Social-Aware Relay Selection for Cooperative Multicast Device-to-Device Communications

Abstract

Abstract:The increasing use of social networks such as Facebook, Twitter, and Instagram to share photos, video streaming, and music among friends has generated a huge increase in the amount of data traffic over wireless networks. This social behavior has triggered new communication paradigms such as device-to-device (D2D) and relaying communication schemes, which are both considered as strong drivers for the next fifth-generation (5G) cellular systems. Recently, the social-aware layer and its relationship to and influence on the physical communications layer have gained great attention as emerging focus points. We focus here on the case of relaying communications to pursue the multicast data dissemination to a group of users forming a social community through a relay node, according to the extension of the D2D mode to the case of device-to-many devices. Moreover, in our case, the source selects the device to act as the relay among different users of the multicast group by taking into account both the propagation link conditions and the relay social-trust level with the constraint of minimizing the end-to-end content delivery delay. An optimization procedure is also proposed in order to achieve the best performance. Finally, numerical results are provided to highlight the advantages of considering the impact of social level on the end-to-end delivery delay in the integrated social-physical network in comparison with the classical relay-assisted multicast communications for which the relay social-trust level is not considered.

Social-Aware Peer Selection for Device-to-Device Communications in Dense Small-Cell Networks

“…The two closeness coefficients are added together to get the cumulative closeness coefficient denoted by ∆, which is then used to calculate the signal-to-noise ratio and the data rate for the D2D communications as given in (15) and (16), respectively. Finally, we use the calculated data rate to compute the throughput and energy efficiency for D2D communications as given in (17) and (18) respectively. The BS probes the candidate's UEs for D2D communications.…”

“…The distance (D) between DUEs is the most important physical attribute. If we assume that Tx is currently located at polar coordinates (X Tx , θ Tx ) while Rx is at (X Rx , θ Rx ), where 0 ≤ θ Tx , θ Rx ≤ 2Π; then, the distance D (Tx, Rx) between Tx and Rx can be determined from their current locations [18] using…”

“…Then, we compute I and εε for D2D communications in macro-and small-cells in (17) and (18) to evaluate the performance of our proposed scheme.…”

“…The parameters used for simulation are given in Table 3. For social attributes, we assigned SFI value as a random number between 0 and 1 [18]. To calculate SCI, we assumed a friends' list (for Tx and Rx, i.e., Φ Tx , Φ Rx ) as…”

Recent Advances on Cellular D2D Communications

“…Militano, Orsino, Araniti and Iera [8] exploited the social relationships among D2D users to model the trust level between them, and proposed a social trust-based solution for enhancing the performance of D2D-enhanced cooperative content uploading in the presence of packet dropping or corrupting malicious nodes for narrowband-IoT cellular environments. In another cooperative design, Chiti, Fantacci and Pierucci [9] considered the problem of relay-assisted cooperative multicast (one-to-many) D2D communications, and presented a relay selection scheme that considers both propagation link conditions and relay's social trust level with the constraint of minimizing end-to-end delay in an integrated social-physical network.…”