Resource Allocation Technique for Hybrid TDMA-NOMA System with Opportunistic Time Assignment

Wei, Xinchen; Al‐Obiedollah, Haitham; Cumanan, Kanapathippillai; Zhang, Miao; Tang, Jie; Wang, Wei; Dobre, Octavia A.

doi:10.48550/arxiv.2003.02196

Cited by 1 publication

(2 citation statements)

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“…Evaluation, and Complexity Analysis 1) Initial Conditions: Firstly, it is crucial to choose an appropriate set Ψ (0) to ensure the feasibility of the problem in the first iteration of the algorithm [19]. In particular, we choose a set of feasible beamforming vectors which can satisfy all the constraints in the approximated problem…”

Section: Initial Conditions Convergence Analysis Performancementioning

confidence: 99%

“…To exploit different potential benefits, NOMA has been recently integrated with different technologies such as cognitive radio (CR) [13], millimeter-wave [14] [15], multiple-antenna techniques [16], [17], [18] and conventional OMA techniques [19]. In particular, the combination of NOMA with spatial domain multiple access (SDMA) offered by multipleantenna can provide additional benefits, by jointly utilizing both the spatial and power domains.…”

Section: Introductionmentioning

confidence: 99%

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Spectral-Energy Efficiency Trade-off-based Beamforming Design for MISO Non-Orthogonal Multiple Access Systems

Al‐Obiedollah¹,

Cumanan²,

Thiyagalingam³

et al. 2020

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Energy efficiency (EE) and spectral efficiency (SE) are two of the key performance metrics in future wireless networks, covering both design and operational requirements. For previous conventional resource allocation techniques, these two performance metrics have been considered in isolation, resulting in severe performance degradation in either of these metrics. Motivated by this problem, in this paper, we propose a novel beamforming design that jointly considers the trade-off between the two performance metrics in a multiple-input singleoutput non-orthogonal multiple access system. In particular, we formulate a joint SE-EE based design as a multi-objective optimization (MOO) problem to achieve a good trade-off between the two performance metrics. However, this MOO problem is not mathematically tractable and, thus, it is difficult to determine a feasible solution due to the conflicting objectives, where both need to be simultaneously optimized. To overcome this issue, we exploit a priori articulation scheme combined with the weighted sum approach. Using this, we reformulate the original MOO problem as a conventional single objective optimization (SOO) problem. In doing so, we develop an iterative algorithm to solve this non-convex SOO problem using the sequential convex approximation technique. Simulation results are provided to demonstrate the advantages and effectiveness of the proposed approach over the available beamforming designs.

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Section: Initial Conditions Convergence Analysis Performancementioning

confidence: 99%