is found to visit the least amount of nodes of the LSDs in the search tree in each studied case, and found the least complex especially with higher order constellations.

Opportunistic spectrum usage would enable enhancing the efficiency of existing and emerging wireless communication systems. One of the key issues related to those systems is spectrum opportunity estimation. In this paper, we are using a technique utilized earlier in narrowband signal detection, namely the localization algorithm based on double-thresholding (LAD), for sensing the existence of primary user signals in a cognitive radio systems. The LAD method requires no a priori information on the primary user statistics and it has a low computational complexity that will enable a low-cost real-time implementation. The LAD method is able to estimate the number of narrowband signals and their characteristics, including bandwidth and power. A simplified version of the LAD method which uses normalized thresholds (NT) as well as an enhancement of the scheme that uses adjacent cluster combining (ACC) are proposed. Simulation results show that the simplified version of the LAD method is useful in the considered situations, and the enhanced version of the LAD method improves the performance of the LAD and LAD NT methods significantly.

We consider a single-cell multiple-input multiple-output (MIMO) downlink channel where linear transmission and reception strategy is employed. The base station (BS) transmitter is equipped with a scheduler using a simple opportunistic beamforming strategy, which associates an intended user for each of the transmitted data streams. For the case when the channel of the scheduled users is available at the BS, we propose a general method for joint design of linear transmit and receive beamformers, according to weighted sum rate maximization criteria. The proposed method can handle multiple antennas at the BS and at the mobile users with an arbitrary number of data streams per scheduled user. It can also handle a fairly general set of practical power constraints for the transmit beamformers, i.e., we can impose sum power constraints for different subsets of the transmit antennas.

In this paper, Bussgang algorithm is developed as a blind equalizer for multi-carrier multi-input multi-output (MIMO) channels to combat both spatial multiplexing interference and channel variations. Also, the Bussgang technique is extended with adapting the one or two parameters in its nonlinear function. The Bussgang algorithm and its two extended version are simulated and compared to the full known channel (ideal) and decision directed cases. Frame error rate (FER) and bit error rate (BER) of both coded and uncoded cases, are considered as comparison criteria and it is shown that the much performance improvement is achieved compared to decision directed algorithm and each of the three proposed schemes presents the best performance in the different criteria and Eb/NO.

System level simulations in a realistic multi-cell model are needed to evaluate the performance of the adaptive radio link and radio resource management (RRM) algorithms for future wireless communication systems. In this paper, we consider spatial mode adaptation, modulation adaptation, hybrid automatic repeat request (HARQ) and scheduling in a cellular network. In order to simulate a considered multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) physical layer, a novel link frame error rate (FER) prediction method is presented. Link-to-system level method is based on the mutual information metric which takes multiple antenna receiver and multiple antenna channel coding into account. The proposed metric is shown to provide an accurate FER approximation for diverse range of MIMO-OFDM channels also with inter-cell interference and HARQ. System level results illustrate that the MIMO-OFDM system with adaptive space-frequency turbo coded modulation (SFTuCM) provides flexibility and high spectral efficiency for interference limited system. The MIMO-OFDM system with interference averaging frequency hopping channel allocation achieves performance close to the channel condition aware dynamic channel allocation (DCA) with time division multiple access (TDMA).

The use of lattice reduction (LR) has been shown to be a very effective method for signal detection in multiple antenna systems. In this paper, the performance of the sub-optimal soft-output maximum a posteriori (MAP) detector with lattice reduction is studied. We propose a polynomial-time method to generate the desired size candidate list in the reduced lattice. This sub-optimal soft-output MAP detector provides a near-optimal performance in uncorrelated MIMO systems, but with significantly reduced computational complexity.

An iterative receiver design using a space-alternating generalized expectation-maximization (SAGE) based list detection and channel estimation for a multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system is considered. A new SAGE based list detection algorithm is proposed to operate as a receiver front-end to approximate the MAP algorithm with reduced complexity and with minor losses in the performance. The receiver front-end, a decoder, and a channel estimator constitute an iterative receiver to jointly decode the transmitted bits and to estimate the channel state. The proposed receiver front-end shows a good performance when compared to the other MAP approximation, i.e., to the K-best list sphere detector (LSD) and a linear front-end. Furthermore, the whole iterative receiver performs well with the low pilot overhead despite the fast fading channel.

The joint cooperative processing of transmitted signal from several multiple-input multiple-output (MIMO) base station (BS) antenna heads is considered for users located within a soft handover (SHO) region. The mathematical framework for the SHO based MIMO system is derived and the joint design of linear transmit and receive beamformers in a MIMO multiuser transmission subject to per BS power constraints is considered. Solution for the maximization of the minimum weighted SINR per data stream criterion is proposed. The proposed algorithm is shown to provide very efficient solutions despite of the fact that the global optimum cannot be guaranteed due to the non-convexity of the problem. Moreover, a less complex but still efficient allocation method based on zero forcing transmission is provided for the same optimization criterion.

