Opportunistic Cooperation in Cognitive Femtocell Networks

We investigate opportunistic cooperation between secondary (femtocell) users and primary (macrocell) users in cognitive femtocell networks. We consider two models for such cooperation. In the first model, called the Cooperative Relay Model, a secondary user cannot transmit its own data concurrently...

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Bibliographic Details
Published in:IEEE journal on selected areas in communications 2012-04, Vol.30 (3), p.607-616
Main Authors: Urgaonkar, R., Neely, M. J.
Format: Article
Language:English
Subjects:
Cognitive Femtocell
Data models
Interference
Markov processes
Opportunistic Cooperation
Optimal Control
Optimization
Receivers
Relays
Resource Allocation
Resource management
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Summary:We investigate opportunistic cooperation between secondary (femtocell) users and primary (macrocell) users in cognitive femtocell networks. We consider two models for such cooperation. In the first model, called the Cooperative Relay Model, a secondary user cannot transmit its own data concurrently with a primary user. However, it can employ cooperative relaying of primary user data in order to improve the latter's effective transmission rate. In the second model, called the Interference Model, a secondary user is allowed to transmit its data concurrently with a primary user. However, the secondary user can "cooperate" by deferring its transmissions when the primary user is busy. In both models, the secondary users must make intelligent cooperation decisions as they seek to maximize their own throughput subject to average power constraints. The decision options are different during idle and busy periods of the primary user, and the decisions in turn influence the durations of these periods according to a controllable infinite state Markov chain. Such problems can be formulated as constrained Markov decision problems, and conventional solution techniques require either extensive knowledge of the system dynamics or learning based approaches that suffer from large convergence times. However, using a generalized Lyapunov optimization technique, we design a novel greedy and online control algorithm that overcomes these challenges. Remarkably, this algorithm does not require any knowledge of the network arrival rates and is provably optimal.
ISSN:0733-8716
1558-0008
DOI:10.1109/JSAC.2012.120410