Name
Title | ||
|---|---|---|
A Novel Adaptive Call Admission Control Scheme for Distributed Reinforcement Learning Based Dynamic Spectrum Access in Cellular Networks. |
Abstract | ||
|---|---|---|
This paper introduces a novel Q-value based adaptive call admission control scheme (Q-CAC) for distributed reinforcement learning (RL) based dynamic spectrum access (DSA) in mobile cellular networks, which provides a good quality of service (QoS) without the need for spectrum sensing. A DSA algorithm has been developed in this paper using the stateless Q-learning algorithm with Win-or-Learn-Fast (WoLF) learning rates. Its performance was analysed using the spatial distribution of the probabilities of call blocking (BP) and dropping (DP) across the network and compared to that of a 100% accurate spectrum sensing based DSA scheme. The Q-CAC scheme demonstrated good controllability of the blocking probability using a Q-value based call admission threshold parameter. It significantly reduced spatial fluctuations in BP and DP, thus providing more cells with acceptable quality of service (QoS). |
Year | Venue | Field |
|---|---|---|
2013 | ISWCS | Controllability,Computer science,Call Admission Control,Quality of service,Computer network,Real-time computing,Digital Signature Algorithm,Cellular network,Call blocking,Stateless protocol,Distributed computing,Reinforcement learning |
DocType | ISBN | Citations |
Conference | 978-3-8007-3529-7 | 4 |
PageRank | References | Authors |
0.77 | 8 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nils Morozs | 1 | 55 | 7.98 |
| Tim Clarke | 2 | 102 | 20.02 |
| David Grace | 3 | 281 | 35.65 |