To fully utilize multiplexing and array gains of massive multiple-input multiple-output (MIMO), the downlink channel state information (CSI) must be acquired at the base station (BS). In frequency division duplexing (FDD) massive MIMO systems, the downlink CSI is generally estimated at the user equipment (UE) and then fed back to the BS. The huge number of antennas at the BS leads to overwhelming feedback overhead. To address this issue, we propose a framework, named CS-ReNet. In this framework, the CSI is first compressed at the UE based on the compressive sensing (CS) technology and then reconstructed at the BS using a deep learning (DL)-based signal recovery solver, named ReNet. We analyze the CSI quality at the BS in terms of the normalized mean-squared error (NMSE) and cosine similarity. Simulation results demonstrate that the proposed method outperforms the existing CS-based and some DL-based methods.

Faster-than-Nyquist (FTN) signaling can improve the bandwidth utilization. In this paper, we will provide a comprehensive survey on the topic. The history and the applications of FTN signaling are first introduced. Then, the basic principles and the system framework of FTN signaling are presented. Next, more details on transmitter and receiver optimization are discussed. Finally, the current research challenges on FTN signaling are identified and conclusions are provided.

Identification of line-of-sight (LOS) and non-line-of-sight (NLOS) propagation conditions is very useful in ultra wideband (UWB) localization systems. In the identification, supervised machine learning is often used, but it requires exorbitant efforts to maintain and label the LOS and NLOS database. In this paper, we apply unsupervised machine learning approach called “ expectation maximization (EM) for Gaussian mixture models (GMM)” to classify LOS and NLOS components. The key advantage of applying unsupervised machine learning is that it does not require any rigorous and explicit labeling of database at a certain location. Simulation results demonstrate that by using the proposed algorithm LOS and NLOS signals can be classified with 86.50 percent correct rate, 12.70 percent false negative, and 0.8 percent false positive rate. We also compare the proposed algorithm with the existing cutting-edge supervised machine learning algorithms in terms of computational complexity and signals classification performance.

This paper investigates a full-duplex (FD) secure communication system with the assistance of an intelligent reflecting surface (IRS). Compared with the traditional FD system, the IRS-assisted FD communication not only greatly improves the spectrum efficiency but also provides a new way to enhance physical layer security due to the overlapping of multiple signals at the eavesdropper. Furthermore, we consider a more practical scenario without perfect channel state information (CSI) because it is very difficult to obtain the perfect CSI especially for cascaded channels via IRS. In addition, the eavesdropper is usually passive and hidden which will not actively exchange CSI with the user, which leads to an obstacle for obtaining the perfect CSI of eavesdropping channels. To this end, a worst-case achievable security rate (ASR) optimization problem is formulated under the bounded CSI error model. Due to the existence of non-convexity and highly coupled variables, this problem is extremely challenging. To directly tackle the nonconvexity of the considered optimization problem, similar to successive convex approximation (SCA), we first transform the original problem into its equivalent convex optimization problem directly, and finally obtain the optimal solution of the original non-convex problem by iteratively calculating the convex optimization problem. On this basis, we iteratively solve the transmission beamforming and IRS phase shift through Alternate Optimization (AO). In particular, when optimizing the phase shift coefficient, a penalty convex-concave procedure solution is proposed. Simulation results demonstrate that our proposed robust secure beamforming scheme can effectively improve ASR, and also outperforms the nonrobust one.

This article studies the beamforming optimization for intelligent reflecting surface (IRS) assisted multiple-input single-output (MISO) wireless communication system. We establish a deep transfer learning (DTL)-based framework to learn how to optimize the phase shifts at the IRS side. Based on it, we also design a loss function to implement unsupervised training without a large number of labeled data samples. Finally, we extend the optimization problem to discrete phase shift constraint to solve the hardware limitation. The simulation verifies that the proposed DTL-based approach can achieve similar performance compared with upper bound while substantially reducing the computational complexity.

This paper proposes a novel active reconfigurable intelligent surface (RIS) assisted secure and robust cooperative beamforming for cognitive satellite-terrestrial networks. Specifically, an active RIS enhances interference at an eavesdropper and reduces interference at a satellite user both from a ground base station (BS), aiming to ensure the secure transmission performance of satellite users in a primary network. Meanwhile, the transmission performance of a terrestrial user in a secondary network is expected to be enhanced without affecting the satellite user. Furthermore, the impact of imperfect channel state information is also reconsidered due to the amplifying effect of active RIS. Accordingly, we focus on minimizing the transmit power at the ground BS while guaranteeing the achievable secrecy rate of the satellite user and the achievable rate of the terrestrial user. Based on the non-convex cooperative beamforming problem, an efficient alternating optimization scheme is developed. Finally, the numerical results demonstrate the potential of active RIS on power saving under secure and robust cognitive satellite-terrestrial transmission.