虚拟多速率无线局域网动态关联控制的元启发式解决方案

Seyed Dawood Sajjadi Torshizi, Maryam Tanha, Jianping Pan
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引用次数: 8

摘要

在人口密集的城市地区,无线局域网(wlan)的混乱部署是许多互联网服务提供商(isp)和Wi-Fi用户共同面临的问题之一。它会导致吞吐量的大幅降低,并阻碍带宽在用户之间的均衡分配。这些网络大多是独立管理的,它们之间没有合作。此外,传统的关联机制选择具有最强接收信号强度指标(RSSI)的接入点(ap),这加剧了这种情况。本文提出了一种基于动态关联控制的虚拟wlan公平带宽分配的通用近最优解决方案。该方案是在蚁群优化(ACO)的基础上发展起来的一种元启发式算法,用于在贪婪客户端之间提供比例公平(PF)。实际上,它为使用通用的、虚拟化的或重叠的WLAN基础设施为其客户服务的isp提供了一种通用的集中式解决方案。我们通过数值分析评估了ACO-PF在下行和上行场景下的有效性,对比了现有的流行方案。该技术在大规模无线局域网的实现和运行时间方面具有较低的复杂性,并且可以很容易地针对不同的目标函数进行开发和定制。此外,它是在测试平台环境中实现的,以调查真实部署场景的关键挑战。
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Meta-Heuristic Solution for Dynamic Association Control in Virtualized Multi-Rate WLANs
Chaotic deployment of Wireless Local Area Networks (WLANs) in dense urban areas is one of the common issues of many Internet Service Providers (ISPs) and Wi-Fi users. It results in a substantial reduction of the throughput and impedes the balanced distribution of bandwidth among the users. Most of these networks are managed independently and there is no cooperation among them. Moreover, the conventional association mechanism that selects the Access Points (APs) with the strongest Received Signal Strength Indicator (RSSI) aggravates this situation. In this paper, we present a versatile near-optimal solution for the fair bandwidth distribution over virtualized WLANs through dynamic association control. The proposed scheme is called ACO-PF, which is developed on top of Ant Colony Optimization (ACO) as a meta-heuristic technique to provide Proportional Fairness (PF) among the greedy clients. In fact, it presents a generic and centralized solution for ISPs that are using a common, virtualized or overlapped WLAN infrastructure for serving their customers. We have evaluated the efficacy of ACO-PF through numerical analysis versus popular existing schemes for both downlink and uplink scenarios. Our proposed technique has less complexity in terms of the implementation and running time for largescale WLANs and it can be easily developed and customized for different objective functions. In addition, it is implemented in a testbed environment to investigate the key challenges of real deployment scenarios.
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