Applications of Deep Learning for Millimeter Wave: A Review

Millimeter Wave (mm-wave) has been considered as significant importance in various communication systems. It has achieved a greater attention to meet the capacity requirement of the future 5G network. Since mm-wave has a high frequency (30 to 300 GHz) using orthodox technologies for mm wave is more challenging. Thus advanced technology i.e. Deep Learning (DL) is a pragmatic approach to analyze a massive amount of data. Firstly, to find out how DL has beaten traditional approaches, this review briefly explores, the different methods of DL for mm wave are. Secondly, the review of the multiple applications in mm wave such as beam and blockages prediction, beam spacing, beamforming for mm wave OFDM system, precoding for mm-wave, channel estimation for mm-wave, sparse channel estimation, and hybrid precoding and fingerprinting-based indoor localization with mm wave is concisely explained. Last but not least, several studies have proved that DL has superior efficiency for mm wave than conventional approaches.

A new framework for utilizing side information in sparse representation

An underdetermined system of linear equation has infinitely number of answers. To find a specific solution, regularization method is used. For this propose, we define a cost function based on desired features of the solution and that answer with the best matches to these function is selected as the desired solution. In case of sparse solution, zero-norm function is selected as the cost function. In many engineering cases, there is side information which are omitted because of the zero-norm function. Finding a way to conquer zero-norm function limitation, will help to improve estimation of the desired parameter. In this regard, we utilize maximum a posterior (MAP) estimation and modify the prior information such that both sparsity and side information are utilized. As a consequence, a framework to utilize side information into sparse representation algorithms is proposed. We also test our proposed framework in orthogonal frequency division multiplexing (OFDM) sparse channel estimation problem which indicates, by utilizing our proposed system, the performance of the system improves and fewer resources are required for estimating the channel.

Multi Stage Kalman Filter (MSKF) Based Time-Varying Sparse Channel Estimation with Fast Convergence

<div>Multi Stage Kalman Filter (MSKF) Based Time-Varying Sparse Channel Estimation with Fast Convergence</div>Submitted to IEEE Journal