A novel low-complexity and provably convergent algorithm is proposed to find the sum capacity for vector Gaussian broadcast channels. Unlike the recently proposed sum-power constraint iterative waterfilling (SPC-IWF) algorithms, it has lower complexity and requires no additional precautions to ensure the convergence. We have proved analytically the convergence with probability one, and the computer simulations show that the proposed algorithm converges faster than the earlier variants of SPC-IWF algorithms. We formulate the problem in the context of a multiple-input multiple-output orthogonal frequency-division multiplexing system and discuss the simplifications provided by the block-diagonal channel structure

Multiple-input multiple-output (MIMO) communications based on multiple transmit and receive antennae will be applied to enhance the data rates of the third generation (3G) cellular systems or wideband code division multiple access (WCDMA) and in particular, their high speed downlink packet access (HSDPA) services. This causes both spatial multiplexing interference (SMI) and downlink multiple access interference (MAI). In this paper, we derive an improved linear minimum mean square error (LMMSE) detector approximation suitable for MIMO-WCDMA systems. We also propose a new two-stage receiver architecture to suppress the effects of both MAI and SMI in HSDPA communications. SMI often dominates MAI, since it does not benefit from spreading gain. Therefore, a spatial maximum a posteriori (MAP) detector is used to suppress SMI, and a LMMSE based channel equalizer is applied as its front- end. The performance of the resulting LMMSE-MAP receiver structure is studied via Monte Carlo computer simulations with assumptions very realistically mimicking those of the HSDPA specification. The results show that it clearly outperforms the rake receiver or the improved LMMSE equalizer approximation, which, on the other hand, is also shown to be superior to the more conventional LMMSE equalizers.

In a time-division-duplex communication system, the channel knowledge can be obtained at the transmitter side due to channel reciprocity and it can be used to increase the spectral efficiency of a multiple-input multiple-output (MIMO) communications. However, the interference structure between transmission directions does not necessarily correlate. The obtained quality of service at the receiver may differ significantly from the desired one if the transmission parameters are assigned based on the reverse link measurements only. In this paper, the performance of an orthogonal frequency division multiplexing (OFDM) cellular system with adaptive MIMO transmission is studied in the presence of non-reciprocal inter-cell interference when the downlink interference structure is known at the receiver and only limited feedback information about the interference is available at the transmitter. The results are compared to those with perfectly known interference structure per each sub-carrier. The system level impact of realistic interference non-reciprocity scenarios is studied via network simulations. Linear minimum mean squared error (MMSE) filter is applied at the receiver to suppress the impact of structured inter-cell interference together with a simple and bandwidth efficient closed-loop compensation algorithm. Both link and system level simulation results show that the proposed compensation algorithm with a simple scalar power offset feedback combined with interference suppression at the receiver results in nearly the same performance as the ideal case

The joint cooperative processing of transmitted signal from several multiple-input multiple-output (MIMO) base station (BS) antenna heads is considered for users located within a soft handover (SHO) region. The system level gains and trade-offs from cooperative SHO processing are investigated. The impact of the size of the SHO region, overhead from the increased hardware and physical (time, frequency) resource utilization, and different non-reciprocal inter-cell interference distributions due to SHO are evaluated. Practical user, bit and power allocation method with different BS power constraints is provided for the proposed cooperative multiuser MIMO transmission. The overhead from SHO processing can be significant, and the call blocking probability can be dramatically increased. However, the overhead can be mitigated by using space division multiple access for users that have identical SHO active set composition. Also, the dropping probability is decreased, and thus, the total outage probability with SHO is less than without SHO. The users located at the SHO region may enjoy from greatly increased transmission rates. This translates to significant overall system level gains from the cooperative SHO processing. The proposed soft handover scheme can be used to provide more evenly distributed service over the entire cellular network.

Mobile positioning has drawn significant attention in recent years. In dealing with the non-line-of-sight (NLOS) propagation error, the dominant error source in the mobile positioning, most previous research in this area has focused on the NLOS identification and mitigation. In this paper, we investigate new positioning algorithms to take advantage of the NLOS propagation paths rather than cancelling them. Based on the prior information about the NLOS path, a least squares based position estimation algorithm is developed and its performance in terms of root mean square error (RMSE) is also analyzed. Furthermore, the maximum likelihood based algorithm is presented to jointly estimate the mobile's and scatterers' positions. The Cramer-Rao lower bound on the RMSE is derived for the benchmark of the performance comparison. Finally, the performances of the proposed algorithms are evaluated analytically and via computer simulations. Numerical results demonstrate that the simulated results closely match the derived analytical results.

Block diagonalization (BD) of multiple users combined with coordinated transmitter-receiver processing and scheduling between users is a simple and straightforward method to utilize the available spatial degrees of freedom in downlink multi-user (MU) multiple-input multiple-output (MIMO) channel for increasing the system throughput. In this paper, an efficient user, bit and power allocation algorithm with low signalling overhead (LSO) is proposed for maximizing the downlink spectral efficiency of the adaptive multiuser MIMO-OFDM system with BD decomposition. A greedy allocation algorithm with Hughes-Hartogs loading is also proposed and used as a reference for LSO algorithm. For a small number of users, the performance of LSO algorithm is shown to be very close to the greedy algorithm. Iterative BD decomposition with a single reiteration is shown to provide significant gains to the non-iterative solution. Linear minimum mean square error (LMMSE) filter is applied at the receiver to suppress the remaining multi access interference from incomplete orthogonalization together with a simple compensation algorithm with low rate scalar feedback to the transmitter. The proposed MU MIMO-OFDM system is also shown to be robust against imperfect channel state information at the transmitter, providing always superior performance to a time switched single user MIMO-OFDM solution.

