Massive multiple-input multiple-output (MIMO) technology utilizes large antenna arrays at the base-station (BS) to support a large number of users with the same time/frequency resources. Uplink signal detection poses a significant challenge in massive MIMO systems. Although minimum-mean square-error based detectors become the mainstay of classical massive MIMO systems, they require a large-dimensional matrix inversion, which is computationally extensive. This paper proposes a new massive MIMO detector based on the two-parameter over relaxation (TOR) algorithm. The relaxation and acceleration parameters are carefully selected on the basis of the spectral radius to achieve a good balance between the performance and the complexity. Compared to existing linear detectors, the proposed TOR based detector is more stable because of its relaxation and acceleration parameters. The results show that the proposed massive MIMO detector achieves a remarkable performance gain and overall complexity reduction compared to existing detectors, particularly when the number of users is comparable to the number of BS antennas. The proposed TOR detector achieves better performance than the existing detectors when using the same number of iterations.

Integrated sensing and communications (ISAC) has emerged as a promising solution for addressing spectrum congestion in sixth-generation communication systems. In this work, we consider the deployment of ISAC in a full-duplex cell-free (FD-CF) multi-input multi-output (MIMO) system that is aided by a reconfigurable intelligent surface (RIS). To overcome the performance limitations of a single ISAC base station (BS), we consider multiple FD access points (APs) that simultaneously perform target detection and multi-user uplink (UL) communication, assisted by a RIS. We aim to maximize the weighted sum of the output radar and communication signal-to-interference-plus-noise ratios (SINRs) by jointly designing the radar and communication receive beamformers, UL transmission powers, joint downlink (DL) sensing beamformers, and RIS reflection coefficients. The total UL, DL power budgets, and the RIS phase shift unit-modulus constraints are considered to guarantee the balance between sensing and communication requirements. The resulting problem is non-convex and rather formidable to solve. Nonetheless, an efficient solution to this problem is developed based on alternating optimization, which utilizes majorization-minimization (MM), fractional programming (FP), and the penalty method. Simulations demonstrate the effectiveness of the proposed solution and the advantages of deploying RIS to assist integrated cooperative sensing and communication (ICSAC) in FD-CF MIMO systems.

The evolution of communication networks towards multi-functionality has paved the way for integrated sensing, communication, and power transfer (ISCAPT) systems, enabling efficient data transmission, environmental sensing, and wireless energy transfer. However, conventional ISCAPT architectures face inherent trade-offs between high-rate communication, precise sensing, and efficient energy transfer, exacerbated by interference and dynamic channel conditions. To address these limitations, rate-splitting multiple access (RSMA) and reconfigurable intelligent surfaces (RIS) are integrated into ISCAPT systems. Hence, in this paper, we consider active RIS-aided RSMA ISCAPT to maximize the sum communication rate while ensuring the sensing performance, harvesting adequate energy, and satisfying the transmit power budgets of the BS and RIS. To achieve this goal, the transmit beamforming matrix, RIS reflection matrix, power splitting factor, radar receive beamforming, and common rate allocation are jointly designed. However, the formulated maximization problem is challenging due to its non-convex nature and the coupling among the optimization variables. To efficiently tackle this issue, the formulated problem is decomposed into three sub-problems, which are reformulated into quadratic-constrained-quadratic programs (QCQPs). Then, an alternating optimization (AO)-aided majorization-minimization (MM) and successive convex approximation (SCA) algorithm is proposed to iteratively optimize these sub-problems. Simulation studies are conducted to demonstrate the effectiveness of the proposed framework and illustrate trade-offs compared to established benchmarks.

Ambient backscatter communication (AmBC) has attracted significant attention as a viable paradigm for energy efficient communication in the next generation of Internet-of-Things (IoT). Channel estimation, a vital task in AmBC receiver design, becomes more challenging when accounting for more practical scenarios such as frequency selective fading and the presence of hardware impairments such as I/Q imbalance. Full-duplex communication further complicates channel estimation, due to the presence of self-interference. In this work, we consider OFDM-based full-duplex AmBC systems affected by I/Q imbalance, and propose two methods for the joint estimation of the channel and I/Q imbalance. The first algorithm is a pilot-based estimator, while the second is a semi-blind estimator based on the concept of decision-directed (DD) estimation. In addition, we propose a novel design of appropriate pilot sequences to optimize the performance of the proposed methods. As benchmarks on estimation performance, we obtain analytical expressions for the pilot-based and semi-blind Cramer-Rao bounds (CRBs). Our simulations demonstrate that both proposed estimators converge to their respective bounds. The proposed methods offer different tradeoffs between accuracy and computational complexity, which are suitable for different use cases.

