Massive MIMO attains high spectral and power efficiency transmission by leveraging a large number of transmit antennas. However, to capture the benefits of massive MIMO, each antenna should be accompanied with a dedicated RF chain, and consequently, the hardware costs would scale up tremendously with the increase of the antennas. Cheap implementations of massive MIMO have recently gained considerable attention, and constant modulus (CM) signaling is seen as a promising solution, owing to its low peak-to-average power ratio (PAPR). This work investigates the physical-layer (PHY) security in massive MIMO with an emphasis on the CM signaling. In particular, we consider a transmitter with massive antennas broadcasts a common confidential information to a group of legitimate receivers, and a number of eavesdroppers overhear the transmission and attempt to intercept the information. Our goal is to design the CM beamforming at the transmitter so that the multicast secrecy rate is maximized. This secrecy rate maximization (SRM) problem is generally NP-hard. To tackle it, two tractable approaches are developed. The first one employs the semidefinite relaxation (SDR) technique and the Charnes-Copper transformation to obtain a convex relaxation of the SRM problem. However, due to the dimension lifting of SDR, this approach is feasible only for small to medium antenna sizes. The second approach leverages the Dinkelbach method to work directly over the beamformer domain; a custom-build nonconvex alternating direction method of multipliers (ADMM) algorithm is proposed to efficiently perform each Dinkelbach update. Simulation results demonstrate that the second approach is computationally more efficient and can achieve nearly optimal performance when the number of antennas is large.

Open-set signal recognition provides a new approach for verifying the robustness of models by introducing novel unknown signal classes into the model testing and breaking the conventional closed-set assumption, which has become very popular in real-world scenarios. In the present work, we propose an efficient open-set signal recognition algorithm, which contains three key sub-modules: the signal representation sub-module based on a vision transformer (ViT) structure, a set distance metric sub-module based on Wasserstein distance, and a class space compression sub-module based on reciprocal point separation and central loss. In this algorithm, the representing features of signals are established based on transformer-based neural networks, i.e., ViT, in order to extract global information about time series-related data. The employed reciprocal point is used in modeling the potential unknown space without using the corresponding samples, while the distance metric between different class spaces is mathematically modeled in terms of the Wasserstein distance instead of the classical Euclidean distance. Numerical experiments on different open-set signal recognition tasks show that the proposed algorithm can significantly improve the recognition efficiency in both known and unknown categories.

In most existing robust array beam pattern synthesis studies, the bounded-sphere model is used to describe the steering vector (SV) uncertainties. In this letter, instead of bounding the norm of SV perturbations as a whole, we explore the amplitude and phase perturbations of each SV element separately, thereby obtaining a tighter SV uncertainty model. On the basis of this model, we formulate the robust pattern synthesis problem from the perspective of the min-max optimization, which aims to minimize the maximum side lobe response, while preserving the main lobe response. However, this problem is difficult due to the infinitely many nonconvex constraints. As a compromise, we employ the worst-case criterion and recast the problem as a convex second-order cone program (SOCP). To solve the SOCP, we further design an alternating direction method of multipliers based algorithm, which is computationally efficient by coming up with closed-form solutions in each step.

Reconfigurable intelligent surfaces (RISs) hold significant promise for enhancing physical layer security (PLS). However, conventional RISs are typically modeled using diagonal scattering matrices, capturing only independent reflections from each reflecting element, which limits their flexibility in channel manipulation. In contrast, beyond-diagonal RISs (BD-RISs) employ non-diagonal scattering matrices enabled by active and tunable inter-element connections through a shared impedance network. This architecture significantly enhances channel shaping capabilities, creating new opportunities for advanced PLS techniques. This paper investigates PLS in a multiple-input multiple-output (MIMO) system assisted by BD-RISs, where a multi-antenna transmitter sends confidential information to a multi-antenna legitimate user while a multi-antenna eavesdropper attempts interception. To maximize the secrecy rate (SR), we formulate it as a non-convex optimization problem by jointly optimizing the transmit beamforming and BD-RIS REs under power and structural constraints. To solve this problem, we first introduce an auxiliary variable to decouple BD-RIS constraints. We then propose a low-complexity penalty product Riemannian conjugate gradient descent (P-PRCGD) method, which combines the augmented Lagrangian (AL) approach with the product manifold gradient descent (PMGD) method to obtain a Karush-Kuhn-Tucker (KKT) solution. Simulation results confirm that BD-RIS-assisted systems significantly outperform conventional RIS-assisted systems in PLS performance.

Admission control is an effective solution to managing a wireless network with a large user set. It dynamically rejects users in bad conditions, and thereby guarantees high quality-of-service (QoS) for the others. Unfortunately, a frequently-varying admissible user set requests remarkable power to continually re-establish the transmission link. In this work, we explore the stability of admissible user set, and propose a joint long-term admission control and beamforming problem for a network with one multi-antenna base station (BS) and multiple single-antenna users. We consider the downlink transmission in a time period comprised of multiple time slices. By jointly optimizing the admissible users and the BS transmit beamformers in different time slices, we aim to balance the size of user set and the network total power cost for both QoS guarantee and user status switching. We develop two (offline and online) algorithms to find some efficient suboptimal solutions of this NP-hard problem. The offline algorithm requires all channel state information (CSI), and then solves the admissible users and beamformers in different time slices in one shot, based on successive upper-bound minimization (SUM). To support real-time data transmission, we further design an alternating direction method of multipliers (ADMM)-based online algorithm, which outputs the admissible users and beamformers in different time slices successively, utilizing the previous admissible user set, the actual value of current CSI, and the distribution of future CSI. Simulations validate the performance of the two algorithms, and show that the online algorithm is an efficient practical alternative to the offline algorithm.

Recently, intelligent reflecting surface (IRS)-assisted communication has gained considerable attention due to its advantage in extending the coverage and compensating the path loss with low-cost passive metasurface. This paper considers the uplink channel estimation for IRS-aided multiuser massive multi-input single-output (MISO) communications with one-bit ADCs at the base station (BS). The use of one-bit ADC is impelled by the low-cost and power efficient implementation of massive antennas techniques. However, the passiveness of IRS and the lack of signal level information after one-bit quantization make the IRS channel estimation challenging. To tackle this problem, we exploit the structured sparsity of the user-IRS-BS cascaded channels and develop three channel estimators, each of which utilizes the structured sparsity at different levels. Specifically, the first estimator exploits the elementwise sparsity of the cascaded channel and employs the sparse Bayesian learning (SBL) to infer the channel responses via the type-II maximum likelihood (ML) estimation. However, due to the one-bit quantization, the type-II ML in general is intractable. As such, a variational expectation-maximization (EM) algorithm is custom-derived to iteratively compute an ML solution. The second estimator utilizes the common row-structured sparsity induced by the IRS-to-BS channel shared among the users, and develops another type-II ML solution via the block SBL (BSBL) and the variational EM. To further improve the performance of BSBL, a third two-stage estimator is proposed, which can utilize both the common row-structured sparsity and the column-structured sparsity arising from the limited scattering around the users. Simulation results show that the more diverse structured sparsity is exploited, the better estimation performance is achieved, and that the proposed estimators are superior to state-of-the-art one-bit estimators.