A Deep Unfolding-Based Scalarization Approach for Power Control in D2D Networks

TL;DR

This paper introduces a deep unfolding-based scalarization method for power control in D2D networks, transforming a non-convex optimization problem into a trainable neural network that achieves comparable performance to existing benchmarks with lower complexity.

Contribution

It proposes a novel deep unfolding approach to optimize weighted-sum-rate in D2D networks, enabling efficient training and strong generalization over network parameters.

Findings

Unfolded algorithm performs comparably to FPLinQ in most cases.

Lower computational complexity than traditional methods.

Strong generalizability across different network sizes and conditions.

Abstract

Optimizing network utility in device-to-device networks is typically formulated as a non-convex optimization problem. This paper addresses the scenario where the optimization variables are from a bounded but continuous set, allowing each device to perform power control. The power at each link is optimized to maximize a desired network utility. Specifically, we consider the weighted-sum-rate. The state of the art benchmark for this problem is fractional programming with quadratic transform, known as FPLinQ. We propose a scalarization approach to transform the weighted-sum-rate, developing an iterative algorithm that depends on step sizes, a reference, and a direction vector. By employing the deep unfolding approach, we optimize these parameters by presenting the iterative algorithm as a finite sequence of steps, enabling it to be trained as a deep neural network. Numerical experiments demonstrate that the unfolded algorithm performs comparably to the benchmark in most cases while exhibiting lower complexity. Furthermore, the unfolded algorithm shows strong generalizability in terms of varying the number of users, the signal-to-noise ratio and arbitrary weights. The weighted-sum-rate maximizer can be integrated into a low-complexity fairness scheduler, updating priority weights via virtual queues and Lyapunov Drift Plus Penalty. This is demonstrated through experiments using proportional and max-min fairness.