Ambient backscatter communication (AmBC) has attracted attention as an enabling technology for green device-to-device (D2D) communication for the next generation of Internet-of-Things (IoT) networks. Most existing works assume that the signals of the source and the backscattering device (BD) are perfectly synchronized at the reader. Perfect synchronization is not feasible in practice, and the timing offset between the two signals can significantly degrade the performance of the reader’s receiver. In this work, we consider an asynchronous AmBC system and solve the problem of joint timing offset and channel estimation at the reader. Assuming generic Nyquist pulse-shaping filters, we develop a pilot-based maximum likelihood (ML) estimator, as well as a semi-blind estimator using the expectation maximization (EM) algorithm. For the special case of the rectangular pulse, we also derive the corresponding ML and EM estimators, and an approximate low-complexity EM (LCEM) algorithm. As theoretical benchmarks, we obtain the Cramer-Rao-bound (CRB) for pilot-based estimation, while for semi-blind estimation the modified CRB (MCRB) is obtained. The performance of the proposed algorithms is investigated using simulations, showing that both the ML and EM approach their corresponding CRBs, and that the EM-type algorithms provide superior estimation and detection accuracy, at the expense of higher complexity.

Ambient backscatter communication (AmBC) is a highly promising technology that enables the ubiquitous deployment of low-cost, low-power devices to support the next generation of Internet-of-Things (IoT) applications. This paper addresses channel estimation for full-duplex multi-antenna AmBC systems. This is highly challenging due to the large number of channel parameters resulting from the use of multiple antennas, the presence of self-interference, and the dependence of the backscattering channel on the state of the backscattering device. Considering both pilot-based and semi-blind estimation strategies, we propose three solutions for this problem. The first is the pilot-based maximum-likelihood (ML) estimator. The second is a semi-blind estimator based on the expectation maximization (EM) framework, which provides higher accuracy than the ML, at the cost of higher computational complexity. The third is a semi-blind estimator based on the decision-directed (DD) strategy, which provides a tradeoff between the ML and the EM. Additionally, we derive the exact Cramer-Rao bound (CRB) for pilot-based estimation and the modified CRB for semi-blind estimation. Simulations show that the ML and the EM perform very close to their respective CRBs, and that the semi-blind estimators offer significantly higher estimation accuracy, as well as superior symbol-error-rate performance, compared to the ML estimator.

Abstract With the advent of fifth generation (5G) systems and the Internet‐of‐Things (IoT), the number of interconnected wireless devices is increasing significantly. Protocols that allow these deceives to interconnect peer‐to‐peer through wireless links are becoming of interest. The major challenge is the inevitable interference among the simultaneously activated wireless links. Given a set of wireless links, this article addresses the non‐deterministic polynomial‐time (NP) hard problem of selecting the maximum subset of links that can be simultaneously activated at their respective signal‐to‐interference‐plus‐noise‐ratio (SINR) targets. The contribution of this article is two‐fold. First, we introduce a new genetic algorithm (GA) constraint‐handling mechanism, and prove analytically that finding optimal link schedules is guaranteed. Second, we develop a novel parallelized GA to solve the problem. Through serial algorithm analysis, we utilize data decomposition as well as exploratory decomposition in order to achieve significant running time speedup, which scales well with problem size. Our numerical results for openMP parallelization illustrate 6.5 and 5.4 reduction in computation time as compared to the serial versions of the GA and hybrid genetic algorithm (HGA), respectively. Moreover, the parallelization of the GA and HGA result in a speedup of 10.4 and 5.4 , respectively, using master‐slave multithreading.

