Resource Allocation using Genetic Algorithm in Multimedia Wireless Networks

Resource allocations in wireless networks is a very challenging task, at one hand wireless networks have scarce resources and suffers from many limitations. At the other hand, typical resource allocation problems requires extensive amount of computations and are usually NP-hard problems. Hence, there is dire need for effective and feasible solutions. Resource allocation problems are concerned in distributing the available network’s resources to all active users in a fair way. Although fairness is hard to define, this work considers the fairness aspects for both, the users and the network operator (service provider). Bio-inspired algorithm are used in many context to provide simple and effective solution tochallenging problems. This works employs Genetic Algorithm to provide effective solution to resource allocation problem for multimedia allocation in wireless networks. The performance of the proposed solution is evaluated using simulation. The obtained simulation results show that the proposed solutionachieved better performance

A Novel Airborne Self-organising Architecture for 5G+ Networks

Network Flying Platforms (NFPs) such as unmanned aerial vehicles, unmanned balloons or drones flying at low/medium/high altitude can be employed to enhance network coverage and capacity by deploying a swarm of flying platforms that implement novel radio resource management techniques. In this paper, we propose a novel layered architecture where NFPs, of various types and flying at low/medium/high layers in a swarm of flying platforms, are considered as an integrated part of the future cellular networks to inject additional capacity and expand the coverage for exceptional scenarios (sports events, concerts, etc.) and hard-to-reach areas (rural or sparsely populated areas). Successful roll-out of the proposed architecture depends on several factors including, but are not limited to: network optimisation for NFP placement and association, safety operations of NFP for network/equipment security, and reliability for NFP transport and control/signaling mechanisms. In this work, we formulate the optimum placement of NFP at a Lower Layer (LL) by exploiting the airborne Self-organising Network (SON) features. Our initial simulations show the NFP-LL can serve more User Equipment (UE)s using this placement technique.Comment: 5 pages, 2 figures, conference paper in IEEE VTC-Fall 2017, in Proceedings IEEE Vehicular Technology Conference (VTC-Fall 2017), Toronto, Canada, Sep. 201

Fast and efficient energy-oriented cell assignment in heterogeneous networks

The cell assignment problem is combinatorial, with increased complexity when it is tackled considering resource allocation. This paper models joint cell assignment and resource allocation for cellular heterogeneous networks, and formalizes cell assignment as an optimization problem. Exact algorithms can find optimal solutions to the cell assignment problem, but their execution time increases drastically with realistic network deployments. In turn, heuristics are able to find solutions in reasonable execution times, but they get usually stuck in local optima, thus failing to find optimal solutions. Metaheuristic approaches have been successful in finding solutions closer to the optimum one to combinatorial problems for large instances. In this paper we propose a fast and efficient heuristic that yields very competitive cell assignment solutions compared to those obtained with three of the most widely-used metaheuristics, which are known to find solutions close to the optimum due to the nature of their search space exploration. Our heuristic approach adds energy expenditure reduction in its algorithmic design. Through simulation and formal statistical analysis, the proposed scheme has been proved to produce efficient assignments in terms of the number of served users, resource allocation and energy savings, while being an order of magnitude faster than metaheuritsic-based approaches.This paper has been supported by the National Council of Research and Technology (CONACYT) through Grant FONCICYT/272278 and the ERANetLAC (Network of the European Union, Latin America, and the Caribbean Countries) Project ELAC2015/T100761. This paper is partially supported also by the ADVICE Project, TEC2015-71329 (MINECO/FEDER) and the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No 777067 (NECOS Project)

Recent advances in radio resource management for heterogeneous LTE/LTE-A networks

As heterogeneous networks (HetNets) emerge as one of the most promising developments toward realizing the target specifications of Long Term Evolution (LTE) and LTE-Advanced (LTE-A) networks, radio resource management (RRM) research for such networks has, in recent times, been intensively pursued. Clearly, recent research mainly concentrates on the aspect of interference mitigation. Other RRM aspects, such as radio resource utilization, fairness, complexity, and QoS, have not been given much attention. In this paper, we aim to provide an overview of the key challenges arising from HetNets and highlight their importance. Subsequently, we present a comprehensive survey of the RRM schemes that have been studied in recent years for LTE/LTE-A HetNets, with a particular focus on those for femtocells and relay nodes. Furthermore, we classify these RRM schemes according to their underlying approaches. In addition, these RRM schemes are qualitatively analyzed and compared to each other. We also identify a number of potential research directions for future RRM development. Finally, we discuss the lack of current RRM research and the importance of multi-objective RRM studies

D11.2 Consolidated results on the performance limits of wireless communications

Deliverable D11.2 del projecte europeu NEWCOM#The report presents the Intermediate Results of N# JRAs on Performance Limits of Wireless Communications and highlights the fundamental issues that have been investigated by the WP1.1. The report illustrates the Joint Research Activities (JRAs) already identified during the first year of the project which are currently ongoing. For each activity there is a description, an illustration of the adherence and relevance with the identified fundamental open issues, a short presentation of the preliminary results, and a roadmap for the joint research work in the next year. Appendices for each JRA give technical details on the scientific activity in each JRA.Peer ReviewedPreprin