A MIMO broadcast channel with linear transceiver schemes is considered in this paper. The base station (BS) is equipped with a scheduler, using a simple opportunistic beamforming strategy, which associates an intended user for each of the transmitted data streams. For the case when the channel of the scheduled users is available at the BS we propose an iterative algorithm for joint design of the transmit and the receive beamformers according to mean square error minimization criterion. The proposed method can handle multiple antennas at the BS and at the mobile user with single and/or multiple data streams per scheduled user. The optimization problems encountered in the beamformer design (e.g., covariance rank constraint) are not convex in general. Therefore, the problem of finding the global optimum is intrinsically non-tractable. However, by exploiting the uplink-downlink SINR duality, we decompose the original optimization problem as a series of simpler optimization problems which can be efficiently solved by using standard convex optimization tools. There is no guarantee that the global optimum has been found due to the nonconvexity of the problem, but, the simulations show that the algorithms converge fast to a solution, which can be a local optimum, but is still efficient

The lattice reduction (LR) aided detectors for multiple-input multiple-output (MIMO) systems have been shown to have a better performance than conventional linear detectors like zero forcing (ZF) or minimum mean square (MMSE) detectors. In this paper, we study the performance of the LR aided detectors for multiple input multiple output orthogonal frequency division multiplexing (MIMO-OFDM) systems in spatially correlated fading channels. Also, the computational complexity of the LR based linear detectors is studied with comparison to the conventional detectors. The simulation results show that the LR aided detectors are more robust to the spatial correlation than the conventional linear detectors and also have a low computational complexity close to the conventional linear detectors

This paper presents a subspace-based algorithm for the simultaneous estimation of angle of arrivals (AOAs) and angle of departures (AODs) from an estimated spatial signature at the receive antenna array. The algorithm exploits a 2-D unitary ESPRIT-like technique to separate and estimate the phase shifts due to the AOAs and AODs with automatic pairing of the two parameter sets. The Cramer-Rao lower bound (CRLB) on the AOAs and AODs estimates is provided. The performance of the algorithm is illustrated based on the computer simulation. It shows that the proposed algorithm is an efficient estimator which is able to achieve the CRLB

In this paper, the complexity and performance of two novel list sphere detector (LSD) algorithms are studied and evaluated in multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) system. The LSDs are based on the K-best and the Schnorr-Euchner enumeration (SEE) algorithms. The required list sizes for LSD algorithms are determined for a 2times2 system with 4-quadrature amplitude modulation (QAM), 16-QAM, and 64-QAM. The complexity of the algorithms is compared by studying the number of visited nodes per received symbol vector by the algorithm in computer simulations. The SEE based LSD algorithm is found to be a less complex and a feasible choice for implementation compared to the K-best based LSD algorithm

The use of lattice reduction (LR) has been shown to be a very effective method for signal detection in multiple antenna systems. In this paper, the performance of the sub-optimal soft-output maximum a posteriori (MAP) detector with lattice reduction is studied in multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) system. We propose two polynomial-time methods to generate the desired size candidate list in the reduced lattice. The sub-optimal soft-output MAP detector with these list generation methods provides a near-optimal performance in uncorrelated MIMO-OFDM systems, but with significantly reduced computational complexity

The joint cooperative processing of transmitted signal from several multiple-input multiple-output (MIMO) base station (BS) antenna heads is considered for users located within a soft handover (SHO) region. Downlink space-frequency bit and power allocation problem with different BS power constraints is studied for the considered adaptive MIMO-OFDM system. The performance of the proposed heuristic loading method is shown to be close to the optimal convex optimization method with per BS power constraints. It is shown that the highest SHO gains are achieved with a small power imbalance between the received BS powers at low signal-to-noise ratio (SNR), where the achievable rates can be even doubled. On the other hand, the gain from joint processing in SHO quickly diminishes as the imbalance increases, especially at low SNR. Moreover, the results indicate that the joint processing can be even detrimental for the system performance if a coarse phase synchronization between BS antenna head is not guaranteed, as some additional fading on the target SNR values is introduced

Multiple-input multiple-output (MIMO) concept has been recognized as one of the key approaches for providing required bandwidth efficiency in the evolution of 3G wireless systems. However, the choice of the particular MIMO transceiver scheme is still an open issue and it is under intensive discussion in standardization bodies. In this paper we tackle the topic of the choice of the appropriate MIMO scheme for the long term evolution of 3G systems. We show that, among the considered schemes, quasiorthogonal matrix modulation approach is the most appealing one in 2 times 2 and 4 times 2 configurations

Expectation-maximization (EM) based joint decoding and channel parameter estimation for multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system is considered. A new iterative receiver structure is proposed to operate together with space-frequency turbo coded modulation (SFTuCM) using decoder outputs to refine the channel parameter estimates. The proposed receiver performs well with low pilot overhead despite the fast changing channel state. The proposed receiver improves not only the error probability performance but remarkable improvement is achieved in the channel parameter estimation accuracy when compared to pilot based estimation with the same overhead

The use of lattice reduction (LR) has been shown to be a very effective method for signal detection in multiple antenna systems. In this letter, the performance of the suboptimal soft-output maximum a posteriori (MAP) detector with lattice reduction is studied. We propose two polynomial-time methods for flat fading channels to generate the desired size candidate list in the reduced lattice. The suboptimal soft-output MAP detector with these list generation methods provides a near-optimal performance in uncorrelated multiple-input multiple-output systems but with significantly reduced computational complexity.

