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Q2A-NOMA : A Q-Learning-based QoS-Aware NOMA System Design for Diverse Data Rate Requirements
- Akhtar, M. W. (author)
- Hassan, S. A. (author)
-
- Mahmood, Aamir, 1980- (author)
- Mittuniversitetet,Institutionen för informationssystem och –teknologi
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- Jung, H. (author)
- Qureshi, H. K. (author)
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- (creator_code:org_t)
- 2022
- 2022
- English.
- In: IEEE Transactions on Industrial Informatics. - 1551-3203 .- 1941-0050. ; 18:11, s. 7549-7559
- Journal article (peer-reviewed)
Abstract
Subject headings
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- Wireless use cases in industrial internet-of-thing (IIoT) networks often require guaranteed data rates ranging from a few kilobits per second to a few gigabits per second. Supporting such a requirement in a single radio access technique is difficult, especially when bandwidth is limited. Although non-orthogonal multiple access (NOMA) can improve the system capacity by simultaneously serving multiple devices, its performance suffers from strong user interference. In this paper, we propose a Q-learning-based algorithm for handling many-to-many matching problems such as bandwidth partitioning, device assignment to sub-bands, interference-aware access mode selection (orthogonal multiple access (OMA), or NOMA), and power allocation to each device. The learning technique maximizes system throughput and spectral efficiency (SE) while maintaining quality-of-service (QoS) for a maximum number of devices. The simulation results show that the proposed technique can significantly increase overall system throughput and SE while meeting heterogeneous QoS criteria.
Subject headings
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering (hsv//eng)
Keyword
- 5G and Beyond
- industrial IoT
- massive connectivity
- NOMA
- Q-learning
Publication and Content Type
- ref (subject category)
- art (subject category)
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