Publications


On the impact of clustering for Energy critical Public Safety Networks

Abstract:
In the event of man made disasters the critical infrastructure is not available. In the absence of any centralized control it is important to devise energy efficient techniques for routing. This paper presents a comparison of clustering techniques for enabling energy-aware routing in Device to Device (D2D) communication for Public Safety Networks. This work compares different clustering schemes in terms of throughput, energy consumption, residual energy and number of dead nodes through extensive simulations. Clustering with gateway outperforms both clustering without gateway and no clustering by 90% and 100%, respectively in terms of throughput. Similarly an improvement of 50% and 100% is observed in terms of energy consumption over the other two schemes. Clustering with a provision of gateway nodes is an energy efficient mechanism for D2D communication since it increases the overall network lifetime.


A Study on Beamforming for Coverage of Emergency Areas from UAVs

Abstract:
In this paper we present a study about the use of beamforming (BF) for devices discovery from flying platforms as the Unmanned Aerial Vehicles (UAVs). This type of application is meant to be exploited in emergency scenarios characterized by the absence of a network infrastructure; the purpose is to search and identify the devices (and consequently the persons) involved in a critical scenario in a limited area without the possibility of connecting to a mobile network. The use of an antenna array from the UAV is supposed to increase the sensitivity towards devices with weak signals and/or difficult propagation conditions. Our preliminary results indicate the effectiveness of a scanning method based on BF techniques for discovering and detecting User Equipment (UEs) on the ground. The results provide an insight on the capability level of BF solutions in these conditions w.r.t. to the size of the area to be covered.


Impact of Power Allocation on Device-to-Device Discovery Processes

Abstract:
Device to device (D2D) communications is one of the key-technologies for advanced releases of LTE and 5G. The centralized base station (or eNodeB), which controls everything in traditional mobile networks, cannot be the only solution when the number of mobile users and devices increase, causing service outages, low spectral efficiency, and high latency. An important technology that can help to solve some of the issues related to traffic overhead and to fulfill the requirements of 5G is D2D communication. In this paper, we focus on D2D communication in a 5G decentralized emergency scenario, where decentralized means that there is no communication or control from the eNodeB. One of the main issues in D2D communications, is the efficiency of the discovery process between couples of devices able to interconnect directly, especially in the decentralized case. In this context, we explore and compare the impact of different power allocation strategies with increasing numbers of D2D devices and different system parameters.


A Machine Learning Approach to Achieving Energy Efficiency in Relay-Assisted LTE-A Downlink System

Abstract:
In recent years, Energy Efficiency (EE) has become a critical design metric for cellular systems. In order to achieve EE, a fine balance between throughput and fairness must also be ensured. To this end, in this paper we have presented various resource block (RB) allocation schemes in relay-assisted Long Term Evolution-Advanced (LTE-A) networks. Driven by equal power and Bisection-based Power Allocation (BOPA) algorithm, the Maximum Throughput (MT) and an alternating MT and proportional fairness (PF)-based SAMM (abbreviated with Authors’ names) RB allocation scheme is presented for a single relay. In the case of multiple relays, the dependency of RB and power allocation on relay deployment and users’ association is first addressed through a k-mean clustering approach. Secondly, to reduce the computational cost of RB and power allocation, a two-step neural network (NN) process (SAMM NN) is presented that uses SAMM-based unsupervised learning for RB allocation and BOPA-based supervised learning for power allocation. The results for all the schemes are compared in terms of EE and user throughput. For a single relay, SAMM BOPA offers the best EE, whereas SAMM equal power provides the best fairness. In the case of multiple relays, the results indicate SAMM NN achieves better EE compared to SAMM equal power and BOPA, and it also achieves better throughput fairness compared to MT equal power and MT BOPA.


Cell Coverage Analysis of a Low Altitude Aerial Base Station in Wind Perturbations

Abstract:
The use of Unmanned Aerial Vehicles (UAVs) as Aerial Base Station (ABSs) is emerging as an effective technique to provide high capacity wireless networks to ground users. In this paper, cell coverage of a low altitude UAV is investigated for supporting such networks. An analytical framework for cell coverage area of an ABS is provided for Suburban, Urban and Urban high rise environments using a solid angle approach including radio link propagation effects in air-to-ground channel obtained from ray tracing simulations. Here, we account for the change in Euler angles such as roll, pitch and yaw due to perturbations by wind gusts or intentional maneuvers which leads to an increase in the geometrical coverage area by approximately 40-50 %, given same transmission power and antenna gain of the ABS.


Device-to-Device Discovery and Localization Assisted by UAVs in Pervasive Public Safety Networks

Abstract:
Device-to-device (D2D) can be a key paradigm to design Pervasive Public safety Networks (PPNs) which could allow the User equipments (UEs) to communicate directly in disaster scenarios. Recently, the use of Unmanned Aerial Vehicles (UAVs) has been suggested in PSNs to enhance situational awareness and disseminate critical information to the deployed Base Station (BS) by providing reliable connectivity. In this paper, we are interested in direct discovery, one of the functions provided by Proximity Services (ProSe). We consider a disaster situation when no core network is available and transmit the discovery message over UAV-to-UE link. Simulation results are presented and discussed based on the root-MUSIC algorithm to locate the affected UE assisted by UAV, achieving a ca.one meter accuracy at over 200 m. Furthermore, we analyse the performance of the link by calculating Packet Error Ratio (PER) and throughput, achieving up to 11 Mbps.


Channel Characterization at 2.4 GHz for Aerial Base Station

Abstract:
The paradigm shift towards high data rate demands of mobile users in IMT-2020 commonly known as 5G, led to the possibility of using Aerial Base Stations (ABS) to fulfill such requirements. However, for implementation of ABS, an appropriate air-to-ground channel model is needed. It is an important factor to incorporate the understanding of the channel fading behavior before designing the system. In this article, we present novel channel propagation results obtained from ray tracing simulations for different environments, such as Suburban, Urban and Urban-High-Rise, according to ITU Radio-communication parameters. The details of different channel characteristics such as Spatial Correlation and Cumulative Distribution Function for Small Scale Parameters as Delay Spread and Angle-of-Arrival are presented for different ABS heights. We also focus on various channel modeling approaches and frameworks for 3D channel models.