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Integrated sensing and communications (ISAC) has emerged as a means to efficiently utilize spectrum and thereby save cost and power. At the higher end of the spectrum, ISAC systems operate at wideband using large antenna arrays to meet the stringent demands for high-resolution sensing and enhanced communications capacity. On the other hand, the overall design should satisfy energy-efficiency and hardware constraints such as operating on low resolution components for a practical scenario. Therefore, this letter presents the design of Hybrid ANalog and Digital BeAmformers with Low resoLution (HANDBALL) digital-to-analog converters (DACs). We introduce a greedy-search-based approach to design the analog beamformers for multi-user multi-target ISAC scenario. Then, the quantization distortion is taken into account in order to design the baseband beamformer with low resolution DACs. We evaluated performance of the proposed HANDBALL technique in terms of both spectral efficiency and sensing beampattern, providing a satisfactory sensing and communication performance for both one-bit and few-bit designs.

Uncrewed Aerial Vehicles (UAVs) provide a compelling solution for data collection in Internet of Things (IoT) networks due to their mobility and adaptability. However, the line-of-sight dominance in their channels may result in severe interference to ground users during UAV operations. To address this, we present an optimization framework that concurrently optimizes UAV trajectories and transmit powers. Our approach efficiently results in the collection of data from a variety of IoT sensors while (a) minimizing the UAVs flying time and (b) mitigating interference with terrestrial networks. Given the complex nature of such an optimization problem, this paper leverages reinforcement learning, specifically the twin delayed deep deterministic policy gradient algorithm, where a distributed learning algorithm is presented. Experimental results validate the efficacy of our proposed approach, demonstrating its capability to significantly enhance data collection in IoT networks while minimizing UAV flight time and interference with ground user links.

A joint design of both sensing and communication can lead to substantial enhancement for both subsystems in terms of size and cost as well as spectrum and hardware efficiency. In the last decade, integrated sensing and communications (ISAC) has emerged as a means to efficiently utilize the spectrum on a single and shared hardware platform. Recent studies focused on developing multifunction approaches to share the spectrum between radar sensing and communications. Index modulation (IM) is one particular approach to incorporating information-bearing communication symbols into the emitted radar waveforms. While IM has been well investigated in communications-only systems, the implementation adoption of the IM concept in ISAC has recently attracted researchers to achieve improved energy/spectral efficiency while maintaining satisfactory radar sensing performance. This article focuses on recent studies on IM-ISAC and presents in detail the analytical background and relevance of the major IM-ISAC applications.

As the demand for wireless connectivity continues to soar, the fifth generation and beyond wireless networks are exploring new ways to efficiently utilize the wireless spectrum and reduce hardware costs. One such approach is the integration of sensing and communications (ISAC) paradigms to jointly access the spectrum. Recent ISAC studies have focused on upper millimeter-wave and low terahertz bands to exploit ultrawide bandwidths. At these frequencies, hybrid beamformers that employ fewer radio-frequency chains are employed to offset expensive hardware but at the cost of lower multiplexing gains. Wideband hybrid beamforming also suffers from the beam-split effect arising from the subcarrier-independent (SI) analog beamformers. To overcome these limitations, we introduce a spatial path index modulation (SPIM) ISAC architecture, which transmits additional information bits via modulating the spatial paths between the base station and communications users. We design the SPIM-ISAC beamformers by estimating both radar and communications parameters through our proposed beam-split-aware algorithms. We then develop a family of hybrid beamforming techniques–hybrid, SI, subcarrier-dependent analog-only, and beam-split-aware beamformers–for SPIM-ISAC. Numerical experiments demonstrate that the proposed approach exhibits significantly improved spectral efficiency performance in the presence of beam-split when compared with even fully digital non-SPIM beamformers.

Reconfigurable Intelligent Surfaces (RIS)-assisted systems are promising technology in next-generation wireless networks, but are susceptible to spoofing attacks due to their broadcast nature. This letter reveals the unique characteristics of RIS-aided multiple-input multiple-output (MIMO) systems, that improve channel entropy compared to conventional MIMO. By capitalizing on the additional paths introduced by the cascaded channel and the distinctive double-structured sparsity inherent in its virtual representation, we develop a novel channel-based physical layer authentication (PLA) approach. In particular, we construct a robust signature for authentication purposes by extracting the intrinsic RIS features of the virtual angle of arrivals and departures indices. Furthermore, the distribution of the digital signature is analyzed to derive analytical expressions for the false alarm and detection probabilities of the proposed scheme. Simulation results show that the proposed approach surpasses the limitations of previous works, with 14.89% and 72% authentication performance improvements in detection and false alarm rates, respectively.

