Saman Atapattu

Energy Detection Based Cooperative Spectrum Sensing in Cognitive Radio Networks

Detection performance of an energy detector used for cooperative spectrum sensing in a cognitive radio network is investigated over channels with both multipath fading and shadowing. The analysis focuses on two fusion strategies: data fusion and decision fusion. Under data fusion, upper bounds for average detection probabilities are derived for four scenarios: 1) single cognitive relay; 2) multiple cognitive relays; 3) multiple cognitive relays with direct link; and 4) multi-hop cognitive relays. Under decision fusion, the exact detection and false alarm probabilities are derived under the generalized k-out-of-n fusion rule at the fusion center with consideration of errors in the reporting channel due to fading. The results are extended to a multi-hop network as well. Our analysis is validated by numerical and simulation results. Although this research focuses on Rayleigh multipath fading and lognormal shadowing, the analytical framework can be extended to channels with Nakagami-m multipath fading and lognormal shadowing as well.

Performance of an Energy Detector over Channels with Both Multipath Fading and Shadowing

This paper analyzes the performance of an energy detector over wireless channels with composite multipath fading and shadowing effects. These effects are modeled by using the K and K_G channel models. Closed-form average detection probabilities are derived for both K and K_G channel models for the no-diversity reception case. A simple approximation is also derived for large values of energy threshold in the energy detector. The analysis is then extended to cases with diversity receptions including maximal ratio combining (MRC) and selection combining (SC). Analytical results are verified by Monte Carlo simulation and by numerical methods. Receiver operating characteristic (ROC) curves are presented for different degrees of multipath fading and shadowing. Finally, the Rayleigh-lognormal distribution and the K distribution are numerically compared, and the validity of the K channel model for representing the impact of shadowing on the performance of energy detection is affirmed.

Analysis of area under the ROC curve of energy detection

A simple figure of merit to describe the performance of an energy detector is desirable. The area under the receiver operating characteristic (ROC) curve, denoted (AUC), is such a measure, which varies between 1/2 and 1. If the detectors performance is no better than flipping a coin, then the AUC is 1/2 , and it increases to one as the detector performance improves. However, in the wireless literature, the AUC measure has gone unnoticed. In this paper, to address this gap, we comprehensively analyze the AUC of an energy detector with no-diversity reception and with several popular diversity schemes. The channel model is assumed to be Nakagami-m fading. First, the average AUC is derived for the case of no-diversity reception. Second, the average AUC is derived for diversity reception cases including maximal ratio combining (MRC), square-law combining (SLC) and selection combining (SC). Further, for Rayleigh fading channels, the impacts of channel estimation errors and fading correlations are analyzed. High SNR (signal-to-noise ratio) approximations and the detection diversity gain are also derived. The analytical results are verified by numerical computations and by Monte-Carlo simulations.

A Mixture Gamma Distribution to Model the SNR of Wireless Channels

Composite fading (i.e., multipath fading and shadowing together) has increasingly been analyzed by means of the K channel and related models. Nevertheless, these models do have computational and analytical difficulties. Motivated by this context, we propose a mixture gamma (MG) distribution for the signal-to-noise ratio (SNR) of wireless channels. Not only is it a more accurate model for composite fading, but is also a versatile approximation for any fading SNR. As this distribution consists of N (≥ 1) component gamma distributions, we show how its parameters can be determined by using probability density function (PDF) or moment generating function (MGF) matching. We demonstrate the accuracy of the MG model by computing the mean square error (MSE) or the Kullback-Leibler (KL) divergence or by comparing the moments. With this model, performance metrics such as the average channel capacity, the outage probability, the symbol error rate (SER), and the detection capability of an energy detector are readily derived.

Relay Selection Schemes and Performance Analysis Approximations for Two-Way Networks

This paper studies relay selection schemes for two-way amplify-and-forward (AF) relay networks. For a network with two users that exchange information via multiple AF relays, we first consider a single-relay selection (SRS) scheme based on the maximization of the worse signal-to-noise ratio (SNR) of the two end users. The cumulative distribution function (CDF) of the worse SNR of the two users and its approximations are obtained, based on which the block error rate (BLER), the diversity order, the outage probability, and the sum-rate of the two-way network are derived. Then, with the help of a relay ordering, a multiple-relay selection (MRS) scheme is developed. The training overhead and feedback requirement for the implementation of the relay selection schemes are discussed. Numerical and simulation results are provided to corroborate the analytical results.

Relay Based Cooperative Spectrum Sensing in Cognitive Radio Networks

In this paper, we exploit cooperative spectrum sensing technique for applications in a relay based cognitive radio network. Relays are assigned in cognitive radio networks to transmit the primary users signal to a cognitive coordinator. This research is focused on the detection of primary user in single or multiple cognitive relay scenarios. The performance of energy detector is analyzed for independent Rayleigh fading channels. False alarm and detection probabilities are derived theoretically with or without direct communication between the primary user and the cognitive coordinator. An upper bound is also given for detection probability. Our analysis is validated by numerical and simulation results.

Spectrum Sensing via Energy Detector in Low SNR

As in IEEE 802.22 proposal, the spectrum sensing technique should be capable enough to sense the primary signal with very low receiver sensitivity such as at -116 dBm. In this paper, the detection performance of an energy detector used for the spectrum sensing in cognitive radio networks is investigated under very low signal-to-noise ratio (SNR) level. The analysis focuses on the derivation of a closed-form expression for the average missed-detection probability over Rayleigh fading and Nakagami-

Energy detection of primary signals over η - μ fading channels

m

Relay Selection and Performance Analysis in Multiple-User Networks

fading channels. Subsequently, the detection threshold is optimized as minimizing the total error rate. The analysis is validated by numerical and simulation results. The sensing requirements defined in IEEE 802.22 are also discussed with numerical examples.

Representation of Composite Fading and Shadowing Distributions by Using Mixtures of Gamma Distributions

In this paper1, the performance analysis of an energy detector is exploited under the η-μ fading channel model. The received unknown signal at the energy detector is considered as the primary signal transmitted by a primary network user (e.g. cognitive radio network). This research is focused on derivation of the closed-form average detection probability over the η - μ fading channel model. Further, results are extended to two diversity reception cases such as maximal ratio combining (MRC) and square-law combining (SLC) techniques with independent fading channels. Detection performance is discussed with receiver operating characteristics (ROC) curves. Our analysis is validated by numerical and simulation results.