Owing to their controlled mobility, extensive coverage and low cost, unmanned aerial vehicles (UAVs) have significant promise in the field of military, civilian and commercial applications, which has sparked a lot of research interest. This paper explores the application of the UAV in the symbiotic radio (SR) system. Specifically, we study the multi-user SR system assisted by the UAV and intelligent reflecting surface (IRS), and the modulation scheme with high flexibility is adopted to embed the IRS symbols into the UAV signals. First, we focus on minimizing the maximum bit error rate (BER) among all ground devices (GDs) while meeting the minimum communication rate requirements by jointly designing the active transmit beamforming and flight trajectory of the UAV as well as IRS reflective phase shifts. Then, we develop an iterative algorithm based on block coordinate descent (BCD) technology to address this optimization problem. Specifically, the optimization problem is decoupled into three sub-problems to be alternately solved, which are solved by invoking the semidefinite relaxation (SDR) and successive convex approximation (SCA) techniques. Moreover, the convergence performance and computational complexity of the proposed algorithm are analyzed. Finally, simulation results show the proposed algorithm can significantly improve the quality of IRS transmission.

In this letter, we consider a downlink non-orthogonal multiple access (NOMA) system where a base station (BS) simultaneously communicates with multiple users. These users are grouped on multiple unequal-bandwidth subchannels. The power and bandwidth allocation are jointly optimized in order to maximize the system energy efficiency (EE) under transmit power constraint at the BS and minimum rate constraints for individual users. The optimization problem is nonconvex because of the fractional objective function and the nonconvex constraints. To deal with the original problem, it is first converted into an equivalent DC (difference of two convex functions) program by introducing slack variables, and then an iterative algorithm based on the constrained concave-convex procedure is proposed to solve it. Our numerical results demonstrate that the proposed algorithm has better EE performance compared to the NOMA scheme with equal-bandwidth allocation and conventional orthogonal multiple access scheme.

This article proposes a novel covert communication framework utilizing movable antennas (MAs) to enable covert communications in which the evading detection eavesdropper aided by a reconfigurable intelligent surface (RIS). The trajectories of the MAs over the entire time slot, transmit beamforming, and the phase shift of the RIS in each time slot are jointly optimized to improve the covert rate. Specifically, the movement trajectories of the MAs are modeled as a Markov decision process (MDP), optimized by developing a novel deep reinforcement learning (DRL) approach. Furthermore, an alternating optimization (AO) algorithm is designed to jointly optimize the beamforming and phase. In particular, penalty-based two-layer iterative algorithm is proposed to guarantee that the solution satisfies the rank-one constraints. Numerical results show that the proposed MA-assisted covert communications system significantly outperforms conventional fixed-position antenna (FPA) schemes in terms of covert rate.

Due to the high propagation path loss in the millimeter wave (mm-wave) system, it is necessary to use the large antenna array for obtaining the high beamforming gain in mm-wave systems. In order to reduce the cost and complexity of hardware, an architecture called hybrid precoding is proposed, which divides the precoding operation into the analog domain and the digital domain. However, this poses a challenge for channel estimation in mm-wave systems. In addition, due to the great amount of users and antennas, the feedback overhead of channel estimation is also an issue that needs to be considered. In the letter, we consider the mm-wave massive multiple-input-multiple-output (MIMO) system and propose a federated learning (FL)-base channel state information (CSI) estimation and feedback (FCEF) scheme, where each user trains the local model through the local dataset and exchanges the model parameters with the base station (BS). Thanks to upload model parameters rather than huge channel data, the FCEF scheme has lower transmission overhead than centralized learning (CL). The architecture of the proposed FCEF model is described in the letter, which consists of a CSI estimation and compression (CEC) network and a CSI recovery (CR) network. Numerical results demonstrate that the proposed FCEF scheme has good performance for CSI estimation and feedback in mm-wave MIMO systems, which is close to that of the CL-based scheme while ensuring lower transmission overhead.

In this letter, we investigate the beamforming design for millimeter wave (mmWave) joint unicast and multicast (JUM) systems, in which the common and private signals are transmitted simultaneously. Due to the hardware limitation, the conventional fully digital beamforming methods is infeasible. We propose a suboptimal low-complexity hybrid zero-forcing (ZF) beamforming scheme for the mmWave JUM systems. The analog precoder and combiner are firstly optimized to maximize the effective channel gains. A ZF algorithm is then used to decide the unicast beamforming directions for cancelling the inter-user interference so that the optimization problem is reduced.With the unicast beamforming directions, the unicast power allocation and multicast beamformer are finally optimized by employing successive convex approximation (SCA) iteratively. The simulation results demonstrate that the sum-rate of the proposed algorithm is slightly inferior to the fully digital beamforming algorithm, but outperforms the orthogonal multiple access (OMA) scheme.