In cognitive radio, the detection probability of primary user affects the signal receiving performance for both primary and secondary users significantly. In this paper, a new Dempster-Shafer （D-S） algorithm with credit scale for decision fusion in spectrum sensing is proposed for the purpose to improve the performance of detection in cognitive radio. The validity of this method is established by simulation in the environment of multiple cognitive users who know their signal to noise ratios （SNR） and a central node. The channels between the cognitive users and the central node are considered to be additive white Ganssian noise （AWGN）. Compared with traditional data fusion rules, the proposed D-S algorithm with credit scale provides a better detection performance.
针对CDMA上行链路系统中,基站已知小区内用户的扩频码而对小区外用户扩频码未知的情况,提出一种基于子空间方法的最小均方误差(minimum mean square error,MMSE)群盲多用户检测算法。该算法利用所有已知的扩频码有效消除了多址干扰,采用改进的紧缩近似投影子空间(projection approximation subspacetracking with deflation,PASTd)跟踪算法实现信号子空间的自适应跟踪,提高了收敛速度。仿真结果表明,所提算法收敛速度快,输出信干噪比和误码率性能优于PASTd盲多用户检测,逼近奇异值分解(singular value decom-position,SVD)群盲多用户检测,并保持了较低的计算复杂度。
