One of the major challenges facing the realization of cognitive radios (CRs) in future mobile and wireless communications is the issue of high energy consumption. Since future network infrastructure will host real-time services requiring immediate satisfaction, the issue of high energy consumption will hinder the full realization of CRs. This means that to offer the required quality of service (QoS) in an energy-efficient manner, resource management strategies need to allow for effective trade-offs between QoS provisioning and energy saving. To address this issue, this paper focuses on single base station (BS) management, where resource consumption efficiency is obtained by solving a dynamic resource allocation (RA) problem using bipartite matching. A deep learning (DL) predictive control scheme is used to predict the traffic load for better energy saving using a stacked auto-encoder (SAE). Considered here was a base station (BS) processor with both processor sharing (PS) and first-come-first-served (FCFS) sharing disciplines under quite general assumptions about the arrival and service processes. The workload arrivals are defined by a Markovian arrival process while the service is general. The possible impatience of customers is taken into account in terms of the required delays. In this way, the BS processor is treated as a hybrid switching system that chooses a better packet scheduling scheme between mean slowdown (MS) FCFS and MS PS. The simulation results presented in this paper indicate that the proposed predictive control scheme achieves better energy saving as the traffic load increases, and that the processing of workload using MS PS achieves substantially superior energy saving compared to MS FCFS.

中文翻译：

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基于深度学习的分布式认知无线电网络QoS配置与节能方案

在未来的移动和无线通信中实现认知无线电 (CR) 面临的主要挑战之一是高能耗问题。由于未来的网络基础设施将承载需要即时满足的实时服务，高能耗问题将阻碍 CR 的全面实现。这意味着要以节能的方式提供所需的服务质量 (QoS)，资源管理策略需要考虑到 QoS 供应和节能之间的有效权衡。为了解决这个问题，本文侧重于单基站 (BS) 管理，其中通过使用二分匹配解决动态资源分配 (RA) 问题来获得资源消耗效率。深度学习 (DL) 预测控制方案用于预测交通负载，以使用堆叠式自动编码器 (SAE) 更好地节能。这里考虑的是一个基站 (BS) 处理器，它具有处理器共享 (PS) 和先来先服务 (FCFS) 共享原则，并且在关于到达和服务过程的非常普遍的假设下。工作负载到达由马尔可夫到达过程定义，而服务是一般的。在所需的延迟方面考虑了客户可能的不耐烦。通过这种方式，BS 处理器被视为一个混合交换系统，它在平均减速 (MS) FCFS 和 MS PS 之间选择更好的分组调度方案。