Incorporating rate-splitting multiple access (RSMA) into integrated sensing and communication (ISAC) presents a significant security challenge, particularly in scenarios where the location of a potential eavesdropper (Eve) is unidentified. Splitting users' messages into common and private streams exposes them to eavesdropping, with the common stream dedicated for sensing and accessible to multiple users. In response to this challenge, this paper proposes a novel approach that leverages active reconfigurable intelligent surface (RIS)-aided beamforming and artificial noise (AN) to enhance the security of RSMA-enabled ISAC. Specifically, we first derive the ergodic private secrecy rate (EPSR) based on a mathematical approximation of the average Eve channel gain. An optimization problem is then formulated to maximize the minimum EPSR, while satisfying the minimum required thresholds on the ergodic common secrecy rate, the radar sensing and the RIS power budget. To address this non-convex problem, a novel optimization strategy is developed, whereby we alternatively optimize the transmit beamforming matrix for the common and private streams, rate splitting, AN, RIS reflection coefficient matrix, and radar receive beamformer. Successive convex approximation (SCA) and Majorization-Minimization (MM) are employed to convexify the beamforming and RIS sub-problems. Simulations are conducted to showcase the effectiveness of the proposed framework against established benchmarks.

The rapid advancement of 5G and the emergence of 6G technologies have intensified the need for robust solutions to challenges in digital communication systems, such as channel estimation, interference management, network optimization, adaptive modulation, and spectrum sensing. Traditional methods like Least Squares (LS) estimation and fixed power allocation struggle with dynamic wireless environments, leading to suboptimal performance. This paper proposes a novel hybrid Machine Learning (ML) framework that integrates a Deep Neural Network (DNN) for channel estimation, a Deep Q-Network (DQN) for interference management, and a Support Vector Machine (SVM) for spectrum sensing, optimized through a unified loss function. The framework is implemented in MATLAB and evaluated on a 5G-like Orthogonal Frequency Division Multiplexing (OFDM) system under Rayleigh fading. Results show a reduction in Mean Squared Error (MSE) for channel estimation, improvement in Signal-to-Interference Ratio (SIR), and increase in spectrum sensing accuracy compared to baseline methods. This work highlights the transformative potential of ML in enhancing reliability and efficiency in modern communication systems.

Semantic communications promise to spearhead the contextual data processing and routing in future networks. To this end, and to the best of our knowledge for the first time, this letter addresses the problem of finding the route with maximum spectral efficiency in multihop semantic communication systems. Our formulation relies on the recently introduced notion of semantic spectral efficiency, and a closed-form expression thereof that relies on data regression and a generalized logistic function approximation. We present a polynomial-time algorithm that provides provably optimal solutions to the problem. The proposed algorithm relies on recent advances in non-isotonic routing metrics optimization. Our numerical results further illustrate the performance of the proposed approach.

This paper investigates the performance of a multi-reconfigurable intelligent surface (RIS)-assisted fluid antenna system (FAS). In this system, a single-antenna transmitter communicates with a receiver equipped with a planar FAS through multiple RISs in the absence of a direct link. To enhance the system performance, we propose two novel selection schemes: Max-Max and Max-Sum. In particular, the Max-Max scheme selects the best combination of a single RIS and a single fluid antenna (FA) port that offers the maximum signal-to-noise ratio (SNR) at the receiver. On the other hand, the Max-Sum scheme selects one RIS while activating all FA ports providing the highest overall SNR. We conduct a detailed performance analysis of the proposed system under Nakagami-m fading channels. First, we derive the cumulative distribution function (CDF) of the SNR for both selection schemes. The derived CDF is then used to obtain approximate theoretical expressions for the outage probability (OP) and the delay outage rate (DOR). Next, a high-SNR asymptotic analysis is carried out to provide further insights into the system performance in terms of diversity and coding gains. Finally, the analytical results are validated through extensive Monte Carlo simulations, demonstrating their accuracy and providing a comprehensive understanding of the system's performance.

