Intercell Interference Management in OFDMA Networks: A Decentralized Approach Based onReinforcement Learning

Bernardo, Francisco; Agustí, R.; Perez-Romero, J.; Sallent, O.

doi:10.1109/tsmcc.2010.2099654

Cited by 29 publications

(18 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hence sophisticated resource allocation and interference management schemes are required. Therefore a range of ML algorithms have also been invoked for interference management relying on their environmental awareness and learning capability [186]- [188].…”

Section: Representative Techniques In Wireless Networkmentioning

confidence: 99%

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Wang

Jiang

Zhang

et al. 2020

IEEE Commun. Surv. Tutorials

455

200

View full text Add to dashboard Cite

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (Het-Nets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.

show abstract

Section: Representative Techniques In Wireless Networkmentioning

confidence: 99%

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Wang

Jiang

Zhang

et al. 2020

IEEE Commun. Surv. Tutorials

455

200

View full text Add to dashboard Cite

show abstract

“…To the best of our knowledge and according to some of more recent surveys, i.e., [22], there is only little work in the literature that is considering a machine learning for frequency assignment in small cell networks. In [23], the authors propose a machine learning approach based on reinforcement learning in a multi-agent system according to which the frequency assignment actions are taken in a decentralized fashion without having a complete knowledge on actions taken by other small cells. Such decentralized approach may lead to frequent changes in frequency assignments, which may cause unpredictable levels of interference among small cells and degradation of performance.…”

Section: Related Work and Proposed Contributionmentioning

confidence: 99%

Machine learning-based dynamic frequency and bandwidth allocation in self-organized LTE dense small cell deployments

Bojović

Meshkova

Baldo

et al. 2016

J Wireless Com Network

View full text Add to dashboard Cite

Self-organizing networks (SONs) are expected to minimize operational and capital expenditure of the operators while improving the end users' quality of experience. To achieve these goals, the SON solutions are expected to learn from the environment and to be able to dynamically adapt to it. In this work, we propose a learning-based approach for self-optimization in SON deployments. In the proposed approach, the learning capability has the central role to perform the estimation of key performance indicators (KPIs) which are then exploited for the selection of the optimal network configuration. We apply this approach to the use case of dynamic frequency and bandwidth assignments (DFBA) in long-term evolution (LTE) residential small cell network deployments. For the implementation of the learning capability and the estimation of KPIs, we select and investigate various machine learning and statistical regression techniques. We provide a comprehensive analysis and comparison of these techniques evaluating the different factors that can influence the accuracy of the KPI predictions and consequently the performance of the network. Finally, we evaluate the performance of learning-based DFBA solution and compare it with the legacy approach and against an optimal exhaustive search for best configuration. The results show that the learning-based DFBA achieves on average a performance improvement of 33 % over approaches that are based on analytical models, reaching 95 % of the optimal network performance while leveraging just a small number of network measurements.

show abstract

“…Assuming that the noise power is much less significant than the interference (i.e., a large number of ICI sources are present), then the noise can be neglected [10], [11], [12] and hence the first term of the denominator of (6) becomes significantly higher than the sum of the other two terms. Furthermore, assume that all signal transmission has to go through at least one relay station.…”

Section: System Modelmentioning

confidence: 99%

On the impact of RD link in resource allocation for multi-cell OFDMA cooperative relay networks with partial CSI

Odeh

Safaei

Franklin

2012

2012 International Symposium on Communications and Information Technologies (ISCIT)

View full text Add to dashboard Cite

This paper investigates the impact of the relayto-destination channel gain on subcarrier allocation for uplink OFDMA based cooperative relay networks using multiple amplify-and-forward (AF) relaying protocols. The closed form outage probability is derived for the system under partial channel state information (PCSI) and considering the presence of intercell interference (ICI). The results show that the impact of the link between the relay station and the destination is very low when the ICI is high. Hence, under this condition, the channel information of this link can be ignored during the resource allocation which significantly reduces the complexity of the resource allocation processes. Furthermore, this paper quantifies the level of interference at which the RD link can be ignored.

show abstract

Intercell Interference Management in OFDMA Networks: A Decentralized Approach Based onReinforcement Learning

Cited by 29 publications

References 22 publications

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Machine learning-based dynamic frequency and bandwidth allocation in self-organized LTE dense small cell deployments

On the impact of RD link in resource allocation for multi-cell OFDMA cooperative relay networks with partial CSI

Contact Info

Product

Resources

About