| YAN Zhenzhen (闫珍珍),LI Bo,YANG Mao,YAN Zhongjiang.[J].高技术通讯(英文),2022,28(1):1~9 |
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| A genetic algorithm based hybrid non-orthogonal multiple access protocol |
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| DOI：10.3772/j.issn.1006-6748.2022.01.001 |
| 中文关键词: |
| 英文关键词: non-orthogonal multiple access (NOMA), resource allocation, sparse code multiple access (SCMA), genetic algorithm, hybrid non-orthogonal multiple access |
| 基金项目: |
| Author Name | Affiliation | | YAN Zhenzhen (闫珍珍) | (School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710072, P.R.China) | | LI Bo | (School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710072, P.R.China) | | YANG Mao | (School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710072, P.R.China) | | YAN Zhongjiang | (School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710072, P.R.China) |
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| 中文摘要: |
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| 英文摘要: |
| Both high-dense wireless connectivity and ultra-huge network capacity are main challenges of next generation broadband networks. As one of its key promising technologies, non-orthogonal multiple access (NOMA) scheme can solve those challenges and meet those needs to some extent, in the way that different user equipments (UEs) multiplex on the same resource. Researchers around the world have presented numerous NOMA solutions. Among those, sparse code multiple access (SCMA) technology is a typical NOMA solution. It supports scheduled access and random access which can be called granted access and grant-free access respectively. But resources allocated to granted UEs and grant-free UEs are strictly separated. In order to improve resource utilization, a hybrid non-orthogonal multiple access scheme is proposed. It allows granted UEs and grant-free UEs sharing the same resource unit in terms of fine-grained integration. On the basis, a resource allocation method is further brought forward based on genetic algorithm. It optimizes resource allocation of all UEs by mapping resource distribution issue to an optimization problem. Comparing throughputs of four methods, simulation results demonstrate the proposed genetic algorithm has better throughput gain. |
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