Journal
IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS
Volume 30, Issue 3, Pages 607-616Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSAC.2012.120410
Keywords
Resource Allocation; Opportunistic Cooperation; Optimal Control; Cognitive Femtocell
Funding
- DARPA IT-MANET [W911NF-07-0028]
- NSF [CCF-0747525, 0964479]
- Network Science Collaborative Technology Alliance
- U.S. Army Research Laboratory [W911NF-09-2-0053]
- Direct For Computer & Info Scie & Enginr
- Division of Computing and Communication Foundations [GRANTS:14031097, 0747525, 0964479] Funding Source: National Science Foundation
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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.
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