Minimize MIMO OFDM interference and noise ratio using polynomial-time algorithm

In the distributed transmit antenna MIMO OFDM system, each transmitting antenna has different frequency offset between each transmitting antenna and receiver due to the use of independent crystal oscillator. This paper proposes Polynomial-time algorithm for correcting the frequency offset in a received signal by maximizing the conditional average signal. The algorithm focus on reducing to interference and noise ratio of each subcarrier on the receiving antenna by frequency offset. The simulation result shows the performance of the proposed algorithm is slightly improved compared with the existing frequency offset correction algorithm, and the complexity is reduced by 50% or more

On Investigations of Machine Learning and Deep Learning Techniques for MIMO Detection

This paper reviews in detail the various types of multiple input multiple output (MIMO) detector algorithms. The current MIMO detectors are not suitable for massive MIMO (mMIMO) scenarios where there are a large number of antennas. Their performance degrades with the increase in number of antennas in the MIMO system. For combatting the issues, machine learning (ML) and deep learning (DL) based detection algorithms are being researched and developed. An extensive survey of these detectors is provided in this paper, alongwith their advantages and challenges. The issues discussed have to be resolved before using them for final deployment