Recently, sparsity-aware least mean square (LMS) algorithms have been proposed to improve the performance of the standard LMS algorithm for various sparse signals, such as the well-known zero-attracting LMS (ZA-LMS) algorithm and its reweighted ZA-LMS (RZA-LMS) algorithm. To utilize the sparsity of the channels in wireless communication and one of the inherent advantages of the RZA-LMS algorithm, we propose an adaptive reweighted zero-attracting sigmoid functioned variable-step-size LMS (ARZA-SVSS-LMS) algorithm by the use of variable-step-size techniques and parameter adjustment method. As a result, the proposed ARZA-SVSS-LMS algorithm can achieve faster convergence speed and better steady-state performance, which are verified in a sparse channel and compared with those of other popular LMS algorithms. The simulation results show that the proposed ARZA-SVSS-LMS algorithm outperforms the standard LMS algorithm and the previously proposed sparsity-aware algorithms for dealing with sparse signals. Copyright
SUMMARYAn ISI-free power roll-off pulse, the roll-off characteristic of which is tunable with one power parameter, is proposed. It is shown that the proposed pulse is advantageous in terms of the probability of error for pulse detection in the presence of a timing error among currently known good pulses, among which the raised cosine pulse, "better than" raised cosine pulse, and polynomial pulse are considered. key words: intersymbol interference, pulse analysis, pulse shaping, timing error
To make use of the sparsity property of broadband multipath wireless communication channels, we mathematically propose an lp-norm-constrained proportionate normalized least-mean-square (LP-PNLMS) sparse channel estimation algorithm. A general lp-norm is weighted by the gain matrix and is incorporated into the cost function of the proportionate normalized least-mean-square (PNLMS) algorithm. This integration is equivalent to adding a zero attractor to the iterations, by which the convergence speed and steady-state performance of the inactive taps are significantly improved. Our simulation results demonstrate that the proposed algorithm can effectively improve the estimation performance of the PNLMS-based algorithm for sparse channel estimation applications.
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