This work examines the efficacy of deep learning (DL) based non-orthogonal multiple access (NOMA) receivers in vehicular communications (VC). Analytical formulations for the outage probability (OP), symbol error rate (SER), and ergodic sum rate for the researched vehicle networks are established Rusing i.i.d. Nakagami-m fading links. Standard receivers, such as least square (LS) and minimum mean square error (MMSE), are outperformed by the stacked long-short term memory (S-LSTM) based DL-NOMA receiver. Under real time propagation circumstances, including the cyclic prefix (CP) and clipping distortion, the simulation curves compare the performance of MMSE and LS receivers with that of the DL-NOMA receiver. According to numerical statistics, NOMA outperforms conventional orthogonal multiple access (OMA) by roughly 20% and has a high sum rate when considering i.i.d. fading links.
Efficient consumption of available resources and fulfillment of increasing demands are the two main challenges which are addressed by exploring advanced multiple access schemes along with efficient modulation techniques. To this end, non-orthogonal multiple access (NOMA) is discussed as a promising scheme for future 5G traffic. NOMA enables the users to share same resource block, permitting certain level of interference. In this paper, we propose filtered OFDM (F-OFDM) as a transmission waveform for NOMA systems, as it offers all the advantages of OFDM with the additional provision of sub-band filtering to satisfy the diverse services of the users. We examine F-OFDM in both downlink and uplink NOMA systems. Error-related performances of both downlink and uplink F-OFDM NOMA systems are analyzed and compared with conventional OFDM NOMA system over Nakagami-m fading channel. The results show that the error performance of F-OFDM NOMA is better than that of OFDM NOMA. An improvement of about 2 dB and 1 dB in bit error rate is achieved in downlink and uplink F-OFDM NOMA, respectively. Monte Carlo simulations are conducted for different values of fading parameter m, supporting the obtained analytical results.
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