We study the energy efficiency of pinching-antenna systems (PASSs) by developing a consistent formulation for power distribution in these systems. The per-antenna power distribution in PASSs is not controlled explicitly by a power allocation policy, but rather implicitly through tuning of pinching couplings and locations. Both these factors are tunable: (i) pinching locations are tuned using movable elements, and (ii) couplings can be tuned by varying the effective coupling length of the pinching elements. While the former is feasible to be addressed dynamically in settings with low user mobility, the latter cannot be addressed at a high rate. We thus develop a class of hybrid dynamic-static algorithms, which maximize the energy efficiency by updating the system parameters at different rates. Our experimental results depict that dynamic tuning of pinching locations can significantly boost energy efficiency of PASSs.

Pinching-antenna systems (PASSs) are a recent flexible-antenna technology that is realized by attaching simple components, referred to aspinching elements, to dielectric waveguides. This work explores the potential of deploying PASS for uplink and downlink transmission in multiuser MIMO settings. For downlink PASS-aided communication, we formulate the optimal hybrid beamforming, in which the digital precoding matrix at the access point and the pinching locations on the waveguides are jointly optimized to maximize the achievable weighted sum-rate. We discuss the key challenges in this design problem and propose two low-complexity algorithms to iteratively update the precoding matrix and activated pinching locations.We further formulate the design problem for uplink transmission in a PASS and develop an iterative scheme for the underlying hybrid multiuser detection problem. We validate the proposed schemes through extensive numerical experiments. The results demonstrate that using a PASS, the throughput in both uplink and downlink is significantly enhanced compared to baseline MIMO architectures, such as massive MIMO and classical hybrid analog-digital designs. This highlights the great potential of the PASS, making it a promising reconfigurable antenna technology for next-generation wireless systems.

In this paper, we propose a robust design for overt-the-air federated edge learning (OTA-FEEL) that leverages sensing capabilities at the parameter server (PS) to mitigate the impact of target echoes on the analog model aggregation. We derive novel expressions for the Cramér-Rao bound of the target response and mean squared error (MSE) of the estimated global model to measure sensing and aggregation quality. We then develop a joint scheduling and beamforming framework that optimizes the OTA-FEEL performance while maintaining desired sensing and communication quality. The resulting scheduling problem reduces to a combinatorial mixed-integer nonlinear programming problem (MINLP). We develop a low-complexity hierarchical method based on the matching pursuit algorithm that uses a step-wise strategy to omit the least effective devices in each iteration based on a metric that captures both the aggregation and sensing quality. Numerical results show that accurate sensing effectively suppresses target echoes on the uplink, preserving model aggregation quality despite interference.

Employing wireless systems with dual sensing and communications functionalities is becoming critical in next generation of wireless networks. In this paper, we propose a robust design for over-the-air federated edge learning (OTA-FEEL) that leverages sensing capabilities at the parameter server (PS) to mitigate the impact of target echoes on the analog model aggregation. We first derive novel expressions for the Cramér-Rao bound of the target response and mean squared error (MSE) of the estimated global model to measure radar sensing and model aggregation quality, respectively. Then, we develop a joint scheduling and beamforming framework that optimizes the OTA-FEEL performance while keeping the sensing and communication quality, determined respectively in terms of Cramér-Rao bound and achievable downlink rate, in a desired range. The resulting scheduling problem reduces to a combinatorial mixed-integer nonlinear programming problem (MINLP). We develop a low-complexity hierarchical method based on the matching pursuit algorithm used widely for sparse recovery in the literature of compressed sensing. The proposed algorithm uses a step-wise strategy to omit the least effective devices in each iteration based on a metric that captures both the aggregation and sensing quality of the system. It further invokes alternating optimization scheme to iteratively update the downlink beamforming and uplink post-processing by marginally optimizing them in each iteration. Convergence and complexity analysis of the proposed algorithm is presented. Numerical evaluations on MNIST and CIFAR-10 datasets demonstrate the effectiveness of our proposed algorithm. The results show that by leveraging accurate sensing, the target echoes on the uplink signal can be effectively suppressed, ensuring the quality of model aggregation to remain intact despite the interference.