The integration of reconfigurable intelligent surfaces (RISs) and grant-free non-orthogonal multiple access (GF-NOMA) has emerged as a promising solution for enhancing spectral efficiency and massive connectivity in future wireless networks. This paper proposes a GF-NOMA communication network enabled by simultaneously transmitting and reflecting RISs (STAR-RIS). In the proposed GF-NOMA, all user equipments (UEs) have instantaneous access to resource blocks (RBs) without the need for grant acquisition and power control as in the traditional grant-based NOMA schemes. Specifically, we have considered two regimes of interest: 1) the max-min fair (MMF) regime and 2) the max-sum throughput (MST) regime. To achieve the required power disparity, a two-level power control mechanism is proposed; initially, the UEs are clustered according to their channel gains. Additionally, we introduce a multi-level GF-NOMA (MGF-NOMA) scheme that adjusts the transmit power levels for each UE in the cluster. The second level of power disparity is achieved through the assignment of STAR-RISs to the clusters and optimal partitioning of STAR-RIS to support each of the cluster members. Furthermore, we have also derived the closed-form equations for the optimal partitioning of STAR-RIS within the clusters for both regimes of interest. Simulation results demonstrate that the proposed STAR-RIS-aided MGF-NOMA yields a gain of 60% and 20% in the MST regime with active and passive RIS realization, respectively. Furthermore, the active and passive RIS-based MGF-NOMA achieve nearly the equivalent fairness that can be obtained through optimal power control in the MMF regime. The finding emphasizes the potential of integrating STAR-RIS with GF-NOMA as a robust and promising solution for future wireless communication systems.

Terahertz (THz) band is envisioned for the future sixth generation wireless systems thanks to its abundant bandwidth and very narrow beamwidth. These features are one of the key enabling factors for high resolution sensing with milli-degree level direction-of-arrival (DOA) estimation. Therefore, this letter investigates the DOA estimation problem in THz systems in the presence of two major error sources: 1) gain-phase mismatches, which occur due to the deviations in the radio-frequency circuitry; 2) beam-squint, which is caused because of the deviations in the generated beams at different subcarriers due to ultra-wide bandwidth. An auto-calibration approach, namely NoisE subspAce correcTion technique for MUltiple SIgnal Classification (NEAT-MUSIC), is proposed based on the correction of the noise subspace for accurate DOA estimation in the presence of gain-phase mismatches and beam-squint. To gauge the performance of the proposed approach, the Cramér-Rao bounds are also derived. Numerical results show the effectiveness of the proposed approach.

Next-generation wireless networks strive for higher communication rates, ultra-low latency, seamless connectivity, and high-resolution sensing capabilities. To meet these demands, terahertz (THz) band signal processing is envisioned as a key technology offering wide bandwidth and sub-millimeter wavelength. Furthermore, THz integrated sensing and communications (ISAC) paradigm has emerged to jointly access the spectrum and reduce the hardware costs through a unified platform. To address the challenges in THz propagation, THz-ISAC systems employ extremely large antenna arrays to improve the beamforming gain for communications with high data rates and sensing with high resolution. However, the cost and power consumption of implementing fully digital beamformers are prohibitive. While hybrid analog/digital beamforming can be a potential solution, the use of subcarrier-independent analog beamformers leads to the beam-squint phenomenon where different subcarriers observe distinct directions because of adopting the same analog beamformer across all subcarriers. In this paper, we develop a sparse array architecture for THz-ISAC with hybrid beamforming to provide a cost-effective solution. We analyze the antenna selection problem under beam-squint influence and introduce a manifold optimization approach for hybrid beamforming design. To reduce computational and memory costs, we propose novel algorithms leveraging grouped subarrays, quantized performance metrics, and sequential optimization. These approaches yield a significant reduction in the number of possible subarray configurations, which enables us to devise a neural network with classification model to accurately perform antenna selection. Numerical simulations show that the proposed approach exhibits up to 95% lower complexity for large antenna arrays while maintaining satisfactory communications with approximately 6% loss in the achievable rate.

Integrated sensing and communications (ISAC) has emerged as a means to efficiently utilize spectrum and thereby save cost and power. At the higher end of the spectrum, ISAC systems operate at wideband using large antenna arrays to meet the stringent demands for high-resolution sensing and enhanced communications capacity. However, the wideband implementation entails beam-squint, that is, deviations in the generated beam directions because of the narrowband assumption in the analog components. This causes significant degradation in the communications capacity, target detection, and parameter estimation. This article presents the design challenges caused by beam-squint and its mitigation in ISAC systems. In this context, we also discuss several ISAC design perspectives, including far-/near-field beamforming, channel/direction estimation, sparse array design, and index modulation. There are also several research opportunities in waveform design, beam training, and array processing to adequately address beam-squint in ISAC.

