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.
翻译:从深层次学习(DL)技术的进步、深层次源-通道联合编码(JSCC)技术的进步中受益,这表明它具有提高无线传输性能的巨大潜力;然而,大多数现有工作侧重于基于DL的无线传输器设计,同时忽视无线系统的资源配置问题;在本文中,我们考虑到一个下行的资源分配问题,即一个基地站(BS)共同优化压缩率(CR)和电力分配以及资源块(RB)的分配,根据悬浮度和性能限制,最大限度地增加成功收到所请求内容并达到预期质量的用户数量;为解决这一问题,我们首先将其分解为两个子问题,而不丧失最佳性;第一个子问题就是最大限度地减少分配给RB的每个用户所需的传输能力;我们通过搜索最大可行的压缩率(CRC)和电源分配以及资源块组合(RB),从封闭式表达最佳传输能力;第二个目标是通过最佳用户-RB配对使获得支持的用户人数最大化,我们通过利用双层搜索和Karmarma资源配置方法确认拟议资源配置的用户结果,以此解决这些结果。