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.

With the increase of access point (AP) density and the exponential growth of mobile devices supported by ultradense networks (UDNs), user-centric overlapped clustering relying on AP cooperation is becoming a promising design principle for guaranteeing the quality of service (QoS) required by each user equipment. This ambitious goal can be achieved by the joint design of user-centric overlapped clustering and resource allocation. In this context, both the traffic-load balancing and the limited availability of orthogonal resource blocks (RBs) have to be carefully considered in UDNs. To tackle these challenges, we formulate a joint user-centric overlapped clustering and resource allocation problem with the goal of maximizing the system's spectral efficiency. To efficiently solve this problem, it is decoupled into two subproblems, which can be solved independently with the aid of our graph-theoretical framework. We first develop a novel distributed three-stage user-centric clustering solution, which is aware of the APs' traffic-loads. Then, based on the overlapped clusters constructed, we propose a novel two-stage graph-based user-centric resource allocation scheme for mitigating the resultant inter-cluster interference, despite the limited availability of orthogonal RBs. Our numerical results confirm the benefits of the proposed framework and show that it outperforms the benchmark solutions in terms of both its per area aggregated user rate and user rate.

Reconfigurable intelligent surfaces (RISs) are envisioned to be a disruptive wireless communication technique that is capable of reconfiguring the wireless propagation environment. In this paper, we study a free-space RIS-assisted multiple-input single-output (MISO) communication system in far-field operation. To maximize the received power from the physical and electromagnetic nature point of view, a comprehensive optimization, including beamforming of the transmitter, phase shifts of the RIS, orientation and position of the RIS is formulated and addressed. After exploiting the property of line-of-sight (LoS) links, we derive closed-form solutions of beamforming and phase shifts. For the non-trivial RIS position optimization problem in arbitrary three-dimensional space, a dimensional-reducing theory is proved. The simulation results show that the proposed closed-form beamforming and phase shifts approach the upper bound of the received power. The robustness of our proposed solutions in terms of the perturbation is also verified. Moreover, the RIS significantly enhances the performance of the mmWave/THz communication system.

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.

In Internet of Vehicles (IoV), information freshness is a significant indicator to indemnify road traffic safety, which is measured by Age of Information (AoI). In this article, we consider the coexistence scenario of vehicular user pairs and cellular users, where the base station (BS) acts as an agent to allocate channels and transmit power for vehicular user pairs. With the goal of minimizing the sum of the average AoI of all links and the average power consumption of all vehicular user pairs, we formulate this optimization problem as a discrete-time Markov decision process (MDP) problem and adopt the trust region policy optimization (TRPO) algorithm, which has the advantage of fast convergence and high stability. Then, an AoI-aware joint spectrum and power dynamic allocation scheme based on the TRPO algorithm is proposed. Simulation results show that the TRPO-based scheme significantly outperforms both the deep $Q$ network (DQN)-based scheme and the random scheme in terms of average cumulative reward, convergence speed, and stability.

User-centric (UC) clustering has recently emerged as a promising paradigm for enhancing the connectivity of mobile users by grouping an appropriate number of access points (APs), thus paving the way for seamlessly connected vehicular networks. However, high-velocity vehicular user equipment (VUE) may encounter overly frequent handovers (HOs) due to their multiple associations, which increases the risk of throughput-reduction, call dropping and energy wastage. We mitigate this problem by reducing the HO overhead imposed on the heterogeneous UC (HUC) cluster migration process of vehicular networks. Specifically, we first conceive a novel hybrid HUC cluster migration strategy, adaptively switching between horizontal and vertical HOs for supporting both vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. Then, a decision-making problem is formulated for balancing the benefits of HUC cluster migration and the total HO overhead, subject to realistic HUC clustering constraints. Since the problem formulated is a dynamic decision-making problem in the face of unknown vehicle mobility, we propose a sequential HUC cluster migration solution based on max-bipartite matching theory imposing a low complexity. As a design alternative, we also propose a holistic solution relying on model-free deep reinforcement learning (DRL). Finally, our numerical results reveal the superiority of the proposed cluster migration design in terms of striking a compelling trade-off between the per-user average data rate (PAR) and the number of HOs in different scenarios.