Non-terrestrial networks (NTNs) are one of the key enablers in sixth-generation (6G) wireless networks; with their rapid growth, system-level metrics analysis becomes more crucial in NTN design. Applying stochastic geometry (SG) as a system-level analysis tool in the context of NTN offers novel insights into the network trade-offs. In this article, we study and highlight NTN's common system-level metrics from three perspectives: NTN types, typical communication issues, and application scenarios. In addition to summarizing existing research, we study the best-suited SG models for different platforms and system-level metrics that have not been well studied in the literature. In addition, we showcase NTN-dominated prospective application scenarios. Finally, we implement a performance analysis of system-level metrics for these applications based on SG models.

Recently, the use of uncrewed aerial vehicles (UAVs) in joint sensing and communication applications has received a lot of attention. However, integrating UAVs in current cellular systems presents major challenges related to trajectory optimization and interference management among others. This paper considers a multi-cell network including a UAV, which senses and forwards the sensory data from different events to the central base station. Particularly, the current manuscript covers how to design the UAV’s ( ${i}$ ) 3D trajectory, (ii) power allocation, and (iii) sensing scheduling such that (a) a set of events are sensed, (b) interference to neighboring cells is kept at bay, and (c) the amount of energy required by the UAV is minimized. The resulting nonconvex optimization problem is tackled through a combination of ( ${i}$ ) low-complexity binary optimization, (ii) successive convex approximation, and (iii) the Lagrangian method. Simulation results over a range of various key parameters have shown the merits of our approach, which consumes 33%-200% less energy compared to different benchmarks.

Reconfigurable intelligent surfaces (RISs) play a vital role in future wireless systems with the capability of enhancing propagation environments by intelligently reflecting the signals toward the target receivers. However, optimal tuning of the phase shifters at the RIS is challenging due to the passive nature of reflective elements and the high complexity of acquiring channel state information (CSI). Furthermore, the joint active beamforming and RIS reflection beam design is a tedious task due to the high computational complexity and the dynamic nature of the wireless environment. Today’s cellular networks establish data transmission by relying on pre-defined generic beamforming codebooks, which are neither site-specific nor adaptive to the changes in the wireless environment. Moreover, identifying the best beam is typically performed using an exhaustive search approach that prohibits the use of large codebook sizes due to the resulting high beam training overhead. Depending merely on the binary received signal strength, this work develops a multi-agent deep reinforcement learning (MA-DRL) framework that jointly designs the active and the passive reflection beam codebooks for the BS and the RIS, reflectively. To accelerate learning convergence and reduce the search space, the proposed model divides the RIS into multiple partitions and associates beam patterns to the surrounding environments with low computational complexity. Moreover, a hierarchical beam training solution is proposed to further reduce the beam training overhead of the single-beam training approach. Simulation results show that the proposed MA-DRL approach can provide a 97% beam training overhead reduction over the discrete Fourier transform (DFT) codebook.

Artificial intelligence (AI) is expected to be an integral part of radio resource management (RRM) in sixth-generation (6G) networks. However, the opaque nature of complex deep learning (DL) models lacks explainability and robustness, posing a significant hindrance to adoption in practice. Furthermore, wireless communication experts and stakeholders, concerned about potential vulnerabilities, such as data privacy issues or biased decision-making, express reluctance to fully embrace these AI technologies. To this end, this article sheds light on the importance and means of achieving explainability and robustness toward trustworthy AI-based RRM solutions for 6G networks. We outline a range of explainable and robust AI techniques for feature visualization and attribution; model simplification and interpretability; model compression; and sensitivity analysis, then explain how they can be leveraged for RRM. Two case studies are presented to demonstrate the application of explainability and robustness in wireless network design. The former case focuses on exploiting explainable AI methods to simplify the model by reducing the input size of deep reinforcement learning agents for scalable RRM of vehicular networks. On the other hand, the latter case highlights the importance of providing interpretable explanations of credible and confident decisions of a DL-based beam alignment solution in massive multiple-input multiple-output systems. Analyses of these cases provide a generic explainability pipeline and a credibility assessment tool for checking model robustness that can be applied to any pre-trained DL-based RRM method. Overall, the proposed framework offers a promising avenue for improving the practicality and trustworthiness of AI-empowered RRM.

This study delves into the capabilities of reconfigurable intelligent surfaces (RISs) in enhancing bidirectional non-orthogonal multiple access (NOMA) networks. The proposed approach partitions RIS to optimize the channel conditions for NOMA users, improving NOMA gain and eliminating the requirement for uplink (UL) power control. The proposed approach is rigorously evaluated under four practical operational regimes; 1) Quality-of-Service (QoS) sufficient regime, 2) RIS and power efficient regime, 3) max-min fair regime, and 4) maximum throughput regime, each subject to both UL and downlink (DL) QoS constraints. By leveraging decoupled nature of RIS portions and base station (BS) transmit power, closed-form solutions are derived to show how optimal RIS partitioning can meet UL-QoS requirements while optimal BS power control can ensure DL-QoS compliance. Analytical findings are validated by simulations, highlighting the significant benefits that RISs can bring to the NOMA networks in the aforementioned operational scenarios.

