Deep Learning-Based Resource Allocation Scheme for Heterogeneous NOMA Networks

Abstract

In this paper, we consider downlink power-domain non-orthogonal multiple access (NOMA) in heterogeneous networks (HetNets) and propose resource allocation algorithms for subchannels and transmit powers to improve the sum rate performance while satisfying a minimum data-rate requirement. The proposed subchannel allocation scheme is an iterative algorithm to achieve NOMA gain by selecting the best subchannel from the viewpoint of each user, without the constraint of the number of NOMA users on each subchannel. The proposed power allocation scheme for NOMA is a deep neural network (DNN)-based unsupervised learning algorithm, where the output of the subchannel allocation scheme is used, and unsupervised learning is adopted to reduce the training complexity, as compared to supervised learning. Through simulation, we show that the proposed subchannel allocation scheme provides better sum rates compared to the conventional two-sided matching scheme, and the proposed power allocation scheme achieves a comparable sum rate to the interior point method (IPM).

Deep Learning-Based Cross-Layer Power Allocation for Downlink Cell-Free Massive Multiple-Input–Multiple-Output Video Communication Systems

“…In order to gain a deeper understanding of the fundamental problem structure, ref. [21] introduces variables {µ kl } in (12), which are related to the square roots of the power allocation coefficients {ρ il }. In this research, the optimization problems under consideration do not enforce constraints on µ kl ≥ 0, following a similar approach as discussed in [25].…”

“…Deep learning [8][9][10][11] plays a crucial role in developing a learning-based power allocation model. By leveraging deep learning techniques, we can effectively capture complex patterns and correlations within the system, leading to more accurate and efficient power/resource allocation decisions [12][13][14][15].…”