Xiao Xiang, Zeng Fanzi, Hu Zhenzhen, Jiao Lei
College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China.
College of Information and Electronic Engineering, Hunan City University, Yiyang 413000, China.
Sensors (Basel). 2020 Jul 7;20(13):3800. doi: 10.3390/s20133800.
Cognitive radio networks (CRNs), which allow secondary users (SUs) to dynamically access a network without affecting the primary users (PUs), have been widely regarded as an effective approach to mitigate the shortage of spectrum resources and the inefficiency of spectrum utilization. However, the SUs suffer from frequent spectrum handoffs and transmission limitations. In this paper, considering the quality of service (QoS) requirements of PUs and SUs, we propose a novel dynamic flow-adaptive spectrum leasing with channel aggregation. Specifically, we design an adaptive leasing algorithm, which adaptively adjusts the portion of leased channels based on the number of ongoing and buffered PU flows. Furthermore, in the leased spectrum band, the SU flows with access priority employ dynamic spectrum access of channel aggregation, which enables one flow to occupy multiple channels for transmission in a dynamically changing environment. For performance evaluation, the continuous time Markov chain (CTMC) is developed to model our proposed strategy and conduct theoretical analyses. Numerical results demonstrate that the proposed strategy effectively improves the spectrum utilization and network capacity, while significantly reducing the forced termination probability and blocking probability of SU flows.
认知无线电网络（CRNs）允许次要用户（SUs）在不影响主要用户（PUs）的情况下动态接入网络，已被广泛视为缓解频谱资源短缺和频谱利用效率低下问题的有效方法。然而，次要用户面临频繁的频谱切换和传输限制。在本文中，考虑到主要用户和次要用户的服务质量（QoS）要求，我们提出了一种新颖的具有信道聚合的动态流自适应频谱租赁方案。具体而言，我们设计了一种自适应租赁算法，该算法根据正在进行的和缓冲的主要用户流的数量自适应地调整租赁信道的比例。此外，在租赁的频段中，具有接入优先级的次要用户流采用信道聚合的动态频谱接入方式，这使得一个流能够在动态变化的环境中占用多个信道进行传输。为了进行性能评估，我们开发了连续时间马尔可夫链（CTMC）来对我们提出的策略进行建模并进行理论分析。数值结果表明，所提出的策略有效地提高了频谱利用率和网络容量，同时显著降低了次要用户流的强制终止概率和阻塞概率。