In this paper, a narrowband multiple-channel transmission scheme with multiple transmit antennas is proposed and analyzed. The channelization is based on space-time signature matrices, which do not expand bandwidth, unlike conventional schemes such as code-division or time-division multiplexing. The channels can be used by multiple independent users in an uplink or downlink scenario (multiple access or broadcast channels, respectively), or by one user in a multiplexing scenario. The data transmitted on each channel is convolutionally encoded, interleaved, and then space-time block encoded before space-time channelization. Each channel has a unique interleaver and space-time signature, but the convolutional encoder and space-time block code encoder can be identical across channels. It is shown that asymptotic single-user-like performance can be achieved with optimal detection and decoding in a Rayleigh fading channel. Practical receiver algorithms are developed based on the iterative (turbo) detection technique. The simulation results demonstrate that these suboptimal receivers achieve single-user performance at moderate signal-to-noise ratios, and moderate user loads. In the single-user multiplexing case, a significant performance gain over single-channel transmission with the same data rate is obtained.

In this paper, a novel simple generalized Chase algorithm (GCA) based multiuser detector (MUD) is proposed for MC-CDMA systems. After getting the initial solution from the front-end frequency domain linear minimum mean-squared error (LMMSE) receiver, different Chase-type algorithms are adopted to extend the potential solution list. Among the list, the candidate which can achieve the minimum distance to the received signal, is selected as the final solution. Both bit- and symbol-level receivers for high level modulation schemes are considered. Compared to the conventional MMSE receiver, the proposed receiver can provide superior performance with little increase in complexity. Moreover, the robustness of the proposed receiver is studied by incorporating a semi-blind subspace based channel estimation.

In this paper, we propose a novel method for creating constellations of unitary space-time signals for non-coherent multiple-antenna communication link, where neither the transmitter nor the receiver knows the fading coefficients. By employing a theorem for the Clarke subdifferential of the sum of the k largest singular values of the unitary matrix, we present a numerical optimization procedure for finding good constellations of unitary space-time signals and report the best constellations found by this procedure. These constellations of unitary space-time signal improve the performance significantly upon previously best known constellations.

The studies of wireless communication systems with multiple transmit and receive antennas have shown to provide large gains over single antenna systems. The recent studies have focused on the spectral efficiency improvements, while the achievable data rates of wireless radio links allowing flexible frequency band usage are much less considered. We present closed-from expressions for the achievable data rates of wireless radio links with multiple antennas operating over frequency selective fading channels including one-sided antenna correlation. In transmitter side the cases with no channel state information (CSI), covariance feedback and ideal CSI are evaluated. Perfect CSI is assumed at the receiver. Numerical results show examples of the upper limits for achievable data rates of the GSM radio links. Furthermore, the numerical results quantify the importance and the relation of different transmitter CSI, antenna correlation and bandwidth usage to the radio link behavior and data rates

The spectral resources of high capacity cellular networks are desired to be utilized efficiently using aggressive frequency reuse. The consequence of the frequency reuse in each cell is inter-cell interference. In this paper, we consider downlink multi-carrier code-division multiple-access (MC-CDMA) in a multi-cell environment. In order to achieve good error rate performance and high spectral efficiency, multiple transmit and receive antennas with strong space-frequency channel coding and layered space-time (LST) architecture are applied. We study the performance of the minimum mean squared error (MMSE) based receivers in interference limited environment. The inter-cell interference suppression in MMSE detection and iterative detection method with soft co-antenna interference (CAI) cancellation are considered. The results indicate that the considered MC-CDMA system requiring no channel knowledge at the transmitter is suitable for high date rate downlink packet transmission in a cellular environment with relative high user load. The inter-cell interference suppression in MMSE detection provides remarkable performance improvement, especially in the case of spatially correlated antennas.

This paper treats the optimal design, placement and power allocation of pilot symbols for frequency-selective correlated multiple-input multiple-output (MIMO) channel estimation. We extend the correlated MIMO channel model in terms of virtual channel representation proposed by Sayeed to the frequency-selective case. We show that the principle of the optimal design and placement of pilot symbols proposed by Dong and Tong can be applied to the correlated MIMO channel. On top of that, optimal power allocation of pilot symbols constrained by total pilot symbol power is presented so that the minimum Cramer-Rao lower bound can be achieved. In general, the closed-form of optimal power allocation of pilot symbols does not exist. However, in case of high tap-to-noise ratio, an approximate closed-form of power allocation scheme is derived.

The effect of channel estimation errors on the symbol error rate of space time block code (STBC) over uncorrelated multiple-input multiple-output (MIMO) channel has been given by Hochwald etc.. On top of that, we derive an exact expression for the bit error probability of the quadrature phase-shift keying modulated STBC coded orthogonal frequency division multiplexing system over correlated MIMO channels. The closed-form BEP formula include the impacts, not only of fading and receiver noise, but also that of the channel estimation errors and spatial correlation occurred at the receiver side.

