Improved local spectrum sensing for cognitive radio networks

Abstract

The successful deployment of dynamic spectrum access requires cognitive radio (CR) to more accurately find the unoccupied portion of the spectrum. An accurate spectrum sensing technique can reduce the probability of false alarms and misdetection. Cooperative spectrum sensing is usually employed to achieve accuracy and improve reliability, but at the cost of cooperation overhead among CR users. This overhead can be reduced by improving local spectrum sensing accuracy. Several signal processing techniques for transmitter detection have been proposed in the literature but more sophisticated approaches are needed to enhance sensing efficiency. This article proposes a two-stage local spectrum sensing approach. In the first stage, each CR performs existing spectrum sensing techniques, i.e., energy detection, matched filter detection, and cyclostationary detection. In the second stage, the output from each technique is combined using fuzzy logic in order to deduce the presence or absence of a primary transmitter. Simulation results verify that our proposed technique outperforms existing local spectrum sensing techniques. The proposed approach shows significant improvement in sensing accuracy by exhibiting a higher probability of detection and low false alarms. The mean detection time of the proposed scheme is equivalent to that of cyclostationary detection.

Heterogeneous Spectrum Sensing in Cognitive Radio Network using Traditional Energy Detection and Matched Filter

“…We consider a heterogeneous cooperative spectrum sensing for a centralized network consisting of a cognitive base station (fusion centre) and a number of nodes collaborating in a non fading (AWGN) environment [17] with perfect reporting and sensing channels. The network assumed employs the optimized energy detection described in section II, at every node, for local sensing and adopts hard combination for information fusion at the fusion centre.…”

“…The network assumed employs the optimized energy detection described in section II, at every node, for local sensing and adopts hard combination for information fusion at the fusion centre. The simulations are done in MATLAB version R2013a for SNR= -14 db, the number of received signal samples L=100 [18] and for three sets of SU nodes such that the ratio of number of nodes operating on traditional ED to the number of nodes operating on MF is always 2.33 for optimal results. In this paper, the technique for detection of the spectrum using a heterogeneous model employing the use of energy detection and matched filter is presented.…”

I3S: Intelligent spectrum sensing scheme for cognitive radio networks

“…In [5], a two-stage fuzzy logic-based detection (FLD) scheme is proposed. In the first stage, each CR performs existing spectrum sensing techniques, i.e., energy detection, matched filter detection, and cyclostationary detection.…”

“…This approach can be very healthy for CR applications for disaster management; smart grid, and so on to get reliable sensing results with minimum sensing time. Many improved local sensing schemes are proposed in [5][6][7][8][9][10][11][12], including our own fuzzy logic-based and SNR-based adaptive spectrum sensing for improved local sensing. In the proposed scheme, channels with known PU waveform will be sensed by matched filter detection and rest of the channels by the detectors which do not need dedicated circuitry and prior knowledge of PU waveform.…”

Optimal Entropy-Based Cooperative Spectrum Sensing for Maritime Cognitive Radio Networks

“…The most renowned local spectrum sensing schemes are energy detection, entropy detector, matched filter and cyclostationary feature detection [4]. There are many improved local sensing schemes [5,6] and advanced cooperative spectrum sensing schemes [7,8] proposed in the literature. However, none of them has been effective at overcoming the unique challenges associated with MCRN, which includes radio wave propagation over water, surface reflection and wave occlusions.…”