Integrated sensing and communication (ISAC) has become a key strategy, where a comprehensive framework transcends a single radar functionality by incorporating various types of sensors, thus facilitating richer interactions between multimodal sensing and communication. As efforts to standardize ISAC intensify, this article embarks on an exploration of the integration of multiple access schemes with ISAC, with a special focus on non-orthogonal multiple access (NOMA). Various dimensions of ISAC-NOMA are covered, comprehensively spanning deployment scenarios, integration challenges, and synergistic opportunities, alongside other wireless communication technologies. In particular, the article delves into intricate aspects such as interference management, user-target clustering, power allocation (PA), beamforming, and cooperative communication schemes. To provide further clarity to our discussions, we introduce two case studies supported by simulation findings: joint PA and user-target clustering, and simultaneous beam codebook design for sensing and communication. Furthermore, the article elaborates on the potential application of ISAC-NOMA in cutting-edge wireless paradigms such as cell-free massive multiple input multiple output networks, reconfigurable intelligent surfaces, non-terrestrial networks, and ambient backscatter communication.

This paper presents the algorithm and very-large-scale integration (VLSI) architecture of a high-throughput and highly efficient independent component analysis (ICA) processor for self-interference cancellation (SIC) in in-band full-duplex (IBFD) systems. This is the first VLSI architecture reported in the literature based on the state-of-the-art entropy bound minimization (EBM) approach. A novel ICA algorithm is presented in this paper with momentum gradient descent optimization. Simulation results show that the number of iterations for the proposed algorithm is significantly reduced compared to the conventional ICA algorithms. Furthermore, a novel early-distribution estimation scheme is proposed in the designed ICA processor to compute multiple distribution functions with low latency and low complexity. The processing flow and the efficiency for the hardware utilization are specifically designed so that the processing speed is maximized with minimum employment of hardware components. The proposed ICA processor is designed and implemented based on the application-specific-integrated circuit (ASIC) flow. The post-layout estimations show that compared with the conventional EBM-based scheme, the proposed design improves the throughput and efficiency by 30x. In addition, compared to prior designs shown in the literature, the proposed ICA processor also demonstrates a significant enhancement in terms of throughput and efficiency.

Recently, resistive RAM (ReRAM)-based hardware accelerators showed unprecedented performance compared the digital accelerators. Technology scaling causes an inevitable increase in interconnect wire resistance, which leads to IR drops that could limit the performance of ReRAM-based accelerators. These IR drops deteriorate the signal integrity and quality, especially in the crossbar structures which are used to build high-density ReRAMs. Hence, finding a software solution, which can predict the effect of IR drop without involving expensive hardware or SPICE simulations, is very desirable. In this article, we propose two neural networks models to predict the impact of the IR drop problem. These models are used to evaluate the performance of the different deep neural network (DNN) models including binary and quantized neural networks showing similar performance (i.e., recognition accuracy) to the golden validation (i.e., SPICE-based DNN validation). In addition, these predication models are incorporated into the DNN training framework to efficiently retrain the DNN models and bridge the accuracy drop. To further enhance the validation accuracy, we propose incremental training methods. The DNN validation results, done through SPICE simulations, show very high improvement in performance close to the baseline performance, which demonstrates the efficacy of the proposed method even with challenging datasets, such as CIFAR10 and SVHN.

This paper presents architectural trade-off analysis for accelerating two (Type I, II) fixed-point long short-term memory (LSTM) network based on circulant matrix-vector multiplications (MVMs) using radix- $r$ offset binary coding (OBC) scheme. Type I MVM architecture rotates the weights with the proposed modulo-cum interleaver and uses partial product generators (PPGs) with a single generation unit across a column. It is hardware-optimized using a single adder tree through time-multiplexing. Meanwhile, Type II MVM architecture rotates the inputs with the proposed store-cum interleaver and uses single PPGs with a single generation unit across a row. It is time-optimized by unfolding shift-accumulate unit to a shift-add tree followed by pipelining. A new design for element-wise multiplication using radix- $r$ PPG is also presented. Both the designs are extended to their block-circulant variants for certain accuracy requirements. Post-synthesis of Type I and II architectures for a different model, kernel, radix sizes and clock frequencies result in several efficient designs. Compared with the prior scheme, Type I architecture for $128 \times 128$ with $r=2$ on 28 nm FDSOI technology at 800 MHz occupies 32.27% lesser area, consumes 67.89% lesser power at the same throughput, while Type II architecture at the expense of area and power provides $40\times $ higher throughput.

This article proposes a technique to calibrate a low-cost inertial measurement unit (IMU) in the field without relying on expensive laboratory-based equipment. The main parameters to be calibrated in any IMU are misalignment factors, sensitivity scale factors, and gyro and accelerometer biases, all of which are temperature-dependent. Usually, IMU manufacturers have highly accurate and expensive rate tables and centrifuges that can provide high-resolution angular rates to the equipment under test but require costly regular maintenance and highly trained technicians. We propose an alternative method of performing this calibration by hand in the field, using a special Kalman filter that benefits from zero-velocity measurement updates. The performance of the proposed calibration technique is evaluated and validated via extensive simulations and real experiments.

