This paper focuses on the performance analysis and comparison of hard decision (HD) and soft decision (SD) based approaches for cooperative spectrum sensing in the presence of reporting channel errors. For cooperative sensing (CS) in cognitive radio networks, a distributed detection approach with displaced sensors and a fusion center (FC) is employed. For HD based CS, each secondary user (SU) sends a one-bit hard local decision to the FC. For SD based CS, each SU sends a quantized version of a local decision statistic such as the log-likelihood ratio or any suitable sufficient statistic. The decision statistics are sent through channels that may cause errors. The effects of channel errors are incorporated in the analysis through the bit error probability (BEP). For HD based CS, the counting rule or the -out-of-rule is used at the FC. For SD based CS, the optimal fusion rule in the presence of reporting channel errors is derived and its distribution is established. A comparison of the two schemes is conducted to show thatthere is a performance gain in using SD based CS even in the presence of reporting channel errors. In addition, a BEP wall is shown to exist for CS such that if the BEP is above a certain value, then irrespective of the received signal strength corresponding to the primary user, the constraints on false alarm probability and detection probability cannot be met. It is shown that the performance of HD based CS is very sensitive to the BEP wall phenomenon while the SD based CS is more robust in that sense.
The main focus of this paper is to present a performance limitation of collaborative spectrum sensing in cognitive radios with imperfect reporting channels. We consider hard decision (HD) based cooperative sensing (CS), in which each SU sends a one-bit binary decision corresponding to the absence or the presence of primary user (PU) to a fusion center (FC). Each SU sends the hard decision over a reporting channel that may cause bit errors. The effect of reporting channel errors is modeled through the widely used bit error probability (BEP). The FC fuses the local binary decisions from all the SUs to make a final decision. Counting rule or K-out-of-N fusion rule is considered for CS and its performance is studied using analytical tools and simulations. Under the constraints on the error probabilities of false alarm and missed detection, a performance limitation in the form of a BEP wall is shown to exist for the counting rule. If the BEP of the reporting channel is above the BEP wall value, then constraints on the cooperative detection performance cannot be met at the FC irrespective of the received signal quality on the listening channel or the sensing time at the SUs. Expressions for the BEP walls are presented for K-out-of-N fusion rules in terms of the error probabilities at the FC and the number of SUs collaborating. The BEP wall values are shown to be sufficiently low to be of practical importance.
A simple and efficient spectrum sensing scheme for Orthogonal Frequency Division Multiplexing (OFDM) signals of primary user in cognitive radio systems is proposed in this paper. A detector exploiting the well-known autocorrelation property of cyclic prefix (CP) based OFDM signals is developed. The proposed scheme is then extended to the case of many secondary users collaborating in order to detect the primary user in the face of shadowing and fading. The amount of information each user sends to other users or fusion center is constrained by censoring scheme where only informative decision statistics are sent. Censoring allows reducing the power consumption in battery operated mobile terminals. The statistical properties of the decision statistics are established. Limits on the censoring region are found under constraints on false-alarm and transmission rates. The distribution of the test statistics for cooperative detection with censoring is approximated using characteristic functions. The performance of the scheme is studied by simulations.
The analyzed system model in this paper is a distributed parallel detection network in which each secondary user (SU) evaluates the energy-based test statistic from the received observations and sends it to a fusion center (FC), which makes the final decision. Uncertainty in the noise variance at each SU is modeled as an unknown constant in a certain interval around the nominal noise variance. It is assumed that the SUs are heterogeneous in that the nominal noise variances and the uncertainty intervals can be different for different SUs. Moreover, the received signal power at each SU may be different. For the considered system model, the paper presents important results for two interrelated themes on cooperative energy detection (CED) in the presence of noise uncertainty (NU). First, the expressions for generalized SNR walls are derived for the classical CED fusion rule, i.e., sum of energies from all SUs. Second, a Dempster-Shafer theory (DST) based CED is proposed in the presence of NU with heterogeneous sensors. In the proposed scheme, the test statistic from each SU is the energy-based basic mass assignment (BMA) values, which are first discounted depending on the uncertainty level associated with the SU and then fused at the FC using the Dempster rule of combination to arrive at the global decision. It is shown that the proposed scheme outperforms the traditional sum fusion rule in terms of detection performance as well as the location of SNR wall.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.