Cognitive Radio Network (CRN) provides effective capabilities for resource allocation with the valuable spectrum resources in the network. It provides the effective allocation of resources to the unlicensed users or Secondary Users (SUs) to access the spectrum those are unused by the licensed users or Primary Users (Pus). This paper develops an Optimal Relay Selection scheme with the spectrum-sharing scheme in CRN. The proposed Cross-Layer Spider Swarm Shifting is implemented in CRN for the optimal relay selection with Spider Swarm Optimization (SSO). The shortest path is estimated with the data shifting model for the data transmission path in the CRN. This study examines a cognitive relay network (CRN) with interference restrictions imposed by a mobile end user (MU). Half-duplex communication is used in the proposed system model between a single primary user (PU) and a single secondary user (SU). Between the SU source and SU destination, an amplify and forward (AF) relaying mechanism is also used. While other nodes (SU Source, SU relays, and PU) are supposed to be immobile in this scenario, the mobile end user (SU destination) is assumed to travel at high vehicle speeds. The suggested method achieves variety by placing a selection combiner at the SU destination and dynamically selecting the optimal relay for transmission based on the greatest signal-to-noise (SNR) ratio. The performance of the proposed Cross-Layer Spider Swarm Shifting model is compared with the Spectrum Sharing Optimization with QoS Guarantee (SSO-QG). The comparative analysis expressed that the proposed Cross-Layer Spider Swarm Shifting model delay is reduced by 15% compared with SSO-QG. Additionally, the proposed Cross-Layer Spider Swarm Shifting exhibits the improved network performance of ~25% higher throughput compared with SSO-QG.

翻译：认知无线网络（CRN）通过利用网络中宝贵的频谱资源，为资源分配提供了有效能力。它能够将频谱资源高效分配给未授权用户或次要用户（SU），使其接入被授权用户或主要用户（PU）未使用的频谱。本文提出了一种基于频谱共享方案的认知无线网络最优中继选择方案。研究中采用所提出的跨层蜘蛛群迁移模型，结合蜘蛛群优化（SSO）算法实现认知无线网络中的最优中继选择。通过数据迁移模型估算认知无线网络中数据传输路径的最短路径。本研究考察了受移动终端用户（MU）干扰限制的认知中继网络（CRN）。所提出的系统模型在主用户（PU）和次用户（SU）之间采用半双工通信模式，并在次用户源节点与次用户目的节点之间使用放大转发（AF）中继机制。在此场景中，假设其他节点（次用户源节点、次用户中继节点和主用户）保持静止，而移动终端用户（次用户目的节点）以高速车辆速度移动。所提方法通过在次用户目的节点处设置选择合并器，并基于最大信噪比（SNR）动态选择最优中继进行传输，从而获得分集增益。将所提出的跨层蜘蛛群迁移模型的性能与基于服务质量保证的频谱共享优化（SSO-QG）模型进行了比较。对比分析表明，与SSO-QG相比，所提出的跨层蜘蛛群迁移模型时延降低了15%，且网络吞吐量提高了约25%，展现出更优的网络性能。