In a distributed wireless network with a large number of nodes, competitive access of nodes may result in the deterioration of throughput and energy. Therefore, in this paper we propose an intelligent access algorithm based on the mean field game (MFG). First, we formulate the competitive access process between nodes as a game Query ID="Q1" Text="Please check and confirm that the authors and their respective affiliations have been correctly identified and amend if necessary." process by a stochastic differential game model, which maximizes the energy efficiency of nodes and obtain the optimal behavior strategy while meeting the requirements of channel access. However, as the number of nodes increases, the dimension of the matrix used to characterize the interaction between nodes becomes too large, which increases the complexity of the solution procedure. Therefore, we introduce the MFG and the interaction between nodes can be approximately transformed into the interaction between nodes and the mean field, which not only reduces the complexity, but also reduces the computational overhead. In addition, the HJB-FPK equation is solved to obtain the Nash equilibrium of the MFG. Finally, a backoff strategy based on the Markov model is proposed, and the node obtains the corresponding backoff strategy according to the network situation and its own state. Simulation results show that the proposed algorithm has good performances on optimizing network throughput and energy efficiency for a large scale multi-hop wireless network.
Internet of Things nodes need new charging methods, and RF energy is not well utilized. Backscatter can harvest RF energy from the environment and use the idle spectrum to realize wireless power supply communication. In this article, each enhanced backscatter device (eBD) can select from a variety of cooperative receivers (CR) to provide different transmission services in three modes: energy harvesting (EH), relay communication (RelayCom), and backscatter communication (BackCom). To analyze the throughput of the secondary network, we model the three working modes, respectively. We describe the problem as the objective function of three variables: the period α of EH when there are no obstacles, period
μ
of RelayCom with obstacles, and period
γ
of BackCom with obstacles. The ideal time allocation strategy is then investigated. Then, we apply the particle swarm optimization algorithm to obtain a solution that maximizes the throughput. The simulation results show that compared to the communication network using backscatter or relay protocol alone, the proposed backscatter-assisted wireless relay communication model can greatly improve the throughput and coverage of the secondary system.
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