No abstract

In this paper, we propose a group partition approach for scalable throughput optimization of capacity-region-aware Device-to-Device communications. Instead of the conventional collision model, we adopt network information theory that allows a receiving device to simultaneously decode multiple packets from multiple transmitting devices, as long as the vector of transmitting rates is inside the capacity region. We propose a novel approach to partition devices into subgroups based on coloring a conflict graph and then solve an optimization problem for each subgroup. In particular, the formation of the conflict graph explicitly takes the capacity region into consideration. Simulation results show that the proposed approach could significantly outperform the greedy algorithm.

No abstract

In this paper, we propose using group partition and dynamic rate adaptation for scalable throughput optimization of capacity-region-aware device-to-device communications. We adopt network information theory that allows a receiving device to simultaneously decode multiple packets from multiple transmitting devices, as long as the vector of transmitting rates is inside the capacity region. Based on graph theory, devices are first partitioned into subgroups. To optimize the throughput of a subgroup, instead of directly solving an integer-linear programming problem, we propose using a fast iterative algorithm to select active devices and using aggression levels for rate adaptation based on channel state information. Simulation results show that the proposed algorithm is scalable and could significantly outperform the greedy algorithm by more than 50%.

No abstract