The synergism of reconfigurable intelligent surfaces (RIS) and artificial noise (AN) shows significant promise in improving physical layer security in wireless networks. Accordingly, this letter proposes the virtual partitioning of RIS elements into two parts such that the phase shifts of the different partitions are configured to improve the intended signal at a legitimate user and enhance the impact of AN on an illegitimate user, respectively. To this aim, two problems are defined to jointly optimize the partitioning ratio, and signal/noise transmit power levels for two main objectives. First, we maximize secrecy capacity by satisfying users’ quality of service (QoS). Second, we optimize transmit power to establish a secure link by satisfying the QoS of the legitimate user. We provide closed-form solutions subject to the rate constraints on both legitimate and illegitimate users. Simulation results validate the closed-from solutions and show that the proposed RIS-partitioning method dramatically improves SC compared to benchmark methods.

Although Resistive RAMs can support highly efficient matrix–vector multiplication, which is very useful for machine learning and other applications, the nonideal behavior of hardware, such as stuck-at fault (SAF) and IR drop is an important concern in making ReRAM crossbar array-based deep learning accelerators. Previous work has addressed the nonideality problem through either redundancy in hardware, which requires a permanent increase of hardware cost, or software retraining, which may be even more costly or unacceptable due to its need for a training dataset as well as high computation overhead. In this article, we propose a very lightweight method that can be applied on top of existing hardware or software solutions. Our method, called forward-parameter tuning (FPT), takes advantage of a certain statistical property existing in the activation data of neural network layers, and can mitigate the impact of mild nonidealities in ReRAM crossbar arrays (RCAs) for deep learning applications without using any hardware, a dataset, or gradient-based training. Our experimental results using MNIST, CIFAR-10, and CIFAR-100, and ImageNet datasets in binary and multibit networks demonstrate that our technique is very effective, both alone and together with previous methods, up to 20% fault rate, which is higher than even some of the previous remapping methods. We also evaluate our method in the presence of other nonidealities, such as variability and IR drop. Furthermore, we provide an analysis based on the concept of the effective fault rate (EFR), which not only demonstrates that EFR can be a useful tool to predict the accuracy of faulty RCA-based neural networks but also explains why mitigating the SAF problem is more difficult with multibit neural networks.

Reconfigurable intelligent surface (RIS) assisted wireless systems require accurate channel state information (CSI) to control wireless channels and improve both the bandwidth and energy efficiency. However, CSI acquisition is non-trivial for two reasons: 1) the passive nature of RIS does not allow transceiving and processing pilot signals, and 2) the dimensions of the cascaded channel between transceivers increases with the large number of RIS elements, which yields high training overhead and computational complexity. While prior art has mainly focused on frequency-flat channel estimation, this paper proposes novel data-driven and compressive sensing based approaches for estimating both frequency-flat and frequency-selective cascaded channels of RIS-assisted multi-user millimeter-wave large multiple input multiple output (MIMO) systems with limited training overhead. The proposed methods exploit the common sparsity property among the different subcarriers and the double-structured sparsity property of the angular cascaded channel matrices as different angular cascaded channels observed by different users share completely common non-zero rows and user-specific column supports. The proposed data-driven cascaded channel estimation approaches use denoising neural networks to accurately detect channel supports. Alternatively, when data-training capabilities are not available, the compressive sensing based orthogonal matching pursuit (OMP) approach relies on sparsity properties and applies simultaneous OMP to detect the channel supports. Simulation results show that the pilot overhead required by the proposed scheme is lower than existing schemes. When compared to other OMP approaches that achieve an NMSE gap of 5 to 6 dB with respect to the Oracle least square lower bound, the proposed algorithms reduce the lower bound gap to only 1 dB, while reducing complexity by more than two orders of magnitude.

Dietary patterns can be the primary reason for many chronic diseases such as diabetes and obesity. State-of-the-art wearable sensor technologies can play a critical role in assisting patients in managing their eating habits by providing meaningful statistics on critical parameters such as the onset, duration, and frequency of eating. For an accurate yet fast food intake recognition, this work presents a novel Machine Learning (ML) based framework that shows promising results by leveraging optimized support vector machine (SVM) classifiers. The SVM classifiers are trained on three comprehensive datasets: OREBA, FIC, and CLEMSON. The developed framework outperforms existing algorithms by achieving F1-scores of $\bf 92\%$, $\bf 94\%$, $\bf 95\%$, and $\bf 85\%$ on OREBA-SHA, OREBA-DIS, FIC, and CLEMSON datasets, respectively. In order to assess the generalization aspects, the proposed SVM framework is also trained on one of the three databases while being tested on the others and achieves acceptable F1-scores in all cases. The proposed algorithm is well suited for real-time applications since inference is made using a few support vector parameters compared to thousands in peer deep neural networks models.

