Performance of an energy detector over η-μ/lognormal composite channel with cooperative sensing. Issue 8 (3rd August 2018)

• Record Type: Journal Article
• Authors: Bhatt, Manisha; Soni, Sanjay Kumar
• Is Part Of: International journal of electronics. Volume 105:Issue 8(2018)
• Page Start: 1273
• Page End: 1285
• Publication Date: 2018-08-03
• Subjects: η-μ/lognormal composite distribution -- complementary receiver operating characteristic (CROC) -- diversity -- energy detection -- cooperative spectrum sensing; Electronics -- Periodicals; Electronic control -- Periodicals; 621.381
• DOI: 10.1080/00207217.2018.1440428
• Languages: English
• ISSNs: 0020-7217

Abstract

ABSTRACT: The η-μ/lognormal composite distribution corresponds to a physical realistic scenario where the multipath effect is characterised by well-known η-μ generalised model and shadowing effect is captured by the lognormal (LN) distribution. In this work, an approximate closed-form of η-μ/LN distribution is derived using Gauss-Hermite (GH) integration. Further, the derived probability density function is utilised to develop the closed-form expression as well as infinite series representation of the average probability of energy detection (PD) and average area under the receiver operating characteristics curve (AUC) along with their respective upper bound of the truncation error. Maximal ratio combining (MRC) microdiversity technique is applied over η-μ/LN composite fading model and then its average PD is derived. In order to reduce the total probability of error, the optimisation of the detection threshold parameter is done. The result clearly shows the significant improvement in the detection probability when the optimised threshold parameter is used. Further, an extensive analysis of the optimisation of the cooperative spectrum sensing (CSS) over η-μ/LN distribution with 'v-out-of-U' rule at fusion center assuming erroneous feedback channels is also carried out. The closed-form expressions are validated by comparing them with exact results and Monte Carlo simulation.