AI-based resource management in future mobile networks

Η υποστίριξη και ενίσχυση των δίκτυων 5ης γενιάς και πέρα από αλγόριθμους Τεχνητής Νοημοσύνης για την επίλυση προβλημάτων βελτιστοποίησης δικτύου, μελετάται πρόσφατα προκειμένου η νέα γενιά των δικτύων να ανταποκριθεί στις απαιτήσεις ποιότητας υπηρεσίας σχετικά με την κάλυψη, τη χωρητικότητα των χρηστών και το κόστος εγκατάστασης. Μία από τις βασικές ανάγκες είναι η βελτιστοποίηση στην διαδικασία της εγκατάστασης σταθμών βάσης δικτύου. Σε αυτή την εργασία προτείνεται μια μετα-ευριστική μέθοδος, με όνομα «Γενετικός Αλγόριθμός» (Genetic Algorithm) για την επίλυση προβλημάτων βελτιστοποίησης λαμβάνοντας υπόψη τους περιορισμούς ζήτησης. Ο κύριος στόχος είναι η παρουσίαση της εναλλακτικής αυτής λύσης, η οποία είναι η χρήση του Γενετικού Αλγόριθμου, για τη βελτιστοποίηση της διαδικασίας εγκατάστασης των σταθμών βάσης του δικτύου. Με την χρήση του αλγορίθμου για την εγκατάσταση σταθμών βάσης παρέχονται οι ίδιες υπηρεσίες με πριν και ελαχιστοποιείται την κατανάλωση ενέργειας της υποδομής του δικτύου, λαμβάνοντας υπόψιν ομοιογενή και ετερογενή σενάρια σταθμών βάσης. Οι προσομοιώσεις πραγματοποιήθηκαν σε γλώσσα προγραμματισμού Python και τα καλύτερα αποτελέσματα εγκατάστασης παρουσιάστηκαν και αποθηκεύτηκαν. Έγινε σύγκριση της εγκατάστασης αποκλειστικά μακρο-σταθμών βάσης με μικρότερου μεγέθους (σε κάλυψη) σταθμών βάσης πάνω από την υπάρχουσα. Με την χρήση των μικρότερων σταθμών βάσης, η εγκατάσταση του δικτύου θα επιτρέψει βελτιώσεις στην κάλυψη των χρηστών και θα μειώσει το κόστος, την κατανάλωση ενέργειας και τις παρεμβολές μεταξύ των κυψελών. Όλα τα σενάρια μελετήθηκαν σε 3 περιοχές με διαφορετική πυκνότητα χρηστών (A, B και C). Ως προς την ικανοποίηση των απαιτήσεων αναφορικά με την ποιότητα υπηρεσιών και των κινητών συσκευών, η ανάπτυξη μικρών σταθμών βάσης είναι επωφελής, συγκεκριμένα σε περιοχές hotspot.The 5G and beyond networks supported by Artificial Intelligence algorithms in solving network optimization problems are recently studied to meet the quality-of-service requirements regarding coverage, capacity, and cost. One of the essential necessities is the optimized deployment of network base stations. This work proposes the meta-heuristic algorithm Genetic Algorithm to solve optimization problems considering the demand constraints. The main goal is present the alternative solution, which is using the Genetic Algorithm to optimize BSs network deployment. This deployment provides the same services as existing deployments and minimizes the network infrastructure's energy consumption, including using homogenous and heterogenous scenarios of base stations. The simulations were performed in Python programming language, and the results as the best plans for each generation were presented and saved. A comparison of the macro base station deployment and small base station deployment was made on top of the existing one. By applying the small base stations, the network deployment will enable user coverage enhancements and reduce the deployment cost, energy consumption, and inter-cell interference. All the scenarios were assembled in user density area A, user density area B, and user density area C areas of interest. In meeting the requirements for QoS and UE, the small base station deployment is beneficial, namely in hotspot areas

Towards UAV Assisted 5G Public Safety Network

Ensuring ubiquitous mission-critical public safety communications (PSC) to all the first responders in the public safety network is crucial at an emergency site. The first responders heavily rely on mission-critical PSC to save lives, property, and national infrastructure during a natural or human-made emergency. The recent advancements in LTE/LTE-Advanced/5G mobile technologies supported by unmanned aerial vehicles (UAV) have great potential to revolutionize PSC. However, limited spectrum allocation for LTE-based PSC demands improved channel capacity and spectral efficiency. An additional challenge in designing an LTE-based PSC network is achieving at least 95% coverage of the geographical area and human population with broadband rates. The coverage requirement and efficient spectrum use in the PSC network can be realized through the dense deployment of small cells (both terrestrial and aerial). However, there are several challenges with the dense deployment of small cells in an air-ground heterogeneous network (AG-HetNet). The main challenges which are addressed in this research work are integrating UAVs as both aerial user and aerial base-stations, mitigating inter-cell interference, capacity and coverage enhancements, and optimizing deployment locations of aerial base-stations. First, LTE signals were investigated using NS-3 simulation and software-defined radio experiment to gain knowledge on the quality of service experienced by the user equipment (UE). Using this understanding, a two-tier LTE-Advanced AG-HetNet with macro base-stations and unmanned aerial base-stations (UABS) is designed, while considering time-domain inter-cell interference coordination techniques. We maximize the capacity of this AG-HetNet in case of a damaged PSC infrastructure by jointly optimizing the inter-cell interference parameters and UABS locations using a meta-heuristic genetic algorithm (GA) and the brute-force technique. Finally, considering the latest specifications in 3GPP, a more realistic three-tier LTE-Advanced AG-HetNet is proposed with macro base-stations, pico base-stations, and ground UEs as terrestrial nodes and UABS and aerial UEs as aerial nodes. Using meta-heuristic techniques such as GA and elitist harmony search algorithm based on the GA, the critical network elements such as energy efficiency, inter-cell interference parameters, and UABS locations are all jointly optimized to maximize the capacity and coverage of the AG-HetNet