Block diagonalization (BD) combined with coordinated transmitter-receiver processing and scheduling between users is a simple and straightforward linear method to utilize the available spatial degrees of freedom in downlink multi-user multiple-input multiple-output (MIMO) channel for increasing the system throughput. In this paper, an efficient scheduling algorithm is proposed to maximize the downlink spectral efficiency of the BD method for any number of users and antennas at both the transmitter and the receivers. The performance is compared to the spectral efficiency available with several other scheduling algorithms and to the sum-rate capacity. It is shown that the performance of the BD method with the proposed scheduling approaches the sum rate capacity at high signal-to-noise ratio (SNR) as the number of users increases. It is also noticed that due to the inherent noise amplification problem with linear BD method, the maximum spectral efficiency is often achieved by transmitting to less users/beams than the spatial dimensions available, especially, at low SNR region with a high number of transmit antennas and with a low number of users.

An adaptive method for suppressing interference with an unknown model is presented. The method is based on computation of several transforms in parallel and selecting the most suitable one depending on a priori unknown the interference type based on a compression gain (CG) metric, which is related to the interference concentration in the transform domain. It is also possible to estimate the a priori parameters required by some transforms. The method can be expanded to utilize an arbitrary number of transforms. It is shown through computer simulations that the CG is a good metric for choosing the proper domain for interference excision.

The channelized radiometer is a well-known intercept receiver. We analyze its performance when different constant false alarm rate (CFAR) strategies are used to set a detection threshold based on values of some reference cells. The studied conventional methods are cell-averaging (CA) and order statistics. The proposed iterative methods for setting the detection threshold are forward consecutive mean excision (FCME) with the CA scaling factors in (final) detection decision (FCME+CA), backward consecutive mean excision (BCME) with the CA scaling factors in detection (BCME+CA) and a method that uses the CA scaling factors in both censoring and detection (CA+CA). The results show that iterative CFAR methods can improve detection performance compared to the baseline methods.

We analyze the impact of a uniform quantizer on the false-alarm probability of a total power radiometer. Different possibilities to set the detection threshold are discussed. The main emphasis is on methods that use the estimated noise level. In particular, we analyze the cell-averaging (CA) constant false-alarm rate threshold setting strategy. The numerical results show that the CA strategy offers the desired false-alarm probability.

This letter introduces diagnostic impulse-detection methods in general, and, based on those, presents three iterative impulse detectors for communication receivers with an antenna array. These detectors are implemented either in a backward or forward search mode. The forward methods are shown to perform better. They can detect all the impulses, even though 90% of all the samples are corrupted.

For the single-user communications pragmatic yet powerful methods known as layered space-time (LST) architectures provide means to increase the user data rate of a multiple-input multiple-output (MIMO) antenna system dramatically. To achieve better error rate performance, the LST transmission can be further accompanied with forward error correction coding. Future wireless communication systems, however, require high data rates also in multiuser environments. For broadband multiuser wireless communications, multicarrier code-division multiple-access (MC-CDMA) has emerged as an attractive technique due to its low equalization complexity and robust performance in multipath fading channels. Utilizing strong channel coding and LST architectures with MC-CDMA, high spectral efficiency and good error rate performance can be obtained in diverse environments. In this paper, single and multi-antenna turbo coded downlink MC-CDMA is combined with the concept of LST architectures. Due to the inevitable complexity restrictions, a suboptimal receiver interface for the underlying system is designed and its performance is evaluated in fading channels. The results demonstrate that significant improvement in both error rate performance and the system throughput can be achieved also with multiuser communications by using these MIMO techniques.

This paper proposes a novel bandwidth-efficient multiuser scheme with multiple antennas. By assigning each user a two-dimensional signature matrix and user-specific interleaver, K users can can occupy the same bandwidth as the single user. Both theoretical analysis and simulation results show that the single-user like performance can be achieved asymptotically.

We present a novel joint multiuser detection method based on sphere packing lattice decoding and semi-blind channel estimation for multicarrier code-division multiple-access (MC-CDMA) systems. After modelling MC-CDMA as a sphere packing lattice, a low-complexity maximum-likelihood (ML) detection, sphere decoding algorithm, is applied to jointly detect all users. The impacts of channel estimation errors are studied by incorporating a semi-blind subspace based channel estimation in the receiver. The selection of search radius and the complexity of the receiver are also investigated. Another promising detection technique, genetic algorithm (GA) based multiuser detector (MLJD) is also studied. Simulation results demonstrate the superior performance of the semi-blind receiver compared to the conventional receivers for MC-CDMA and its robustness to channel estimation errors.

We evaluate the performance of single- and multi-antenna multi-carrier code division multiple access (MC-CDMA) downlink (base station to mobile terminal) systems in multi-cell environments. We first propose a minimum mean square error (MMSE) filter for a single input single output (SISO) MC-CDMA downlink system. Then, we apply it to a SIMO (single input multiple output) system with a conventional turbo coding. Furthermore, we compare the performance of SISO (1 /spl times/ 1) and SIMO (1 /spl times/ 2) MC-CDMA systems with that of a multiple input multiple output (MIMO) (2 /spl times/ 2) system employing space-time turbo coded modulation (STTuCM) in a multi-cell environment with 7 cells by computer simulation. Based on the computer simulation results, it is found that the considered MIMO system can achieve twofold capacity with the same transmission power in the multi-cell environment.

