Abstract-In this paper, we present a methodology and a tool to derive simple but yet accurate stochastic Markov processes for the description of the energy scavenged by outdoor solar sources. In particular, we target photovoltaic panels with small form factors, as those exploited by embedded communication devices such as wireless sensor nodes or, concerning modern cellular system technology, by small-cells. Our models are especially useful for the theoretical investigation and the simulation of energetically self-sufficient communication systems including these devices.The Markov models that we derive in this paper are obtained from extensive solar radiation databases, that are widely available online. Basically, from hourly radiance patterns, we derive the corresponding amount of energy (current and voltage) that is accumulated over time, and we finally use it to represent the scavenged energy in terms of its relevant statistics. Toward this end, two clustering approaches for the raw radiance data are described and the resulting Markov models are compared against the empirical distributions.Our results indicate that Markov models with just two states provide a rough characterization of the real data traces. While these could be sufficiently accurate for certain applications, slightly increasing the number of states to, e.g., eight, allows the representation of the real energy inflow process with an excellent level of accuracy in terms of first and second order statistics.Our tool has been developed using Matlab TM and is available under the GPL license at [1].
Predictive analysis on mobile network traffic is becoming of fundamental importance for the next generation cellular network. Proactively knowing the user demands, allows the system for an optimal resource allocation. In this paper, we study the mobile traffic of an LTE base station and we design a system for the traffic prediction using Recurrent Neural Networks. The mobile traffic information is gathered from the Physical Downlink Control CHannel (PDCCH) of the LTE using the passive tool presented in [1]. Using this tool we are able to collect all the control information at 1 ms resolution from the base station. This information comprises the resource blocks, the transport block size and the modulation scheme assigned to each user connected to the eNodeB. The design of the prediction system includes long short term memory units. With respect to a Multilayer Perceptron Network, or other artificial neurons structures, recurrent networks are advantageous for problems with sequential data (e.g. language modeling) [2]. In our case, we state the problem as a supervised multivariate prediction of the mobile traffic, where the objective is to minimize the prediction error given the information extracted from the PDCCH. We evaluate the one-step prediction and the long-term prediction errors of the proposed methodology, considering different numbers for the duration of the observed values, which determines the memory length of the LSTM network and how much information must be stored for a precise traffic prediction.
In this article, we cover eco-friendly cellular networks, discussing the benefits that ambient energy harvesting offers in terms of energy consumption and profit. We advocate for future networks where energy harvesting will be massively employed to power network elements; even further, communication networks will seamlessly blend with future power grids. This vision entails the fact that future base stations may trade some of the excess energy they harvest so as to make a profit and provide ancillary services to the electricity grid. We start by discussing recent developments in the energy harvesting field, and then deliberate on the way future energy markets are expected to evolve and the new fundamental trade-offs that arise when energy can be traded. Performance estimates are given throughout to support our arguments, and open research issues in this emerging field are discussed.
Abstract-We consider a two-tier urban Heterogeneous Network where small cells powered with renewable energy are deployed in order to provide capacity extension and to offload macro base stations. We use reinforcement learning techniques to concoct an algorithm that autonomously learns energy inflow and traffic demand patterns. This algorithm is based on a decentralized multi-agent Q-learning technique that, by interacting with the environment, obtains optimal policies aimed at improving the system performance in terms of drop rate, throughput and energy efficiency. Simulation results show that our solution effectively adapts to changing environmental conditions and meets most of our performance objectives. At the end of the paper we identify areas for improvement.
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