In this work, we study the secrecy performance of a reconfigurable intelligent surfaces (RIS)-aided wireless communication system in the presence of an eavesdropping user. Specifically, we assume that the RIS is placed between the source and the legitimate user to create a smart environment and used to improve the link security. In particular, analytical results for the secrecy outage probability (SOP) is derived. We also provide an asymptotic analysis to investigate the effect of the main parameters on the secrecy performance of our proposed system, such as the number of the reflectors in the RIS and the average signal-to-noise ratios. Finally, we verify our analytical results via simulations. Results show the positive effect of utilizing the RIS for enhancing wireless systems secrecy performance.

By considering the different cumulant combinations of the 2FSK, 4FSK, 2PSK, 4PSK, 2ASK and 4ASK, this paper established new identification parameters to achieve the recognition of those digital modulations. The Deep Neural Network (DNN) was also employed to improve the recognition rate, which was designed to classify the signal based on the distinct feature of each signal type that was extracted with high order cumulants. Extensive simulations demonstrated the exceptional classification performance for new key features based on high order cumulants. The over-all success rate of the proposed algorithm was over 99% at the signal to noise ratio (SNR) of -5dB and 100% at the SNR of -2dB. The results of the experiments also showed the robustness of the proposed method for a variety of conditions, such as frequency offset, multi-path and so on.

A near-field wideband beamforming scheme is investigated for reconfigurable intelligent surface (RIS) assisted multiple-input multiple-output (MIMO) systems, in which a deep learning-based end-to-end (E2E) optimization framework is proposed to maximize the system spectral efficiency. To deal with the near-field double beam split effect, the base station is equipped with frequency-dependent hybrid precoding architecture by introducing sub-connected true time delay (TTD) units, while two specific RIS architectures, namely true time delay-based RIS (TTD-RIS) and virtual subarray-based RIS (SA-RIS), are exploited to realize the frequency-dependent passive beamforming at the RIS. Furthermore, the efficient E2E beamforming models without explicit channel state information are proposed, which jointly exploits the uplink channel training module and the downlink wideband beamforming module. In the proposed network architecture of the E2E models, the classical communication signal processing methods, i.e., polarized filtering and sparsity transform, are leveraged to develop a signal-guided beamforming network. Numerical results show that the proposed E2E models have superior beamforming performance and robustness to conventional beamforming benchmarks. Furthermore, the tradeoff between the beamforming gain and the hardware complexity is investigated for different frequency-dependent RIS architectures, in which the TTD-RIS can achieve better spectral efficiency than the SA-RIS while requiring additional energy consumption and hardware cost.

Vehicular edge computing (VEC) effectively reduces the computing load of vehicles by offloading computing tasks from vehicle terminals to edge servers. However, offloading of tasks increase in quantity the transmission time and energy of the network. In order to reduce the computing load of edge servers and improve the system response, a shared offloading strategy based on deep reinforcement learning is proposed for the complex computing environment of Internet of Vehicles (IoVs). The shared offloading strategy exploits the commonality of vehicles task requests, similar computing tasks coming from different vehicles can share the computing results of former task submitted. The shared offloading strategy can be adapted to the complex scenarios of the IoVs. Each vehicle can share the offloading conditions of the VEC servers, and then adaptively select three computing modes: local execution, task offloading, and shared offloading. In this article, the network state and offloading strategy space are the input of the deep reinforcement learning (DRL). Through the DRL, each task unit selects the offloading strategy with the optimal energy consumption at each time period in the dynamic IoVs transmission and computing environment. Compared with the existing proposals and DRL-based algorithms, it can effectively reduce the delay and energy consumption required for tasks offloading.

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.