Capacity Optimizing Resource Allocation in Joint Source-Channel Coding Systems with QoS Constraints

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 is solved by utilizing bisection search as well as interior-point algorithm. To reduce the computational complexity, we propose a heuristic greedy algorithm to obtain a simplified problem. Then the Bregman alternating direction method of multipliers (BADMM) based algorithm is adopted to decompose the simplified problem into several subproblems that can be computed in parallel. Simulation results validate the effectiveness of the proposed resource allocation methods in terms of the number of satisfied users with given resources. It is also shown that the BADMM-based algorithm can significantly reduce the computational complexity and retain high performance.