scholarly journals Indoor massive multiple-input multiple-output channel characterization and performance evaluation

2017 ◽  
Vol 18 (6) ◽  
pp. 773-787 ◽  
Author(s):  
Jian-zhi Li ◽  
Bo Ai ◽  
Rui-si He ◽  
Qi Wang ◽  
Mi Yang ◽  
...  
Keyword(s):  
Performance Evaluation ◽  
Multiple Input Multiple Output ◽  
Output Channel ◽  
Channel Characterization ◽  
Multiple Input ◽  
Input Multiple Output ◽  
And Performance

scholarly journals Channel capacity and performance evaluation of precoded MIMO-OFDM system with large-size constellation

2019 ◽  
Vol 9 (6) ◽  
pp. 5024
Author(s):  
Hussein A. Leftah ◽  
Huda N. Alminshid
Keyword(s):  
Fading Channels ◽  
Channel Capacity ◽  
Orthogonal Frequency Division Multiplexing ◽  
Multiple Input Multiple Output ◽  
Ofdm System ◽  
Large Size ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Mimo Ofdm ◽  
And Performance

<p>Multiple input-multiple output (MIMO) is a multipath diversity exploring approach which is emerged with orthogonal frequency division multiplexing (OFDM) to produce MIMO-OFDM that is widely used in wireless communications. This paper presents a discrete Hart-ley transform (DHT) precoded MIMO-OFDM system over multipath frequency-selective fading channel with large-size quadrature amplitude modulation (16-QAM, 64-QAM and 256-QAM). A mathematical models for the BER and channel capacity over mutlipath fading channels are also derived in this paper. Average Bit-error-rate (BER) and channel capacity of the presented system is considered and compared with that of the traditional MIMO-OFDM. Simulation results shows that the transmission performance and channel capacity of the proposed schemes is better than that of the traditional MIMO-OFDM without a pre-coder.</p>

Investigation of the fifth generation non-orthogonal multiple access technique for defense applications using deep learning

2021 ◽  
pp. 154851292110228
Author(s):  
Ravisankar Malladi ◽  
Manoj Kumar Beuria ◽  
Ravi Shankar ◽  
Sudhansu Sekhar Singh
Keyword(s):  
Deep Learning ◽  
Interference Cancellation ◽  
Multiple Access ◽  
Multiple Input Multiple Output ◽  
Optimal Solution ◽  
Channel State ◽  
Fifth Generation ◽  
Multiple Input ◽  
Input Multiple Output ◽  
And Performance

In modern wireless communication scenarios, non-orthogonal multiple access (NOMA) provides high throughput and spectral efficiency for fifth generation (5G) and beyond 5G systems. Traditional NOMA detectors are based on successive interference cancellation (SIC) techniques at both uplink and downlink NOMA transmissions. However, due to imperfect SIC, these detectors are not suitable for defense applications. In this paper, we investigate the 5G multiple-input multiple-output NOMA deep learning technique for defense applications and proposed a learning approach that investigates the communication system’s channel state information automatically and identifies the initial transmission sequences. With the use of the proposed deep neural network, the optimal solution is provided, and performance is much better than the traditional SIC-based NOMA detectors. Through simulations, the analytical outcomes are verified.

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