Wavelet Transform Based Sparse Code Multiple

Abstract

Fifth Generation (5G) of wireless communications that incorporates very high data rates, massive connectivity and very low latency can be fundamental part of 4th Industrial revolution. As far as, physical layer is concerned, Multiple Access (MA) techniques are significant in accommodating users on frequency and time resources for an efficient spectrum utilization, least Multi-Access Interference (MAI) and greater throughput. 5G is transforming user access techniques from orthogonal to non-orthogonal resource sharing methods. Non-Orthogonal Multiple Access (NOMA) technique is the transmission of superimposed signal of multi-users on shared frequency band simultaneously instead of utilizing separate frequency & time slot for each user. NOMA is categoried into Power Domain (PD) NOMA & Code Domain (CD) NOMA.

Sparse Code Multiple Access (SCMA), a type of CD-NOMA that facilitates greater user connectivity for 5G. SCMA utilizes sparse resource allocation and non-orthogonal three dimensional codebooks to increase spectrum efficiency. Superimposed encoded data of all users employs a complex receiver such as Message Passing Algorithm (MPA) to extract user specific signal. In this research work, MAI for overloaded SCMA will be examined and different signal processing techniques will be employed to reduce it. Multi-Carrier (MC) communication techniques such as Fast Fourier Transformed Orthogonal Frequency Division Multiplexing (FFT-OFDM) combined with SCMA are analysed in literature and offers satisfactory performance for wireless communications. Recently proposed MC modulation schemes such as Wavelet Transformed OFDM (WT-OFDM) remained relatively unexplored with reference to SCMA. WT provides better decomposition & reconstruction as compared to FFT which is helpful for interference mitigation in a signal. Therefore, Wavelet Transform (WT) is expected to improve Signal to Interference and Noise Ratio (SINR) for SCMA users. Simulation models of SCMA and MC-SCMA schemes are presented that utilize MPA receiver to decode data of all superimposed users and show that WT-SCMA is superior to FFT-SCMA in the context of BER performance. Analytical models are derived for SINR employing Maximal Ratio Combining (MRC) to examine MAI for a particular user in the SCMA/MC-SCMA systems. SINR comparison shows that WT-SCMA outperforms FFT-SCMA.