In this paper, we explore the possibilities and advantages of cooperative relaying with the addition of non-orthogonal multiple access (NOMA). First, the possibilities of NOMA for fifth-generation (5G) and beyond networks is discussed followed by the generalized structures for the cooperative NOMA. Then, advanced NOMA communication is investigated where NOMA is integrated with advanced transmission technologies for the further improvement in cooperative NOMA. Hereinafter, resource allocation is investigated and, finally, the major challenges and issues are highlighted.

Twenty years in the RF Analog/Mixed Signal Design and EDA software industries doing design, customer support, application engineering, technical writing, training development and delivery, project management, AE and business management. Current focus is on telephony and IVR technologies.Realization of software development as a true passion of mine led to the pursuit of a Master's degree in Computer Science at Florida Atlantic University, graduated in December, 2013. Dr. Ravi T. Shankar, Florida Atlantic UniversityRavi Shankar has a PhD in Electrical and Computer Engineering from the University of Wisconsin, Madison, WI, and an MBA from Florida Atlantic University, Boca Raton, FL. He is currently a senior professor with the Computer and Electrical Engineering and Computer Science department at Florida Atlantic University. His current research interests are on K-12 education, engineering learning theories, and education data mining. He has been well funded by the high tech industry over the years. He has 7 US patents, of which 3 have been commercialized by the university. He has published earlier work on the use of the semantic web for medical applications at another conference. This work is part of an ongoing teaching and research project that leverages our collaborative teaching in smart phone app development. We plan to leverage this in generalizing the course offering so other interdisciplinary groups' efforts are facilitated. Dr. Diana Mitsova, Florida Atlantic UniversityDiana Mitsova has a background in research design, statistical and spatial analysis, as well as environmental planning and modeling using geographic information systems, and interactive computer simulation. Her primary area of research involves the impact of urban development on ecosystems and other environmentally sensitive areas.Her recent publications focus on the impact of climate-related stressors on coastal communities and the implementation of planning approaches related to enhancing coastal resilience to natural hazards.

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In modern wireless communication scenarios, non-orthogonal multiple access (NOMA) provides high throughput and spectral efficiency for fifth generation (5G) and beyond 5G systems. Traditional NOMA detectors are based on successive interference cancellation (SIC) techniques at both uplink and downlink NOMA transmissions. However, due to imperfect SIC, these detectors are not suitable for defense applications. In this paper, we investigate the 5G multiple-input multiple-output NOMA deep learning technique for defense applications and proposed a learning approach that investigates the communication system’s channel state information automatically and identifies the initial transmission sequences. With the use of the proposed deep neural network, the optimal solution is provided, and performance is much better than the traditional SIC-based NOMA detectors. Through simulations, the analytical outcomes are verified.

Non-orthogonal multiple access (NOMA) networks play an important role in defense communication scenarios. Deep learning (DL)-based solutions are being considered as viable ways to solve the issues in fifth-generation (5G) and beyond 5G (B5G) wireless networks, since they can provide a more realistic solution in the real-world wireless environment. In this work, we consider the deep Q-Network (DQN) algorithm-based user pairing downlink (D/L) NOMA network. We have applied the convex optimization (CO) technique and optimized the sum rate of all the wireless users. First, the near-far (N-F) pairing and near-near and far-far (N-N and F-F) pairing strategies are investigated for the multiple numbers of users, and a closed-form (CF) expression of the achievable rate is derived. After that, the optimal power allocation (OPA) factors are derived using the CO technique. Through simulations, it has been demonstrated that the DQN algorithms perform much better than the deep reinforcement learning (DRL) and conventional orthogonal frequency-division multiple access (OFDMA) schemes. The sum-rate performance significantly increases with OPA factors. The simulation results are in close agreement with the analytical results.