In this paper, we study the handover (HO) and power allocation problem in a two-tier heterogeneous network (HetNet), which consists of a macro base station and some millimeter-wave (mmWave) small base stations. We establish an HO management and power allocation scheme to maximize the overall throughput while reducing the HO frequency. In particular, considering the interrelationship among decisions made by different user equipments (UEs), we first model the HO and power allocation problem as a fully cooperative multi-agent task, in which all agents, i.e., UEs, have the same target. Then, to solve the multi-agent task and get decentralized policies for each UE, we develop a multi-agent reinforcement learning (MARL) algorithm based on the proximal policy optimization (PPO) method, by introducing the centralized training with decentralized execution framework. That is, we use global information to train policies for each UE, and after training is finished, each UE obtains a decentralized policy, which can be implemented only based on each UE's local observation. Specially, we introduce the counterfactual baseline to address the credit assignment problem in centralized learning. Due to the centralized training, the decentralized polices learned by multi-agent PPO (MAPPO) can work more cooperatively. Finally, the simulation results demonstrate that our method can achieve better performance comparing with other existing works.

With increasingly sophisticated electromagnetic environment of combat platforms, radar communication integration has attracted wide interests. In this letter, instead of designing a new integrated signal, we employ multiple-phase shift keying direct sequence spread spectrum (MPSK-DSSS) signal as radar communication integrated signal due to its advantage of outstanding correlation. On the one hand, the closed form expression of average ambiguity function is obtained, by which we can analyze the relationship between parameters of integrated signal and radar range resolution. On the other hand, to improve radar peak sidelobe level (PSL) influenced by random communication signals, we propose a joint optimization method based on sequence mapping at the transmitter and sidelobe suppression at the radar receiver. Simulation results show that the sidelobe levels of the optimized integrated signal is effectively suppressed at the cost of slight communication rate loss.

A reconfigurable intelligent surface (RIS) assisted scheme is proposed for direction-of-arrival (DOA) estimation in this work. In the scheme, the RIS is utilized to provide a reflected path, and the RIS-reflected signal is received by array antennas together with the direct-path signal. The model consisting of the direct-path signal and the RIS-reflected signal is proposed, and the Cramer-Rao Bound (CRB) is derived based on the signal model to assess the ultimate estimation performance. In order to enhance the estimation performance of RIS-assisted scheme, two possible strategies for the phases design of RIS are presented. The first possibility considers RIS phase shifts that maximize the coherence of signals from the direct path and the RIS-reflected path. The other possibility considers to obtain the phase shifts that minimize the CRB of DOA estimation in the proposed scheme through a manifold optimization method. Moreover, DOA estimation methods are proposed for the RIS-assisted scheme to validate the scheme feasibility. Target tracking is proposed as a practical application for the RIS-assisted scheme. In the end, both theoretical analysis and simulation results illustrate that the accuracy of DOA estimation and target tracking has been improved with the aid of RIS.

This paper considers an adversarial scenario between a legitimate eavesdropper and a suspicious communication pair. All three nodes are equipped with multiple antennas. The eavesdropper, which operates in a full-duplex model, aims to wiretap the dubious communication pair via proactive jamming. On the other hand, the suspicious transmitter, which can send artificial noise (AN) to disturb the wiretap channel, aims to guarantee secrecy. More specifically, the eavesdropper adjusts jamming power to enhance the wiretap rate, while the suspicious transmitter jointly adapts the transmit power and noise power against the eavesdropping. Considering the partial observation and complicated interactions between the eavesdropper and the suspicious pair in unknown system dynamics, we model the problem as an imperfect-information stochastic game. To approach the Nash equilibrium solution of the eavesdropping game, we develop a multi-agent reinforcement learning (MARL) algorithm, termed neural fictitious self-play with soft actor-critic (NFSP-SAC), by combining the fictitious self-play (FSP) with a deep reinforcement learning algorithm, SAC. The introduction of SAC enables FSP to handle the problems with continuous and high dimension observation and action space. The simulation results demonstrate that the power allocation policies learned by our method empirically converge to a Nash equilibrium, while the compared reinforcement learning algorithms suffer from severe fluctuations during the learning process.

Multiple-input multiple-output (MIMO) has been used in wireless communications to increase data rates via multiplexing or improve performance via diversity. Additionally, in radar systems, MIMO promises improved parameter identifiability and target resolution. Frequency diverse (FD)-MIMO radar can effectively distinguish targets that are closely spaced in the same angle cell by exploiting its range-angle-dependent transmit beamform. Therefore, in this paper, we first propose an integrated waveform design by embedding weighted, phase-modulated communication signals in the FD-MIMO radar waveform. Then, we derive the Cramer-Rao lower bound (CRLB) of the location estimation and analyze the communication performance achieved when the communication receiver extracts information from the integrated signals. Next, transmit beamforming is optimized to achieve the best tradeoff between the radar and communication performances of the system. Due to the nonconvexity of the optimization problem, we apply sequential parametric convex approximation (SPCA) and semidefinite relaxation (SDR) methods to solve the problem. The simulation results reveal the effects of some parameters, such as the frequency interval, symbol period, and communication symbol sequence, on radar and communication performance and demonstrate the tradeoff between radar and communication obtained by optimizing the transmit beamforming vector.