Blind joint maximum likelihood channel estimation and data detection for single-input multiple-output systems

Abstract

Abstract-Blind and semiblind adaptive schemes are proposed for joint maximum likelihood (ML) channel estimation and data detection for multiple-input multiple-output (MIMO) systems. The joint ML optimisation over channel and data is decomposed into an iterative two-level optimisation loop. An efficient global optimisation search algorithm called the repeated weighted boosting search is employed at the upper level to identify the unknown MIMO channel model while an enhanced ML sphere detector called the optimised hierarchy reduced search algorithm aided ML detector is used at the lower level to perform the ML detection of the transmitted data. A simulation example is included to demonstrate the effectiveness of these two schemes.

Blind sequential detection for sparse ISI channels

“…The method introduced in [24], for example, finds an ML solution for a joint single-input multiple-output channel and sequence estimation problem using a two-step procedure: (1) channel estimates are obtained for every possible data sequence, and (2) the ML data sequence and corresponding channel estimate are selected. A least-squares (LS) approach is used to implement step 1, and the VA is used to implement step 2.…”

A Novel Semi-blind Channel Estimation Scheme for Rayleigh Flat Fading MIMO channels (joint LS estimation and ML detection)

“…As depicted in Figure 2, in an iterative structure, output of the estimator is applied to the detector for detecting data bits and also output of detector is applied to the estimator as virtual bits and to estimate the channel again. This iterative procedure runs until a criterion is achieved [36][37][38][39][40][41][42]. For example, difference in estimation for two successive iterations is lower than a level.…”

Joint Maximum Likelihood Channel Estimation and Data Detection for MIMO Systems

“…The RWBS algorithm is a simple yet efficient global search algorithm. In [21], both the genetic algorithm (GA) and the RWBS algorithm were used to find the ML channel and data estimation for single-input multiple-output systems, and it was seen that the RWBS algorithm achieved slightly better accuracy at the same convergence speed as the GA. The RWBS algorithm has additional advantages of requiring minimum programming effort and having fewer algorithmic parameters to tune.…”

“…In this paper we propose blind and semi-blind joint maximum likelihood (ML) channel and data estimation schemes for MIMO channels. Our work extends the approach developed in [18]- [21], in which the joint ML optimisation process for channel and data estimation is decomposed into two levels. At the upper level a global optimisation algorithm searches for an optimal channel estimate, while at the lower level an ML data detector decodes the transmitted data.…”