Learning-Based Coexistence in Two-Tier Heterogeneous Networks with Cognitive Small Cells

TL;DR

This paper introduces a learning-based method for cognitive small cells in two-tier HetNets to optimize their access probability while respecting interference constraints, significantly improving transmission opportunities.

Contribution

It proposes a novel learning algorithm that uses distance information from MBS signals to optimize access probability under interference constraints.

Findings

Achieves up to 60% increase in access probability.

Outperforms existing methods in interference management.

Provides a closed-form expression for access probability.

Abstract

We study the coexistence problem in a two-tier heterogeneous network (HetNet) with cognitive small cells. In particular, we consider an underlay HetNet, where the cognitive small base station (C-SBS) is allowed to use the frequency bands of the macro cell with an access probability (AP) as long as the C-SBS satisfies a preset interference probability (IP) constraint at macro users (MUs). To enhance the AP (or transmission opportunity) of the C-SBS, we propose a learning-based algorithm for the C-SBS and exploit the distance information between the macro base station (MBS) and MUs. Generally, the signal from the MBS to a specific MU contains the distance information between the MBS to the MU. We enable the C-SBS to analyze the MBS signal on a target frequency band, and learn the distance information between the MBS and the corresponding MU. With the learnt distance information, we calculate the upper bound of the probability that the C-SBS may interfere with the MU, and design an AP with a closed-form expression under the IP constraint. Numerical results indicate that the proposed algorithm outperforms the existing methods up to $60\%$ AP (or transmission opportunity).