Abstract This paper considers the problem of joint timing‐offset and channel estimation for physical‐layer network coding systems operating in frequency‐selective environments. Three different algorithms are investigated for the joint estimation of the channel coefficients and the fractional timing offset. The first algorithm is based on the maximum‐likelihood (ML) criterion assuming baud‐rate (BR) sampling. The second algorithm also assumes BR sampling and is based on the special properties of Zadoff‐Chu training sequences. In the third algorithm, oversampling at double the baud‐rate (DBR) is used and the least‐squares (LS) estimation criterion applied. While the above algorithms assume that the integer timing offset is known, three generalized‐likelihood‐ratio tests (GLRTs) are also considered for integer offset error correction that integrate very well with the proposed estimation algorithms. Our simulation studies show that the DBR‐LS estimator provides the highest estimation accuracy, significantly outperforming both BR estimators and performing very close to the corresponding Cramer–Rao bound. A gain of 4 dB is observed in symbol‐error‐rate performance using the DBR‐LS algorithm. The DBR‐GLRT also provides substantially higher probability of error correction.

In this paper, we consider the problem of joint timing-offset and channel estimation for amplify-and-forward (AF) two-way relay networks (TWRNs). This problem is solved for generic pulse-shaping filters, taking into account the filter truncation in practical communication and considering both pilot-based and semi-blind estimation strategies. Beginning with pilot-based estimation, we propose a novel Maximum-likelihood joint timing-offset and channel estimator, as well as an alternative estimator based on the special properties of Zadoff-Chu sequences. The first algorithm offers high accuracy, almost overlapping with the Cramer-Rao bound (CRB), while the second offers very low computational complexity. We then develop a semi-blind estimator based on the expectation maximization (EM) framework, exploiting the underlying Hidden Markov Model to apply Baum-Welch forward-backward recursion. The semi-blind CRB is also obtained as an indicator of the best achievable performance. Using simulations, we show that the semi-blind algorithm yields superior accuracy to pilot-based estimation, as well as improved symbol-error-rates and performs very close to the semi-blind CRB. Additionally, a low-complexity approximate EM algorithm is proposed for the case of rectangular pulses. Finally, we consider the possibility of errors in integer-offset estimation and propose pilot-based and semi-blind generalized likelihood ratio test (GLRT) schemes for correcting such errors.

In this paper, we consider the problem of channel estimation for asynchronous amplify-and-forward (AF) two-way relay networks. We propose a novel semi-blind channel estimation algorithm for this problem based on the expectation- maximization (EM) framework. The proposed EM algorithm has low complexity, and only a small number of EM iterations are needed to achieve convergence. The semi-blind Cramer-Rao bound (CRB) for channel estimation in asynchronous AF two-way relay networks is also obtained. Using simulations, we show that the proposed EM algorithm significantly outperforms pilot-based estimation by using only a limited number of received data samples in addition to the pilot samples. Furthermore, the achieved mean-squared error performance almost overlaps with the obtained CRB. Finally, a semi-blind generalized likelihood ratio testing (GLRT) method is proposed to tackle sequence arriving order (SAO) detection at the terminals and is shown to yield a higher probability of detection than the pilot-based GLRT for SAO detection.

The recently approved IEEE 802.11ac WLAN standard has dramatically boosted Wi-Fi capabilities through supporting more data streams, larger bandwidth and more efficient frame-aggregation, leading to significantly higher data rates. The multitude of data rates and MIMO configurations to choose from has made it crucial to develop efficient algorithms for rate adaptation and MIMO mode selection (spatial multiplexing versus spatial diversity) in order to realize the promised gains in data rate. At the MAC layer, the optimal level of frame aggregation needs to be chosen in order to maximize the throughput. The adaptation approach uses very limited feedback provided within the Wi-Fi packet acknowledgement protocol. In this paper, we develop a framework for jointly selecting the data rate, the MIMO mode and the frame aggregation configuration at the access point (AP) using subcarrier-level channel state information (CSI). We use approximate expressions for the BER based on the union bound for the first-event error probability in order to obtain approximate throughput expressions for the different rates and MIMO modes. Assuming A-MPDU aggregation, we also show that for each data rate and MIMO-mode pair, there is a corresponding optimal aggregation level, which we find analytically. Our numerical results reveal that the proposed joint selection scheme yields significant improvements in throughput.

