Detecting signals in multiple-input multiple-output (MIMO) systems is still a big problem that digital receivers need a lot of computing power to solve. Channel hardening makes linear detectors like zero-forcing (ZF) and minimum mean-square error (MMSE) work well in 5 G massive MIMO systems. But as we move to 6 G, these methods do not work as well when channels are not orthogonal or have low rank. Channel hardening is not always feasible in 6 G. Therefore, detection frameworks employing convex or norm-based constraints are essential to maintain high accuracy and predictable complexity. We propose a multiple-input multiple-output (MIMO) detection framework based on the alternating direction method of multipliers (ADMM) that combines the $\ell_{\infty}$-box and $\ell_{2}$-ball formulations using a shared adaptive-boundary mechanism. This boundary changes in real time based on the current signal-to-noise ratio (SNR), which means that the detector can stay strong even when noise levels and constellation densities change without making the calculations more difficult. Simulation results show that the suggested adaptive-boundary ADMM works much better than fixed-box and MMSE detectors for M-QAM constellations, with a much lower bit-error-rate (BER), while still being stable across a range of signal-to-noise ratios (SNRs). When using constantenvelope modulations like M-PSK, the $\ell_{2}$-norm constraint better fits the shape of the signal and makes it easier to find. These results show that both adaptive-boundary and norm-constrained methods improve detection performance without adding much to the cost of computing.

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.

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.

Error correcting codes (ECCs) are frequently avoided by power-limited devices due to hardware complexity and financial limitations. During transmission, memory errors produced by these devices may spread over the wireless channel, making decoding more difficult at the receiver. It can be a crucial problem in a large MIMO system. We address this challenge in this article by proposing a two-stage solution. The first stage employs an equalizer-based detection method to lessen interference and communication channel impairments, while the second stage uses gradient boosted decision tree (GBDT) and neural network (NN) algorithms to forecast and fix memory errors. Using the GBDT and NN model through the Scikit-Learn library, we used Python simulations to show the effectiveness of our method. Following approximate inversion-based equalization and NN-based error detection at the massive MIMO base station receiver, the results show a detection accuracy of over 99%. Finally, we present a motivating example demonstrating how our proposed model can be integrated into the base station.

(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.

Wideband sub-terahertz (THz) transmitters require very high sample rates at their digital front-ends, often reaching multi-gigasample-per-second (GSPS) levels. However, the maximum clock frequency of contemporary field programmable gate array (FPGA) fabric remains limited to a few hundred megahertz, creating a fundamental throughput gap between required sample rates and feasible hardware clocks. In this work, we present an FPGA implementation of a fractional sample rate converter (SRC) tailored for very high throughput transmitter applications. The proposed architecture is based on a generalized polyphase formulation that enables an arbitrary number of parallel output lanes independent of the interpolation factor, thereby removing the conventional constraint that ties the degree of parallelism to the conversion ratio. The method is evaluated for a 15/8 sample-rate conversion applied to a $\mathbf{1 6 0 0 ~ M H z}$ baseband signal using a 16-lane parallel configuration. A VLSI architecture of the proposed parallel fractional SRC is implemented on a Radio Frequency System-on-Chip (RFSoC) kit, and implementation results are presented.

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.

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.

Reconfigurable intelligent surfaces (RIS) can reflect an incoming signal toward a target location to enhance the received signal quality. In most studies, it is assumed that the base station (BS) provides configuration data to an RIS controller. However, for a passive RIS with a high number of elements, this data transmission from the BS can introduce significant delays. This challenge is especially pronounced in vehicular communications, where reflection coefficients must be updated even for a few millimeters of movement. To overcome this limitation, we propose a self-tuning RIS controller that independently calculates the reflection coefficients with minimal input from the BS. Our RIS controller leverages the user’s location information to compute the reflection coefficients in three steps: channel gain calculation, steering vector calculation, and optimal phase control. A pipelined architecture is developed to support these steps in real-time. The proposed VLSI architecture is implemented on an Artix-7 XC7A100TCSG324-1 FPGA to calculate reflection coefficients for a vehicle moving at a 100 kmh speed.

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.

The design of low-complexity data detection techniques for massive multiple-input multiple-output (mMIMO) systems continues to attract considerable industry and research attention due to the critical need to achieve the right tradeoff between complexity and performance, especially with the signed quadrature spatial modulation (SQSM) scheme. However, the SQSM scheme attains a high spectral efficiency and good performance but suffers from a high computational complexity with mMIMO systems. In this article, we propose an efficient low-complexity detection framework for the SQSM scheme. Sparsity detection is amalgamated in this article with minimum mean-square error (MMSE) detector by decoupling the detection of the real and imaginary vector streams. Unfortunately, the MMSE-based detector has a matrix inversion which incurs a high computational complexity. Therefore, we employed several iterative methods; i.e., conjugate gradient and Gauss-Seidel, to avoid the exact matrix inversion, and hence, the computational complexity is significantly reduced. Moreover, the proposed framework can host other iterative methods such as the JA, successive over relaxation, accelerated over relaxation, Neumann series, Newton iteration, two-parameter over relaxation, and Richardson methods. The proposed detection framework attains a significant complexity reduction with a small or insignificant deterioration in the performance.

Due to hardware complexity and cost constraints, power-limited devices often avoid using error-correcting codes (ECC). The memory errors generated from these devices can propagate through the wireless channel during transmission, making the decoding at the receiver more challenging. In a massive MIMO system, which is equipped with a large number of antennas to support a large number of device users, this issue can be critical. In this paper, We propose a two-stage solution to address this challenge. The first stage employs an equalizer-based detection mechanism to mitigate communication channel impairments and interference while the second stage employs a Gradient Boosted Decision Tree (GBDT) algorithm to predict and correct memory errors originated at the devices. We demonstrated the efficacy of our approach through Python simulations, utilizing the GBDT model via the Scikit-Learn library. The results indicate a detection accuracy of over 99 % after approximate-inversion based equalization and GBDT-based error detection at the massive MIMO base station receiver. Finally, we design a static random access memory (SRAM) with Cadence Spectre to illustrate the correlation between voltage scaling and error rates, demonstrating that the proposed solution can be utilized to reduce the operating voltage of the user devices.

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.