跨层资源分配与调度:多用户MIMO-OFDM系统

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"A Cross-layer Resource Allocation and Scheduling for multiuser space-time block coded MIMO-OFDM systems" 本文是IEEE中的一篇研究论文,探讨了在多用户MIMO-OFDM系统中的跨层资源分配与调度策略。MIMO(Multiple-Input Multiple-Output)-OFDM(Orthogonal Frequency Division Multiplexing)是一种结合了多输入多输出技术与正交频分复用技术的无线通信系统,常用于提高无线通信系统的容量和性能。在这种系统中,多个天线被用来同时传输和接收数据,而OFDM则通过在多个正交子载波上分配数据来利用频率多样性。 论文特别关注的是,在不完全了解信道状态信息(Channel State Information, CSI)的情况下,如何采用空间时块编码的多天线系统结合OFDM进行资源分配。在物理层,空间时块编码用于增加系统的抗干扰能力和可靠性,而OFDM则有助于对抗频率选择性衰落。然而,由于信道信息的不完美性,资源分配和调度变得更加复杂。 提出的跨层优化方法独特之处在于,它同时考虑了物理层的子载波、比特和功率分配,以及数据链路层的调度。这种联合设计旨在最大化系统性能,特别是在多用户环境下,确保每个用户的公平性和整体系统的效率。 仿真结果证明,相较于传统的MIMO-OFDM系统,该跨层优化与设计方法能显著提高吞吐量、降低平均包延迟,并增强系统的稳定性。这意味着,通过智能地调整资源分配策略,即使在信道条件不稳定的情况下,也能实现更高效的数据传输和服务质量。 此外,这样的跨层方法也考虑到了网络的动态性和用户需求的变化,因此具有更强的适应性。它不仅优化了物理层的资源利用,还通过数据链路层的智能调度,确保了网络资源与用户需求之间的动态匹配,从而提升了整个系统的整体性能。 这篇论文为多用户MIMO-OFDM系统的资源管理提供了一种创新的解决方案,强调了跨层设计在克服信道不确定性、提升系统性能方面的重要性。这种方法对于现代无线通信网络的设计和优化具有重要的理论和实践价值。
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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 ,,actor模型和critic模型在哪

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