Deep neural networks (DNNs), as a subset of machine learning (ML) techniques, entail that real-world data can be learned, and decisions can be made in real time. However, their wide adoption is hindered by a number of software and hardware limitations. The existing general-purpose hardware platforms used to accelerate DNNs are facing new challenges associated with the growing amount of data and are exponentially increasing the complexity of computations. Emerging nonvolatile memory (NVM) devices and the compute-in-memory (CIM) paradigm are creating a new hardware architecture generation with increased computing and storage capabilities. In particular, the shift toward resistive random access memory (ReRAM)-based in-memory computing has great potential in the implementation of area- and power-efficient inference and in training large-scale neural network architectures. These can accelerate the process of IoT-enabled AI technologies entering our daily lives. In this survey, we review the state-of-the-art ReRAM-based DNN many-core accelerators, and their superiority compared to CMOS counterparts was shown. The review covers different aspects of hardware and software realization of DNN accelerators, their present limitations, and prospects. In particular, a comparison of the accelerators shows the need for the introduction of new performance metrics and benchmarking standards. In addition, the major concerns regarding the efficient design of accelerators include a lack of accuracy in simulation tools for software and hardware codesign.

Reconfigurable intelligent surfaces (RISs) have recently been presented as a revolutionary technique and recognized as one of the candidates for beyond fifth-generation wireless networks. The RIS technology becomes especially attractive due to its capability to operate in a full-duplex (FD) mode, which is able to ideally double the half-duplex radio spectrum efficiency. This article analyzes the performance of an RIS-aided multihop FD relaying system, where an intermediate FD relay is employed to defeat the far-field path-loss effect inherent to RIS communication links. To this end, we analytically derive exact and asymptotic closed-form expressions for the outage probability, average spectral efficiency, and average bit error rate. The RIS-aided system demonstrates effective performance improvement for the proposed network. However, from the results, it is noticed that the increase in the number of RIS units within a certain communication link does not provide linear performance improvement due to the RIS channel's far-field path-loss model. Thus, mixing active FD relaying techniques with passive RIS platforms can be an ideal solution to improve further system performance, such as outage probability, average spectral efficiency, and average bit error rate in RIS-enabled networks.

An independent component analysis (ICA) has been used in many applications, including self-interference cancellation (SIC) for in-band full-duplex (IBFD) wireless systems and anomaly detection in industrial Internet of Things (IoT). This article presents a high-throughput and highly efficient configurable preprocessing accelerator for the ICA algorithm. The proposed ICA accelerator has three major blocks that perform data centering, covariance matrix for computation, and eigenvalue decomposition (EVD). Specifically, the proposed accelerator is based on a high-performance matrix multiplication array (MMA). The proposed MMA architecture uses time-multiplexed processing, so that the efficiency of hardware utilization is greatly enhanced. Furthermore, the processing flow utilizes parallel processing, such that the centering, the calculation of the covariance matrix, and the EVD are conducted simultaneously and are individually pipelined to maximize throughput. This article presents the architecture, circuit design, and performance estimates based on post-layout extraction of the proposed preprocessing ICA accelerator. The proposed design achieves a throughput of 40.7 kMatrices/s at a complexity of 73.3 kGE.

The performance of a biologically plausible spiking neural network (SNN) largely depends on the model parameters and neural dynamics. This article proposes a parameter optimization scheme for improving the performance of a biologically plausible SNN and a parallel on-field-programmable gate array (FPGA) online learning neuromorphic platform for the digital implementation based on two numerical methods, namely, the Euler and third-order Runge–Kutta (RK3) methods. The optimization scheme explores the impact of biological time constants on information transmission in the SNN and improves the convergence rate of the SNN on digit recognition with a suitable choice of the time constants. The parallel digital implementation leads to a significant speedup over software simulation on a general-purpose CPU. The parallel implementation with the Euler method enables around $180\times $ ( $20\times $ ) training (inference) speedup over a Pytorch-based SNN simulation on CPU. Moreover, compared with previous work, our parallel implementation shows more than $300\times $ ( $240\times $ ) improvement on speed and $180\times $ ( $250\times $ ) reduction in energy consumption for training (inference). In addition, due to the high-order accuracy, the RK3 method is demonstrated to gain $2\times $ training speedup over the Euler method, which makes it suitable for online training in real-time applications.

