Thesis Defense : Resource allocation and optimization for the non-orthogonal multiple access

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Non-orthogonal multiple access (NOMA) is a promising technology to increase spectral efficiency and enable massive connectivity in future wireless networks. In contrast to orthogonal schemes, such as OFDMA, NOMA can serve multiple users on the same frequency and time resource by superposing their signal in the power domain. One of the key challenges for radio resource management (RRM) in NOMA systems is to solve the joint subcarrier and power allocation (JSPA) problem.
In this thesis, we present a novel optimization framework to study a general class of JSPA problems. This framework employs a generic objective function that can be used to represent the popular weighted sum-rate (WSR), proportional fairness, harmonic mean and max-min fairness utilities. Our work also integrates various realistic constraints. We prove under this framework that JSPA is NP-hard to solve in general. In addition, we study its computational complexity and approximability in various special cases, for different objective functions and constraints.
In this framework, we first consider the WSR maximization problem subject to cellular power constraints. We propose three new algorithms: Opt-JSPA computes an optimal solution with lower complexity than current optimal schemes in the literature. It can be used as an optimal benchmark in simulations. However, its pseudo-polynomial time complexity remains impractical for real-world systems with low latency requirements. To further reduce the complexity, we propose a fully polynomial-time approximation scheme called Ɛ-JSPA, which allows tight trade-offs between performance guarantee and complexity. To the best of our knowledge, Ɛ-JSPA is the first polynomial-time approximation scheme proposed for this problem. Finally, Grad-JSPA is a heuristic based on gradient descent. Numerical results show that it achieves near-optimal WSR with much lower complexity than existing optimal methods.
As a second application of our framework, we study individual power constraints. Power control is solved optimally by gradient descent methods. Then, we develop three heuristics: DGA, DPGA, and DIWA, which solve the JSPA problem for centralized and distributed settings. Their performance and computational complexity are compared through simulations.