Optimal Resource Allocation for Random Multiple Access Oriented SCMA-V2X Networks
Bo Zhao, Jiajia Liu, Bomin Mao, Shouqing Li
Abstract
In this paper, an optimal resource allocation scheme is proposed for random multiple access (RMA) oriented sparse code multiple access (SCMA) based vehicle-to-everything (V2X) networks. We consider an uplink RMA oriented SCMA, where vehicle-to-infrastructure (V2I) users and vehicle-to-vehicle (V2V) users are considered to randomly choose codebooks for transmission from a shared codebook pool. To avoid mutual interference between V2I users and V2V users, the total bandwidth is decomposed into two parts for transmission of V2I users and V2V users. A joint user-codebook selection and bandwidth allocation optimization problem is formulated to maximize the sum rate of V2I users while guaranteeing the reliability requirement of V2V users. This problem is a mixed-integer programming (MIP) problem with probabilistic constraint, thus it is impractical to directly solve. To solve the problem, the probabilistic constraint is converted into a non-probabilistic one by approximation. Subsequently, efficient but suboptimal staged algorithms are proposed to solve the joint optimization problem. Firstly, a new decoupled Q-learning based user-codebook selection algorithm (DQL-UCSA) is proposed to find the optimal user-codebook selection relationships, which completely address codebook collision problem. Under the optimal user-codebook selection relationships determined, the joint optimization problem is transformed into a single objective bandwidth allocation problem. Then, we solve the single objective problem using a convex optimization method, which maximizes the sum rate of V2I users. Simulation results show the DQL-UCSA can converge quickly and enable a significant performance improvement by addressing codebook collisions. Besides, the proposed RMA-SCMA with optimal bandwidth allocation (OBA) is significantly superior to conventional schemes in terms of sum rate.