Density-Aware Smart Grid Node Allocation in Heterogeneous Radio Access Technology Environments

Smart grid (SG) is an intelligent enhancement of the conventional energy grid allowing a smarter management. In order to be implemented, SG needs to rely on a communication network connecting different node types, implementing the SG services, with different communication and energy requirements. Heterogeneous network (Het-Net) solutions are very attractive, gaining from the allocation of different radio access technologies (RATs) to the different SG node types; however, due to the heterogeneity of the system, an efficient radio resource optimization and energy management are a complex task. Through the exploitation of the most significant key performance indicators (KPIs) of the SG node types and the key features of the RATs, a joint communication and energy cost function are here defined. Through this approach it is possible to optimally assign the nodes to the RATs while respecting their requirements. In particular, we show the effect of different nodes’ density scenarios on the proposed allocation algorithm

Low noise amplifier design for radio telescope system

The purpose of this project is to design a low noise amplifier (LNA) for a radio telescope. The LNA is an electronic amplifier used in communication systems to amplify extremely weak signals captured by antennae. This thesis describes the procedure of designing an LNA for 7 GHz frequency. Noise matching is an important technique which was considered in the design process. The designed LNA achieved a gain of 30 dB with 1 GHz bandwidth. The noise figure achieved was less than 2.9 dB. Return-losses were improved by 6 dB with a new proposed optimization method

On the Application of Machine Learning to the Design of UAV-Based 5G Radio Access Networks

A groundbreaking design of radio access networks (RANs) is needed to fulfill 5G traffic requirements. To this aim, a cost-effective and flexible strategy consists of complementing terrestrial RANs with unmanned aerial vehicles (UAVs). However, several problems must be solved in order to effectively deploy such UAV-based RANs (U-RANs). Indeed, due to the high complexity and heterogeneity of these networks, model-based design approaches, often relying on restrictive assumptions and constraints, exhibit severe limitation in real-world scenarios. Moreover, design of a set of appropriate protocols for such U-RANs is a highly sophisticated task. In this context, machine learning (ML) emerges as a useful tool to obtain practical and effective solutions. In this paper, we discuss why, how, and which types of ML methods are useful for designing U-RANs, by focusing in particular on supervised and reinforcement learning strategies

Cognitive Radio Based Smart Grid Networks

The conventional power grid has several drawbacks and a new powerful Smart Grid perspective has been recently introduced. The Smart Grid principle, allowing to efficiently manage an electrical grid network, needs to exploit a communication network for interconnecting the Smart Grid devices. An increasing interest is toward wireless communications due to their higher flexibility. Within this context Cognitive Radio (CR) techniques has been introduced aiming to exploit more efficiently the radio spectrum resources; to this aim CR based Smart Grids will be reviewed as a possible solution for implementing an effective Smart Grid Network. Furthermore, a set of new methods will be introduced to solve some challenges introduced by CR based Smart Grids

Smart meters density effects on the number of collectors in a Smart Grid

The conventional power grids are not efficient today due to their aged infrastructure, requiring a more effective management of their functions. This goal is at the base of the Smart Grid (SG) concept that aim at introducing intelligence in the energy grid. Among several SG devices, smart meters (SMs) work in the demand side of the power grids and their number is increasing massively in the last years. A SM is a SG electronic device which records electric energy consumption in certain time intervals for communicating such information to the utility and SG Control Station through the collectors for monitoring, demanding response and billing services. The wireless communication systems have an important role in the SG functions. However, the spectrum scarcity due to growing number of users is becoming an important problem. The number of SMs is a function of area size and SM density. The inter arrival rate in a single collector which supports a certain number of SMs, follows Poisson distribution. Moreover, the amount of data that a single collector receives from SMs, increase exponentially by increasing the number of SMs. Thus, introducing an algorithm to avoid the exponential data increasing by defining the minimum number of collectors for a certain number of SMs can be a suitable solution to manage SG communication infrastructure. The definition of the threshold number of SMs and its relation with SMs density which should be supported by a single collector is a primary step to reach the goal. This paper focuses on definition of an algorithm aiming to obtain the minimum number of collectors respect to the number of SMs as a function of SM density and to maximize the spectrum efficiency of the collectors