A Review on Training and Blind Equalization Algorithms for Wireless Communications

Every wireless communication system comes with an innate problem of multipath propagation, which results in spreading the resultant symbols on a time scale and thus causes the symbols to overlap and end up in Inter-symbol Interference (ISI). The overall signal is distorted and the receiver is unable to recover the original signal. ISI from the signal must be removed and the signal must be brought back to its original form as it was sent or as close to it as possible; and process of equalization is used in all wireless communication system for this purpose. Two types of equalization processes are common in modern wireless communication systems. Training based equalization requires the sender block of communication system to constantly send a pilot/training signal in order to update the receiver about the original signal. The receiver removes the ISI and extracts the unadulterated signal. The second equalization process is called blind equalization and it does not require any pilot signal. The receiver only needs to know the type of constellation scheme used in modulation and then the original signal is extracted based on that information. In this paper we have thoroughly reviewed four equalization algorithms, two from each type of equalization for 16-QAM constellation and 64-QAM constellation. We came up with constellation diagrams for each equalization algorithm and comparison of BER, residual ISI and MSE for 16-QAM and 64-QAM is done through simulations. In case of LMS and RLS algorithm for 16 QAM, the performance of RLS gets slightly better than LMS at 6 dB, however, at around 12–14 dB and onwards the BER of RLS leads and there is a significantly better BER than LMS. In future, we will compare these algorithms in order to figure out the best algorithms for the current and upcoming 5G and 6G communication technologies.

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