Research Article
On the Performance of an ELAA-Enabled Near-Field Monostatic Backscatter Communication System
@INPROCEEDINGS{10.1007/978-3-032-44094-5_6, author={Muhammad Abdullah Khan and Muhammad Usman and Haejoon Jung}, title={On the Performance of an ELAA-Enabled Near-Field Monostatic Backscatter Communication System}, proceedings={Industrial Networks and Intelligent Systems. 12th EAI International Conference, INISCOM 2026, Da Nang, Vietnam, February 26--27, 2026, Proceedings}, proceedings_a={INISCOM}, year={2026}, month={11}, keywords={Extremely large antenna array Deep reinforcement learning Backscatter communication Service fairness}, doi={10.1007/978-3-032-44094-5_6} }- Muhammad Abdullah Khan
Muhammad Usman
Haejoon Jung
Year: 2026
On the Performance of an ELAA-Enabled Near-Field Monostatic Backscatter Communication System
INISCOM
Springer
DOI: 10.1007/978-3-032-44094-5_6
Abstract
Next-generation wireless systems are anticipated to exploit extremely large antenna arrays (ELAAs) operating in the near-field regime to realize enhanced spatial focusing and advanced interference management for supporting emerging immersive application paradigms. However, the rapid growth of battery-constrained IoT ecosystems demands scalable and practical energy-efficient solutions to meet their massive connectivity and reliability requirements. Backscatter communication systems, which facilitate battery-free data transmission by harvesting energy from incident electromagnetic radiation and reflecting modulated information, have been deemed a promising energy-efficient solution for future networks. Thus, incorporating ELAAs into backscatter communication presents a compelling approach for effective energy transfer and interference management in IoT deployments. Motivated by this, we propose a novel ELAA-enabled monostatic backscatter communication system with backscatter tags distributed in the near-field of the base station. To characterize the system performance, we investigate the rate fairness concern by formulating a min-rate maximization problem under a transmit-power constraint and propose a deep reinforcement learning (DRL)-based proximal policy optimization (PPO) framework to solve the challenging non-convex problem. Simulation results demonstrate the performance gains achieved by the proposed system and validate the effectiveness of the optimization framework.