Energy Efficient Power and Channel Allocation in Underlay Device to Multi Device Communications

Abstract

In this paper, we optimize the energy efficiency (bits/s/Hz/J) of device-to-multi-device (D2MD) wireless communications. While the device-to-device scenario has been extensively studied to improve the spectral efficiency in cellular networks, the use of multicast communications opens the possibility of reusing the spectrum resources also inside the groups. The optimization problem is formulated as a mixed integer non-linear joint optimization for the power control and allocation of resource blocks (RBs) to each group. Our model explicitly considers resource sharing by letting co-channel transmission over a RB (up to a maximum of r transmitters) and/or transmission through s different channels in each group. We use an iterative decomposition approach, using first matching theory to find a stable even if sub-optimal channel allocation, to then optimize the transmission power vectors in each group via fractional programming. Additionally, within this framework, both the network energy efficiency and the max-min individual energy efficiency are investigated. We characterize numerically the energy-efficient capacity region, and our results show that the normalized energy efficiency is nearly optimal (above 90 percent of the network capacity) for a wide range of minimum-rate constraints. This performance is better than that of other matching-based techniques previously proposed.

Adaptive mesh size (AMS) algorithm for energy efficiency maximisation in underlay D2D communication

“…Hmila et al [20] investigated the reuse of spectrum resources for groups based on multicast communications. Firstly, they considered the optimisation problem as a mixed integer non-linear to control the power.…”

Energy-Efficient Resource Allocation for Machine-Type Communications in Smart Grid Based on a Matching With Externalities Approach

“…But there only exists few literatures taking into account many-to-one scenarios via joint optimization for the power control and allocation of resource from the perspective of energy efficiency. For instance, to improve the spectrum efficiency in cellular networks, Hmila et al [26] considered resource sharing by letting co-channel transmission over an RB (up to a maximum number of transmitters). And in [27], in order to maximize the system sum rate under the constraints of the signal to interference plus noise ratio (SINR) for both transmitter and cellular user equipments, a novel resource allocation algorithm was proposed based on a many-to-many two-sided matching game.…”

“…Unlike most of the existing works, we consider a multi resource sharing case where a sub-channel can be reused by multiple MTDs, and the problem of channel selection between MTDs and RBs was formulated as a mixed integer non-linear problem (MINLP). Nevertheless, the problem of channel selection is solved by a matching with externalities approach while power control is solved by nonlinear fractional programming [26].…”

A Joint Power and Channel Scheduling Scheme for Underlay D2D Communications in the Cellular Network

“…In recent years, various resource management technique-based literature has attempted to conquer the challenges that D2D technology is confronted with in order to take full advantage of the benefits of D2D communication underlying the cellular networks. The following papers [10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33] represent some of the research aiming to execute interference management. The authors of [10,11,12] investigated the resource allocation mechanism under a cellular system from the game theory point of view.…”

“…A joint resource management scheme considering three different modes was proposed in [27] and the authors formulated the optimization problem as a three-dimensional problem to maximize the the overall EE of D2D communications. In [28], considering both the network EE and the max-min individual EE, matching theory was introduced to solve the channel allocation problem and then a transmission power solution was obtained via fractional programming. The authors in [29] study the joint energy harvesting time slot allocation, power and resource block allocation problem while guaranteeing the QoS of CUEs and the energy harvesting constraints of D2D links.…”