Application of Fast Euclidean Direction Search (FEDS) method for smart antennas in mobile communications systems

This paper presents a new study in an attempt to apply Fast Euclidean Direction Search (FEDS) algorithm on adaptive smart antennas. The novelty in this work is the use of FEDS algorithm in a smart antenna system for mobile communications because up to our knowledge this algorithm is not applied on smart antenna so far. In order to properly evaluate the performance of smart antenna for mobile communication systems, an Additive White Gaussian Noise (AWGN) channel model is required that each received signal at each element in the array includes only an additive, zero mean, Gaussian noise . The ability of the FEDS algorithm to operate in a fast changing signal environment is examined by subjecting the input signal to Rayleigh fading with a Jakes power spectral density which is a popular choice for land mobile communications systems. Through simulation results of smart antennas for AWGN channel, the RLS and FEDS start to converge from the initial iteration and 10 respectively, whereas, the LMS and NLMS start to converge after 50 and 10 iterations respectively. In the presence of the Rayleigh fading channel, the LMS, and NLMS algorithms converge after 60 and 25 iterations respectively, while the RLS convergence from the initial iteration and FEDS converge from the iteration number 25. On the other hands, the FEDS algorithm shows better tracking and estimation of the desired input signal compared with both LMS and NLMS algorithms and comparable with RLS algorithm. The comparison of linear plot of array factor using FEDS and RLS , both algorithms generates the same deeper null for undesired signals of about í30 dB towards the interferer using an AWGN channel, while it generates deeper null of about -23 dB and -30 dB respectively using the Rayleigh fading channel.