Opportunistic Cooperation in Cognitive Femtocell Networks

TL;DR

This paper proposes a novel online control algorithm for opportunistic cooperation in cognitive femtocell networks, improving secondary user throughput while maintaining primary user performance, using Lyapunov optimization.

Contribution

It introduces a new model of cooperation between primary and secondary users in cognitive femtocell networks and develops a provably optimal online control algorithm.

Findings

The algorithm achieves maximum secondary throughput under power constraints.

It effectively balances cooperation and energy consumption.

The approach outperforms traditional dynamic programming methods.

Abstract

We investigate opportunistic cooperation between unlicensed secondary users and legacy primary users in a cognitive radio network. Specifically, we consider a model of a cognitive network where a secondary user can cooperatively transmit with the primary user in order to improve the latter's effective transmission rate. In return, the secondary user gets more opportunities for transmitting its own data when the primary user is idle. This kind of interaction between the primary and secondary users is different from the traditional dynamic spectrum access model in which the secondary users try to avoid interfering with the primary users while seeking transmission opportunities on vacant primary channels. In our model, the secondary users need to balance the desire to cooperate more (to create more transmission opportunities) with the need for maintaining sufficient energy levels for their own transmissions. Such a model is applicable in the emerging area of cognitive femtocell networks. We formulate the problem of maximizing the secondary user throughput subject to a time average power constraint under these settings. This is a constrained Markov Decision Problem and conventional solution techniques based on dynamic programming require either extensive knowledge of the system dynamics or learning based approaches that suffer from large convergence times. However, using the technique of Lyapunov optimization, we design a novel greedy and online control algorithm that overcomes these challenges and is provably optimal.