Group Partition and Dynamic Rate Adaptation for Scalable Capacity-Region-Aware Device-to-Device Communications

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%.

On the Capacity of Fractal D2D Social Networks with Hierarchical Communications

“…Moreover, [57] claimed that the network capacity can be enhanced by increasing the density of D2D users, whilst our findings can provide an additional perspective for [57] by taking into account the topology of D2D network. In social networks, [58] showed that traffic distribution and network capacity can be analyzed by representing a social network as a human contact graph, and further works can be done by characterizing the graph as a fractal network.…”

Optimal Context Aware Transmission Strategy for non-Orthogonal D2D Communications

“…which is easy to implement in a distributed fashion but is static and often over-restrictive, nor on a centralized optimization problem, as in [9], [12], [15], [16], which achieves optimal solutions, but needs full channel state information over all the involved channels.…”

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