Optimizing the Energy Efficiency of Short Term Ultra Reliable Communications in Vehicular Networks

We evaluate the use of HARQ schemes in the context of vehicle to infrastructure communications considering ultra reliable communications in the short term from a channel capacity stand point. We show that it is not possible to meet strict latency requirements with very high reliability without some diversity strategy and propose a solution to determining an optimal limit on the maximum allowed number of retransmissions using Chase combining and simple HARQ to increase energy efficiency. Results show that using the proposed optimizations leads to spending 5 times less energy when compared to only one retransmission in the context of a benchmark test case for urban scenario. In addition, we present an approximation that relates most system parameters and can predict whether or not the link can be closed, which is valuable for system design

Tunable, Concurrent Multiband, Single Chain Radio Architecture for Low Energy 5G-RANs

This invited paper considers a key next step in the design of radio architectures aimed at supporting low energy consumption in 5G heterogeneous radio access networks. State-of-the-art mobile radios usually require one RF transceiver per standard, each working separately at any given time. Software defined radios, while spanning a wide range of standards and frequency bands, also work separately at any specific time. In 5G radio access networks, where continuous, multiband connectivity is envisaged, this conventional radio architecture results in high network power consumption. In this paper, we propose the novel concept of a concurrent multiband frequency-agile radio (CM-FARAD) architecture, which simultaneously supports multiple standards and frequency bands using a single, tunable transceiver. We discuss the subsystem radio design approaches for enabling the CM-FARAD architecture, including antennas, power amplifiers, low noise amplifiers and analogue to digital converters. A working prototype of a dual-band CM-FARAD test-bed is also presented together with measured salient performance characteristics

Optimal Policies of Advanced Sleep Modes for Energy-Efficient 5G networks

We study in this paper optimal control strategy for Advanced Sleep Modes (ASM) in 5G networks. ASM correspond to different levels of sleep modes ranging from deactivation of some components of the base station for several micro-seconds to switching off of almost all of them for one second or more. ASMs are made possible in 5G networks thanks to the definition of so-called lean carrier radio access which allows for configurable signaling periodicities. We model such a system using Markov Decision Processes (MDP) and find optimal sleep policy in terms of a trade-off between saved power consumption versus additional incurred delay for user traffic which has to wait for the network components to be woken-up and serve it. Eventually, for the system not to oscillate between sleep levels, we add a switching component in the cost function and show its impact on the energy reduction versus delay trade-off.Comment: The 18th IEEE International Symposium on Network Computing and Applications (NCA 2019) 26-28 September 2019 Cambridge, MA US

Leveraging D2D Communication to Maximize the Spectral Efficiency of Massive MIMO Systems

In this paper, we investigate offloading of UEs in D2D mode for a massive MIMO system, where the base station (BS) is equipped with a large, but finite number of antennas and the total number of UEs is kept fixed. We derive closedform expressions for the bounds of the overall capacity of the system. Our results reveal that there exists an optimal user offload fraction, which maximizes the overall capacity. This fraction is strongly coupled with the network parameters such as the number of antennas at the BS, D2D link distance and the transmit SNR at both the UE and the BS. Given a set of network parameters, careful tuning of the offload fraction can provide up to 5× capacity gains

A New Approach of Data Pre-processing for Data Compression in Smart Grids

The conventional approach to pre-process data for compression is to apply transforms such as the Fourier, the Karhunen-Lo\`{e}ve, or wavelet transforms. One drawback from adopting such an approach is that it is independent of the use of the compressed data, which may induce significant optimality losses when measured in terms of final utility (instead of being measured in terms of distortion). We therefore revisit this paradigm by tayloring the data pre-processing operation to the utility function of the decision-making entity using the compressed (and therefore noisy) data. More specifically, the utility function consists of an Lp-norm, which is very relevant in the area of smart grids. Both a linear and a non-linear use-oriented transforms are designed and compared with conventional data pre-processing techniques, showing that the impact of compression noise can be significantly reduced

Energy Optimization of a Cellular Network with QoS Guarantee

The problem of energy optimization in a cellular network has been studied from different perspectives: sleep patterns, network interference, association of users and base stations, allocation of resources (bandwidth and power), etc. All these aspects have been discussed individually in previous works. However, none of the existing works has succeeded in proposing an accurate solution to an exact mathematical model that takes into account several of these parameters simultaneously. In this work, we propose a modeling of several network parameters (i.e., base station transmission power and activity, user assignment, bandwidth allocation, interference, and data rate) and their interaction in order to minimize the network energy consumption. An exact solution of the proposed model, which solves the energy optimization problem in an LTE network, is presented afterward. The proposed solution guarantees to satisfy all the users with a minimum quality of service (data rate), assuming a proper call admission mechanism has been put in place. The accurate solution allows energy savings of up to 50\% in a moderately loaded network, which leads to energy savings of up to twice than of the heuristic proposed by Piunti \textit{et al.} (2015). Moreover, a strategy to solve the model using column generation method is proposed and investigated. Various numerical results are presented on hexagonal and randomly generated cellular networks