【文章內(nèi)容簡介】 yzed. Assume the control channel between the unlicensed users and the mon receiver is perfect, the local decisions are reported without any error. Let and denote the cumulative distribution function (CDF) of the local test statistic under the hypothesis and , respectively. Then, we have [10]: (9) (10)Obviously,，.If no any local decision is reported to the mon receiver, ., K=0 , we call that fail sensing. For this case, the mon receiver will request the user which has the highest reputation to send its local decision based on conventional energy detection method. Let and denote the probability of fail sensing under hypothesis and , respectively. Here we have: (11) (12)Apparently, and .In our scheme, the false alarm probability ,the detection probability,and the missing probability : (13) = (14) (15)For simplicity, we assume the channel between the unlicensed users and the base station are ideal, the local decision will be reported without any error. So stand for the probability of the event that under hypothesis , all the K users claim and other NK users make no local decisions. = = （16） （17） （18） （19）IV. SIMULATION RESULTSIn this section, some simulation results are presented to illustrate the system performance of our cooperative spectrum sensing algorithm based on reputation. The results of the conventional one threshold energy detection method are also shown for a parison. In our simulation, the mon simulation parameters are given as follows: Table 1. Simulation parameters depicts the performance of cooperative spectrum sensing and ..It can be observed that, pared it with the conventional method, the detection performance has improved significantly. For example, while = , our method achieves extra detection probability. shows the decrease of the normalized transmission bits for different values of fail sensing, . = 0, , , . Compared with conventional method, ., when = 0, the normalized average number of sensing bits is dramatically decreased and bandwidth limited problem of the reporting channel is relieved. For example, when = , almost 44% and 38% reduction of the normalized average number of sensing bits can be obtained for = and = , respectively. In our algorithm, is upper bounded and lower bounded because of the probability of fail sensing and the false alarm probability are based on (7), (13).Fig ., Fig .,=00,V. CONCLUSIONIn this paper, a new scheme in cooperative spectrum sensing for cognitive radio networks under bandwidth constraints was proposed. In our method, only the secondary users with reliable information are allowed to report their sensing results. When no user has reliable information, only he secondary user with highest reputation will report its sensing result. We analyzed the closed expression for the probability of the detection and the falsealarm. From the preliminary simulation results, we demonstrated the average number of sensing bits decrease greatly and the sensing performance is also improved.REFERENCES[1] Federal Communications Commission. Spectrum Policy Task Force, Rep. ET Docket no. 02135 [R]. Nov. 2002.[2] J. Mitola and G. Q. Maguire. Cognitive radio: Making software radios more personal[C],IEEE Personal Communication. vol. 6, pp. 13–18, Aug. 1999.[3] S. Haykin. Cognitive radio: brainempowered wireless munications [J]. IEEE J. Sel. Areas Communication. vol. 23, pp. 201–220, Feb. 2005.[4] AKYLDIZ IF. Next generation/dynamic spectrum access/cognitive radio wireless networks: A Survey [J]. ELSEVIER Computer Networks, 2006(50):21272159.[5] D. Cabric, S. M. Mishra, and R. W. Brodersen. Implementation issues in spectrum sensing for cognitive radios[C]// in Proc. Of A silomar Conf. on Signals