Cognitive radio (CR) technology is to assure the better utilization of the spectrum, permitting the non-licensed users to utilize the unused bands provided to the Licensed or primary users, which is made effective through the application of the Orthogonal Frequency Division Multiplexing (OFDM). The effective utilization of the OFDM channels through temporarily allocating the bands to the unlicensed users avoids interference. In order to ensure the reliable spectrum sensing in CR with effective spectral efficiency, the channel estimation (CE) is enabled in the OFDM-based multi-input and multi-output systems. The channel estimation is facilitated through the pilot-based sequential approach for the least square CE in OFDM-based CR. The optimal insertion of the pilots in the input data symbols before transmitting the signal through the fading OFDM channels in CR is made through the proposed rider grey wolf optimization, which is the integration of the grey wolf optimization and rider optimization algorithm. The estimation is done through the least square of the original channel state and the estimated symbols. The experimental analysis is progressed both in Rician and Rayleigh environments with minimal mean square error of 1.28E−06 and bit error rate of 0.000626.

认知无线电（CR）技术可确保更好地利用频谱，允许非许可用户使用提供给许可用户或主要用户的未使用频带，这通过应用正交频分复用（OFDM）得以有效）。通过临时将频段分配给非许可用户来有效利用OFDM信道可避免干扰。为了确保CR具有有效的频谱效率，可靠的频谱检测，在基于OFDM的多输入和多输出系统中启用了信道估计（CE）。通过基于导频的顺序方法可以简化基于OFDM的CR中最小平方CE的信道估计。通过提出的骑手灰太狼优化算法，将灰狼优化算法和骑手优化算法相结合，可以在CR中通过衰落的OFDM信道传输信号之前，在输入数据符号中进行导频的最佳插入。通过原始信道状态和估计符号的最小二乘来进行估计。在Rician和Rayleigh环境中均进行了实验分析，最小均方误差为1.28E-06，误码率为0.000626。