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 nonlinear 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% 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.

In this paper, we address the optimization of the energy efficiency of underlay multicast device-to-device (D2MD) communications on cellular networks. In particular, we maximize the energy efficiency of both the global network and the individual users considering various fairness factors such as maximum power and minimum rate constraints. For this, we employ a canonical mixed-integer non-linear formulation of the joint power control and resource allocation problem. To cope with its NP-hard nature, we propose a two-stage semi-distributed solution. In the first stage, we find a stable, yet sub-optimal, channel allocation for D2MD groups using a cooperative coalitional game framework that allows co-channel transmission over a set of shared resource blocks and/or transmission over several different channels per D2MD group. In the second stage, a central entity determines the optimal transmission power for each user in the system via fractional programming. We performed extensive simulations to analyze the resulting energy efficiency and attainable transmission rates. The results show that the performance of our semi-distributed approach is very close to that obtained with a pure optimal centralized one.

The energy demands of Ethernet links have been an active focus of research in the recent years. This work has enabled a new generation of energy-efficient Ethernet (EEE) interfaces able to adapt their power consumption to the actual traffic demands, thus yielding significant energy savings. With the energy consumption of single network connections being a solved problem, in this paper, we focus on the energy demands of link aggregates that are commonly used to increase the capacity of a network connection. We build on known energy models of single EEE links to derive the energy demands of the whole aggregate as a function on how the traffic load is spread among its powered links. We then provide a practical method to share the load that minimizes overall energy consumption with controlled packet delay and prove that it is valid for a wide range of EEE links. Finally, we validate our method with both synthetic and real traffic traces captured in Internet backbones.

Non-orthogonal multiple access (NOMA) techniques have emerged in the past years as a solution to approximate the throughput performance of wireless communications systems to their theoretical capacity region. We consider in this paper an optimization-based model for multicast device-to-device (MD2D) communications where the channels are not orthogonal and may be (partially or fully) shared among the transmitters in each cluster. This setting leads naturally to the introduction of NOMA transmitters and receivers who use successive interference cancellation (SIC) to separate the superposed signals. To analyze the role of NOMA in MD2D, its performance impact, potential performance gains and possible shortcomings, we formulate a model that includes SIC operations in the decoders, so that higher rates can be attained when several sources transmit on the same channel(s). We also investigate the energy efficiency of the network (global and max-min) through a dynamic power control algorithm and present a centralized and a semi-distributed solution to these optimization problems. Through numerical simulations, we show that NOMA is able to improve both the sum-rate and the max-min rate of a MD2D network even from a small degree of resource sharing. Furthermore, these gains also improve the global energy efficiency on the network, but not always the max-min energy efficiency of the devices.

Underlay device to device multicast communication brings great benefits to cellular networks in term of energy and spectral efficiency, since a group of users can communicate directly by reusing cellular resources. In this paper, we propose a scheme to maximize network energy efficiency (EE) through transmission power control. In addition, to get a compromise between fairness and the overall EE, we analyse a second scheme with fairness factors to maximize minimum individual EE. The problem is formulated as a fractional programming optimization and solved with iterative algorithm. Two different EE metrics are analysed via extensive numerical simulations with a spatial Poisson process for the users’ locations, and applying different clustering algorithms as K-nearest neighbour, distance limit and DBSCAN.

In this paper, we study new schemes to maximize network and users' minimum EE energy efficiency through transmission power control. The problem is formulated as fractional optimization programming and solved with a successive convex approximation. The EE metrics are analysed via extensive numerical simulations under different clustering algorithms, namely a variant of nearest neighbor clustering and clusters with a distance limit between transmitters and receivers.