Price-Based Resource Allocation for Spectrum-Sharing Femtocell Networks: A Stackelberg Game Approach

This paper investigates the price-based resource allocation strategies for the uplink transmission of a spectrum-sharing femtocell network, in which a central macrocell is underlaid with distributed femtocells, all operating over the same frequency band as the macrocell. Assuming that the macrocell base station (MBS) protects itself by pricing the interference from the femtocell users, a Stackelberg game is formulated to study the joint utility maximization of the macrocell and the femtocells subject to a maximum tolerable interference power constraint at the MBS. Especially, two practical femtocell channel models: sparsely deployed scenario for rural areas and densely deployed scenario for urban areas, are investigated. For each scenario, two pricing schemes: uniform pricing and non-uniform pricing, are proposed. Then, the Stackelberg equilibriums for these proposed games are studied, and an effective distributed interference price bargaining algorithm with guaranteed convergence is proposed for the uniform-pricing case. Finally, numerical examples are presented to verify the proposed studies. It is shown that the proposed algorithms are effective in resource allocation and macrocell protection requiring minimal network overhead for spectrum-sharing-based two-tier femtocell networks.Comment: 27 pages, 7 figures, Submitted to JSA

Using LTE-Sim in New Hanover Decision Algorithm for 2-Tier Macrocell-Femtocell LTE Network

Deployment of mini macrocell base stations can also be referred to as femtocells improve quality of service of indoor and outdoor users. Nevertheless, mobility management remains a key issue with regards to their deployment. This paper is leaned towards this issue, with in-depth focus on the most important aspect of mobility management - handover. In handover management, making a handover decision in the LTE two-tier macrocell femtocell network is a crucial research area. Decision algorithms in this research, are classified and comparatively analyzed according to received signal strength, user equipment speed, cost function and interference. However, it was observed that most of the discussed decision algorithms fail to consider cell selection with hybrid access policy in a single macrocell multiple femtocell scenario, another observation was a majority of these algorithms lack the incorporation of user equipment residence parameter. Not including this parameter boosts the number of unnecessary handover occurrence. To deal with these issues, a sophisticated handover decision algorithm is proposed. The proposed algorithm considers the user’s velocity, received signal strength, residence time as well as the femtocell base station’s access policy. Simulation results have shown that the proposed algorithm reduces the number of unnecessary handovers when compared to conventional received signal strength based handover decision algorithm

Docitive Networks. A Step Beyond Cognition

Projecte fet en col.laboració amb Centre Tecnològic de Telecomunicacions de CatalunyaCatalà: En les Xarxes Docents es por ta més enllà la idea d'elaborar decisions intel ligents. Per mitjà de compartir informació entre els nodes, amb l'objectiu primordial de reduir la complexitat i millorar el rendiment de les Xarxes Cognitives. Per a això es revisen alguns conceptes importants de les bases de l'Aprenentatge Automàtic, prestant especial atenció a l'aprenentatge per reforç. També es fa una visió de la Teoria de Jocs Evolutius i de la dinàmica de rèpliques. Finalment, simulacions ,basades en el projecte TIC-BUNGEE, es mostren per validar els conceptes introduïts.Castellano: Las Redes Docentes llevan más alla la idea de elaborar decisiones inteligentes, por medio de compartir información entre los nodos, con el objetivo primordial de reducir la complejidad y mejorar el rendimiento de las Redes Cognitiva. Para ello se revisan algunos conceptos importantes de las bases del Aprendizaje Automático, prestando especial atencion al aprendizaje por refuerzo, también damos una visón de la Teoría de Juegos Evolutivos y de la replicación de dinamicas. Por último, las simulaciones basadas en el proyecto TIC-BUNGEE se muestran para validar los conceptos introducidos.English: The Docitive Networks further use the idea of drawing intelligent decisions by means of sharing information between nodes with the prime aim of reduce complexity and enhance performance of Congnitive Networks. To this end we review some important concepts form Machine Learning, paying special atention to Reinforcement Learning, we also go insight Evolutionary Game Theory and Replicator Dynamics. Finally, simulations Based on ICT-BUNGEE project are shown to validate the introduced concepts

An energy-centric handover decision algorithm for the integrated LTE macrocell–femtocell network

Femtocells are attracting a fast increasing interest nowadays, as a promising solution to improve indoor coverage and system capacity. Due to the short transmit-receive distance, femtocells can greatly lower transmit power, prolong handset battery life, and enhance the user-perceived Quality of Service (QoS). On the other hand, technical challenges still remain, mainly including interference mitigation, security and mobility management, intercepting wide deployment and adoption by both mobile operators and end users. This paper introduces a novel energy-centric handover decision policy and its accompanied algorithm, towards minimizing the power consumption at the mobile terminal side in the integrated LTE macrocell–femtocell network. The proposed policy is shown to extend the widely-adopted strongest cell policy, by suitably adapting the handover hysteresis margin in accordance with standardized LTE measurements on the tagged user’s neighbor cells. Performance evaluation results show that significantly lower interference and power consumption can be attained for the cost of a moderately increased number of network-wide handover executions events