Massive multiple-input multiple-output (MIMO) serves as a cornerstone technology for beyond fifth-generation $(5 \mathrm{G})$ wireless networks, delivering significant improvements in both spectral and energy efficiency. However, one of the key challenges lies in designing signal detection algorithms that achieve high performance while maintaining low computational complexity. Iterative detection techniques have gained significant attention, where they progressively improve the accuracy of detection through a series of successive updates or iterations. In this work, we propose two novel hybrid massive MIMO detectors: the improved Newton method (INI) combined with Gauss-Seidel (GS) and the INI combined with successive overrelaxation (SOR). The motivation behind this work is to combine low-complexity iterative methods with the adaptive and recursive estimation power of INI, resulting in a more efficient and accurate detection process. By first applying a lightweight INI-based initialization and then refining the estimate using efficient iterative updates, the proposed detectors can accelerate convergence with significantly reduced complexity. Simulation results demonstrate superior performance, outperforming existing up-to-date detectors, making them well-suited for practical deployments.

This paper presents a framework for estimating and tracking massive multiple-input multiple-output (mMIMO) low-Earth-orbit (LEO) satellite channels under uncompensated Doppler. The approach begins with a pilot-based minimum mean square error (MMSE) estimate, followed by Doppler estimation and data-aided channel estimation using either a decision-directed MMSE (DD-MMSE) or an expectation-maximization (EM)-based estimator. The proposed framework achieves improved channel and Doppler estimation accuracy compared to existing methods. Results demonstrate that the DD-MMSE variant offers lower complexity, while the EM variant provides higher estimation accuracy.

Massive multiple-input multiple-output low-Earth-orbit communication channels are highly time-varying due to severe Doppler shifts and propagation delays. While satellite-mobility-induced Doppler shifts can be compensated using known ephemeris data, those caused by user mobility require accurate user positioning information; the absence of such information contributes to amplified channel aging in conventional channel estimators. To address this challenge, we propose a data-aided channel estimator based on the expectation-maximization (EM) algorithm, combined with a discrete Legendre polynomial basis expansion method (DLP-BEM), to estimate the channel under imperfect Doppler compensation. The EM algorithm iteratively exploits hidden data symbols for improved channel estimation, while DLP-BEM regularizes the process by projecting the channel estimate onto a lower-dimensional subspace that mitigates estimation errors. Simulation results demonstrate the superiority of the proposed framework over existing methods in terms of normalized mean square error and symbol error rate.

(CF) massive multiple-input multiple-output (mMIMO) is emerging as a key technology for sixth-generation (6G) communication systems, offering nearly uniform service for users across various areas while effectively managing interference compared to traditional mMIMO systems. However, data detection in CF-mMIMO environments requires sophisticated signal processing techniques. While both linear and nonlinear detectors have demonstrated strong performance, the exploration of iterative detection methods in CF-mMIMO has been limited. This paper addresses this research gap by examining the performance of five efficient iterative scalable CFmMIMO detectors based on approximate/avoid matrix inversion techniques: Newton iteration, Gauss-Seidel, Jacobi, accelerated over-relaxation, and successive over-relaxation. Additionally, we propose an efficient detector based on sphere decoding (CF-SD) for scalable CF-mMIMO systems. Simulation results indicate that the linear iterative methods can achieve performance that approximates that of the minimum mean square error detector, while also maintaining a lower computational burden. In addition, while the CF-SD detector demonstrates considerable performance enhancements, it requires higher computational complexity compared to its linear iterative counterparts.

Timing offset and channel estimation plays an important role in the performance of amplify-and-forward two-way relay networks. The state-of-the art approach models the system as a Hidden Markov Model (HMM) and performs semi-blind estimation using expectation maximization (EM). The HMM formulation allows the use of the Baum Welch (BW) forward-backward algorithm to calculate the posterior probabilities of the data. One of the main challenges of this algorithm is reducing the computational complexity to allow fast real-time implementation. In this work, the opportunities of parallelization are studied based on the time and performance analysis. We propose different parallelization techniques of compute-intensive tasks in semi-blind joint timing-offset and channel estimation. Multi-threading, message passing interface (MPI) and hybrid (multi-threading/ MPI) models are implemented and tested. The results show that the hybrid model is superior to the other models. In particular, the hybrid parallel model can achieve 10.4X speedup over the serial implementation of semi-blind joint timing-offset and channel estimation, while multithreading and MPI achieve only 6.5X and 1.5X speedup, respectively. Moreover, our proposed hybrid parallel model achieved better performance than other existing models.