This work studies a low-complexity design for re-configurable intelligent surface (RIS)-aided multiuser multiple-input multiple-output systems. The base station (BS) applies receive antenna selection to connect a subset of its antennas to the available radio frequency chains. For this setting, the BS switching network, uplink precoders, and RIS phase-shifts are jointly designed, such that the uplink sum-rate is maximized. The principle design problem reduces to an NP-hard mixed-integer optimization. We hence invoke the weighted minimum mean squared error technique and the penalty dual decomposition method to develop a tractable iterative algorithm that approxi-mates the optimal design effectively. Our numerical investigations verify the efficiency of the proposed algorithm and its superior performance as compared with the benchmark.

Over-the-air federated learning (OTA-FL) is an emerging technique to reduce the computation and communication overload caused by the orthogonal transmissions of the model updates in conventional federated learning (FL). This reduction is achieved at the expense of introducing aggregation error that can be efficiently suppressed by means of receive beamforming via large array-antennas. This paper studies OTA-FL in massive multiple-input multiple-output (MIMO) systems with limited number of radio frequency (RF)-chains. For this setting, the beamforming for over-the-air model aggregation needs to be addressed jointly with antenna selection. This leads to an NP-hard problem due to its combinatorial nature. We develop three different algorithms to solve the problem. First, we use the penalty dual decomposition (PDD) technique and propose a two-tier algorithm for joint antenna selection and beamforming. The second algorithm interprets the antenna selection task as a sparse recovery problem and invokes the least absolute shrinkage and selection operator (Lasso) algorithm to approximate the sparse solution. The third algorithm invokes the same sparse recovery based interpretation, but employs the low-complexity method of fast iterative soft-thresholding to find a sparse solution. Convergence and complexity analysis is presented for all the algorithms. The numerical investigations depict that the two algorithms based on the sparse recovery interpretation outperform the PDD-based algorithm, when the number of RF-chains at the edge server is much smaller than its array size. However, as the number of RF-chains increases, the PDD-based algorithm outperforms. Our simulations further depict that learning performance with all the antennas being active at the parameter server (PS) can be closely tracked by selecting less than 20% of the antennas at the PS.

It is widely believed that large IRS-aided MIMO settings maintain the fundamental features of massive MIMO systems. This work gives a rigorous proof that confirms this belief. We show that using a large passive IRS, the end-to-end MIMO channel between the transmitter and the receiver always hardens, even if the IRS elements are strongly correlated. For fading direct and reflection links between the transmitter and the receiver, our derivations demonstrate that for a large number of reflecting elements on the IRS, the capacity of the end-to-end channel is accurately approximated by a real-valued Gaussian random variable whose variance goes to zero as the number of IRS elements grows unboundedly large. The order of this drop depends on how the physical dimensions of the IRS grow. We derive this order explicitly. Numerical experiments show that the closed-form approximation very closely matches the histogram of the capacity term, even in practical scenarios. As a sample application of the results, we characterize the dimensional trade-off between the transmitter and the IRS. The result is intuitive: For a target performance, the larger the IRS is, the fewer transmit antennas are required.

In intelligent reflecting surface (IRS)-aided multiple-input multiple-output (MIMO) systems, the IRS can be utilized to suppress the information leakage towards malicious terminals. This can lead to significant secrecy gains. This work exploits these gains via a tractablejointdesign of downlink beamformers and IRS phase-shifts. In this respect, we consider a generic IRS-aided MIMO wiretap setting and invoke fractional programming and alternating optimization to iteratively find the beamformers and phase-shifts that maximize the achievable weighted secrecy sum-rate. Our design is comprised of two low-complexity algorithms. Performance of the proposed algorithms are numerically evaluated and compared to the benchmark. The results reveal that integrating IRSs into MIMO systems not only boosts the secrecy performance, but also improves the robustness against passive eavesdropping.

