A joint multicast/D2D learning-based approach to LTE traffic offloading

Abstract

Multicast is the obvious choice for disseminating popular data on cellular networks. In spite of having better spectral efficiency than unicast, its performance is bounded by the user with the worst channel in the cell. To overcome this limitation, we propose to combine multicast with device-to-device (D2D) communications over an orthogonal channel. Such a strategy improves the efficiency of the dissemination process while saving resources at the base station. It is quite challenging, however, to decide which users should be served through multicast transmissions and which ones should receive the content via D2D communications. The progress of content dissemination through D2D communications depends on how users meet while on the move. The optimal decision for each content depends both on the status of the LTE channel (when the multicast transmission is executed) and on the evolution of the mobility process of the nodes from there on. We propose a learning solution based on a multi-armed bandit algorithm that dynamically selects the best allocation of users between multicast and D2D to guarantee the timely delivery of data. Numerical evaluations are performed to compare our proposal with the state-of-the-art scheme and an optimal but unfeasible strategy. We confirm that a proper mix of multicast and D2D helps operators save resources at the base station and that the learning algorithm can autonomously find a near-optimal configuration in a reasonable time.

D2D data offloading in vehicular environments with optimal delivery time selection

“…To generate the vehicles speed in inpu to the simulator, we used the PDF (2). The considered parameters are the content timeout τ c , the speed range [v min , v max ], and the maximum nominal transmission range for D2D communications r [ v m i n , v m a x ] [12,32] The sharing timeout was set to 600 s.The remaining system parameters, kept fixed as well, are shown in Table 2.…”

“…In [11], in a scenario in which content delivery mostly relies on D2Doffloading, a strategy for I2D re-injection of contents in the network is proposed to mitigate the effect of temporal content starving in a certain areas. In [12], in the framework of a content dissemination problem (i.e., when contents need to reach all the nodes, without having been explicitly requested), the authors propose a mixed I2D-multicast and D2D-relaying reinforcement-learning-based strategy, which determines which users should receive the contents through D2D relaying from a neighboring device or through a direct I2D transmission. The above mentioned works, although providing interesting insights from the perspective of offloading efficiency maximization, devote less attention to performance metrics which are closer to physical quantities, like energy consumption and spectrum efficiency.…”

“…Here the term "relative trajectory" has the meaning that the trajectory refers to the coordinate system integral with vehicle A's motion 12. It is worth recalling that we are considering only one spatial dimension.…”

Performance Analysis of a Device-to-Device Offloading Scheme for Vehicular Networks

“…Thus, we have obtained (5). Finally, using (2) in (38), with a few integral calculus steps, it is easy to obtain (6).…”

Should I seed or should I not: On the remuneration of seeders in D2D offloading

“…More related to our work, heuristics are presented in [2], [9]. A learning approach can help identify the best data carriers [10] or broadcast modulation [25]. The optimization techniques presented in this work were initially developed as a solution of aeronautic problems, where engineers wanted to control a system by minimizing a certain performance index.…”