Threshold Based Censoring of Cognitive Radios in Rician Fading Channel

Abstract

This paper describes about the performance analysis and comparison between hard decision (majority rule) logic and soft decision (maximal ratio combining) logic approaches using cooperative spectrum sensing (CSS) with censoring scheme. We have considered both sensing channel (S-channel) and reporting channel (R-channel) are affected by Rician fading. Radio links present in the channel gets heavily faded and the information received at the fusion center (FC) becomes erroneous because of fading effect present in the channel. Threshold based censoring scheme is used to eliminate the heavily faded cognitive radios (CRs) in R-channel. Two fusion rules namely, majority logic and maximal ratio combining (MRC) are considered individually at the fusion center to estimate the performance of CSS by censoring of R-channel links. The performance is evaluated in terms of missed detection probability (Q m ) and total error probability (Q m þ Q f ) in both majority logic and MRC logic. Comparison table between perfect and imperfect channels is provided to know which fusion rule (MRC, majority) performs better under Rician fading. Simulations are performed under both perfect and imperfect channels by varying the network parameters like probability of false alarm (P f ), S-channel SNR, R-channel SNR, Rician fading parameter (K) and number of CR users (N).

Optimized cooperative spectrum sensing network analysis in nonfading and fading environments

“…Finally, the actual decision of PU is made at FC using fusion rules. The received signal at the input of j th CR is shown in : …”

Spectral Vacancy Prediction Using Time Series Forecasting for Cognitive Radio Applications

“…System parameter performance with an IED scheme is examined over channel models is done in [12]. The analogous study in [13][14][15][16] using Nakagami-m and Weibull fading channel with ED method is done since it does not require prior information about the channel. A classifier [16] with energy statistics of PU along with decisions about the existence of PU is used for training.…”