This paper characterizes the performance of massive multiuser spatial modulation MIMO systems, when a regularized form of the least-squares method is used for detection. For a generic distortion function and right unitarily invariant channel matrices, the per-antenna transmit rate and the asymptotic distortion achieved by this class of detectors are derived. Invoking an asymptotic characterization, we address two particular applications. Namely, we derive the error rate achieved by the computationally-intractable optimal Bayesian detector, and we propose an efficient approach to tune LASSO-type detectors. We further validate our derivations through various numerical experiments.

Using intelligent reflecting surfaces (IRSs), wireless propagation channels can be manipulated such that information leakage to eavesdropping terminals in a multiple-input multiple-output (MIMO) setting is significantly suppressed. This observation illustrates the potential secrecy gains of IRS-aided MIMO systems. This work develops a novel low-complexity algorithm by which these potential gains are exploited. Invoking methods from fractional programming, the algorithm iteratively designs the digital precoder at the transmitter and tunes the IRS elements, such that the weighted secrecy sum-rate is maximized. It is shown that as the algorithm iterates, the weighted secrecy sum-rate evolves in a non-decreasing way. Numerical investigations confirm the efficiency of the proposed algorithm.

Passively overheard massive multiple-input multiple-output (MIMO) settings are capable of suppressing eavesdroppers via narrow beamforming towards legitimate receivers. This implies that secrecy is obtained almost for free in these settings. This study shows that this is a valid property for a large class of low-complexity massive MIMO transmitters. The investigations consider two dominant approaches for complexity reduction, namely antenna selection and hybrid analog-digital precoding. It is shown that using either approach, the information leakage per achievable sum-rate vanishes as the number of transmit antennas grows large. The results demonstrate that, as the transmit array size grows large, the normalized information leakage obtained by antenna selection and hybrid analog-digital precoding converges to zero double-logarithmically and logarithmically, respectively. The analytical results are confirmed for various benchmark architectures via numerical simulations.

Recently, a hybrid analog-digital architecture has been proposed for multiuser MIMO transmission in the millimeter-wave spectrum using reflect-arrays. The architecture exhibits scalability and high energy-efficiency while keeping the transmitter cost-efficient. Inspired by this architecture, we design a secure multiuser hybrid analog-digital precoding scheme. This scheme utilizes the method of regularized least-squares to shape the downlink beamformers, such that the signal received via malicious terminals is effectively suppressed. Numerical investigations depict high robustness of this scheme against the density of malicious terminals, as well as the quality of overhearing channels. Such properties along with high efficiency of the transmitter imply that the proposed scheme is an effective candidate for secure communication in multiuser millimeter-wave massive MIMO systems.

Most detection algorithms in spatial modulation (SM) are formulated as linear regression via the regularized least-squares (RLS) method. In this method, the transmit signal is estimated by minimizing the residual sum of squares penalized with some regularization. This paper studies the asymptotic performance of a generic RLS-based detection algorithm employed for recovery of SM signals. We derive analytically the asymptotic average mean squared error and the error rate for the class of bi-unitarily invariant channel matrices. The analytic results are employed to study the performance of SM detection via the box-LASSO. The analysis demonstrates that the performance characterization for i.i.d. Gaussian channel matrices is valid for matrices with non-Gaussian entries, as well. This justifies the partially approved conjecture given in [1]. The derivations further extend the former studies to scenarios with non-i.i.d. channel matrices. Numerical investigations validate the analysis, even for practical system dimensions.

Joint user selection and precoding in multiuser MIMO settings can be interpreted as group sparse recovery in linear models. In this problem, a signal with group sparsity is to be reconstructed from an underdetermined system of equations. This paper utilizes this equivalent interpretation and develops a computationally tractable algorithm based on the method of group LASSO. Compared to the state of the art, the proposed scheme shows performance enhancements in two different respects: higher achievable sum-rate and lower interference at the non-selected user terminals.

