We consider a class of resource allocation problems given a set of unconditional constraints whose objective function satisfies Bellman's optimality principle. Such problems are ubiquitous in wireless communication, signal processing, and networking. These constrained combinatorial optimization problems are, in general, NP-Hard. This paper proposes two algorithms to solve this class of problems using a dynamic programming framework assisted by an information-theoretic measure. We demonstrate that the proposed algorithms ensure optimal solutions under carefully chosen conditions and use significantly reduced computational resources. We substantiate our claims by solving the power-constrained bit allocation problem in 5G massive Multiple-Input Multiple-Output receivers using the proposed approach.
翻译:我们考虑了一系列资源分配问题,因为一系列无条件的限制,其客观功能符合Bellman的最佳性原则。这些问题在无线通信、信号处理和联网方面普遍存在。这些受限制的组合优化问题一般是NP-Hard。本文建议采用两种算法,在信息理论措施的协助下,利用动态的编程框架解决这一类问题。我们证明,拟议的算法确保了在谨慎选择的条件下找到最佳解决办法,并大大削减了计算资源。我们通过使用拟议办法解决5G大规模多投入多产出多产出接收器中受节能限制的点分配问题,证实了我们的要求。