Design, implementation and experimental validation of a 5G energy-aware reconfigurable hotspot

Flexibility and energy efficiency are considered two principal requirements of future fifth generation (5G) systems. From an architectural point of view, centralized processing and a dense deployment of small cells will play a vital role in enabling the efficient and dynamic operation of 5G networks. In this context, reconfigurable hotspots will provide on-demand services and adapt their operation in accordance to traffic re quirements, constituting a vital element of the heterogeneous 5G network infrastructure. In this paper we present a reconfigurable hotspot which is able to flexibly distribute its underlying communication functions across the network, as well as to adapt various parameters affecting the generation of the transmitted signal. The reconfiguration of the hotspot focuses on minimizing its energy footprint, while accounting for the current operative requirements. A real-time hotspot prototype has been developed to facilitate the realistic evaluation of the energy saving gains of the proposed scheme. The development flexibly combines software (SW) and hardware (HW) accelerated (HWA) functions in order to enable the agile reconfiguration of the hotspot. Actual power consumption measurements are presented for various relevant 5G networking scenarios and hotspot configurations. This thorough characterization of the energy footprint of the different subsystems of the prototype allows to map reconfiguration strategies to different use cases. Finally, the energy-aware design and implementation of the hotspot prototype is widely detailed in an effort to underline its importance to the provision of the flexibility and energy efficiency to future 5G systems.This work was supported by the European Commission in the framework of the H2020-ICT-2014-2 project Flex5Gware (Grant agreement no. 671563). The work of CTTC was also partially supported by the Generalitat de Catalunya (2017 SGR 891) and by the Spanish Government under project TEC2014-58341-C4-4-R

A Survey of Prediction and Classification Techniques in Multicore Processor Systems

In multicore processor systems, being able to accurately predict the future provides new optimization opportunities, which otherwise could not be exploited. For example, an oracle able to predict a certain application\u27s behavior running on a smart phone could direct the power manager to switch to appropriate dynamic voltage and frequency scaling modes that would guarantee minimum levels of desired performance while saving energy consumption and thereby prolonging battery life. Using predictions enables systems to become proactive rather than continue to operate in a reactive manner. This prediction-based proactive approach has become increasingly popular in the design and optimization of integrated circuits and of multicore processor systems. Prediction transforms from simple forecasting to sophisticated machine learning based prediction and classification that learns from existing data, employs data mining, and predicts future behavior. This can be exploited by novel optimization techniques that can span across all layers of the computing stack. In this survey paper, we present a discussion of the most popular techniques on prediction and classification in the general context of computing systems with emphasis on multicore processors. The paper is far from comprehensive, but, it will help the reader interested in employing prediction in optimization of multicore processor systems

Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs