Self-Organizing Networks: A Packet Scheduling Approach for Coverage/Capacity Optimization in 4G Networks Using Reinforcement Learning
Open accessAbstract
The next generation mobile networks LTE and LTE-A are all-IP based networks. In such IP based networks, the issue of Quality of Service (QoS) is becoming more and more critical with the increase in network size and heterogeneity. In this paper, a Reinforcement Learning (RL) based framework for QoS enhancement is proposed. The framework achieves the coverage/capacity optimization by adjusting the scheduling strategy. The proposed self-optimization algorithm uses coverage/capacity compromise in Packet Scheduling (PS) to maximize the capacity of an eNB subject to the condition that minimum coverage constraint is not violated. Each eNB has an associated agent that dynamically changes the scheduling parameter value of an eNB. The agent uses the RL technique of Fuzzy Q-Learning (FQL) to learn the optimal scheduling parameter. The learning framework is designed to operate in an environment with varying traffic, user positions, and propagation conditions. A comprehensive analysis on the obtained simulation results is presented, which shows that the proposed approach can significantly improve the network coverage as well as capacity in terms of throughput. DOI: http://dx.doi.org/10.5755/j01.eee.20.9.4786
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A model-free distributed reinforcement learning method for power allocation is proposed, in which CSI and QoS indicators are exploited to adapt transmit power.
“In this regard, a model-free distributed reinforcement learning method for power allocation is proposed [79], in which Channel State Information (CSI) and QoS indicators are exploited to adapt the transmit power.”
From Quantum Machine Learning for 6G Communication Networks: State-of-the-Art and Vision for the Future (2019) · cites this paper as [79]
The Abstract and Introduction describe a reinforcement-learning agent at each eNB, but explicitly identify the adapted variable as the α-fair packet-scheduling parameter. The claimed power-allocation method is contrary to the paper’s stated contribution; the Simulation Scenario lists transmit power only as a simulation parameter. No page numbers are available in our copy. (LLM, conf=0.88)
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Research works printed but not linked: 23
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Cited by
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Quantum Machine Learning for 6G Communication Networks: State-of-the-Art and Vision for the Future
Nawaz, S. J., Sharma, S. K., Wyne, S., Patwary, M. N., & Asaduzzaman, M. (2019) · IEEE Access · doi:10.1109/access.2019.2909490 · Cited by 658 · see citing works
Self-citation
- Authors
- Tiwana, M. I., Nawaz, S. J., Ikram, A. A., & Tiwana, M. I.
- Affiliations
- COMSATS University Islamabad (PK)
- National University of Computer and Emerging Sciences (PK)
- ORCID
- Nawaz, Syed Junaid
- Venue
- Elektronika ir Elektrotechnika
- Published
- 2014-11-05
- Volume
- 20
- Issue
- 9
- Pages
- 59-64
- ISSN
- 1392-1215 (ISSN-L)
- 2029-5731
- OpenAlex
- W2044988590
- Cited by
- 5
- References
- 0
- Citations by year
- 2021: 1 · 2020: 1 · 2019: 1 · 2017: 1 · 2016: 1
- FWCI
- 0.35
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Elektronika ir Elektrotechnika (publishedVersion, cc-by)
- Greater South Information System
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Wireless Network Optimization
- Physical Sciences › Computer Science › Computer Networks and Communications › Wireless Communication Networks Research
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Traffic and Congestion Control
- Keywords
- packet scheduling, reinforcement learning, fuzzy Q-learning, LTE, LTE-Advanced, quality of service, self-organizing networks, throughput maximization, heterogeneous networks
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