Max-Log-MAP Optimal MU-MIMO Receiver for Joint Data Detection and Interferer Modulation Classification

Automatic Modulation Classification Using Interacting Multiple Model Kalman Filter for Channel Estimation

“…Research on the JCEDD approach in the framework of blind AMC applications is extensive, and only a few notable studies are mentioned herein [10,[28][29][30][31][32][33][34]. The interest in JCEDD techniques is not new and growth is expected to continue, not only with AMC applications, but also with other pinnacle functions of CR systems like the SS and ACM.…”

“…The information exchange process is decomposed into two optimization loops, the outer and the inner. The outer loop is a global optimization algorithm, which searches for an optimal CSI estimate, while the inner loop is an ML algorithm that identifies the transmitted symbols [28][29][30][31][32][33]. These loops are interweaved, and sometimes called the upper level and the lower level, in tandem.…”

“…The JCEDD was also extended to MIMO-OFDM systems in the framework of sparse Bayesian learning techniques in [10]. It was also applied to the unknown co-scheduled user's modulation constellation size as part of the MU-MIMO detector in [33]. The per-survivor processing of best optimization path using the VA algorithm as an effective trellis search engine in the context of JCEDD was elaborated in [29,30].…”

“…Considering the MIMO configuration, this can be done by applying the MLE on the transmitted symbols and the channel matrix and then maximizing the joint conditional PDF over and together. Alternatively, this can be achieved by minimizing the following joint cost function [28][29][30][31][32][33][34]…”

“…where all the constants have been ignored as they will cancel each other while calculating LRTs. Namely, the joint ML for channel and data estimation is obtained as below [28][29][30][31][32][33][34] (̂,̂) = {̌,̌[ℱ( ,̌)]}…”