The Internet of Bodies (IoB) is a network of smart objects placed in, on, and around the human body, allowing for intra- and inter-body communications. This position article aims to provide a glimpse into the opportunities created by implantable, injectable, ingestible, and wearable IoB devices. The article starts with a thorough discussion of application-specific design goals, technical challenges, and enabling communication standards. We discuss why the highly radiative nature of radio frequency (RF) systems results in inefficient communication due to over-ex-tended coverage, causing interference and becoming susceptible to eavesdropping. Alternatively, body channel communication (BCC) uses the human body as a transmission medium by coupling harmless electrical signals, yielding secure and efficient communication thanks to better channel conditions and lower signal leakage than over-the-air RF systems. Numerical results show that various BCC topologies can respectively reach 8–12 Mb/s and 1.5-3 Mb/s max-sum and max-min rates with 1 MHz bandwidth and -30 dBm transmission power, which is three orders of magnitude lower than safety limits. Moreover, the BCC is capable of accommodating tens of IoB nodes up to 1 Mb/s rates, which is sufficient for most IoB applications. Furthermore, as the cyber and biological worlds meet, security risks and privacy concerns take center stage, leading to a discussion of multi-faceted legal, societal, ethical, and political issues related to technology governance.

Recent breakthroughs in neuromorphic computing show that local forms of gradient descent learning are compatible with Spiking Neural Networks (SNNs) and synaptic plasticity. Although SNNs can be scalably implemented using neuromorphic VLSI, an architecture that can learn using gradient-descent in situ is still missing. In this paper, we propose a local, gradient-based, error-triggered learning algorithm with online ternary weight updates. The proposed algorithm enables online training of multi-layer SNNs with memristive neuromorphic hardware showing a small loss in the performance compared with the state-of-the-art. We also propose a hardware architecture based on memristive crossbar arrays to perform the required vector-matrix multiplications. The necessary peripheral circuitry including presynaptic, post-synaptic and write circuits required for online training, have been designed in the subthreshold regime for power saving with a standard 180 nm CMOS process.

In this letter, we propose a modified version of Fast Independent Component Analysis (FICA) algorithm to solve the self-interference cancellation (SIC) problem in In-band Full Duplex (IBFD) communication systems. The complex mixing problem is mathematically formulated to suit the real-valued blind source separation (BSS) algorithms. In addition, we propose a method to estimate the ambiguity factors associated with ICA lumped together with the channels and residual separation error. Experiments were performed on an FD platform where FICA-based BSS was applied for SIC in the frequency domain. Experimental results show superior performance compared to least squares SIC by up to 6 dB gain in the SNR.

Human Body Communication (HBC) is an emerging technology that uses the human body as a communication channel. It offers significant advantages over traditional RF techniques in terms of power consumption and security. In recent developments, Electro-quasistatic HBC (EQS-HBC) in the frequency band below 1 MHz has been employed to enable communication without signal radiation beyond the body, effectively turning the body into a wired communication medium. This paper delves into the application of the EQS band for HBC. Experimental results show the determinantal effect of intermittent noise that sporadically disrupts communications across the band of interest. To address this challenge, we introduce an innovative frequency-hopping transceiver system, which allows the transmitter to seamlessly adapt to different frequencies. In addition, we present a miniature transmitter design, incorporating a simplified micro-controller unit (MCU) to facilitate the implementation of HBC. Furthermore, to validate this proposed design, we present a fully functional prototype of an HBC system that effectively employs frequency hopping techniques for practical applications.

This letter presents a Wi-Fi compliant self-interference (SI) active cancellation (AC) technique for amplify and forward full-duplex relay (AF-FDR) utilizing orthogonal frequency division multiplexing. First, we describe the system model of AF-FDR, where the relay transparently extends the range of the main access point (AP) to reach a station located outside the AP’s radio coverage. We highlight the SI (and its suppression) impact on FDR, due to simultaneous transmission/reception on the same frequency. We then present a novel Wi-Fi compliant estimation technique of the SI channel and its delay parameters, which are later used for AC, achieving SI suppression of up to an additional 35 dB. The proposed AF-FDR system is simulated in a fading channel environment with Doppler frequency of up to 10 Hz. Simulation results show cancellation gains within 1 dB of static channels with an SI training overhead of 1.5%.

The complexity of the computational problems is rising faster than the computational platforms’ capabilities which are also becoming increasingly costly to operate due to their increased need for energy. This forces researchers to find alternative paradigms and methods for efficient computing. One promising paradigm is accelerating compute-intensive kernels using in-memory computing accelerators, where data movements are significantly reduced. Another increasingly popular method for improving energy efficiency is approximate computing. In this paper, we propose a methodology for efficient approximate in-memory computing. To maximize energy savings for a given approximation constraints, a hybrid approach is presented combining both voltage and precision scaling. This can be applied to an associative memory-based architecture that can be implemented today using CMOS memories (SRAM) but can be seamlessly scaled to emerging ReRAM-based memory technology later with minimal effort. For the evaluation of the proposed methodology, a diverse set of domains is covered, such as image processing, machine learning, machine vision, and digital signal processing. When compared to full-precision, unscaled implementations, average energy savings of $5.17{\times}$ and $59.11{\times}$ , and speedups of $2.1{\times}$ and $3.24{\times}$ in SRAM-based and ReRAM-based architectures, respectively, are reported.