This paper studies a fluid-antenna-enabled integrated bistatic sensing and backscatter communication system for future networks where connectivity, power delivery, and environmental awareness are jointly supported by the same infrastructure. A multi-antenna base station (BS) with transmitting fluid antennas serves downlink users, energizes passive tags, and illuminates radar targets, while a spatially separated multi-antenna reader decodes tag backscatter and processes radar echoes to avoid the strong self-interference that would otherwise obscure weak returns at the BS. The coexistence of tags and targets, however, induces severe near–far disparities and multi-signal interference, which can be mitigated by fluid antennas through additional spatial degrees of freedom that reshape the multi-hop channels. We formulate a transmit-power minimization problem that jointly optimizes the BS information beamformers, sensing covariance matrix, reader receive beamformers, tag reflection coefficients, and fluid-antenna (FA) positions under heterogeneous quality of service constraints for communication, backscatter, and sensing, as well as energy-harvesting and FA geometry requirements. To tackle the resulting non-convex problem, we develop an alternating-optimization block-coordinate framework that solves four tractable subproblems using semidefinite relaxation, majorization–minimization, and successive convex approximation. Numerical results show consistent transmit-power savings over fixed-position antennas and zero-forcing baselines, achieving about 13.7

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 communication (ISAC) systems face critical security vulnerabilities when dual-functional waveforms are used for covert operations, as static antenna architectures inherently lack the spatial agility to harmonize communication reliability, covertness, and sensing accuracy. To address this challenge, we propose a novel movable antenna (MA)-assisted covert ISAC framework integrated with non-orthogonal multiple access (NOMA), where a multi-antenna ISAC base station (BS) dynamically serves two types of user-pairs, public pairs demanding high throughput and covert pairs requiring low-probability-of-detection transmissions, through shared communication-and-sensing (C&S) beams. Each user, in a pair, is equipped with a single MA to enable dynamic spatial reconfiguration and obscure covert signals from warden. To achieve these goals, we minimize the Cramér-Rao bound (CRB) for target estimation while satisfying communication rate requirements, covertness constraints, as well as power allocation and successive interference cancellation (SIC) feasibility. The formulated problem involves highly coupled variables of the transmit beamforming vectors, the NOMA power coefficients, and the MA position vectors of the users. The paper solves the resulting non-convex optimization problem using a block coordinate descent (BCD) algorithm that decomposes it into two subproblems: 1) beamforming and power allocation via successive convex approximation (SCA) augmented with the proper penalty correction step, and 2) MA position optimization using gradient-assisted SCA. Extensive simulations demonstrate significant gains and trade-offs over fixed position antennas and orthogonal multiple access benchmarks.

Cell-free (CF) massive multiple-input multiple-output (mMIMO) is a promising state-of-the-art technology that has been developed to solve the inter-cell interference problem in mMIMO. In a CF-mMIMO system, a large number of access points are distributed over a geographical area, serving a massive number of user equipments with no cell boundaries. It has shown great potential in improving the network performance in terms of user capacity, throughput, and network coverage, emerging as one of the key technologies for the upcoming sixth-generation networks. In the literature, a plethora of CF-mMIMO signal processing algorithms have been proposed. This paper aims to provide insights into the signal processing techniques for CF-mMIMO, with particular focal points on channel estimation, receive combining, data detection and transmit precoding. We review the CF-mMIMO signal processing algorithms and classify them so that readers can distinguish between different algorithms from a wide range of solutions. Specifically, we present the pilot-based, semi-blind, and machine learning-empowered channel estimation approaches. We also cover the hard, soft, and deep learning-based data detection/decoding methods. In addition, we discuss various transmit precoding techniques with uplink-downlink duality, over-the-air, utility maximization, machine learning, and dual-functional communication and sensing techniques. As part of enriching the reader’s knowledge, we offer an analytical and simulation-based comparison across different configurations as well as between centralized and distributed processing. We also summarized the computational complexity expressions of various signal processing methods to facilitate a clear and direct comparison for the reader. A discussion of the advantages and disadvantages of each technique is also provided. In addition, a comparative analysis is given in terms of complexity, scalability, hardware requirements, applicability, spectral efficiency, energy efficiency, accuracy, and latency. To the best of the authors’ knowledge, this comprehensive survey is the first to extensively include the signal processing aspects utilized in CF-mMIMO with respect to different system models and emerging technologies.

