A cognitive radio network can be employed in any wireless communication systems, including military communications, public safety, emergency networks, aeronautical communications, and wireless-based Internet of Things, to enhance spectral efficiency. The performance of a cognitive radio network (CRN) can be enhanced through the use of cooperative relays with buffers; however, this incurs additional delays which can be reduced by using virtual duplex relaying that requires selection of a suitable relay pair. In a virtual duplex mode, we mimic full-duplex links by using simultaneous two half-duplex links, one transmitting and the other one receiving, in such a way that the overall effect of duplex mode is achieved. The relays are generally selected based on signal-to-interference-plus-noise ratio (SINR). However, other factors such as power consumption and buffer capacity can also have a significant impact on relay selection. In this work, a multiobjective relay selection scheme is proposed that simultaneously takes into account throughput, delay performance, battery power, and buffer status (i.e., both occupied and available) at the relay nodes while maintaining the required SINR. The proposed scheme involves the formulation of four objective functions to, respectively, maximize throughput and buffer space availability while minimizing the delay and battery power consumption. The weighted sum approach is then used to combine these objective functions to form the multiobjective optimization problem and an optimal solution is obtained. The assignments of weights to objectives have been done using the rank sum (RS) method, and several quality-of-service (QoS) profiles have been considered by varying the assignment of weights. The results gathered through simulations demonstrate that the proposed scheme efficiently determines the optimal solution for each application scenario and selects the best relay for the respective QoS profile. The results are further verified by using the genetic algorithm (GA) and particle swarm optimization (PSO) techniques. Both techniques gave identical solutions, thus validating our claim.

中文翻译：

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认知无线电网络中面向QoS的最佳中继选择

认知无线电网络可用于任何无线通信系统中，包括军事通信，公共安全，应急网络，航空通信和基于无线的物联网，以提高频谱效率。可以通过使用带有缓冲区的协作中继来增强认知无线电网络（CRN）的性能。然而，这引起了额外的延迟，这可以通过使用需要选择合适的中继对的虚拟双工中继来减少。在虚拟双工模式下，我们通过同时使用两个半双工链路（一个发送和另一个接收）来模拟全双工链路，从而实现了双工模式的整体效果。通常根据信号干扰加噪声比（SINR）选择继电器。然而，诸如功耗和缓冲容量之类的其他因素也可能对继电器的选择产生重大影响。在这项工作中，提出了一种多目标中继选择方案，该方案同时考虑了中继节点处的吞吐量，延迟性能，电池电量和缓冲区状态（即，已占用和可用），同时保持了所需的SINR。提出的方案涉及四个目标函数的制定，以分别最大化吞吐量和缓冲区空间可用性，同时最大程度地减少延迟和电池功耗。然后使用加权和方法将这些目标函数组合起来，以形成多目标优化问题，并获得最优解。已使用等级总和（RS）方法完成了对目标的权重分配，并且已经通过改变权重分配来考虑几个服务质量（QoS）配置文件。通过仿真收集的结果表明，所提出的方案有效地确定了每种应用场景的最佳解决方案，并为相应的QoS配置文件选择了最佳中继。通过使用遗传算法（GA）和粒子群优化（PSO）技术进一步验证了结果。两种技术都给出了相同的解决方案，从而验证了我们的主张。通过使用遗传算法（GA）和粒子群优化（PSO）技术进一步验证了结果。两种技术都给出了相同的解决方案，从而验证了我们的主张。通过使用遗传算法（GA）和粒子群优化（PSO）技术进一步验证了结果。两种技术都给出了相同的解决方案，从而验证了我们的主张。