A multi-criteria BS switching-off algorithm for 5G heterogeneous cellular networks with hybrid energy sources

International audienceIn this paper, we study Base Station (BS) switching-off and offloading for next generation 5G heterogeneous (macro/femto) networks supplied with hybrid energy sources. This type of network will form the basis of the high-data rate energy- efficient cellular networks in the years to come. A novel generalized multimetric algorithm is presented. Our proposal is conceived to operate in highly heterogeneous Radio Access Network (RAN) environments, as expected for 5G, where BSs with different characteristics of coverage, radio resources and power consumption coexist. The approach uses a set of metrics with a modifiable priority hierarchy in order to filter, sort and select the BS neighbors, which receive traffic during redistribution and offloading of the BSs to be put into sleep mode. In our analysis, we study the impact of BS power model trends for active, idle and sleep modes on the BS switching-off. We highlight how the continuous evolution of BS components and the introduction of renewable energy technologies play a significant role to be considered in the decision making. The multimetric approach proposed makes it possible to define and accomplish defined network performance goals by adding specific emphasis on aspects like QoS, energy savings or green equipment utilization

Game-theoretic Scalable Offloading for Video Streaming Services over LTE and WiFi Networks

This paper presents a game-theoretic scalable offloading system that provides seamless video streaming services by effectively offloading parts of video traffic in all video streaming services to a WiFi network to alleviate cellular network congestion. The system also consolidates multiple physical paths in a cost-effective manner. In the proposed system, the fountain encoding symbols of compressed video data are transmitted through long term evolution (LTE) and WiFi networks concurrently to flexibly control the amount of video traffic through the WiFi network as well as mitigate video quality degradation caused by wireless channel errors. Furthermore, the progressive second price auction mechanism is employed to allocate the limited LTE resources to multiple user equipment in order to maximize social welfare while converging to the epsilon-Nash equilibrium. Specifically, we design an application-centric resource valuation that explicitly considers both the realistic wireless network conditions and characteristics of video streaming services. In addition, the scalability and convergence properties of the proposed system are verified both theoretically and experimentally. The proposed system is implemented using network simulator 3. Simulation results are provided to demonstrate the performance improvement of the proposed system.111Nsciescopu

Scalability study of backhaul capacity sensitive network selection scheme in LTE-wifi HetNet

Wireless Heterogeneous Network (HetNet) with small cells presents a new backhauling challenge which differs from those of experienced by conventional macro-cells. In practice, the choice of backhaul technology for these small cells whether fiber, xDSL, point–to-point and point-to-multipoint wireless, or multi-hop/mesh networks, is often governed by availability and cost, and not by required capacity. Therefore, the resulting backhaul capacity of the small cells in HetNet is likely to be non-uniform due to the mixture of backhaul technologies adopted. In such an environment, a question then arises whether a network selection strategy that considers the small cells’ backhaul capacity will improve the end users’ usage experience. In this paper, a novel Dynamic Backhaul Capacity Sensitive (DyBaCS) network selection schemes (NSS) is proposed and compared with two commonly used network NSSs, namely WiFi First (WF) and Physical Data Rate (PDR) in an LTE-WiFi HetNet environment. The proposed scheme is evaluated in terms of average connection or user throughput1and fairness among users. The effects of varying WiFi backhaul capacity (uniform and non-uniform distribution), WiFi-LTE coverage ratio, user density and WiFi access points (APs) density within the HetNet form the focus of this paper. Results show that the DyBaCS scheme generally provides superior fairness and user throughput performance across the range of backhaul capacity considered. Besides, DyBaCS is able to scale much better than WF and PDR across different user and WiFi densities

Taking Benefit of Diversity in RAN to Offload Delay-tolerant Traffic and Improve End-Users QoE

International audienceSince the introduction of smartphones and tablets into the market, mobile data traffic has shown an exponential growth. To cope with such mobile data explosion, offloading techniques were proposed. The main aim of offloading is to avoid transporting unnecessary traffic in costly networks without creating any additional incremental revenue. Offloading is addressed in this paper where we focus on the the interworking between 3GPP access and WiFi. Thus, we propose an economical model for offloading, capturing simultaneously the subscribers' conditions and the operators' preferences. Such model enables the operators to specify an offload strategy to increase their economical benefits without impacting their subscribers QoE

CRIME: Input-Dependent Collaborative Inference for Recurrent Neural Networks

The excellent accuracy of Recurrent Neural Networks (RNNs) for time-series and natural language processing comes at the cost of computational complexity. Therefore, the choice between edge and cloud computing for RNN inference, with the goal of minimizing response time or energy consumption, is not trivial. An edge approach must deal with the aforementioned complexity, while a cloud solution pays large time and energy costs for data transmission. Collaborative inference is a technique that tries to obtain the best of both worlds, by splitting the inference task among a network of collaborating devices. While already investigated for other types of neural networks, collaborative inference for RNNs poses completely new challenges, such as the strong influence of input length on processing time and energy, and is greatly unexplored.In this paper, we introduce a Collaborative RNN Inference Mapping Engine(CRIME), which automatically selects the best inference device for each input. CRIME is flexible with respect to the connection topology among collaborating devices, and adapts to changes in the connections statuses and in the devices loads. With experiments on several RNNs and datasets, we show that CRIME can reduce the execution time (or end-node energy) by more than 25% compared to any single-device approach