Energy Efficient Mobility Management for the Macrocell – Femtocell LTE Network

Femtocells will play a key role in future deployments of the 3rd Generation Partnership Project (3GPP) the Long Term Evolution (LTE) system, as they are expected to enhance system capacity, and greatly improve the energy-efficiency in a cost-effective manner. Due to the short transmit-receive distance, femtocells prolong handset battery life and enhance the Quality of Service (QoS) perceived by the end users. However, large-scale femtocell deployment comprises many technical challenges, mainly including security, interference and mobility management. Under the viewpoint of energy-efficient mobility management, this chapter discusses the key features of the femtocell technology and presents a novel energy-efficient handover decision policy for the macrocell – femtocell LTE network. The proposed HO decision policy aims at reducing the transmit power of the LTE mobile terminals in a backwards compatible with the standard LTE handover decision procedure. Simulation results show that significantly lower energy and power consumption can be attained if the proposed approach is employed, at the cost of a moderately increased number of handover executions events

A survey of self organisation in future cellular networks

This article surveys the literature over the period of the last decade on the emerging field of self organisation as applied to wireless cellular communication networks. Self organisation has been extensively studied and applied in adhoc networks, wireless sensor networks and autonomic computer networks; however in the context of wireless cellular networks, this is the first attempt to put in perspective the various efforts in form of a tutorial/survey. We provide a comprehensive survey of the existing literature, projects and standards in self organising cellular networks. Additionally, we also aim to present a clear understanding of this active research area, identifying a clear taxonomy and guidelines for design of self organising mechanisms. We compare strength and weakness of existing solutions and highlight the key research areas for further development. This paper serves as a guide and a starting point for anyone willing to delve into research on self organisation in wireless cellular communication networks

Docitive Networks. A Step Beyond Cognition

Projecte fet en col.laboració amb Centre Tecnològic de Telecomunicacions de CatalunyaCatalà: En les Xarxes Docents es por ta més enllà la idea d'elaborar decisions intel ligents. Per mitjà de compartir informació entre els nodes, amb l'objectiu primordial de reduir la complexitat i millorar el rendiment de les Xarxes Cognitives. Per a això es revisen alguns conceptes importants de les bases de l'Aprenentatge Automàtic, prestant especial atenció a l'aprenentatge per reforç. També es fa una visió de la Teoria de Jocs Evolutius i de la dinàmica de rèpliques. Finalment, simulacions ,basades en el projecte TIC-BUNGEE, es mostren per validar els conceptes introduïts.Castellano: Las Redes Docentes llevan más alla la idea de elaborar decisiones inteligentes, por medio de compartir información entre los nodos, con el objetivo primordial de reducir la complejidad y mejorar el rendimiento de las Redes Cognitiva. Para ello se revisan algunos conceptos importantes de las bases del Aprendizaje Automático, prestando especial atencion al aprendizaje por refuerzo, también damos una visón de la Teoría de Juegos Evolutivos y de la replicación de dinamicas. Por último, las simulaciones basadas en el proyecto TIC-BUNGEE se muestran para validar los conceptos introducidos.English: The Docitive Networks further use the idea of drawing intelligent decisions by means of sharing information between nodes with the prime aim of reduce complexity and enhance performance of Congnitive Networks. To this end we review some important concepts form Machine Learning, paying special atention to Reinforcement Learning, we also go insight Evolutionary Game Theory and Replicator Dynamics. Finally, simulations Based on ICT-BUNGEE project are shown to validate the introduced concepts

Rapid Learning Optimization Approach for Battery Recovery-Aware Embedded System Communications

To date, battery optimization for embedded systems still a crucial subject. Actually, the majority of carried out works focus on transmission controls without taking into account the specifications of the batteries themselves. Indeed, an improvementof 70\% is reported by exploiting the battery recovery effect.In this paper, the recovery phenomenon is exploited to design an algorithm that optimizes both the lifetime of the battery and the performance of the studied system. The algorithms from Dynamic programming and Reinforcement learning fields are the first to be considered. When in Dynamic programming prior detailed information are assumed to be available, in reinforcement learning those information becomes unknown and long calculation times are needed to converge toward an optimal policy solution. The paper contribution is about designing a new Rapid Learning Algorithm (RLA) that combines both Dynamic programming and Reinforcement learning features. RLA exploits a reduced model of the system instead of exploring the whole and heavy system state model as Dynamic programming do. The RLA run-time is then shortened. Based on battery stochastic model, the simulation results obtained with RLA are compared to the Dynamic programming and Reinforcement learning algorithms under the same conditions. By taking into account the recovery effect this paper illustrates that the calculation time and the system performance are greatly improved when RLA is adopted