Resource Allocation for Capacity Optimization in Joint Source-Channel Coding Systems

TL;DR

This paper addresses resource allocation in deep joint source-channel coding systems, optimizing compression, power, and resource block assignment to maximize supported users under latency and quality constraints.

Contribution

It introduces a novel resource allocation framework for DL-based JSCC systems, decomposing the problem into two subproblems with closed-form solutions and efficient algorithms.

Findings

Effective resource allocation increases the number of satisfied users.

Closed-form expression for optimal transmit power derived.

Simulation confirms improved user support with proposed method.

Abstract

Benefited from the advances of deep learning (DL) techniques, deep joint source-channel coding (JSCC) has shown its great potential to improve the performance of wireless transmission. However, most of the existing works focus on the DL-based transceiver design of the JSCC model, while ignoring the resource allocation problem in wireless systems. In this paper, we consider a downlink resource allocation problem, where a base station (BS) jointly optimizes the compression ratio (CR) and power allocation as well as resource block (RB) assignment of each user according to the latency and performance constraints to maximize the number of users that successfully receive their requested content with desired quality. To solve this problem, we first decompose it into two subproblems without loss of optimality. The first subproblem is to minimize the required transmission power for each user under given RB allocation. We derive the closed-form expression of the optimal transmit power by searching the maximum feasible compression ratio. The second one aims at maximizing the number of supported users through optimal user-RB pairing, which we solve by utilizing bisection search as well as Karmarka' s algorithm. Simulation results validate the effectiveness of the proposed resource allocation method in terms of the number of satisfied users with given resources.