Multi-Agent Reinforcement Learning for Joint Channel Assignment and Power Allocation in Platoon-Based C-V2X Systems

We consider the problem of joint channel assignment and power allocation in underlaid cellular vehicular-to-everything (C-V2X) systems where multiple vehicle-to-infrastructure (V2I) uplinks share the time-frequency resources with multiple vehicle-to-vehicle (V2V) platoons that enable groups of connected and autonomous vehicles to travel closely together. Due to the nature of fast channel variant in vehicular environment, traditional centralized optimization approach relying on global channel information might not be viable in C-V2X systems with large number of users. Utilizing a reinforcement learning (RL) approach, we propose a distributed resource allocation (RA) algorithm to overcome this challenge. Specifically, we model the RA problem as a multi-agent system. Based solely on the local channel information, each platoon leader, who acts as an agent, collectively interacts with each other and accordingly selects the optimal combination of sub-band and power level to transmit its signals. Toward this end, we utilize the double deep Q-learning algorithm to jointly train the agents under the objectives of simultaneously maximizing the V2I sum-rate and satisfying the packet delivery probability of each V2V link in a desired latency limitation. Simulation results show that our proposed RL-based algorithm achieves a close performance compared to that of the well-known exhaustive search algorithm.Comment: 6 pages, 4 figure

User grouping and power allocation algorithm for UAV-aided NOMA network

Copyright © 2020 The Author(s). To improve the transmission flexibility of emergency communication scenarios, a joint user grouping and power allocation algorithm was proposed for UAV-aided non-orthogonal multiple access (NOMA) system. A sum-rate maximization resource allocation algorithm was formulated under the constraints of the maximum transmit power and the user grouping. To solve the non-convex problem, a graph-based user grouping algorithm was proposed to minimize the relative distance between the UAV and users. Based on the grouping result, the power allocation sub-problem was transformed into a convex one by using the auxiliary-variable approach. Simulation results demonstrate that the proposed algorithm has better performance in terms of sum rates.The National Key Research and Development Program of China(2019YFC1511300);The Natural Science Foundation of Chongqing(cstc2019jcyj-msxmX0666);The Natural Science Foundation of Chongqing(cstc2019jcyj-xfkxX0002);The Open Project of Shaanxi Key Laboratory of Information Communication Network and Security(ICNS201904