A large-scale fully digital receive antenna array can provide very high-resolution direction of arrival (DOA) estimation, but resulting in a significantly high RF-chain circuit cost. Thus, a hybrid analog and digital (HAD) structure is preferred. Two phase alignment (PA) methods, HAD PA (HADPA) and hybrid digital and analog PA (HDAPA), are proposed to estimate DOA based on the parametric method. Compared to analog PA (APA), they can significantly reduce the complexity in the PA phases. Subsequently, a fast root multiple signal classification HDAPA (root-MUSIC-HDAPA) method is proposed specially for this hybrid structure to implement an approximately analytical solution. Due to the HAD structure, there exists the effect of direction-finding ambiguity. A smart strategy of maximizing the average receive power is adopted to delete those spurious solutions and preserve the true optimal solution by linear searching over a set of limited finite candidate directions. This results in a significant reduction in computational complexity. Eventually, the Cramer-Rao lower bound (CRLB) of finding emitter direction using the HAD structure is derived. Simulation results show that our proposed methods, root-MUSIC-HDAPA and HDAPA, can achieve the hybrid CRLB with their complexities being significantly lower than those of pure linear searching-based methods, such as APA.

In this article, we investigate the anti-jamming problem with joint channel and power allocation for unmanned aerial vehicle (UAV) networks. In particular, we focus on avoiding both mutual interference among UAVs and external malicious jamming to maximize the system Quality of Experience (QoE) relevant to the power consumption. To simultaneously capture the competition and coordination among UAVs, we first model the problem as a local interaction Markov game and then prove it as an exact potential game with at least one Nash equilibrium. Next, we propose a collaborative multiagent layered Q learning (MALQL)-based anti-jamming communication algorithm to reduce the high dimensionality of the action space and analyze the asymptotic convergence of the proposed algorithm. Simulation results show the effectiveness of the proposed algorithm, which outperforms the traditional multiagent $Q$ learning algorithm when suffering from different jamming strategies.

The fifth generation and beyond wireless communication will support vastly heterogeneous services and use demands such as massive connection, low latency and high transmission rate. Network slicing has been envisaged as an efficient technology to meet these diverse demands. In this paper, we propose a dynamic virtual resources allocation scheme based on the radio access network (RAN) slicing for uplink communications to ensure the quality-of-service (QoS). To maximum the weighted-sum transmission rate performance under delay constraint, formulate a joint optimization problem of subchannel allocation and power control as an infinite-horizon average-reward constrained Markov decision process (CMDP) problem. Based on the equivalent Bellman equation, the optimal control policy is first derived by the value iteration algorithm. However, the optimal policy suffers from the widely known curse-of-dimensionality problem. To address this problem, the linear value function approximation (approximate dynamic programming) is adopted. Then, the subchannel allocation Q-factor is decomposed into the per-slice Q-factor. Furthermore, the Q-factor and Lagrangian multipliers are updated by the use of an online stochastic learning algorithm. Finally, simulation results reveal that the proposed algorithm can meet the delay requirements and improve the user transmission rate compared with baseline schemes.

In this paper, given the beamforming vector of confidential messages and the artificial noise (AN) projection matrix and total power constraint, a power allocation(PA) strategy of maximizing secrecy rate (Max-SR) is proposed for secure directional modulation networks. By the method of Lagrange multiplier, the analytical expression of the proposed PA strategy is derived. To confirm the benefit from the Max SR-based PA strategy, we take all the null-space projection beamforming scheme as an example and derive its closed-form expression of optimal PA strategy. From simulation results, we find the following facts: in the medium and high signal-to-noise ratio (SNR) regions. compared with three typical PA parameters such β = 0.1, 0.5, and 0.9, the optimal PA shows a substantial SR performance gain with maximum gain percent up to more than 60%. In addition, as the PA factor increases from 0 to 1, the achievable SR increase accordingly in the low SNR region, whereas it first increases and the decreases in the medium and high SNR regions, where the SR can be approximately viewed as a convex function of the PA factor. Finally, as the number of antennas increases, the optimal PA factor become large and tends to one in the medium and high SNR regions. In other words, the contribution of AN to SR can be trivial in such a situation.

With the development of federated learning (FL), mobile devices (MDs) are able to train their local models with private data and send them to a central server for aggregation, thereby preventing leakage of sensitive raw data. In this paper, we aim to improve the training performance of FL systems in the context of wireless channels and stochastic energy arrivals of each MD. To this purpose, we dynamically optimize MDs transmission power and training task scheduling. We first model this dynamic programming problem as a constrained Markov decision process (CMDP). Due to high dimensions of the proposed CMDP problem, we propose online stochastic learning methods to simplify the CMDP and design online algorithms to obtain an efficient policy for all MDs. Since there are long-term constraints in our CMDP, we utilize a Lagrange multipliers approach to tackle this issue. Furthermore, we prove the convergence of the proposed online stochastic learning algorithm. Numerical results indicate that the proposed algorithms can achieve better performance than the benchmark algorithms.

Vehicular edge computing (VEC) has become a promising enabler for ultrareliable and low-latency communications (URLLC) vehicular networks by providing computational resources for task offloading. In this article, we investigate an online task offloading problem for heterogeneous VEC (HVEC) network in the face of unknown environment dynamics. To overcome the unavailability of state information, we aim for minimizing the expectation of total offloading energy consumption while satisfying stringent delay requirements by learning the relationship between historical observations and rewards. Hence, this problem constitutes a contextual multiarmed bandit (MAB) problem. By grouping users according to their task preferences, we propose a contextual clustering of bandits-based online vehicular task offloading (CBTO) solution, which is aware of the task popularity. Simulation results reveal that the proposed solution outperforms other contextual and context-free benchmarkers in terms of both offloading energy consumption and delay performance.