SwePub - search: id:"swepub:oai:DiVA.org:miun-4...

Enumeration	Reference	Cover	Find
1.	Akhtar, M. W., et al. (author) Q2A-NOMA : A Q-Learning-based QoS-Aware NOMA System Design for Diverse Data Rate Requirements 2022 In: IEEE Transactions on Industrial Informatics. - 1551-3203 .- 1941-0050. ; 18:11, s. 7549-7559 Journal article (peer-reviewed)abstract 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.

Akhtar, M. W., et al. (author)
Q2A-NOMA : A Q-Learning-based QoS-Aware NOMA System Design for Diverse Data Rate Requirements
2022
In: IEEE Transactions on Industrial Informatics. - 1551-3203 .- 1941-0050. ; 18:11, s. 7549-7559
Journal article (peer-reviewed)abstract
- 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.

Träfflista för sökning "id:"swepub:oai:DiVA.org:miun-44124" "