Matching coalitions for interference classification in large heterogeneous networks

Due to large numbers of interferers, in-band interference is a bottleneck that future wireless heterogeneous networks have to deal with. Recent advances in information theory have shown that interference does not have to be avoided or strongly limited, so that reliable transmissions can occur. In this paper, we propose a novel Radio Resource Management (RRM) approach, capable to exploit those recent advances, by methodically coupling and processing in-band interferers. To do so, we first reuse the design of a previous two interference regime classifier. Then, we propose an algorithm which forms coalitions of interferers over the available spectral resources, in such a way that the spectral efficiency (SE) of the system is maximized. Simulations results show that our proposed RRM algorithm strongly limits the undesired effects of the traditional tradeoff between in-band interference and overall system performance (SE).