In massive MIMO wiretap settings, the base station can significantly suppress eavesdroppers by narrow beamforming toward legitimate terminals. Numerical investigations show that by this approach, secrecy is obtained at no significant cost. We call this property of massive MIMO systems "secrecy for free" and show that it not only holds when all the transmit antennas at the base station are employed, but also when only a single antenna is set active. Using linear precoding, the information leakage to the eavesdroppers can be sufficiently diminished, when the total number of available transmit antennas at the base station grows large, even when only a fixed number of them are selected. This result indicates that passive eavesdropping has no significant impact on massive MIMO systems, regardless of the number of active transmit antennas.

In this paper, we study the impacts of transmit antenna selection on the secrecy performance of massive MIMO systems. We consider a wiretap setting in which a fixed number of transmit antennas are selected and then confidential messages are transmitted over them to a multi-antenna legitimate receiver while being overheard by a multi-antenna eavesdropper. For this setup, we derive an accurate approximation of the instantaneous secrecy rate. Using this approximation, it is shown that in some wiretap settings under antenna selection the growth in the number of active antennas enhances the secrecy performance of the system up to some optimal number and degrades it when this optimal number is surpassed. This observation demonstrates that antenna selection in some massive MIMO settings not only reduces the RF-complexity, but also enhances the secrecy performance. We then consider various scenarios and derive the optimal number of active antennas analytically using our large-system approximation. Numerical investigations show an accurate match between simulations and the analytic results.

Antenna selection is an effective means to address the cost and complexity issues in massive MIMO systems. This paper studies the performance limits of massive MIMO systems under practical antenna selection algorithms. A massive MIMO system is considered in which the transmitter employs only a fixed-size subset of the available antennas with the strongest channel gains. For this setup, the input-output mutual information of the system is shown to be well-approximated by a normal random variable when the number of transmit antennas is large. The mean of this random variable grows proportional to the number of antennas and its variance vanishes in the large-system limit. This behavior of the mutual information generalizes the well-known channel hardening property in massive MIMO systems to the cases with antenna selection. Our investigations show that 90% of the ergodic rate achieved by full antenna selection can be achieved by selecting less than 30% of the transmit antennas. Using large-system analysis, we drive an analytical expression for the number of selected antennas that maximizes the energy efficiency. This number is also derived for the case where a certain fraction of the totally achievable rate is aimed to be achieved. Our numerical investigations demonstrate a close match between the analytical and simulation results even for scenarios with not-so-large dimensions.

This paper studies the impact of transmit antenna selection on the secrecy performance of massive MIMO wiretap channels. We consider a scenario in which a multi-antenna transmitter selects a subset of transmit antennas with the strongest channel gains. Confidential messages are then transmitted to a multi-antenna legitimate receiver while the channel is being overheard by a multi-antenna eavesdropper. For this setup, we approximate the distribution of the instantaneous secrecy rate in the large-system limit. The approximation enables us to investigate the optimal number of selected antennas which maximizes the asymptotic secrecy throughput of the system. We show that increasing the number of selected antennas enhances the secrecy performance of the system up to some optimal value, and that further growth in the number of selected antennas has a destructive effect. Using the large-system approximation, we obtain the optimal number of selected antennas analytically for various scenarios. Our numerical investigations show an accurate match between simulations and the analytic results even for not so large dimensions.

Consider a fading Gaussian MIMO channel with Nttransmit and Nrreceive antennas. The transmitter selects Ltantennas corresponding to the strongest channels. For this setup, we study the distribution of the input-output mutual information when Ntgrows large. We show that, for any Nrand Lt, the distribution of the input-output mutual information is accurately approximated by a Gaussian distribution whose mean grows large and whose variance converges to zero. Our analysis depicts that, in the large limit, the gap between the expectation of the mutual information and its corresponding upper bound, derived by applying Jensen's inequality, converges to a constant which only depends on Nrand Lt. The result extends the scope of channel hardening to the general case of antenna selection with multiple receive and selected transmit antennas. Although the analyses are given for the large-system limit, our numerical investigations indicate the robustness of the approximated distribution even when the number of antennas is not large.