Cooperative Channel Assignment for VANETs Based on Dual Reinforcement Learning

Dynamic channel assignment(DCA)is significant for extending vehicular ad hoc network(VANET)capacity and mitigating congestion.However,the un-known global state information and the lack of centralized control make channel assignment performances a challenging task in a distributed vehicular direct communication scenario.In our preliminary field test for communication under V2X scenario,we find that the existing DCA technology cannot fully meet the communication performance requirements of VANET.In order to improve the communication performance,we firstly demonstrate the feasibility and potential of reinforcement learning(RL)method in joint channel selection decision and access fallback adaptation design in this paper.Besides,a dual reinforcement learning(DRL)-based cooperative DCA(DRL-CDCA)mechanism is proposed.Specifically,DRL-CDCA jointly optimizes the decision-making behaviors of both the channel selection and back-off adaptation based on a multi-agent dual reinforcement learning framework.Besides,nodes locally share and incorporate their individual rewards after each communication to achieve regional consistency optimization.Simulation results show that the proposed DRL-CDCA can better reduce the one-hop packet delay,improve the packet delivery ratio on average when compared with two other existing mechanisms.

The Dynamic Channel Allocation Scheme Based on Stratification and Simulated Annealing Method

This paper proposes a dynamic channel allocation scheme based on cognitive radio （CR）. Firstly, the channel probing based on MMSE criterion is implemented, with which the probability distribution of channels in use by the primary user is given. Next, take the distances between the CR users and the primary user as basis to stratify the CR users, among the layers; the simulated annealing （SA） algorithm is used to implement the channel assigmnent. This algorithm differs from the well-known 0-1 matrix based allocation scheme, and keeps a good tradeoff between complexity, capacity as well as the fairness problems. The simulation results show that this algorithm can improve the allocation efficiency effectively.

Cooperative channel assignment for VANETs based on multiagent reinforcement learning

Dynamic channel assignment(DCA)plays a key role in extending vehicular ad-hoc network capacity and mitigating congestion.However,channel assignment under vehicular direct communication scenarios faces mutual influence of large-scale nodes,the lack of centralized coordination,unknown global state information,and other challenges.To solve this problem,a multiagent reinforcement learning(RL)based cooperative DCA(RLCDCA)mechanism is proposed.Specifically,each vehicular node can successfully learn the proper strategies of channel selection and backoff adaptation from the real-time channel state information(CSI)using two cooperative RL models.In addition,neural networks are constructed as nonlinear Q-function approximators,which facilitates the mapping of the continuously sensed input to the mixed policy output.Nodes are driven to locally share and incorporate their individual rewards such that they can optimize their policies in a distributed collaborative manner.Simulation results show that the proposed multiagent RL-CDCA can better reduce the one-hop packet delay by no less than 73.73%,improve the packet delivery ratio by no less than 12.66%on average in a highly dense situation,and improve the fairness of the global network resource allocation.

Q-learning for dynamic channel assignment in cognitive wireless local area network with fibre-connected distributed antennas

Cognitive wireless local area network with fibre-connected distributed antennas （CWLAN-FDA） is a promising and efficient architecture that combines radio over fiber, cognitive radio and distributed antenna technologies to provide high speed/high capacity wireless access at a reasonable cost. In this paper, a Q-learning approach is applied to implement dynamic channel assignment （DCA） in CWLAN-FDA. The cognitive access points （CAPs） select and assign the best channels among the industrial, scientific, and medical （ISM） band for data packet transmission, given that the objective is to minimize external interference and acquire better network-wide performance. The Q-learning method avoids solving complex optimization problem while being able to explore the states of a CWLAN-FDA system during normal operations. Simulation results reveal that the proposed strategy is effective in reducing outage probability and improving network throughput.