Spreading factor (SF) in code-division multiple-access (CDMA) systems depends on the data rate of the user, and is, therefore, unknown a priori for a communication receiver. In this letter, the blind SF-detection problem in an additive white Gaussian noise channel is studied, and a novel one-step autocorrelation-based SF detector is proposed. Three decision rules are derived to find simple and robust SF detectors for practical communication receivers. Performance of the detectors is studied and compared with the optimal detector via Monte Carlo computer simulations. The autocorrelation-based SF detector appears to give performance close to the optimal detector, assuming perfect knowledge of signal-to-noise ratio (SNR). It is also found to be significantly more robust to SNR estimation errors than the optimal detector.

Receivers based on channel equalization have proven to be one of the most promising candidates for terminal receivers in wideband code-division multiple-access (WCDMA) downlink. The channel equalizers provide multiple access interference (MAI) suppression, and thus ensure adequate receiver performance even with a high number of active users. However, the suitability of the equalizers to soft handover has been unclear. In this paper, adaptive equalizer structures for soft handover are introduced, and their performance is numerically evaluated. The results show a significant performance gain over the conventional RAKE receiver.

A modular LMMSE receiver for the 3GPP WCDMA FDD shortcode option is presented. The LMMSE receiver is implemented with a separate signal processing module placed in front of a conventional detector. The structure, algorithms, numerical aspects, complexity and the performance of the receiver are studied. In performance simulations conforming to 3GPP Rel'99 physical layer specifications the receiver is found to provide a 0.5 - 1 dB gain over the conventional detector in the studied cases.

In this paper the influence of the channel estimation error on the performance of the closed loop Tx diversity algorithms for WCDMA is studied for a high number of Tx antennas. The analysed algorithms are generalised closed loop Tx diversity, all-antenna Tx diversity and one-antenna selection Tx diversity. It was shown that generalised closed loop Tx diversity algorithm is more robust to channel estimation errors that all-antenna Tx diversity algorithm. This is valid for both uncorrelated fading and for correlated fading between Tx antennas. The one-antenna selection Tx diversity algorithm is also robust to the channel estimation errors, but its performance is worse than that of generalised closed loop Tx diversity algorithm.

A design method for recursive space-time trellis codes and parallel concatenated space-time turbo coded modulation was recently proposed as a new framework for building optimized, low-complexity, large equivalent constraint-length space-time trellis codes. In this paper the distance spectrum interpretation of the space-time turbo coded modulation is introduced to evaluate the union bound on the frame error rate over fast fading channels. The distance spectrum gives a good insight on the importance of multiplicities in the space-time coding design. Derived bounds are in some cases very tight and provide a designer with the practical tool for further optimization of the constituent space-time trellis codes within the space-time turbo coded modulation framework. Some interesting examples of the convergence of the sub-optimal iterative decoding were also presented.

A new bandwidth and power efficient signaling scheme is proposed that achieves high data rates over wideband radio channels exploiting the bandwidth efficient OFDM modulation, multiple transmit and receive antennas and large frequency selectivity offered in typical low mobility indoor environments. Owing to its maximum transmit diversity gain and large coding gain, space-frequency turbo coded modulation is demonstrated to perform within 2.5 dB of the 10% outage capacity for the variety of practical wideband MIMO radio channels, strongly out performing other space-frequency coding schemes proposed in literature. A simple way of combining space-frequency coding with OFDM delay diversity for cost effective further increase in bandwidth efficiency by exploiting more than two transmit antennas is also proposed.

The most important 3rd generation cellular communications standard is based on wideband CDMA (WCDMA). Receivers based on TDMA style channel equalization at the chip-level have been proposed for a WCDMA downlink employing long spreading sequences to ensure adequate performance even with a high number of active users. These receivers equalize the channel prior to the despreading, thus restoring the orthogonality of users and resulting in multiple access interference (MAI) suppression. In this paper four adaptive versions of chip-level channel equalizers are studied and their performance is evaluated in a Rayleigh fading multipath channel. The numerical results show a significant performance improvement over the conventional RAKE receiver with a large number of active users.

The performance of two previously proposed interference suppression schemes in a turbo-coded hybrid direct sequence/frequency hopping (DS/FH) system, with the presence of broadband interference, is presented. The analysed schemes are the /spl xi/-filter and polar suppressor, proposed for cancelling interfering signals that can vary from narrowband to wideband, with the bandwidth comparable to that of the desired direct sequence (DS) signal. The classical coding approach is compared to a data-aided approach, where the desired signal estimates are used in the suppression block, with an idea to make the suppression procedure more efficient. Performance comparisons of systems that use reencoding to produce feedback information with the system without reencoding show that an improvement can be obtained in the latter case. Both cases, however, do not show a significant improvement over the classical approach.

