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

• Published in: Photonics 2023-05, Vol.10 (5), p.569
• Main Authors: Mohsan, Syed Agha Hassnain, Li, Yanlong, Zhang, Zejun, Ali, Amjad, Xu, Jing
• Format: Article
• Language: English
• Subjects: Algorithms; Bandwidths; Bit error rate; Code Division Multiple Access; Coefficients; Communications systems; Design; Downlinking; Electric waves; Electromagnetic radiation; Electromagnetic waves; Energy efficiency; Error correction; Fluorescence; Health aspects; Interference; interference coefficient estimation; Light; Long short-term memory; Neural networks; non-orthogonal multiple access; Nonorthogonal multiple access; Optical communication; power allocation; Propagation; Quality of service; quantum dot; Quantum dots; Radiation; Recurrent neural networks; Signal detection; Spectrum allocation; successive interference cancellation; Telecommunication systems; Uplinking; User satisfaction; visible light communication; Wireless communications; Wireless networks; Xu Jing
• ISSN: 2304-6732; 2304-6732
• DOI: 10.3390/photonics10050569

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.