Performance and Complexity Analysis of Blind FIR Channel Identification Algorithms Based on Deterministic Maximum Likelihood in SIMO Systems

Abstract

We analyze two algorithms that have been introduced previously for Deterministic Maximum Likelihood (DML) blind estimation of multiple FIR channels. The first one is a modification of the Iterative Quadratic ML (IQML) algorithm. IQML gives biased estimates of the channel and performs poorly at low SNR due to noise induced bias. The IQML cost function can be “denoised” by eliminating the noise contribution: the resulting algorithm, Denoised IQML (DIQML), gives consistent estimates and outperforms IQML. Furthermore, DIQML is asymptotically globally convergent and hence insensitive to the initialization. Its asymptotic performance does not reach the DML performance though. The second strategy, called Pseudo-Quadratic ML (PQML), is naturally denoised. The denoising in PQML is furthermore more efficient than in DIQML: PQML yields the same asymptotic performance as DML, as opposed to DIQML, but requires a consistent initialization. We furthermore compare DIQML and PQML to the strategy of alternating minimization w.r.t. symbols and channel for solving DML (AQML). An asymptotic performance analysis, a complexity evaluation and simulation results are also presented. The proposed DIQML and PQML algorithms can immediately be applied also to other subspace problems such as frequency estimation of sinusoids in noise or direction of arrival estimation with uniform linear arrays.