scholarly journals Applications of Deep Learning for Millimeter Wave: A Review

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Saveeta Bai ◽  
◽  
Muhammad Rauf ◽  
Abid Khan ◽  
Suresh Kumar ◽  
...  
Keyword(s):  
Deep Learning ◽  
Channel Estimation ◽  
Millimeter Wave ◽  
Communication Systems ◽  
Indoor Localization ◽  
Advanced Technology ◽  
Sparse Channel Estimation ◽  
Wave Channel ◽  
5G Network ◽  
Traditional Approaches

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.

scholarly journals Millimeter Wave Channel Estimation and Beam forming Techniques

2019 ◽  
Vol 9 (2S3) ◽  
pp. 387-391
Keyword(s):  
Channel Estimation ◽  
Millimeter Wave ◽  
Communication Systems ◽  
Beam Forming ◽  
Frequency Range ◽  
Codebook Design ◽  
Wave Channel ◽  
Signal Parameters ◽  
Communication Equipment ◽  
Millimeter Wave Communication

Millimeter-wave(mm-wave) frequencies offer higher bandwidth communication channels. Most of the wireless communication equipment operates with 6GHZ carrier frequencies, where the millimeter-wave communication systems are operated in the frequency range of 30GHZ to 300GHZ. Channel estimation will become a difficult task when the number of antennas is more than the number of radiofrequency chains. Millimeter-wave communication has received large attention from research fraternity and industry. In this paper, we listed the evolution and advancements in antenna beam forming, at the same time we outline some of the channel estimation techniques such as Auxiliary Beam pair (ABP), Adaptive algorithm based Codebook design(AABCD), Estimation of Signal Parameters by Rotational Invariance Technique (ESPRIT).

scholarly journals Research on 5G Millimeter Wave Channel Estimation Algorithm

2019 ◽  
pp. 74-77
Author(s):  
Xu Shuang
Keyword(s):  
Channel Estimation ◽  
Millimeter Wave ◽  
Massive Mimo ◽  
Large Scale ◽  
Low Frequency ◽  
Estimation Algorithm ◽  
Simulation Research ◽  
Wave Technology ◽  
Wave Channel ◽  
Mimo Technology

With the explosive growth in the number of communication users and the huge demand for data from users, Limited low-frequency resources have been far from being satisfied by users. The combination of Massive MIMO technology and millimeter-wave technology has brought new hope to users. In this paper, several basic algorithms are placed under the millimeter wave large-scale antenna channel for simulation research.

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