2022/04/09 by Jithin Jagannath, Jagannath, Jithin, Kian Hamedani +7
Computer Science · Engineering · #Advanced MIMO Systems Optimization #FOS: Computer and information sciences #Full-Duplex Wireless Communications #Machine Learning (cs.LG) #Networking and Internet Architecture (cs.NI) #Radio Frequency Integrated Circuit Design #cs.LG #cs.NI
paper · pdf · doi:10.48550/arxiv.2204.04507
To appear in Proceedings of ACM Workshop on Wireless Security and Machine Learning (WiseML 2022)
arxiv created 2022/04/09 · openalex publication_date 2022/04/09 · arxiv updated 2022/04/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Dynamic resource allocation plays a critical role in the next generation of intelligent wireless communication systems. Machine learning has been leveraged as a powerful tool to make strides in this domain. In most cases, the progress has been limited to simulations due to the challenging nature of hardware deployment of these solutions. In this paper, for the first time, we design and deploy deep reinforcement learning (DRL)-based power control agents on the GPU embedded software defined radios (SDRs). To this end, we propose an end-to-end framework (MR-iNet Gym) where the simulation suite and the embedded SDR development work cohesively to overcome real-world implementation hurdles. To prove feasibility, we consider the problem of distributed power control for code-division multiple access (DS-CDMA)-based LPI/D transceivers. We first build a DS-CDMA ns3 module that interacts with the OpenAI Gym environment. Next, we train the power control DRL agents in this ns3-gym simulation environment in a scenario that replicates our hardware testbed. Next, for edge (embedded on-device) deployment, the trained models are optimized for real-time operation without loss of performance. Hardware-based evaluation verifies the efficiency of DRL agents over traditional distributed constrained power control (DCPC) algorithm. More significantly, as the primary goal, this is the first work that has established the feasibility of deploying DRL to provide optimized distributed resource allocation for next-generation of GPU-embedded radios.