Stochastic gradient algorithms for equalisation in αα-stable noise

This paper addresses the problem of developing a least mean squares (LMS) style decision feedback equaliser algorithm for minimising bit error rate (BER) in impulsive noise environments characterised by the αα-stable distribution. The development exploits the stable nature of the αα-distribution and the concepts built on an earlier work in a Gaussian noise environment. Further, a Wiener-filter-with-limiter solution is also presented and used as a performance bench mark. An improvement in convergence and BER performance is achieved by using a minimum bit error rate (MBER) cost function instead of a conventional LMS-based design. The ability of least BER (LBER) equalisers based on a Gaussian noise assumption to operate in an αα-stable noise environment is also highlighted.