Location information of mobile primary users is one of the essential requirements for an underlay cognitive radio user to utilize the licensed spectrum efficiently. The performance of various location-based applications such as global navigation satellite system, device to device communication in dense urban 5G network also depends on the localization accuracy. In this paper, a collaborative localization scheme based on received signal strength has been proposed. The weighted centroid localization algorithm has been applied in the proposed network scenario to compute location coordinates of the mobile primary user. Since the channel noise effects are random and unavoidable, this paper has focused on the mitigation of the internal noise by designing a suitable reconfigurable FIR filter after the demodulator stage of a cognitive radio receiver circuit to improve precision of signal measurement during primary user localization. The localization error rate has come down to (1.3–1.62) % after internal noise mitigation. The enhancement in the localization accuracy improves the overall spectrum utilization efficiency and reduces the miss detection and false detection probabilities in the proposed underlay network.

移动主要用户的位置信息是底层认知无线电用户有效利用许可频谱的基本要求之一。全球定位卫星系统，密集的城市5G网络中设备到设备通信等各种基于位置的应用程序的性能也取决于定位精度。本文提出了一种基于接收信号强度的协同定位方案。加权质心定位算法已在提出的网络方案中应用，以计算移动主要用户的位置坐标。由于信道噪声的影响是随机且不可避免的，本文致力于通过在认知无线电接收器电路的解调器阶段之后设计合适的可重构FIR滤波器来减轻内部噪声，以提高主要用户定位期间的信号测量精度。消除内部噪声后，定位错误率已降至（1.3–1.62）％。定位精度的提高提高了整体频谱利用效率，并降低了所建议的底层网络中的未命中检测和误检测概率。