Multi-Agent DRL for Cooperative Resource Allocation in C-NOMA-Enabled Multi-UAV Networks With 2-D Hybrid Beamforming
TL;DR
A cooperative resource-allocation framework for C-NOMA-enabled multi-uncrewed aerial vehicle (UAV) millimeter-wave (mmWave) networks with two-dimensional (2D) HB and an EE-maximization problem that jointly considers user subclustering, power allocation (PA), and subchannel assignment (SA) under transmit-power and SIC constraints is developed.
Abstract
Sixth-generation (6G) wireless networks require massive connectivity, high spectral efficiency (SE), and energy efficiency (EE). Although conventional non-orthogonal multiple access (NOMA) improves spectrum utilization by allowing multiple users to share the same subchannel through power-domain multiplexing, applying NOMA to large user groups significantly increases successive interference cancellation (SIC) complexity and intra-group interference. To address this limitation, clustered NOMA (C-NOMA) groups users into small subclusters where SIC is performed over fewer users, thereby improving scalability while reducing decoding complexity. Combining C-NOMA with hybrid beamforming (HB) further enhances SE and lowers power consumption by serving each subcluster through a dedicated analog beam with fewer radio-frequency chains. In this paper, we develop a cooperative resource-allocation framework for C-NOMA-enabled multi-uncrewed aerial vehicle (UAV) millimeter-wave (mmWave) networks with two-dimensional (2D) HB. UAVs equipped with 2D uniform planar array antennas serve as aerial base stations to enhance line-of-sight (LoS) connectivity. We formulate an EE-maximization problem that jointly considers user subclustering, power allocation (PA), and subchannel assignment (SA) under transmit-power and SIC constraints. User subclustering is first performed through head selection and channel-disparity-based pairing to enable efficient C-NOMA transmission. Given the resulting structure, the 2D HB and joint PA/SA optimization problem is solved via a two-stage approach: a heuristic 2D HB construction followed by a multi-agent deep deterministic policy gradient (MADDPG) algorithm for cooperative PA and SA optimization under centralized training and decentralized execution (CTDE). Simulation results demonstrate that the proposed framework consistently improves EE over representative benchmarks across different network configurations.