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.

As revolutionary enablers capable of dynamically influencing communication link quality, Reflecting Intelligent Surfaces (RIS), particularly when mounted on Unmanned Aerial Vehicles (UAV) have emerged as reliable, cost-effective, and flexible tools for enhancing wireless connectivity across a range of real-world scenarios. Similarly, Digital Twin (DT) technology is increasingly recognized as an enabler for future wireless networks. To mitigate the challenges posed by building obstructions or natural disasters that disrupt radio propagation, this work incorporates multiple UAV-RIS to enhance connectivity and improve link quality throughout the network. This paper proposes a Multi-Relay UAV-RIS-assisted DT (MR-UAV-RIS DT) framework, focused on intelligent network rate management to ensure User Fairness (UF) and Load Balancing (LB) in small cell (SC)-based wireless networks. In the proposed system, dual-connectivity user equipments (UEs) are simultaneously connected to both the macrocell (MC) via relay UAV-RIS, and to Small cell-Base stations (SC-BSs), all operating in the mmWave frequency band to deliver high-speed connectivity. To meet UF and LB goals, an optimization problem is formulated to jointly control the active beamforming for both SC-BSs and the MC, as well as the passive beamforming and 3D positions of the UAV-RISs associated with SC-BSs and relay UAV-RIS linked to the MC. Due to the complexity and high dimensionality of the problem, a DT-based multi-task deep reinforcement learning model (DT-MTDRL) is proposed, built upon the Deep Deterministic Policy Gradient (DDPG) algorithm. Simulation results confirm that the proposed model not only ensures a fair rate distribution among users and SCs but also delivers superior performance compared to existing benchmark schemes, while maintaining robust and adaptive connectivity in dynamic and challenging environments.

Ambient backscatter communication (AmBC) has emerged as a highly attractive paradigm for energy-efficient communication. Full-duplex multi-tag AmBC systems provide the scalability and efficient spectrum utilization essential for next generation Internet-of-Things (IoT) networks. However, the presence of multiple tags, self-interference and hardware impairments such as inphase/quadrature (I/Q) imbalance, makes accurate channel estimation indispensable for efficient interference management. The large number of channel parameters and the presence of mirror images of each signal component necessitate careful design of the channel estimation phase to prevent performance degradation. In this work, we propose a novel three-stage training protocol and pilot-based estimation scheme that ensure signal orthogonality and successfully avoid error floors. We also propose two semi-blind estimators, one based on decision-directed (DD) criterion and the other on the expectation conditional maximization (ECM) framework. By exploiting both pilots and data symbols, these two estimators achieve higher estimation accuracy than pilot-based estimation, at the cost of additional complexity. Cramer-Rao bounds (CRBs) for both types of estimation are also derived. The pilot-based estimator and the ECM estimator approach their respective CRBs, while the DD estimator performs mid-way between them. The three proposed solutions support different use cases by offering distinct tradeoffs between performance and computational complexity.

Massive multiple-input multiple-output (MIMO) is a cornerstone technology in beyond 5G (B5G) communication systems due to its ability to achieve exceptional power and spectral efficiency. The development of low-complexity detectors for massive MIMO remains a key area of research, driven by the need to strike a balance between performance and computational complexity, especially as the number of antennas increases at both the transmitter and receiver. In this paper, we propose efficient initialization methods to address these challenges. Instead of the conventional diagonal matrix, we employ the stair matrix and the band matrix in the initialization of the proposed detector based on accelerated overrelaxation. We also employ successive overrelaxation, Gauss-Seidel, and Jacobi methods to improve the performance of the proposed detector. The initialization scaling factors are based on the spectral radius of the iteration matrix. The proposed detectors are evaluated using diverse massive MIMO configurations and multiple modulation schemes and under both perfect and imperfect channel state information (CSI). Extensive simulations show that the proposed detectors achieve significant performance enhancements accompanied by a remarkable reduction in computational complexity, making them highly suitable for practical large-scale systems.

