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Development of users-grouping and power-allocation algorithms for fairness maximization of NOMA-based multibeam satellite networks intended for 5G implementation
Author(s)
Biyoghe, Joel S.
Date Issued
2023
Type
Thesis
Publisher
Cape Peninsula University of Technology
Abstract
Non-Orthgonal Multiple Access (NOMA), Multi-Inpupt Muli-Output (MIMO) and Satellites technologies, are identified as key technology enablers for implementing 5G networks. Thus, the development of NOMA-based Multibeams Satellite Networks (MBSNs) for the realisation of 5G networks is a current research trend in the international telecommunication industry. 5G networks intend to provide extremely high speeds and reliability to all users; and, therefore, set a critical requirement for both high system capacity and high system fairness. However, most reported works on designed subsystems for NOMA-MBSNs focused on maximising the network’s capacity alone, without much regard for the high fairness requirement. Therefore, this research suggests to address this need, by proposing a novel users-grouping algorithm (UGA) and two novel power-allocation algorithms (PAAs); which all seek to maximize the fairness of 2users-NOMA-MBSNs.
The proposed users-grouping algorithm was developed by combining the concept of bipartite-matching between the far-users set and the near-users set, the minimum channel-gains margin restriction, and the minimum channel-correlation coefficient restriction between paired users. The resulting restricted problem was formulated as a restricted Hungarian-matrix problem of channel-correlation coefficients between far and near users; which was then solved by the Restricted Hungarian method. The developed UGA was then implemented and tested on both Matlab and real-time processor (Arm Cortex-R5) platforms. The results showed that the algorithm ensures high channel-gain margin and channel-correlation between paired users and high fairness amongst resulting pairs. Results also demonstrated that the proposed UGA outperforms other existing user-grouping algorithms in terms of resulting paring fairness.
The proposed power-allocations algorithms were designed based on the OCTR-ratios convergence concept (PAA-1) and the Max-Min Fairness Concept (PAA-2), respectively. In each design, since the original fairness maximization power-allocation problem for the NOMA-MBSN is non-convex and NP-hard, it was thus decomposed into two sub-problems, namely, intra-beam and inter-beams power allocations. Each of these sub-problems was then solved using the selected concept amongst the two above; and yielded in intra-beam and inter-beam power-allocation algorithms, respectively. The final algorithm (PAA-1 or PAA-2) combined both sub-algorithms. The developed algorithm in each design (PAA1 or PAA-2) was implemented and tested on both Matlab and the real-time processor (Cortex-R5). In each case, the results demonstrated that the proposed algorithm maximizes the network’s fairness; and exhibits sound superiority to other existing power-allocation algorithms, in achieving network fairness.
In sum, to the author’s best knowledge, all three algorithms proposed are novel contributions in the field of NOMA-MBSN’s development for 5G implementation.
The proposed users-grouping algorithm was developed by combining the concept of bipartite-matching between the far-users set and the near-users set, the minimum channel-gains margin restriction, and the minimum channel-correlation coefficient restriction between paired users. The resulting restricted problem was formulated as a restricted Hungarian-matrix problem of channel-correlation coefficients between far and near users; which was then solved by the Restricted Hungarian method. The developed UGA was then implemented and tested on both Matlab and real-time processor (Arm Cortex-R5) platforms. The results showed that the algorithm ensures high channel-gain margin and channel-correlation between paired users and high fairness amongst resulting pairs. Results also demonstrated that the proposed UGA outperforms other existing user-grouping algorithms in terms of resulting paring fairness.
The proposed power-allocations algorithms were designed based on the OCTR-ratios convergence concept (PAA-1) and the Max-Min Fairness Concept (PAA-2), respectively. In each design, since the original fairness maximization power-allocation problem for the NOMA-MBSN is non-convex and NP-hard, it was thus decomposed into two sub-problems, namely, intra-beam and inter-beams power allocations. Each of these sub-problems was then solved using the selected concept amongst the two above; and yielded in intra-beam and inter-beam power-allocation algorithms, respectively. The final algorithm (PAA-1 or PAA-2) combined both sub-algorithms. The developed algorithm in each design (PAA1 or PAA-2) was implemented and tested on both Matlab and the real-time processor (Cortex-R5). In each case, the results demonstrated that the proposed algorithm maximizes the network’s fairness; and exhibits sound superiority to other existing power-allocation algorithms, in achieving network fairness.
In sum, to the author’s best knowledge, all three algorithms proposed are novel contributions in the field of NOMA-MBSN’s development for 5G implementation.
Additional information
Thesis (DEng (Electrical Engineering))--Cape Peninsula University of Technology, 2023
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