Harnessing the potential of smart grid networks relies on the efficient and secure communication between distributed energy resources (DERs) and the energy management system (EMS), particularly under variable channel conditions and adversarial interference. This interference is further exacerbated with the advent of artificial intelligence (AI)-driven adversarial devices capable of adaptively disrupting communication in real time. To address these challenges, in this study, we formulate the distributed channel access problem, incorporating dynamic channels and intelligent adversarial interference as a partially observable Markov game (POMG). Specifically, we propose a distributed framework based on a centralized training and distributed execution (CTDE) multi-agent reinforcement learning (MARL), enabling DERs to autonomously adapt to dynamic channels and mitigate intelligent interference using only local observations, i.e., without direct information sharing among DERs. Our simulation results indicate that, by integrating an advanced policy evaluation technique and a tailored utility maximization strategy, DERs can collaboratively optimize their transmission decisions, improving the network's aggregate packet success rate (APSR) and resilience. Additionally, the proposed framework outperforms existing methods, ensuring robust and scalable communication in smart grids under diverse conditions.

In this paper, we propose a novel framework to integrate the intelligent reconfigurable surface (IRS) in terahertz (THz) networks, with co-existing ultra-reliable low-latency communication (URLLC) and enhanced mobile broadband (eMBB) services. To meet URLLC latency constraints, the URLLC traffic is scheduled along with eMBB traffic, which poses a significant resource allocation challenge. To address this concern, in this paper, we propose a joint power and service allocation (JPSA) framework to maximize URLLC reliability while ensuring eMBB data rates. Furthermore, to address the challenging NP-hard mixed-integer nonlinear programming (MINLP) problem, we decompose the resource allocation problem into the URLLC power allocation and service allocation sub-problems. More specifically, we suggest a one-to-one matching game for service allocation. Our simulation results demonstrate that the proposed scheme outperforms baseline methods, particularly in terms of enhancing the reliability of URLLC user equipment (uUEs).

While the Internet of Vehicles (IoV) can revolutionize transportation systems through intelligent connectivity, a critical challenge in realizing this potential lies in ensuring efficient channel allocation in the IoV ecosystem, particularly considering dynamic channel conditions and adversarial jamming exacerbated by the emergence of artificial intelligence (AI)-based jamming. To address these challenges, in this study, we use distributed edge intelligence (DEI) to propose a distributed channel access mechanism for the vehicle-to-infrastructure (V2I) mode of IoV networks. Specifically, using an actor-critic-based multiagent reinforcement learning (MARL) framework with a common critic, we model the distributed channel access problem in V2I communications under varying channel conditions and an intelligent jamming device$(\tt {iJD})$interference as a decentralized partially observable stochastic game (Dec-POSG). Furthermore, by addressing challenges, such as partial observations, nonstationarity, and credit assignment, our proposed approach fosters collaboration among intelligent vehicles ($\tt {iV}$s) without direct communication. In addition, our unique counterfactual reasoning-aided action evaluation mechanism and a novel utility function design enable the$\tt {iV}$s to learn mixed collaborative-competitive channel access policies, thereby enhancing channel utilization, mitigating the impact of the$\tt {iJD}$, and improving the network’s sum cross-layer achievable rate (SCLAR).

In quasi-static wireless networks characterized by infrequent changes in the transmission schedules of user equipment (UE), malicious jammers can easily deteriorate network performance. Accordingly, a key challenge in these networks is managing channel access amidst jammers and under dynamic channel conditions. In this context, we propose a robust learning-based mechanism for channel access in multi-cell quasi-static networks under jamming. The network comprises multiple legitimate UEs, including predefined UEs (pUEs) with stochastic predefined schedules and an intelligent UE (iUE) with an undefined transmission schedule, all transmitting over a shared, time-varying uplink channel. Jammers transmit unwanted packets to disturb the pUEs’ and the iUE’s communication. The iUE’s learning process is based on the deep reinforcement learning (DRL) framework, utilizing a residual network (ResNet)-based deep Q-Network (DQN). To coexist in the network and maximize the network’s sum cross-layer achievable rate (SCLAR), the iUE must learn the unknown network dynamics while concurrently adapting to dynamic channel conditions. Our simulation results reveal that, with properly defined state space, action space, and rewards in DRL, the iUE can effectively coexist in the network, maximizing channel utilization and the network’s SCLAR by judiciously selecting transmission time slots and thus avoiding collisions and jamming.

This paper investigates a deep reinforcement learning (DRL)-based approach for managing channel access in wireless networks. Specifically, we consider a scenario in which an intelligent user device (iUD) shares a time-varying uplink wireless channel with several fixed transmission schedule user devices (fUDs) and an unknown-schedule malicious jammer. The iUD aims to harmoniously coexist with the fUDs, avoid the jammer, and adaptively learn an optimal channel access strategy in the face of dynamic channel conditions, to maximize the network's sum cross-layer achievable rate (SCLAR). Through extensive simulations, we demonstrate that when we appropriately define the state space, action space, and rewards within the DRL frame-work, the iUD can effectively coexist with other UDs and optimize the network's SCLAR. We show that the proposed algorithm outperforms the tabular Q-learning and a fully connected deep neural network approach.