Resource Allocation And Optimization Of Multi User Communication For Next Generation Network

Abstract

Existing wireless networks use orthogonal access that serves users as per the number of available resources. On the other hand, Next Generation Networks (NGNs) use the concept of non-orthogonality that serves multiple users on a single resource which consequently enhances device connectivity and spectral efficiency. Non-Orthogonal Multiple Access (NOMA) scheme employs power division multiple access which is sensitive to interferences and noises. This research presents a composite multiple access scheme that is developed by a combination of Power Domain NOMA (PDNOMA) and Orthogonal Beamforming (OBF) to improve the spectral efficiency and reduce the interference between beams in the presence of Impulse Noise (IN). Furthermore, a novel IN mitigation and classification technique is presented using deep learning methods which efficiently minimizes the harmful effects of IN from PD-NOMA-based communication systems. This research can be divided into three phases. The first phase of the research encompasses a new composite multiple access scheme based on PD-NOMA and OBF for exchanging information between smart grid, smart meters (SMs), and other communication units in the presence of IN. In the proposed scheme a cell is divided into sectors and OBF is implemented between sectors to reduce inter beams interference using orthogonalization. Within these sectors, the PD-NOMA scheme is implemented to utilize maximum bandwidth with the help of the successive interference cancellation scheme. According to the simulation and numerical findings, the proposed scheme offers a 3 Mbit/sec higher data rate and 0.24 Mbit/joule greater energy efficiency than the traditional orthogonal frequency division multiple access scheme, leading to better system performance in the case of 10 SMs/users in a sector of a cell. Another significant achievement of the proposed scheme is that it does not cause inter beam interference and provides 17 Mbit/sec higher data rate by using OBF compared to conventional beamforming in the case of 60 SMs/users in 12 sectors of a cell. The second phase deals with the analysis of performance degradation of the link due to the IN-contaminated wireless channel. Statistical formation i.e. the Probability Density Function (PDF) and Cumulative Distribution Function (CDF) is formulated for the channel to estimate the effect of IN. Moreover, two closed-form expressions are derived i.e. instantaneous Signal to Noise Ratio (SNR) by using the PDF and CDF for IN-contaminated wireless channel and Bit Error Rate (BER) by using instantaneous SNR for IN-contaminated PD-NOMA-based system. Finally, in the last phase of the research, a novel IN mitigation and classification technique is presented using deep learning methods for PD-NOMA-based communication systems. The IN detection is performed by first identifying the IN occurrences using a Deep Neural Network (DNN) that learns statistical traits of noisy samples, followed by removal of the harmful effect of IN in the detected occurrences. Compared to the existing IN detection methods, the proposed DNN provided an enhanced BER performance. The proposed method is further tested for high and low IN and weak and strong IN occurrence probabilities. The proposed DNN method detected approximately 0.1 Mbits more true symbols out of 1 Mbits compared to conventional methods. The DNN identified high IN in the incoming noisy PDNOMA symbols with an accuracy of 99% and low impulses with an accuracy of 87% respectively.