A new bandwidth and power efficient signaling scheme is proposed that achieves high data rates over wideband radio channels exploiting the bandwidth efficient OFDM modulation, multiple transmit and receive antennas and large frequency selectivity offered in typical low mobility indoor environments. Owing to its maximum transmit diversity gain and large coding gain, space-frequency turbo coded modulation is demonstrated to perform within 2.5 dB of the 10% outage capacity for the variety of practical wideband MIMO radio channels, strongly outperforming other space-frequency coding schemes recently proposed in the literature. A simple way of combining space-frequency coding with OFDM delay diversity for cost effective exploitation of more than two transmit antennas is also proposed.

In WCDMA systems, the spreading factor (SF) of a user is allowed change as a new radio frame (length 10 ms) starts. The receiver does not know in advance the SF a user is really using. In this paper, techniques to detect the used spreading factor are studied. The problem is formulated as a general SF detection problem for a DS-CDMA systems. The optimal detector as well as the suboptimal autocorrelation based detector were studied. It was found that the SF detection can be devised rather reliably by a simple autocorrelation based detection algorithm.

A hard-decision parallel interference cancellation receiver suitable for closed-loop power controlled CDMA uplink is introduced. Receiver algorithms for cancellation and SIR estimation are presented. A simple model for inter-cell interference is proposed to gain insight into the detector performance in cellular CDMA networks. Multiantenna diversity reception with maximal-ratio combining is employed in reception. The receiver performance is evaluated in fading multipath environments with simulations. Performance gains with regard to the conventional receiver are found in both pico- and macro-cellular environments. Gain from cancellation is found to be larger in more rapidly fading vehicular environments, where power control has less effect on the fluctuation of the received power. Results suggest the utilisation of the near-far robustness of the HD-PIC in power control algorithm design.

Bit-error probability (BEP) analysis for linear multiuser receivers with multiple sensors in frequency selective Rayleigh fading channels is presented. The analysis is applied to evaluate the BEP in antenna diversity reception and in a cellular CDMA system. Diversity and multiuser receivers are compared based on the examples. It is observed that adding new diversity antenna elements improves performance even if the correlation between the antenna elements is relatively large (up to 0.7). However, the large correlation values pose a significant reduction in the diversity gain in comparison to the zero correlation. It is also seen that the macroscopic diversity improves the performance of receivers significantly in cellular CDMA networks. When comparing diversity and multiuser receivers it is concluded that multiuser receivers are necessary to provide low BEPs. It is also highly beneficial to have at least two diversity antennas available, in particular, if there is no multipath diversity provided by the channel. The results also show that the reduction of intercell multiple-access interference yields a significant performance advantage in cellular networks. It is also demonstrated that the combination of spatial diversity and a multiuser receiver provides a significant receiver performance or system capacity gain in comparison to implementing only one of them.

The linear minimum mean-squared-error (LMMSE) criterion can be used to obtain near-far resistant receivers in direct-sequence code-division multiple-access systems. The standard version of the LMMSE receiver (postcombining LMMSE) minimizes the mean-squared error between the filter output and the true transmitted data sequence. Since the detector depends on the channel coefficients of all users, it cannot be implemented adaptively in fading channels due to severe tracking problems. A modified criterion for deriving LMMSE receivers (precombining LMMSE) in fading channels is presented. The precombining LMMSE receiver is independent of the users' complex channel coefficients, and it effectively converts the time-varying Rayleigh fading channel to an equivalent fixed additive white Gaussian noise channel from the point of view of updating the detector. The performance of the LMMSE receivers in fading channels is studied via computer simulations and numerical analysis. The results show that the postcombining LMMSE receiver has potentially larger capacity, but it cannot be used in fast fading channels. The precombining LMMSE receiver has slightly worse capacity than the postcombining LMMSE receiver, but remarkably larger capacity than the conventional RAKE receiver at the signal-to-noise ratios of practical interest.

Bit error probability of linear receivers for multiuser communications is analysed. The results can be applied both for centralised multiuser or joint receivers as well as for decentralised single-user receivers. The analysis is presented for AWGN and known or perfectly estimated fading multipath channels. The analysis is also extended to the estimated fading multipath channel case with data-aided (DA) channel estimation. Two ways to approximate the averaging over the interfering data symbol combinations are considered and compared. One is a semi-analytic method, where only part of the interfering data symbol combinations are taken into consideration in the averaging. The other is the Gaussian approximation. In addition to MAI, the analysis directly incorporates the impact of the interpath interference (IPI) or inter symbol interference (ISI) to the receiver performance. Numerical examples are presented to demonstrate the usefulness of the method.

Parallel interference cancellation (PIC) receivers are among the most promising receiver techniques for future code-division multiple-access (CDMA) systems. Interference cancellation efficiency relies on the knowledge of the number of users and propagation paths needed in multiple-access interference (MAI) estimation. In practice, the exact number of users and paths is not known exactly, e.g., due to inter-cell interference, unknown propagation paths or new users trying to connect to the base station. As a result, there will be some residual interference even with perfect cancellation of the known signal components. In many cases, the inter-cell interference can be large enough to significantly degrade the performance of the PIC receivers. For that reason, residual interference suppression is crucial to guarantee that the PIC receivers can operate reliably. Residual interference can be reduced by applying adaptive antennas to PIC receivers. In this paper, another possibility is considered. The approach taken is to combine PIC receivers with blind adaptive interference suppression techniques in single sensor receivers.

