Cognitive radio (CR) is an emergent communication platform that offers solutions for spectrum scarcity issues. Cognitive radio networks (CRNs) will offer increased bandwidth to mobile consumers through wireless heterogeneous architectures and dynamic spectrum acquisition mechanisms. However, CRNs enforce challenges because of the fluctuating behaviour of the spectrum available and the diverse requirements for a varied range of applications. The functions of spectrum management can resolve those challenges to realize a new paradigm of the network. Secondary users (SUs) can opportunistically explore and employ the blank spaces present in licensed channels. This makes the SU evacuate the licensed channel and then switch to a vacant channel, when an incumbent primary user (PU) interferes with the channel, it causes degradation of SUs because of the frequent switching of channels. Also, the deafness problem is commonly seen in a CRN, where the QoS is critically affected due to the hidden interferences. This research proposes a Genetic Algorithm Optimized Fuzzy decision system for performing channel selection, channel switching, and spectrum allocation in a multi-channel multi-hop CRN. The proposed scheme acts as a decision support system (DSS), focusing on reducing the channel switching rate, hidden node interferences, and efficient spectrum allocation. Meta-heuristic genetic algorithm (GA) optimizes the parameters of the fuzzy decision system (FDS), for obtaining optimized decisions. The proposed DSS in the CR environment is simulated in the MATLAB platform and the results show improved performance concerning throughput and channel utilization.

认知无线电 (CR) 是一个新兴的通信平台，可为频谱稀缺问题提供解决方案。认知无线电网络 (CRN) 将通过无线异构架构和动态频谱获取机制为移动消费者提供更高的带宽。然而，由于可用频谱的波动行为以及各种应用的不同要求，CRN 带来了挑战。频谱管理功能可以解决这些挑战，实现网络的新范式。次要用户 (SU) 可以机会性地探索和使用许可频道中存在的空白空间。这使得 SU 撤离许可信道，然后切换到空闲信道，当现任主用户 (PU) 干扰该信道时，由于频繁切换频道，它会导致 SU 的退化。此外，耳聋问题在 CRN 中很常见，由于隐藏的干扰，QoS 受到严重影响。本研究提出了一种遗传算法优化的模糊决策系统，用于在多信道多跳 CRN 中执行信道选择、信道切换和频谱分配。所提出的方案充当决策支持系统（DSS），侧重于降低信道切换率、隐藏节点干扰和有效的频谱分配。元启发式遗传算法 (GA) 优化模糊决策系统 (FDS) 的参数，以获得优化的决策。建议的 CR 环境中的 DSS 在 MATLAB 平台中进行仿真，结果表明吞吐量和信道利用率方面的性能有所提高。