Learning-Assisted Optimization for Energy-Efficient Scheduling in Deadline-Aware NOMA Systems

in IEEE Transactions on Green Communications and Networking (2019)

Machine Learning based Antenna Selection and Power Allocation in Multi-user MISO Systems

in 2019 IEEE International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt) (2019, June)

Energy Efficient Design for Coded Caching Delivery Phase

in 2019 IEEE International Conference on Recent Advances in Signal Processing, Telecommunications & Computing (SigTelCom) (2019)

Power and Flow Assignment for 5G Integrated Terrestrial-Satellite Backhaul Networks

in IEEE Wireless Communications and Networking Conference, Marrakech, Morocco, April 2019 (2019)

Power and Load Optimization in Interference-Coupled Non-Orthogonal Multiple Access Networks

in IEEE Global Communications Conference (GLOBECOM) 2018 (2018, December)

Efficient Minimum-Energy Scheduling with Machine-Learning based Predictions for Multiuser MISO Systems

in 2018 IEEE International Conference on Communications (ICC) (2018, July)

NOMA Aided Interference Management for Full-Duplex Self-Backhauling HetNets

in IEEE Communications Letters (2018), 22(8), 1696-1699

Latency Minimization for Content Delivery Networks with Wireless Edge Caching

in 2018 IEEE International Conference on Communications (ICC) (2018, May)

Energy-efficient design for latency-tolerant content delivery networks

in 2018 IEEE Wireless Communications and Networking Conference (WCNC) (2018, May)

A Framework for Optimizing Multi-cell NOMA: Delivering Demand with Less Resource

in 2017 IEEE Global Communications Conference (GLOBECOM) (2017, December)

Non-orthogonal multiple access (NOMA) allows multiple users to simultaneously access the same time-frequency resource by using superposition coding and successive interference cancellation (SIC). Thus far ... [more ▼]

Non-orthogonal multiple access (NOMA) allows multiple users to simultaneously access the same time-frequency resource by using superposition coding and successive interference cancellation (SIC). Thus far, most papers on NOMA have focused on performance gain for one or sometimes two base stations. In this paper, we study multi-cell NOMA and provide a general framework for user clustering and power allocation, taking into account inter-cell interference, for optimizing resource allocation of NOMA in multi-cell networks of arbitrary topology. We provide a series of theoretical analysis, to algorithmically enable optimization approaches. The resulting algorithmic notion is very general. Namely, we prove that for any performance metric that monotonically increases in the cells’ resource consumption, we have convergence guarantee for global optimum. We apply the framework with its algorithmic concept to a multi-cell scenario to demonstrate the gain of NOMA in achieving significantly higher efficiency. [less ▲]