为了解决无线定位精度与复杂测距之间的矛盾,提出一种基于接收信号强度比较的非测距定位算法,接收信号强度比较(Received signal strength compare,RSSC)定位算法。为了满足认知无线电网络中主用户的非合作特性,RSSC算法不需要主用户与认知用户合作。通过比较认知用户所测量到的接收信号强度,逐步确定主用户所在的区域,取区域的质心作为主用户的位置估计。根据认知用户密度、用户密度和信噪比3种参数,对RSSC算法的性能进行了分析。实验结果表明,RSSC算法与其他非测距定位算法相比,能够明显提高定位精度。
Considering the dynamic changes and uncertainty features of the radio environment in cognitive wireless networks(CWNs),the environment cognition ability is critical for the performance evaluation of CWNs design and optimization.However,there are no effective metrics to evaluate the ability and gain of information cognition in CWNs from an information theory perspective.Therefore,the novel cognitive information concept is proposed and defined as a metric to evaluate the uncertainty of both the internal and external environments of one system that can be removed by other systems or nodes using cognitive radio techniques.As an intelligent wireless communication system that is aware of its surrounding radio,network,and user multi-domains environment,the more cognitive information it achieves,the higher level cognitive capability it is.In this paper,we define and analyze the mathematical features of cognitive information.Results reveal that the increase of cognitive information can improve the spectrum efficiency and reduce the interference probability simultaneously in CWNs.Thus cognitive information can be regarded as a metric for CWNs optimization.Finally,we apply the theory of cognitive information in the parameters optimization in energy detection and cooperative spectrum sensing.
This paper considers social welfare maximization for spatial resource sharing networks(SRSNs),in which multiple autonomous users are spatially located and mutual influence only occurs between nearby users.To cope with a lack of central control and the restriction that only local information is available,a spatial resource sharing game is proposed.However,individual selfishness in traditional game models generally leads to inefficiency and dilemmas.Inspired by local cooperative behavior in biological sys- tems,a community cooperation mechanism(CCM)is proposed to improve the efficiency of the game.Specifically,when a user makes a decision,it maximizes the aggregate payoffs for its local community rather than selfishly consider itself.It is analytically shown that with the bio-inspired CCM,the social optimum of SRSNs is achieved with an exchange of local information.The proposed bio-inspired CCM is very general and can be applied to various communication networks.
Although multiple-input-multiple-output （MIMO） detection has received much research attention in the past years, to the author＇s knowledge, few detection methods demonstrate optimal/near-optimal performance with low complexity. This paper proposes to incorporate automatic retransmission request （ARQ） with sub-optimal MIMO detectors so as to achieve both favorable performance and low complexity. In the study, retransmission delay induced by ARQ is exploited as a source of improving the detection performance of low complexity algorithms. In particular, the detection performance of sub-optimal algorithms improved by introducing ARQ is analyzed theoretically. A sufficient condition for such scheme to achieve full-diversity performance is also derived which relates detection performance with number of transmission times. Moreover, throughput cost by retransmission is deduced as well as its lower bound. The zero-forcing （ZF） equalizer cooperating with ARQ, as a case study, is shown to have evident performance improvement through theoretical analysis. And numerical results are presented to verify the effectiveness of the proposed scheme which boosts the performance of sub-optimal detector and possesses lower implementation complexity for practical reality simultaneously.
