arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2002.02801eess.SPcs.ITcs.LGmath.ITstat.ML
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动态无小区网络中的多址接入:中断性能和基于深度强化学习的设计

Multiple Access in Dynamic Cell-Free Networks: Outage Performance and Deep Reinforcement Learning-Based Design

University of Manitoba(曼尼托巴大学);University of Toronto(多伦多大学)

Yasser Al-Eryani, Mohamed Akrout, Ekram Hossain

摘要:

在未来的无小区(或无小区)无线网络中,地理区域中的大量设备将在非正交多址接入场景中由大量分布式接入点(AP)同时服务,这些AP与集中式处理池协调。对于这样一个集中式的无小区网络与静态预定义的波束成形设计,我们首先推导出一个封闭形式的表达式的上行链路每用户中断概率。为了显著降低在存在大量设备和AP的情况下联合处理用户信号的复杂度,我们提出了一种新的动态无小区网络架构。在该架构中,分布式AP在一组子组之间被划分(即,聚类),其中每个子组充当配备有分布式天线系统(DAS)的虚拟AP。当簇大小为1时,传统的静态无小区网络是这种动态无小区网络的特殊情况。对于这种动态的无小区网络,我们提出了一个连续的干扰消除(SIC)启用信号检测方法和用户间干扰(IUI)感知DAS的接收分集合并方案。然后,我们制定的一般问题的集群AP和设计的波束成形向量的目标,以最大化的总和速率或最大化的最小速率。为此,我们提出了一种混合深度强化学习(DRL)模型,即深度确定性策略梯度(DDPG)-深度双Q网络(DDQN)模型,以解决低复杂度在线实现的优化问题。DRL模型的总和速率优化显着优于最大化的最小速率方面的平均每用户速率性能。此外,在我们的系统设置中,发现所提出的DDPG-DDQN方案实现通过基于穷举搜索的设计可实现的速率的大约$78\%$。

英文摘要:

In future cell-free (or cell-less) wireless networks, a large number of devices in a geographical area will be served simultaneously in non-orthogonal multiple access scenarios by a large number of distributed access points (APs), which coordinate with a centralized processing pool. For such a centralized cell-free network with static predefined beamforming design, we first derive a closed-form expression of the uplink per-user probability of outage. To significantly reduce the complexity of joint processing of users' signals in presence of a large number of devices and APs, we propose a novel dynamic cell-free network architecture. In this architecture, the distributed APs are partitioned (i.e. clustered) among a set of subgroups with each subgroup acting as a virtual AP equipped with a distributed antenna system (DAS). The conventional static cell-free network is a special case of this dynamic cell-free network when the cluster size is one. For this dynamic cell-free network, we propose a successive interference cancellation (SIC)-enabled signal detection method and an inter-user-interference (IUI)-aware DAS's receive diversity combining scheme. We then formulate the general problem of clustering APs and designing the beamforming vectors with an objective to maximizing the sum rate or maximizing the minimum rate. To this end, we propose a hybrid deep reinforcement learning (DRL) model, namely, a deep deterministic policy gradient (DDPG)-deep double Q-network (DDQN) model, to solve the optimization problem for online implementation with low complexity. The DRL model for sum-rate optimization significantly outperforms that for maximizing the minimum rate in terms of average per-user rate performance. Also, in our system setting, the proposed DDPG-DDQN scheme is found to achieve around $78\%$ of the rate achievable through an exhaustive search-based design.

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