Uplink and Downlink NOMA Based on a Novel Interference Coefficient Estimation Strategy for Next-Generation Optical Wireless Networks

Author:

Mohsan Syed Agha Hassnain12ORCID,Li Yanlong234ORCID,Zhang Zejun2,Ali Amjad2ORCID,Xu Jing1234ORCID

Affiliation:

1. Donghai Laboratory, Zhoushan 316021, China

2. Optical Communications Laboratory, Ocean College, Zhejiang University, Zhoushan 316021, China

3. Ocean Research Center of Zhoushan, Zhejiang University, Zhoushan 316021, China

4. Hainan Institute of Zhejiang University, Sanya 572025, China

Abstract

Non-orthogonal multiple access (NOMA) has been widely recognized as a promising technology to improve the transmission capacity of wireless optical communication systems. NOMA considers the principle of successive interference cancellation (SIC) to separate a user’s signal at the receiver side. To improve the ability of optical signal detection, we developed a quantum dot (QD) fluorescent concentrator incorporated with multiple-input and single-output (MISO) to realize an uplink NOMA-based optical wireless system. However, inaccurate interference assessment of multiple users using the SIC detection algorithm at the receiver side may lead to more prominent error propagation problems and affect the bit error rate (BER) performance of the system. This research aims to propose a novel recurrent neural network-based guided frequency interference coefficient estimation algorithm in a NOMA visible light communication (VLC) system. This algorithm can improve the accuracy of interference estimation compared with the traditional SIC detection algorithm by introducing interference coefficients. It provides a more accurate reconstruction possibility for level-by-level interference cancellation and weakens the influence of error propagation. In addition, we designed uplink and downlink NOMA-VLC communication systems for experimental validation. When the power allocation ratio was in the range of 0.8 to 0.97, the experimental results of the downlink validated that the BER performance of both users satisfied the forward error correction (FEC) limit with the least squares (LS)-SIC and the long short-term memory recurrent neural networks (LSTM)-SIC detection strategy. Moreover, the BER performance of the LSTM-SIC algorithm was better than that of the LS-SIC algorithm for all users when the power allocation ratio was in the range of 0.92 to 0.93. In particular, our proposed system offered a large detection area of 2 cm2 and corresponding aggregate data rate up to 40 Mbps over 1.5 m of free space by using QDs, and we successfully achieved a mean bit error rate (BER) of 2.3 × 10−3 for the two users.

Funder

Science Foundation of Donghai Laboratory

National Natural Science Foundation of China

Strategic Priority Research Program of the Chinese Academy of Sciences

Zhoushan-Zhejiang University Joint Research Project

Publisher

MDPI AG

Subject

Radiology, Nuclear Medicine and imaging,Instrumentation,Atomic and Molecular Physics, and Optics

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