Work

Deep Reinforcement Learning for the Coexistence of eMBB and URLLC Services in NOMA Systems

Year: 2025

Type: conference-paper

Abstract: This paper introduces a novel resource allocation framework for the coexistence of enhanced mobile broadband (eMBB) and ultra-reliable low latency communication (URLLC) services in a non-orthogonal multiple access (NOMA) system. The proposed solution utilizes a two-layer deep reinforcement learning (DRL) algorithm to optimize the dynamic allocation of subbands and power for both URLLC and eMBB users. In the top layer, a deep Q-network (DQN) is employed for discrete subband allocation while the bottom layer uses a multi-agent DRL approach to refine the continuous power allocation. This hierarchical approach ensures that low latency and reliability requirements of the URRLC services are met, without compromising the high throughput demands of eMBB. Extensive simulations validate the efficiency of the proposed algorithm, demonstrating improved system performance in terms of power and subband optimization for both user types.

Source: International Conference on Communications

DOI: 10.1109/icc52391.2025.11161603

Authors: Joseph Doumit, Marie-Josépha Youssef, Charbel Abdel Nour, Catherine Douillard

Institutions: Centre National de la Recherche Scientifique, Laboratoire des Sciences et Techniques de l’Information de la Communication et de la Connaissance, IMT Atlantique, Capital Fund Management (France)

Language: English

Topic: Advanced Wireless Communication Technologies

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: green

Funder: Agence Nationale de la Recherche

Award: Power-efficient radio interface for sub-7GHz distributed massive MIMO infrastructUres