The performance of multi-user multiple input multiple output (MU-MIMO) LTE systems critically depends on how the interference is estimated and managed. In this letter, we propose a practical framework for interference estimation for LTE networks. Moreover, we present a practical suboptimal non-iterative method for managing the interference through utilizing the interference knowledge. Furthermore, we consider the non-ideality of MU-MIMO precoding resulting from limited channel knowledge. Finally, we evaluate the proposed framework benefits through comparisons with other techniques.

The demand for data capacity has been growing, especially in the aviation and maritime industries and is expected to continue to grow in the foreseeable future. The satellite industry is moving to High Throughput Satellite (HTS) that are characterized by large numbers of small spot beams, frequency reuse, higher Equivalent Isotropically Radiated Power (EIRP) and higher Gain to Noise Temperature (G/T) to meet growing capacity demand. Multi-Port Amplifiers (MPA) increase the flexibility of HTS systems by allowing capacity to be allocated dynamically based on changing demand. This will allow capacity requirements to be planned based on the sum of the requirements across all beams rather than the peaks in each HTS beam. The authors propose a ground based solution that will maximize resource utilization of an HTS with an MPA and deliver the capacity dynamically based on demand. Maritime, commercial aviation, satellite based cellular backhaul, and disaster recovery services are the main applications that can benefit from the solution they propose. The authors' results show significant reduction in the overall capacity requirements because of the more efficient utilization of the satellite resources.

In this letter, we consider decentralized estimation of unknown random vectors by using wireless sensors and a fusion center (FC). We adopt a linear model for decentralized estimation of vector sources where both observation models and sensor operations are linear and the multiple access channel (MAC) is coherent. Each sensor observes a different vector source, where vector sources can be correlated. Sensors are designed to minimize the total mean square error (MSE) at the fusion center subject to transmit power constraints at the sensors. While a closed form solution cannot be found, an iterative algorithm is proposed. Simulations are provided to verify the analysis and present the performance of the proposed scheme.

The non‐orthogonal multiple access (NOMA) technique is a prospective solution to support the massive connectivity of an ever‐increasing number of wirelessly connected devices and address the spectrum scarcity issue. In this article, the outage probability and ergodic capacity of a two‐hop cooperative NOMA network with an energy‐limited relaying node are quantified over a generalized α−κ−μstatistical model. The relay acts in an amplify‐and‐forward mode and performs energy harvesting (EH) using the time‐switching and power splitting relaying protocols. Moreover, the impact of hardware impairments (HIs) is incorporated into the performance evaluation. The obtained results prove the importance of HIs and allow one to evaluate the outage probability and ergodic capacity over various statistical models and system parameters. Finally, the results suggest that the optimal performance at a specific scenario depends on the combination of multiple factors, such as channel conditions, HI level, transmit power, and EH protocol. The analytical expressions for outage probability and ergodic capacity (as well as their asymptotic behavior) are derived for wireless powered cooperative NOMA over generalized and non‐homogeneous α−κ−μfading channels, while considering hardware imperfections at transceivers. Derived analytical expressions demonstrate the advantage of cooperative NOMA over its orthogonal counterpart.

In this paper, a low-voltage low-power rail-to-rail constant gmtransconductance amplifier (TA) is introduced. The supply voltages are set at (±1.5 V). The circuit depends on selecting the maximum transconductance (gm) to achieve an almost constant gmover the entire common-mode (CM) range. The circuit is then used to realize a second-order 4 MHz lowpass filter consuming 530 μW, and a fifth-order 450 kHz lowpass elliptic filter consuming 2.3 mW. Both filters can be integrated on silicon without any external connections.

In‐band full‐duplex communication has a great potential to enhance wireless local area networks, where full‐duplex access points can support simultaneous uplink (UL) and downlink (DL) flows over the same frequency channel. This work presents a scheduling and power adaptation technique to manage interference in wireless local area networks with full‐duplex capability. The proposed system focuses primarily on scheduling UL and DL clients that can be efficiently served simultaneously. First, we develop a DL client scheduling algorithm that allows for sum throughput gains with the existence of a UL client. Second, we manage the interference resulting from the UL flow via adjusting the UL transmit power to maximize rate for DL clients that are served in a multiuser multiple‐input–multiple‐output fashion. In‐band full‐duplex communication has a great potential to enhance wireless local area networks, where full‐duplex access points can support simultaneous uplink and downlink flows over the same frequency channel. This work presents scheduling and power adaptation technique to manage interference in wireless local area networks with full‐duplex capability. The proposed algorithms focus on scheduling uplink and downlink clients that can be efficiently served simultaneously in a multiuser multiple‐input–multiple‐output fashion aiming at achieving high sum throughput.