Admission and Eviction Control of Cognitive Radio Users at Wi-Fi 2.0 Hotspots

Abstract

Abstract-Cognitive radio (CR)-based Wi-Fi 2.0 hotspots are introduced as an attractive application of dynamic spectrum access (DSA), at which a wireless service provider (WSP) leases licensed channels via secondary market and offers Internet access to CRenabled customers by opportunistically utilizing the leased spectrum. The CR users access the channels only when they are temporarily unoccupied by their legacy users, and pay a usage charge according to the WSP's pricing policy. In this paper, we study the profit maximization problem of a WSP by deriving the (sub)optimal control of admission (at CR user arrivals) and eviction (upon return of the legacy users) of CR users. We formulate the problem as a semi-Markov decision process (SMDP) with two quality-ofservice (QoS) constraints on arrival-blocking and service-dropping probabilities, which is solved by the linear programming techniques. Using an extensive numerical analysis, we show that the derived policy achieves up to 22.5-44 percent more profit than simple complete-sharing algorithms in the tested scenarios. In addition, we evaluate the impact of the number of leased channels and pricing on the achieved profit, and study the tradeoffs between the two QoS constraints.

Hierarchical Market Competition in a Duopoly Super Wi-Fi Spectrum Market

“…8 Each evicted user is compensated by the reimbursement of I = β ·p i /μ, β > 0, i.e., β times the average service charge without eviction. We also assume β ≤ 1 to make the compensation upper-bounded by what customers pay on average.…”

“…Following the model of preemptive spectrum lease in [8], it is assumed that a leased channel can be preempted by its PUs during the leasing term. Specifically, a channel is modeled as an ON/OFF source [9], [10] as shown in Fig.…”

Joint Eviction and Admission Control for cognitive radio networks

“…It is demonstrated that supporting traffic with certain QoS requirements is possible in CRNs with spectrum overlay based on different MAC protocols, e.g., [1] [2], and scheduling strategies, e.g., [3] [4]. In [5] and [6], it is demonstrated that resources can be well managed in a CRN when serving best effort data traffic in order to optimize the throughput performance. In recent years, many models and algorithm are proposed in [7][8] to analyze the performance, including blocking probability, dropping probability and throughput, in Cognitive Radio Networks (CRNs), such as the optimal reserve channel model proposed in [7], the dynamic heterogeneous spectrums Multiple Channel Reuse Areas (MCRA) model given in [8] and some other models.…”

Dynamic Hierarchical Power Control in Multi-User Cognitive Wi-Fi 2.0 Wireless Networks

“…杨春刚 等:认知 Wi-Fi 2.0 无线网络多用户动态分层功率控制算法 1311 认知无线电技术已经成为无线通信发展的新思路和研究热点 [1−3] .认知无线电技术的核心思想是,具有认 知功能的无线通信设备(即认知用户或认知节点)通过感知无线环境获得当前授权频段的频谱空洞,学习推理更 新已有知识库,进而做出智能的行为预测,并自适应地调节最优传输策略,实现择机共享已有授权频谱空洞,从 而有效提升频谱利用率和缓解当前频谱资源匮乏的现状.当前,按照认知设备与授权用户共存方式和频谱利用 优先级的不同,其动态频谱共享(DSA)方式可分为:开放频谱共享、独占式共享和衬垫式共享 [4,5] .虽然学术界和 工业界已有很多对于独占式共享和衬垫式共享的研究,并确实取得了良好效果,然而与真正商用基于认知无线 电技术的频谱共享系统还有一段距离.同时,以 IEEE 802.11 WLAN 为代表的开放频谱共享在无线产业界引起 极大反响 [6−8] .随着 WLAN 网络大量普及和良好的用户体验,WSP 逐渐意识到开放频谱共享的巨大潜能.然而, 正如 Goldsmith 在文献 认知无线电技术实时择机利用 DTV 频谱衍生出 Wi-Fi 2.0 网络的概念 [9] .Wi-Fi 2.0 网络指的是基于认知无线技 术的在授权频段的频谱空洞实现的类似于 Wi-Fi 的 Internet 接入网络.Wi-Fi 2.0 网络包含认知接入点(CR-AP, 或者称为认知无线服务提供商(wireless service provider,简称 WSP)和认知终端用户(CR-enabled-customer).当 WSP 成功获得由频谱经纪人(spectrum broker)管理的多赢频谱租赁的信道后,即可为认知终端用户提供无线 Internet 接入服务.例如:文献 [10]从 Wi-Fi 2.0 网络中 WSP 的经济收益的角度出发,研究了用户接入和迁出策略 和 WSP 之间的市场竞争;文献 [11]考虑了双头垄断的 Wi-Fi 2.0 网络中 WSP 对于频谱和用户的竞争,采用博弈 论分析了服务关税(service tariffs)和信道利用效率(channel utilization)的纳什均衡解.在文献 [10,11]的基础上,文 献 [12]采用半马尔可夫决策过程研究最优的认知终端用户策略设计. 由于认知 Wi-Fi 2.0 无线网络的感知性、自主性、适变性以及认知设备的灵活捷变性的特征,要求设计动 态自适应地传输机制和资源管理和控制算法,有效约束认知节点自私的策略行为,避免认知节点谎报 QoS 需求 以获得更高优先级或者更多廉价的频谱资源,并进一步遏制干扰.因此,为满足 Wi-Fi 2.0 无线网络的特性,博弈 论必将在认知 Wi-Fi 2.0 无线网络中的研究发挥重要作用 [10,11] .其实,博弈论在开放频谱共享传统的 WLAN 中已 经有大量的研究 [13−17] .目前,大多数研究仍集中在采用纳什非合作博弈模型的纳什均衡解的分析和逼近算法的 设计上,然而研究表明,纳什均衡解不能保证设计策略的帕累托最优性 [13,14] .采用分级定价策略、对于不诚实策 略行为认知节点实行惩罚,可以有效改进有效性.然而,定价策略单一且不能实时跟进网络环境变化,出现惩罚 延时或者过重的情况时有发生,用户之间的公平性更难以保证 [15−17] .同时,上述基于博弈论的资源管理和分配方…”