A Network-Assisted Approach for RAT Selection in Heterogeneous Cellular Networks

When several radio access technologies (e.g., HSPA, LTE, WiFi, and WiMAX) cover the same region, deciding to which one mobiles connect is known as the Radio Access Technology (RAT) selection problem. To reduce network signaling and processing load, decisions are generally delegated to mobile users....

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Bibliographic Details
Published in:IEEE journal on selected areas in communications 2015-06, Vol.33 (6), p.1055-1067
Main Authors: El Helou, Melhem, Ibrahim, Marc, Lahoud, Samer, Khawam, Kinda, Mezher, Dany, Cousin, Bernard
Format: Article
Language:English
Subjects:
Algorithms
Computer Science
Decision making
Decisions
Encoding
heterogeneous cellular networks
Heuristic
Heuristics
Markov analysis
Mobile communication
Mobile computing
Modulation
Networking and Internet Architecture
Networks
Optimization
Parametrization
Policies
Quality of service
Radio access technology selection
reinforcement learning
semi-Markov decision process
Throughput
Tuning
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Summary:When several radio access technologies (e.g., HSPA, LTE, WiFi, and WiMAX) cover the same region, deciding to which one mobiles connect is known as the Radio Access Technology (RAT) selection problem. To reduce network signaling and processing load, decisions are generally delegated to mobile users. Mobile users aim to selfishly maximize their utility. However, as they do not cooperate, their decisions may lead to performance inefficiency. In this paper, to overcome this limitation, we propose a network-assisted approach. The network provides information for the mobiles to make more accurate decisions. By appropriately tuning network information, user decisions are globally expected to meet operator objectives, avoiding undesirable network states. Deriving network information is formulated as a semi-Markov decision process (SMDP), and optimal policies are computed using the Policy Iteration algorithm. Also, and since network parameters may not be easily obtained, a reinforcement learning approach is introduced to derive what to signal to mobiles. The performances of optimal, learning-based, and heuristic policies, such as blocking probability and average throughput, are analyzed. When tuning thresholds are pertinently set, our heuristic achieves performance very close to the optimal solution. Moreover, although it provides lower performance, our learning-based algorithm has the crucial advantage of requiring no prior parameterization.
ISSN:0733-8716
1558-0008
DOI:10.1109/JSAC.2015.2416987