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Dymamic MAC Frame Configuration and PSO-Based Optimal Resource Allocation in Multi-channel Cognitive Radio Ad-Hoc Networks

机译:多通道认知无线电Ad-hoc网络中的动态MAC帧配置和基于PSO的最优资源分配

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摘要

In cluster-based cognitive radio networks, secondary system uses available channels that are not used temporally by primary systems. In multi-channel operational environments, each available channel may have different wireless channel gain and primary activity so that achievable data rate to secondary users (SUs) and required sensing parameter value are channel dependent. SUs also have different energy saving requirements and data traffic demands. Therefore, based on measured channel conditions and user constraints, cluster head needs to decide which channel should be allocated to which SUs and to configure the optimum MAC frame structure for satisfying each SU's service demands. Furthermore, optimization to provide proportional fairness among the cognitive secondary users in resource allocation in terms of energy consumption and data rate is very important. In this paper, a dynamic MAC frame configuration and optimal resource allocation scheme for multi-channel ad-hoc cognitive radio network is proposed. We formulate our dynamic resource allocation model as a constrained optimization problem with multi-objective functions using particle swarm optimization (PSO) algorithm. The proposed PSO scheme guarantees that the allocation captures the individual traffic and energy saving demands and maximizes the objectives functions simultaneously. Simulation results show the proposed scheme can successfully maximize the intended utility function and provide proportional fairness between SUs.
机译:在基于群集的认知无线电网络中,辅助系统使用主要系统不在时间上使用的可用通道。在多通道操作环境中,每个可用信道可以具有不同的无线信道增益和主要活动,从而实现辅助用户(SUS)和所需的感测参数值的可实现数据速率是依赖性的。 SUS还具有不同的节能需求和数据流量需求。因此,基于测量的信道条件和用户约束,群集头需要确定应该分配哪个频道,并配置用于满足每个SU的服务需求的最佳MAC帧结构。此外,在能量消耗和数据速率方面,在资源分配中提供比例公平性的比例公平非常重要。在本文中,提出了一种用于多通道ad-hoc认知无线电网络的动态MAC帧配置和最优资源分配方案。我们将动态资源分配模型作为使用粒子群优化(PSO)算法的多目标函数的约束优化问题。拟议的PSO方案保证了分配捕获各个流量和节能需求,并同时最大化目标功能。仿真结果表明,所提出的方案可以成功地最大化预期的实用功能,并在SUS之间提供比例公平。

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