Ergodic UL/DL capacity analysis of co-located and distributed antenna configuration for high speed train with massive MIMO system

Author(s):  
Maharshi K. Bhatt ◽  
Bhavin S. Sedani ◽  
K. R. Parmar ◽  
Mansi P. Shah
IEEE Access ◽  
2017 ◽  
Vol 5 ◽  
pp. 5936-5946 ◽  
Author(s):  
Xuhong Chen ◽  
Jiaxun Lu ◽  
Tao Li ◽  
Pingyi Fan ◽  
Khaled Ben Letaief

2019 ◽  
Vol 68 (3) ◽  
pp. 2077-2086 ◽  
Author(s):  
Yu Liu ◽  
Cheng-Xiang Wang ◽  
Jie Huang ◽  
Jian Sun ◽  
Wensheng Zhang

Author(s):  
Maharshi K. Bhatt ◽  
Bhavin S. Sedani ◽  
Komal Borisagar

This paper analytically reviews the performance of massive multiple input multiple output (MIMO) system for communication in highly mobility scenarios like high speed Railways. As popularity of high speed train increasing day by day, high data rate wireless communication system for high speed train is extremely required. 5G wireless communication systems must be designed to meet the requirement of high speed broadband services at speed of around 500 km/h, which is the expected speed achievable by HSR systems, at a data rate of 180 Mbps or higher. Significant challenges of high mobility communications are fast time-varying fading, channel estimation errors, doppler diversity, carrier frequency offset, inter carrier interference, high penetration loss and fast and frequent handovers. Therefore, crucial requirement to design high mobility communication channel models or systems prevails. Recently, massive MIMO techniques have been proposed to significantly improve the performance of wireless networks for upcoming 5G technology. Massive MIMO provide high throughput and high energy efficiency in wireless communication channel. In this paper, key findings, challenges and requirements to provide high speed wireless communication onboard the high speed train is pointed out after thorough literature review. In last, future research scope to bridge the research gap by designing efficient channel model by using massive MIMO and other optimization method is mentioned.


Author(s):  
Diwakar Bhardwaj ◽  

Massive MIMO (M-MIMO) system comprises of multiple number of antennas to achieve energy- efficiency and large gains in spectral-efficiency in comparison to existing MIMO technology. High speed and Quality of Experience (QoE) of video data over wireless communication has always been a challenge for the researchers due to scarcity of the bandwidth, fading and interference. The channels with high noise corrupt the transmitted video and results in poor QoE of at the receiver. Therefore, to maintain the quality of transmitted video, it is highly desirable to identify noisy channels and avoid transmission over them. This paper deals with QoE of the transmitted video over Massive MIMO channels. The channels are categorized into two categories: good and bad depending upon the value of Signal to Interference and Noise Ratio (SINR). A channel above the minimum acceptable value (threshold) of SINR is categorized as good channel otherwise bad channel. A Guided MAC layer (GMAC) protocol is designed to transmit the video data over good channels only and to discard the transmission over bad channels.


Electronics ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 36 ◽  
Author(s):  
Seoyoung Yu ◽  
Jeong Woo Lee

We propose a generation scheme for a sounding reference signal (SRS) suitable for supporting a large number of users in massive multi-input multi-output (MIMO) system with a distributed antenna system (DAS) environment. The proposed SRS can alleviate the pilot contamination problem which occurs inherently in the multi-user system due to the limited number of orthogonal sequences. The proposed SRS sequence is generated by applying a well-chosen phase rotation to the conventional LTE/LTE-A SRS sequences without requiring an increased amount of resource usage. We also propose using the correlation-aided channel estimation algorithm as a supplemental scheme to obtain more reliable and refined channel estimation. It is shown that the proposed SRS sequence and the supplemental channel estimation scheme improve significantly the channel estimation performance in multi-user massive MIMO systems.


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