Adaptive signal combining with unequal channel noise variances
Qureshi, A., Kanakis, T. and Rapajic, P. (2010) Adaptive signal combining with unequal channel noise variances. International Journal of Simulation: Systems, Science and Technology, 11 (6). pp. 14-21. ISSN 1473 804x (online), 1473 8031 (print)Full text not available from this repository.
This paper demonstrates the signal combining by use of adaptive algorithms for wireless communication networks. The proposed adaptive combiner operates under different communication channel noise variances on each branch of the multi-antenna receiver. The used algorithm for adaptive signal combining is least mean square (LMS) algorithm based on Newton’s Recursion Method. The proposed algorithm uses inverse of noise variances estimate in step size of adaptive algorithm. It is shown that the adaptive combining filter with LMS converges with respect to signal to noise ratio (SNR) and not to the received power. Simulation results in Gaussian channels with different channel noise variances shows that proposed scheme provide performance very close to wiener’s solution of signal combining. It provide 10−4 bit error rate (BER) at 10.dB SNR. The performance of system in Flat Fading Raleigh channels is about 10−5 at 20 dB SNR. Where as the classical maximum ratio combining, adaptive combining with classical LMS and recursive least square algorithm provide 10−3 bit error rate at 20 dB SNR. The improvement in MSE and BER performance with purposed algoritm is more obvious when two independent signals arrive at the receiver of communication terminal (on multiple antennas) with 10dB of SNR difference, which is very common situation in wireless communication systems.
|Uncontrolled Keywords:||adaptive signal combining, unequal noise variances, RLS, LMS, Newton’s recursion base LMS, optimum signal combining, optimum adaptive signal combining|
|Subjects:||T Technology > T Technology (General)|
T Technology > TK Electrical engineering. Electronics Nuclear engineering
|School / Department / Research Groups:||School of Engineering|
School of Engineering > Department of Computer & Communications Engineering
|Last Modified:||27 Feb 2012 10:54|
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