Cell-free massive multiple-input multiple-output (CF-MMIMO) systems represent a transformative evolution in wireless communication, characterized by a distributed architecture of numerous low-cost, low-power access points (APs) connected to a centralized network controller. Unlike traditional cellular systems, CF-MMIMO eliminates the concept of cells, with every AP collaboratively serving all users simultaneously. This design eradicates inter-cell interference, significantly enhancing network capacity, reliability, and spectral efficiency. As a corner-stone for future 6G networks, CF-MMIMO aligns seamlessly with the vision of ultra-reliable, low-latency communication, massive connectivity, and ubiquitous coverage. By leveraging distributed APs, CF-MMIMO systems pave the way for energy-efficient, high-capacity networks that cater to the demands of next-generation wireless systems. This paper conducts an in-depth exploration of various detection techniques, including minimum mean square error (MMSE), zero-forcing (ZF), linear precoding MMSE (LP-MMSE), and regularized conjugate beamforming (RCB). Through rigorous simulations and performance analysis, the results demonstrate that while ZF achieves the best bit error rate (BER) performance, RCB emerges as a robust contender, especially under practical conditions with imperfect channel state information. MMSE and LP-MMSE exhibit moderate performance, with LP-MMSE being comparatively less efficient in mitigating interference and noise.

Massive multiple-input multiple-output (mMIMO) plays a crucial role in improving the quality-of-service and achieving high power efficiency and spectrum efficiency in beyond fifth generation communication systems. However, data detection in uplink mMIMO is not a trivial task as the computational complexity increases with the number of antennas. The equalization matrix is diagonally dominant, and hence, most of the existing linear detectors use the diagonal matrix. Unfortunately, detection based on a diagonal matrix may require a high number of iterations to converge, which increases the computational complexity. This is highly challenging because of the large number of antennas on both the transmitting and receiving sides. In this paper, we propose a refinement of six linear mMIMO detectors based on a band matrix formulation to accelerate the convergence rate, and hence reduce the complexity. The proposed linear detectors include the Newton iterations method, the Neumann series method, the accelerated over-relaxation method, the successive over-relaxation method, the Gauss-Seidel (GS) method, and the Jacobi method. The computation of the band matrix inverse is also presented in this paper and employed in the proposed detectors. In addition, efficient initialization based on the structure of the band matrix is proposed, which both improves the convergence rate and yields a substantial performance gain. Simulations show that the proposed detectors achieve minimum mean-squared error performance with significant complexity reduction even when the number of users approaches the number of base station antennas. It is also shown that the refined detector based on the GS and band matrix achieves the highest performance gain with the lowest computational complexity.

Massive multiple-input multiple-output (M-MIMO) technology is a critical driver of fifth-generation (5G) and beyond-5G (B5G) communication systems, significantly enhancing capacity, efficiency, and overall network performance. However, the performance of M-MIMO systems is heavily influenced by the detection techniques employed, which also determine the complexity of the system. To address the trade-off between performance and computational efficiency, this study explores advanced signal detection methods within the M-MIMO framework. This paper introduces two novel hybrid detectors, combining the Gauss-Seidel (GS) and Jacobi (JA) methods with an enhanced Newton iteration (NI) approach, designed to minimize iteration time while maintaining high detection accuracy. By leveraging the upgraded NI approach, these detectors achieve efficient data decoding while significantly reducing computational complexity. Extensive analysis and simulation results demonstrate the superiority of the proposed architectures in terms of computational complexity and bit error rate (BER). In particular, the proposed approach achieves a significant reduction in complexity, lowering it from O(NK2+ NK) to O(NK) for Detector 1, and from O(NK2+ NK) to O(NK + K) for Detector 2, effectively demonstrating substantial improvements in both efficiency and performance.