We consider the problem of channel estimation for OFDM-based amplify-and-forward (AF) two-way relay networks (TWRNs). While previous works have adopted a pilot-based approach, we propose a semi-blind approach that exploits both the transmitted pilots as well as the received data samples to improve the estimation performance. Our proposed semi-blind estimator is based on the Gaussian maximum likelihood (GML) criterion which treats that data symbols as Gaussian-distributed nuisance parameters. The GML estimates are obtained using an iterative quasi-Newton method. To assist in the estimation of the individual channels, we adopt a superimposed training strategy at the relay. We design the pilot vectors of the terminals and the relay to optimize the estimation performance. Furthermore, we derive the semi-blind and pilot-based Cramer-Rao bounds (CRBs) to use as performance benchmarks. Finally, we use simulation studies to show that the proposed method provides substantial improvements in estimation accuracy over the conventional pilot-based estimation and that it approaches the semi-blind CRB as SNR increases. These improvements are possible using only a limited number of OFDM data blocks, which demonstrates the practicality of the semi-blind approach.

In this paper, we derive the Cramer-Rao bound (CRB) for semiblind channel estimation in amplify-and-forward two-way relay networks employing square QAM, assuming flat-fading channel conditions. The derived bound is exact as it is based on the true likelihood function that takes into account the statistics of the transmitted data symbols. Using the new bound, we show that exploiting even a limited number of transmitted data symbols in addition to the pilot symbols leads to substantial estimation accuracy improvements over conventional pilot-based estimation. We also propose a semiblind expectation-maximization-based estimation algorithm that performs very close to the exact CRB at an affordable computational cost. The superior accuracy of the semiblind approach makes it possible to significantly reduce the training overhead for channel estimation, thus offering a higher throughput and a better tradeoff between accuracy and spectral efficiency. We also derive the modified CRB, which approximates the exact CRB at high SNR for low modulation orders.

We consider the problem of channel estimation for OFDM-based amplify-and-forward (AF) two-way relay networks (TWRNs). Unlike previous works which were based on a fully pilot-based approach, we propose a semi-blind approach that exploits both the transmitted pilots as well as the received data samples to provide an enhanced estimation performance. Superimposed training is adopted at the relay to assist in the estimation of the individual channels. We base our semi-blind estimator on the maximum-likelihood (ML) criterion and employ an iterative low-complexity Quasi-Newton method to obtain the ML semi-blind channel estimates. As a performance benchmark we derive the semi-blind Cramer-Rao bound (CRB). Using simulation studies, we show that the proposed approach provides a substantial improvement in estimation accuracy over the conventional pilot-based approach.

We consider the problem of channel estimation for amplify-and-forward two-way relays assuming channel reciprocity and M-PSK modulation. In an earlier work, a partially-blind maximum-likelihood estimator was derived by treating the data as deterministic unknowns. We prove that this estimator approaches the true channel with high probability at high signal-to-noise ratio (SNR) but is not consistent. We then propose an alternative estimator which is consistent and has similarly favorable high SNR performance. We also derive the Cramer-Rao bound on the variance of unbiased estimators.

We consider the problem of channel estimation for amplify-and-forward (AF) two-way relay networks (TWRNs). The majority of works on this problem develop pilot-based algorithms that allocate significant resources for training. We will show in this work that such overhead is not necessary when the terminals employ M-ary phase-shift keying (M-PSK). Using the constant-modulus nature of the transmitted symbols, we develop a relaxed blind maximum-likelihood (ML) channel estimator. We study the performance of the ML estimator in the high SNR and large sample-size scenarios, demonstrating that it performs well in both cases. As a benchmark, we also present and analyze an intuitive low-complexity estimator based on sample-averaging. Simulation studies are used to compare the mean-squared error performance of the two algorithms.

We analyze the mean-squared error (MSE) performance of widely linear (WL) and conventional subspace-based channel estimation for single-input multiple-output (SIMO) flat-fading channels employing binary phase-shift-keying (BPSK) modulation when the covariance matrix is estimated using a finite number of samples. The conventional estimator suffers from a phase ambiguity that reduces to a sign ambiguity for the WL estimator. We derive closed-form expressions for the MSE of the two estimators under four different scenarios which vary in the amount and accuracy of the information available for ambiguity resolution. Our work demonstrates that the relative performance of WL and conventional subspace-based estimators is strongly related to the accuracy of ambiguity resolution and shows that the less information available about the actual channel for ambiguity resolution, or the lower the accuracy of this information, the more favorable the WL estimator becomes.