User Association in 5G Networks: A Survey and an Outlook

26 pages; accepted to appear in IEEE Communications Surveys and Tutorial

Energy Efficient User Association and Power Allocation in Millimeter Wave Based Ultra Dense Networks with Energy Harvesting Base Stations

Millimeter wave (mmWave) communication technologies have recently emerged as an attractive solution to meet the exponentially increasing demand on mobile data traffic. Moreover, ultra dense networks (UDNs) combined with mmWave technology are expected to increase both energy efficiency and spectral efficiency. In this paper, user association and power allocation in mmWave based UDNs is considered with attention to load balance constraints, energy harvesting by base stations, user quality of service requirements, energy efficiency, and cross-tier interference limits. The joint user association and power optimization problem is modeled as a mixed-integer programming problem, which is then transformed into a convex optimization problem by relaxing the user association indicator and solved by Lagrangian dual decomposition. An iterative gradient user association and power allocation algorithm is proposed and shown to converge rapidly to an optimal point. The complexity of the proposed algorithm is analyzed and the effectiveness of the proposed scheme compared with existing methods is verified by simulations.Comment: to appear, IEEE Journal on Selected Areas in Communications, 201

Energy-efficient non-orthogonal multiple access for wireless communication system

Non-orthogonal multiple access (NOMA) has been recognized as a potential solution for enhancing the throughput of next-generation wireless communications. NOMA is a potential option for 5G networks due to its superiority in providing better spectrum efficiency (SE) compared to orthogonal multiple access (OMA). From the perspective of green communication, energy efficiency (EE) has become a new performance indicator. A systematic literature review is conducted to investigate the available energy efficient approach researchers have employed in NOMA. We identified 19 subcategories related to EE in NOMA out of 108 publications where 92 publications are from the IEEE website. To help the reader comprehend, a summary for each category is explained and elaborated in detail. From the literature review, it had been observed that NOMA can enhance the EE of wireless communication systems. At the end of this survey, future research particularly in machine learning algorithms such as reinforcement learning (RL) and deep reinforcement learning (DRL) for NOMA are also discussed