Novel Approach for ML Detection with Reduced Complexity

Abstract

For orthogonal frequency division multiplexing (OFDM) technology, if the number of antenna or modulation increases, the most accurate maximum likelihood (ML) detection will become extremely complex. A new suggestion for an algorithm is introduced in this paper with a significantly reduced calculation complexity to perform an optimal ML detection. With the use of minimum mean square error (MMSE) criterion, the suggested method filtered and avoids undependable candidate signals from data streams. While using the metric probability to analysis the dependability of each and every symbol candidate with normalized likelihood functions, the most accurate ML detection is achieved. The authenticity of suggested method is strengthen with the performance examine. To offset a balance between system performance and calculation complexity, a threshold parameter is introduced. The suggested method achieved nearly comparable results as in the ML detection at a bit error rate (BER) of 10−4 with 29 and 16% of true calculations with the conventional QR methods.