Guo, W., Wang, S., Wu, Y. et al. (3 more authors) (2013) Spectral- and energy-efficient antenna tilting in a HetNet using reinforcement learning. In: Wireless Communications and Networking Conference (WCNC), 2013 IEEE. Wireless Communications and Networking Conference (WCNC), 2013 IEEE , 07-10 Apr 2013, Shanghai . , 767 - 772. ISBN 978-1-4673-5938-2
Abstract
In cellular networks, balancing the throughput among users is important to achieve a uniform Quality-of-Service (QoS). This can be accomplished using a variety of cross-layer techniques. In this paper, the authors investigate how the down-tilt of base-station (BS) antennas can be adjusted to maximize the user throughput fairness in a heterogeneous network, considering the impact of both a dynamic user distribution and capacity saturation of different transmission techniques. Finding the optimal down-tilt in a multi-cell interference-limited network is a complex problem, where stochastic channel effects and irregular antenna patterns has yielded no explicit solutions and is computationally expensive. The investigation first demonstrates that a fixed tilt strategy yields good performances for homogeneous networks, but the introduction of HetNet elements adds a high level of sensitivity to the tilt dependent performance. This means that a HetNet must have network-wide knowledge of where BSs, access-points and users are. The paper also demonstrates that transmission techniques that can achieve a higher level of capacity saturation increases the optimal down-tilt angle. A distributed reinforcement learning algorithm is proposed, where BSs do not need knowledge of location data. The algorithm can achieve convergence to a near-optimal solution rapidly (6-15 iterations) and improve the throughput fairness by 45-56% and the energy efficiency by 21-47%, as compared to fixed strategies. Furthermore, the paper shows that a tradeoff between the optimal solution convergence rate and asymptotic performance exists for the self-learning algorithm.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Keywords: | Convergence; Heuristic algorithms; Interference; Learning (artificial intelligence); Throughput; Transmitting antennas |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Electronic and Electrical Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 17 Jun 2014 08:29 |
Last Modified: | 19 Dec 2022 13:25 |
Published Version: | http://dx.doi.org/10.1109/WCNC.2013.6554660 |
Status: | Published |
Refereed: | Yes |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:77287 |
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