双跳多中继LTE-Advanced系统下行链路最优非对称资源分配

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本文主要探讨了"最优的非对称资源分配"(Optimal Asymmetric Resource Allocation, ARA)在双跳多中继长期演进高级(Long Term Evolution Advanced, LTE-Advanced)系统中的应用,特别是在下行链路。研究的焦点是针对OFDM(正交频分复用)传输方式设计的DF(解码转发)策略,这是一种区别于传统方法的新颖尝试。 与之前的研究不同,该算法考虑了时间资源在两个跳之间分配的不对称性。具体来说,当一个蜂窝中有K个中继节点时,总共可能有2K个不等时长的时间槽,这显著增加了系统的度自由度,从而提高了系统的效率和性能。这种设计允许在相同的时段内由多个中继同时为多个用户服务,提升了传输的多样性,增强了系统的可靠性和容量。 文章的核心贡献在于,作者成功地导出了关于最优资源分配的闭式结果。这意味着所需的反馈信息量相对较少,这对于实际网络中的实施来说具有重要的实用性。通过仿真结果,研究者展示了引入多中继、多用户和时间多样性后,优化的非对称资源分配策略能够明显提升系统吞吐量、降低误码率,并且能够有效应对多用户并发场景下的复杂通信需求。 这篇论文为优化双跳多中继LTE-Advanced系统在下行链路中的资源利用提供了创新且高效的解决方案,对于提高无线网络的容量和性能具有重要的理论和实际意义。通过非对称资源分配,网络设计者可以更好地平衡各部分的带宽需求,确保在满足服务质量的同时,实现资源的高效利用。

Algorithm 1: The online LyDROO algorithm for solving (P1). input : Parameters V , {γi, ci}Ni=1, K, training interval δT , Mt update interval δM ; output: Control actions 􏰕xt,yt􏰖Kt=1; 1 Initialize the DNN with random parameters θ1 and empty replay memory, M1 ← 2N; 2 Empty initial data queue Qi(1) = 0 and energy queue Yi(1) = 0, for i = 1,··· ,N; 3 fort=1,2,...,Kdo 4 Observe the input ξt = 􏰕ht, Qi(t), Yi(t)􏰖Ni=1 and update Mt using (8) if mod (t, δM ) = 0; 5 Generate a relaxed offloading action xˆt = Πθt 􏰅ξt􏰆 with the DNN; 6 Quantize xˆt into Mt binary actions 􏰕xti|i = 1, · · · , Mt􏰖 using the NOP method; 7 Compute G􏰅xti,ξt􏰆 by optimizing resource allocation yit in (P2) for each xti; 8 Select the best solution xt = arg max G 􏰅xti , ξt 􏰆 and execute the joint action 􏰅xt , yt 􏰆; { x ti } 9 Update the replay memory by adding (ξt,xt); 10 if mod (t, δT ) = 0 then 11 Uniformly sample a batch of data set {(ξτ , xτ ) | τ ∈ St } from the memory; 12 Train the DNN with {(ξτ , xτ ) | τ ∈ St} and update θt using the Adam algorithm; 13 end 14 t ← t + 1; 15 Update {Qi(t),Yi(t)}N based on 􏰅xt−1,yt−1􏰆 and data arrival observation 􏰙At−1􏰚N using (5) and (7). i=1 i i=1 16 end With the above actor-critic-update loop, the DNN consistently learns from the best and most recent state-action pairs, leading to a better policy πθt that gradually approximates the optimal mapping to solve (P3). We summarize the pseudo-code of LyDROO in Algorithm 1, where the major computational complexity is in line 7 that computes G􏰅xti,ξt􏰆 by solving the optimal resource allocation problems. This in fact indicates that the proposed LyDROO algorithm can be extended to solve (P1) when considering a general non-decreasing concave utility U (rit) in the objective, because the per-frame resource allocation problem to compute G􏰅xti,ξt􏰆 is a convex problem that can be efficiently solved, where the detailed analysis is omitted. In the next subsection, we propose a low-complexity algorithm to obtain G 􏰅xti, ξt􏰆. B. Low-complexity Algorithm for Optimal Resource Allocation Given the value of xt in (P2), we denote the index set of users with xti = 1 as Mt1, and the complementary user set as Mt0. For simplicity of exposition, we drop the superscript t and express the optimal resource allocation problem that computes G 􏰅xt, ξt􏰆 as following (P4) : maximize 􏰀j∈M0 􏰕ajfj/φ − Yj(t)κfj3􏰖 + 􏰀i∈M1 {airi,O − Yi(t)ei,O} (28a) τ,f,eO,rO 17 ,建立了什么模型

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