The bit-error probability of a linear receiver for code-division multiple-access communications is analyzed. The analysis is presented for the additive white Gaussian noise and fading multipath channels also with data-aided channel estimation. Two ways to approximate the averaging over the interfering data symbol combinations are considered and compared. Numerical examples are presented to demonstrate the usefulness of the analysis methods.

In this paper, the performance of some code acquisition schemes are studied in the downlink of DS-CDMA systems. The conventional matched filter, the minimum variance method, the MUSIC algorithms and the eigenvector method based delay acquisition schemes are studied in a frequency-selective fading channel. The three last algorithms are based on different estimation schemes for the inverse of the sample-covariance matrix. Based on the results, the minimum variance based method can be used to improve the acquisition performance in comparison to the conventional non-coherent matched filter method. The MUSIC algorithm becomes useless in highly loaded systems. The best performance, however, was obtained by using a longer despreading interval in the conventional delay estimator. Unfortunately, it would require either unmodulated pilot channel or a control channel with lower data rate, which may not be available in all applications.

Several linear multiuser detectors for code-division multiple access (CDMA) systems can be characterized as an inverse of some form of correlation matrices. If the correlations change, the detectors must be redesigned. An ideal computation of the decorrelating or the linear minimum mean-squared-error (LMMSE) detector requires order K/sup 3/ flops, where K is the number of users. To alleviate the computational complexity, iterative decorrelating and LMMSE detectors are proposed. The iterative detectors use steepest descent (SD), conjugate gradient (CG), and preconditioned conjugate gradient (PCG) algorithms, and require order K/sup 2/ flops per iteration. Their main advantages are the reduced number of flops and their suitability to highly parallel implementations. The correlation coefficient computation can also be embedded into the CG algorithm, which is an advantage with time-varying signature waveforms. The performance of the iterative algorithms is studied via computer simulations.

Linear minimum mean squared error (LMMSE) criterion can be used to obtain near-far resistant receivers in direct-sequence code-division multiple-access (DS-CDMA) systems. The standard version of the LMMSE detector depends on the channel coefficients of all users and cannot be used in frequency-selective fading channels due to fast changes of channel phases which demands for continuous detector updating. In this paper, a modified criterion for deriving LMMSE receivers in fading channels is used, which effectively converts the pathological Rayleigh fading channel to an equivalent fixed AWGN channel from the detector point of view. Thus, the detector is independent from the user's complex channel coefficients. Adaptive versions of the modified LMMSE receiver require estimation of channel delays and complex coefficients. An adaptive least mean squares (LMS) algorithm for DS-CDMA downlink receivers utilizing a pilot channel for channel estimation is presented as an example of a practical implementation of the scheme. The numerical results show that the modified criterion can be applied in downlink receivers in fading channels and the adaptive version converges close to the optimal LMMSE solution.

Near-far resistant data detectors for frequency selective fading channels are proposed for the uplink of code-division multiple-access (CDMA) mobile communication systems. Parallel interference cancellation (PIC) is used to suppress the multiple-access interference (MAI) in the receiver. In order to improve the MAI estimates, the multistage principle is used. In this paper, three main issues have been studied: the effect of feeding back the channel estimates from the last stage with a different number of cancellation stages, the hang-up phenomenon as well as means to avoid that in PIC receivers, and the impact of channel estimators on the receiver performance. The PIC based multistage receiver developed in the course of these studies had good performance in fading channels according to the simulation results.

The performance of a clamped average energy metric with a slow frequency hopping non-coherent M-ary FSK is studied. The channel is considered as an AWGN-channel with narrowband users of another system occupying part of the frequency hopping slots. Hence the frequency hopping system is an overlay system. The coding scheme chosen is the optimal binary convolutional (7,1/3)-code. The clamped average energy metric gives good performance in an overlay system and clearly outperforms hard decision decoding with traditional MFSK-detection.

Decorrelating, linear, minimum mean-squared error (LMMSE), and noise-whitening multiuser detectors for code-division multiple-access systems (CDMA) are ideally infinite memory-length (referred to as IIR) detectors. To obtain practical detectors, which have low implementation complexity and are suitable for CDMA systems with time-variant system parameters (e.g., the number of users, the delays of users, and the signature waveforms), linear finite-memory-length (referred to as FIR) multiuser detectors are studied in this paper. They are obtained by truncating the IIR detectors or by finding optimal FIR detectors. The signature waveforms are not restricted to be time-invariant (periodic over symbol interval). Thus, linear multiuser detection is generalized to systems with spreading sequences longer than the symbol interval. Conditions for the stability of the truncated detectors are discussed. Stable truncated detectors are shown to be near-far resistant if the received powers are upper bounded, and if the memory length is large enough (but finite). Numerical examples demonstrate that moderate memory lengths are sufficient to obtain the performance of the IIR detectors even with a severe near-far problem.

The performance of three sub-optimal soft decision metrics with slow frequency hopping non-coherent M-ary FSK is compared. The channel is considered as an AWGN channel with partial band interference. The coding scheme chosen for the comparison is binary convolutional (7,1/3) code. Normalized energy metrics provide good performance in noise interference channels. Clamped average energy metrics give good performance in partial band tone interference thus suggesting good performance in overlay systems.