Deep Reinforcement Learning-Based Channel Allocation for Wireless LANs With Graph Convolutional Networks

Abstract

For densely deployed wireless local area networks (WLANs), this paper proposes a deep reinforcement learning-based channel allocation scheme that enables the efficient use of experience. The central idea is that an objective function is modeled relative to communication quality as a parametric function of a pair of observed topologies and channels. This is because communication quality in WLANs is significantly influenced by the carrier sensing relationship between access points. The features of the proposed scheme can be summarized by two points. First, we adopt graph convolutional layers in the model to extract the features of the channel vectors with topology information, which is the adjacency matrix of the graph dependent on the carrier sensing relationships. Second, we filter experiences to reduce the duplication of data for learning, which can often adversely influence the generalization performance. Because fixed experiences tend to be repeatedly observed in WLAN channel allocation problems, the duplication of experiences must be avoided. The simulation results demonstrate that the proposed method enables the allocation of channels in densely deployed WLANs such that the system throughput increases. Moreover, improved channel allocation, compared to other existing methods, is achieved in terms of the system throughput. Furthermore, compared to the immediate reward maximization method, the proposed method successfully achieves greater reward channel allocation or realizes the optimal channel allocation while reducing the number of changes. INDEX TERMS Wireless LAN, channel allocation, deep reinforcement learning, graph convolutional networks, replay buffer.

Graph-Based Resource Allocation for Integrated Space and Terrestrial Communications

“…Close to linear gain in occurs with the increase in the number of transmitter–receiver pairs in the testing stage. Further developments are made by the DRL framework for channel allocation, based on GNNs in wireless local area networks (WLANs) with reduction in the number of states, as proposed in [ 34 ]. The graph is formed from the APs, and all of them that are able to detect each other’s signals are connected via edges.…”

Machine Learning in Beyond 5G/6G Networks—State-of-the-Art and Future Trends

“…Simulations show that the algorithm performs very well on realistic LTE and 5G channels and has great potential for B5G systems. In [111], a Markov decision process (MDP)-based algorithm for channel allocation is proposed. The model allocates channels in densely deployed WLANs, leading to enhancement of throughput.…”

Assigning channels in WLANs with channel bonding: A fair and robust strategy

“…The assignment is then performed to maintain fairness between channels and APs. Two algorithms based on reinforcement learning are proposed in [17] and [15]. They consist of a real-time exploration of new configurations and then exploiting the ones that offer good performance.…”

“…They consist of a real-time exploration of new configurations and then exploiting the ones that offer good performance. A dense WLAN scenario is studied in [17]. The proposed method relies on a graph convolutional network to extract the carrier sensing relationships between APs.…”