Device-to-device (D2D) communication is one of the most promising solutions to the dramatic increase of wireless networks traffic load. In D2D communications, mobile nodes can communicate in a semi-autonomous way, with minimal or no control by the base station (BS). In this context, we address the problem of the coexistence of cellular and D2D tiers in the uplink frequencies, where a D2D source is allowed to transmit without direct control of its scheduling by the base station (BS). In order to limit the interference, we add a punishment mechanism triggered by the BS to limit the activity of disturbing terminals. We propose a context- aware channel access mechanism for a D2D source, where the context-awareness is obtained by 1) observing the topology and the wireless transmissions in the proximity of the D2D source, and 2) exploiting the past knowledge learned thanks to a Bayesian network approach. To design the channel access mechanism, we study the tradeoff between maximizing the end-to-end throughput and minimizing the interference to the cellular tier. We then evaluate the performance improvement of the proposed solution, showing the effectiveness of the learning mechanism and the advantages of context awareness.
D2D communications in the uplink: A context-aware approach with punishment
Librino F;
2016
Abstract
Device-to-device (D2D) communication is one of the most promising solutions to the dramatic increase of wireless networks traffic load. In D2D communications, mobile nodes can communicate in a semi-autonomous way, with minimal or no control by the base station (BS). In this context, we address the problem of the coexistence of cellular and D2D tiers in the uplink frequencies, where a D2D source is allowed to transmit without direct control of its scheduling by the base station (BS). In order to limit the interference, we add a punishment mechanism triggered by the BS to limit the activity of disturbing terminals. We propose a context- aware channel access mechanism for a D2D source, where the context-awareness is obtained by 1) observing the topology and the wireless transmissions in the proximity of the D2D source, and 2) exploiting the past knowledge learned thanks to a Bayesian network approach. To design the channel access mechanism, we study the tradeoff between maximizing the end-to-end throughput and minimizing the interference to the cellular tier. We then evaluate the performance improvement of the proposed solution, showing the effectiveness of the learning mechanism and the advantages of context awareness.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.