Abstract
The high dynamics of mobile and wireless networks calls
for intelligent mechanisms to select access networks and
corresponding points of access for the clients and their
active applications. However, one needs to be careful not
to increase the number of handovers substantially as it
may cause large communication overhead to the network. In
this paper, we consider mechanisms located at the
client-side where the greedy selfish behavior should be
regulated by using algorithms which simultaneously
improve the quality of experience (QoE) but do not
disturb much or, in the best case, even improve the
overall network performance. Specifically, we introduce a
Q-learning based QoE-aware access selection algorithm
which enables the clients to learn from past experiences
in order to find the optimal actions. The statuses of the
available points of access are described by a cascade
fuzzy classifier. The Q-learning based solution is
compared to the default mechanism and an opportunistic
fuzzy inference algorithm by simulation. The results
indicate that a Q-learning approach is able to keep the
number of handovers reasonably low while still achieving
a good QoE, thus providing a better approach both from
the user and the network operator perspective
Original language | English |
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Title of host publication | IEEE Network Operations and Management Symposium (NOMS 2014) |
Publisher | IEEE Institute of Electrical and Electronic Engineers |
Number of pages | 4 |
ISBN (Print) | 978-1-4799-0913-1 |
DOIs | |
Publication status | Published - 2014 |
MoE publication type | A4 Article in a conference publication |
Event | 14th IEEE/IFIP Network Operations and Management Symposium: Management in a Software Defined World, NOMS 2014 - Krakow, Poland Duration: 5 May 2014 → 9 May 2014 |
Conference
Conference | 14th IEEE/IFIP Network Operations and Management Symposium: Management in a Software Defined World, NOMS 2014 |
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Abbreviated title | NOMS 2014 |
Country/Territory | Poland |
City | Krakow |
Period | 5/05/14 → 9/05/14 |