Neural Network Detection for Bandwidth-Limited Non-Orthogonal Multiband CAP UVLC System

Abstract

In this paper, we propose a novel sparse data-tosymbol neural network (SDSNN) receiver for bandwidth-limited underwater visible light communication (UVLC) based on nonorthogonal multi-band carrierless amplitude and phase modulation (NM-CAP). Bandwidth limited NM-CAP signals usually carry severe inter-symbol interference (ISI) and inter-band interference (IBI). The SDSNN receiver directly converts the received NM-CAP data with ISI and IBI into quadrature amplitude modulation symbols without distortion for each sub-band. In contrast, the conventional receiver requires the least mean square (LMS) equalizer to cancel ISI, and the subcarrier component extraction with complex independent component analysis (SCE-ICA) to cancel IBI, respectively. SDSNN provides a novel receiving structure to replace postequalization, matched filtering, and SCE-ICA. A blue-LED based UVLC system has been demonstrated utilizing NM-CAP16 with 3 sub-bands. The experimental results show that NM-CAP with the SDSNN receiver case reaches the highest spectral efficiency, where an enhancement of 43%, 20%, 6% has been measured over the orthogonal multi-band CAP case, NM-CAP with LMS equalizer case, and NM-CAP with joint LMS equalizer and SCE-ICA case, respectively. Compared with joint LMS equalizer and SCE-ICA case, the proposed SDSNN receiver can achieve 98% reduction of computational complexity.

Analysis of a Real-Time FSO System Utilizing Xia and SRRC Pulses in Multi-Band Carrier-Less Amplitude and Phase Modulation

“…In recent years, CAP (and its multi-band counterpart, m-CAP) have also been the focus of a huge amount of research interest within the optical wireless community, with a high number of demonstrations performed on visible light communications links [11], [12]. CAP utilises a basis function (normally a square-root raised cosine (SRRC)) and a Hilbert pair to transmit information over the channel.…”

Enhanced Performance of a Cascaded Receiver Consisting of a DNN-Based Waveform-to-Symbol Converter and Modified NN-Based DD-LMS in CAP Underwater VLC System

“…Except for equalizers, DNNs were recently used as a waveform to symbol converter, which could replace conventional demodulation, post-equalization, and down-sampling at the receiving end. In [22], a sparse data-to-symbol neural network (SDSNN) receiver is proposed for UVLC based on nonorthogonal multi-band CAP to mitigate ISI and inter-channel interference (ICI). In [23], a Neural-network-based waveform to symbol converter (NNWSC) can directly convert the received multiband CAP waveform into quadrature amplitude modulation (QAM) symbols to simultaneously handle the ISI and ICI in a fiber-mmWave system.…”

Adaptive Fuzzy Logic Deep-Learning Equalizer for Mitigating Linear and Nonlinear Distortions in Underwater Visible Light Communication Systems

“…Due to radio’s energy absorption in saltwater, radio frequencies are not suited for underwater communications. Due to blue and green light’s ability to pass through water with comparatively minimal absorption, UVLC, which relies on blue and green semiconductor lasers and LEDs, offers high-performance and affordable UWC solutions [ 4 , 5 ].…”