Wireless sensor networks (WSNs) are widely applied in battlefield surveillance, where the data collection (e.g. target tracking or contention zone observation) employed by local sensor nodes needs to send to military bases for tactical decisions. Since data transmissions are susceptible to malicious attacks, jammers of adversaries can successfully block their victim’s communications by transmitting interfering signals to legitimate transmissions. Nowadays, owing to the ability to reconfigure the wireless propagation medium, reconfigurable intelligent surface (RIS) is regarded as an effective tool to enhance transmission performance, especially in the jamming context. This paper considers the anti-jamming communication tactical scenario of a solar-powered RIS network, in which the RIS is used to improve the uplink transmission performance between a wireless device (WD) and a base station (BS). We investigate the long-term anti-jamming communications of the WD powered by a solar energy harvester. Our objective is to jointly assign the optimal amount of transmission energy and the RIS phase shifts to maximize the data rate of the system in the long run. To this end, we formulate an anti-jamming communication optimization problem as a Markov decision process (MDP) framework and then design a deep Q network (DQN)-based algorithm to generate an optimal policy. As a result, the optimal resource allocation is achieved through trial-and-error interactions with environment by observing the predefined rewards and the network state transition. The Python simulation results conducted by averaging 10 ⁴ time slots show that the proposed algorithm is not only able to learn from environment, but also yields better performance than baseline schemes under network changes. Moreover, the performance of RIS communication schemes is verified to be superior to that of without-RIS communication schemes in the jamming context.

Non-orthogonal multiple access (NOMA) is considered a potential technique in fifth-generation (5G). Nevertheless, it is relatively complex when applying NOMA to a massive access scenario. Thus, in this paper, a hybrid NOMA/OMA scheme is considered for uplink wireless transmission systems where multiple cognitive users (CUs) can simultaneously transmit their data to a cognitive base station (CBS). We adopt a user-pairing algorithm in which the CUs are grouped into multiple pairs, and each group is assigned to an orthogonal sub-channel such that each user in a pair applies NOMA to transmit data to the CBS without causing interference with other groups. Subsequently, the signal transmitted by the CUs of each NOMA group can be independently retrieved by using successive interference cancellation (SIC). The CUs are assumed to harvest solar energy to maintain operations. Moreover, joint power and bandwidth allocation is taken into account at the CBS to optimize energy and spectrum efficiency in order to obtain the maximum long-term data rate for the system. To this end, we propose a deep actor-critic reinforcement learning (DACRL) algorithm to respectively model the policy function and value function for the actor and critic of the agent (i.e., the CBS), in which the actor can learn about system dynamics by interacting with the environment. Meanwhile, the critic can evaluate the action taken such that the CBS can optimally assign power and bandwidth to the CUs when the training phase finishes. Numerical results validate the superior performance of the proposed scheme, compared with other conventional schemes.

Nowadays, running applications on devices is restricted by limited computational capability and battery capacity, which needs the aid of mobile edge computing (MEC). This article aims to enhance MEC systems by employing revolutionary techniques: 1) adjusting wireless environment using reconfigurable intelligent surface (RIS), 2) exploiting opportunistically licensed spectrum using cognitive radio (CR), and 3) scavenging renewable energy using energy harvesting. We consider a RIS-assisted CR-MEC network where a secondary user (SU) powered by solar energy attempts to optimize computation. We study the computation rate maximization of SU, where it opportunistically either performs local computation or offloads data to the MEC server on a primary channel. A Markov decision process problem is formulated and then is firstly solved by a proposed deep policy gradient scheme, in which the system directly learns the policy from gradients of actions. To obtain higher stability, we subsequently propose a deep Q-learning scheme to derive a proper solution by maximizing the state-action value function. By taking the advantages of both policy-based and value-based methods, we further develop a deep actor-critic scheme, where an actor selects actions and a critic evaluates actions to acquire an optimal policy.