Reinforcement learning for context awareness and intelligence in wireless networks: Review, new features and open issues

Yau, Kok‐Lim Alvin; Komisarczuk, Peter; Teal, Paul D.

doi:10.1016/j.jnca.2011.08.007

Cited by 124 publications

(59 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Such an investigation makes sense as the feedback helps in controlling SU interference in mission critical PU scenarios. It has been shown that reinforcement learning techniques like Q-learning which makes use of PU feedback can improve the system performance [9]. On the other hand, the practicality of enabling feedback and deciding the type of feedback should be debated more before reaching a proper consensus.…”

Section: B Suggestions For Future Workmentioning

confidence: 99%

IEEE 5G Spectrum Sharing Challenge: A Practical Evaluation of Learning and Feedback

et al. 2016

View full text Add to dashboard Cite

Abstract-We present the results, experiences and takeaways from comparing a diverse set of dynamic spectrum access methods during the IEEE DySPAN 2015 spectrum challenge. Five solutions for coexistence with a given wireless link were implemented and tested in an unknown environment during the conference in Stockholm. The challenge was framed broadly, enabling participants to use their own hardware, antennas, physical layer or medium access control solutions to compete in a unified setup. Each solution was ran two times and ranked using a single metric. Between the two runs the teams were allowed to improve their solution. The metric considered wanted throughput and unwanted interference. In addition to the metric, all solutions were evaluated by a jury. In this paper, we give a detailed overview of the challenge, how we organized it, the participating teams and finally the winners. We conclude with some takeaways on dynamic spectrum access.

show abstract

Section: B Suggestions For Future Workmentioning

confidence: 99%

IEEE 5G Spectrum Sharing Challenge: A Practical Evaluation of Learning and Feedback

et al. 2016

View full text Add to dashboard Cite

show abstract

“…Cognition and learning capabilities have been introduced in different aspects of mobile networks including routing, resource management and dynamic channel selection [14], [15]. We use Q-learning in this paper that is a model-free reinforcement learning technique.…”

Section: Related Workmentioning

confidence: 99%

Traffic Management in 5G Mobile Networks: Selfish Users and Fair Network

Mahmoodi

Jiang

2016

TNC

View full text Add to dashboard Cite

With increasing in heterogeneity of the mobile and wireless networks, including the use of licensed and unlicensed spectrum, and diversity in types of User Equipment (UEs), managing how traffic flows through network while maintaining high level of users' quality of experience is crucial. In this paper, we present a novel traffic management mechanism that maintains the users' quality of experience as well as guaranteeing fairness among users. This traffic management has two separate elements, one that is located at the UE and one that is located at the cloud-based network controller. While each UE maximizes their utility function, which is modelled based on the QoS parameters, selfishly, at the network side, the attempt is to maximize fairness among all users' flows.

show abstract

“…These capabilities contrast with traditional approaches where each host adheres to a predefined set of rules, and responds accordingly. In [18], the authors propose the use of reinforcement learning (RL) to achieve context awareness and intelligence, and also presents an overview of classical RL and three extensions, including events, rules and agent interaction and coordination, to wireless networks.…”

Section: A Related Workmentioning

confidence: 99%

On-demand Bandwidth and Stability Based Unicast Routing in Mobile Adhoc Networks

Basarkod¹,

Manvi²

2014

International Journal of Electronics and Telecommunications

View full text Add to dashboard Cite

Reinforcement learning for context awareness and intelligence in wireless networks: Review, new features and open issues

Cited by 124 publications

References 58 publications

IEEE 5G Spectrum Sharing Challenge: A Practical Evaluation of Learning and Feedback

IEEE 5G Spectrum Sharing Challenge: A Practical Evaluation of Learning and Feedback

Traffic Management in 5G Mobile Networks: Selfish Users and Fair Network

On-demand Bandwidth and Stability Based Unicast Routing in Mobile Adhoc Networks

Contact Info

Product

Resources

About