Graph-based multi-user scheduling for indoor cooperative visible light transmission

Recent progress in the indoor visible light communication (VLC) has shown promising signs of alleviating an increasing strain on the radio frequency spectrum and enhancing transmission capacity. Nevertheless, the indoor VLC usually suffers from inter-channel interference (ICI) due to the dense light-emitting diode (LED) deployment. The ICI is considered as a key factor affecting signal to interference and noise ratio (SINR) and spectral efficiency. To address this challenge, an efficient multi-user scheduling framework that employs interference coordination and cooperative transmission is investigated based on the graph theory. To effectively mitigate ICI and maximize benefit of the cooperative transmission, the cell-centric (CC) and user-centric (UC) clustering are introduced for cooperative transmission. For the CC clustering, the multi-user scheduling problem under the proportional fairness criterion is formulated to maximize spectral efficiency while ensuring user fairness. Such a problem is solved by linear programming and greedy algorithms after transforming it into a maximum weighted independent set problem with the aid of a modified interference graph. For the UC clustering, the multi-user scheduling problem under the max-min criterion is formulated and solved by a proposed heuristic approach based on the bipartite graph theory. Numerical results show that the proposed graph-based scheduling is capable of providing up to 7.7 dB gain in SINR over the non-cooperative transmission. The bipartite graph scheduling offers high spectral efficiency and service fairness index. In the worst case with an occlusion probability of 1, a small SINR penalty of up to 1 dB is observed with the greedy algorithm. It is shown that the graph-based scheduling is robust to receiver rotation and occlusion in terms of spectral efficiency, SINR, and user fairness.