Asymptotic Error Rate Analysis of Selection Combining on Generalized Correlated Nakagami-m Channels

• Published in: IEEE transactions on communications Vol. 60; no. 7; pp. 1765 - 1771
• Main Authors: Xianchang Li, Cheng, Julian
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.07.2012; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Applied sciences; Approximation; Approximation methods; Asymptotic properties; Channels; Correlated fading; Correlation; Covariance matrix; Diversity reception; Error analysis; Errors; Exact sciences and technology; Exact solutions; Fading; generalized Marcum Q-function; Indexing in process; Mathematical analysis; Nakagami-m fading; Outages; selection combining; Signal to noise ratio; Systems, networks and services of telecommunications; Telecommunications; Telecommunications and information theory; Transmission and modulation (techniques and equipments); Upper bound; Fading channels; Estimation error; Error analysis; Error estimation; Outage; Correlated fading; Generalized function; Large signal behavior; Error rate; Marcum function; Covariance matrix; Closed form equation; generalized Marcum Q-function; Nakagami fading; Diversity combining; Nakagami-m fading; selection combining; Signal to noise ratio
• ISSN: 0090-6778; 1558-0857
• DOI: 10.1109/TCOMM.2012.060112.110146

From the canonical representation of the Marcum Q-function, a simple and highly accurate small argument approximation for the Marcum Q-function is first obtained. Utilizing this approximation, we derive the asymptotic error rate and outage probability expressions for multi-branch selection combining over generalized correlated Nakagami-m fading channels. These closed-form solutions can be used to provide rapid and accurate estimation of the error rates and outage probabilities in large signal-to-noise ratio regimes. It is shown that asymptotic error rates and outage probability over correlated Nakagami-m branches can be obtained by scaling the asymptotic error rates and outage probability over independent branches with a factor det m (M), where det(M) is the determinant of matrix M whose elements are the square root of the corresponding elements in the branch power covariance coefficient matrix.