Contribution to resource management in cellular access networks with limited backhaul capacity

La interfaz radio de los sistemas de comunicaciones móviles es normalmente considerada como la única limitación de capacidad en la red de acceso radio. Sin embargo, a medida que se van desplegando nuevas y más eficientes interfaces radio, y de que el tráfico de datos y multimedia va en aumento, existe la creciente preocupación de que la infraestructura de transporte (backhaul) de la red celular pueda convertirse en el cuello de botella en algunos escenarios. En este contexto, la tesis se centra en el desarrollo de técnicas de gestión de recursos que consideran de manera conjunta la gestión de recursos en la interfaz radio y el backhaul. Esto conduce a un nuevo paradigma donde los recursos del backhaul se consideran no sólo en la etapa de dimensionamiento, sino que además son incluidos en la problemática de gestión de recursos. Sobre esta base, el primer objetivo de la tesis consiste en evaluar los requerimientos de capacidad en las redes de acceso radio que usan IP como tecnología de transporte, de acuerdo a las recientes tendencias de la arquitectura de red. En particular, se analiza el impacto que tiene una solución de transporte basada en IP sobre la capacidad de transporte necesaria para satisfacer los requisitos de calidad de servicio en la red de acceso. La evaluación se realiza en el contexto de la red de acceso radio de UMTS, donde se proporciona una caracterización detallada de la interfaz Iub. El análisis de requerimientos de capacidad se lleva a cabo para dos diferentes escenarios: canales dedicados y canales de alta velocidad. Posteriormente, con el objetivo de aprovechar totalmente los recursos disponibles en el acceso radio y el backhaul, esta tesis propone un marco de gestión conjunta de recursos donde la idea principal consiste en incorporar las métricas de la red de transporte dentro del problema de gestión de recursos. A fin de evaluar los beneficios del marco de gestión de recursos propuesto, esta tesis se centra en la evaluación del problema de asignación de base, como estrategia para distribuir el tráfico entre las estaciones base en función de los niveles de carga tanto en la interfaz radio como en el backhaul. Este problema se analiza inicialmente considerando una red de acceso radio genérica, mediante la definición de un modelo analítico basado en cadenas de Markov. Dicho modelo permite calcular la ganancia de capacidad que puede alcanzar la estrategia de asignación de base propuesta. Posteriormente, el análisis de la estrategia propuesta se extiende considerando tecnologías específicas de acceso radio. En particular, en el contexto de redes WCDMA se desarrolla un algoritmo de asignación de base basado en simulatedannealing cuyo objetivo es maximizar una función de utilidad que refleja el grado de satisfacción de las asignaciones respecto los recursos radio y transporte. Finalmente, esta tesis aborda el diseño y evaluación de un algoritmo de asignación de base para los futuros sistemas de banda ancha basados en OFDMA. En este caso, el problema de asignación de base se modela como un problema de optimización mediante el uso de un marco de funciones de utilidad y funciones de coste de recursos. El problema planteado, que considera que existen restricciones de recursos tanto en la interfaz radio como en el backhaul, es mapeado a un problema de optimización conocido como Multiple-Choice Multidimensional Knapsack Problem (MMKP). Posteriormente, se desarrolla un algoritmo de asignación de base heurístico, el cual es evaluado y comparado con esquemas de asignación basados exclusivamente en criterios radio. El algoritmo concebido se basa en el uso de los multiplicadores de Lagrange y está diseñado para aprovechar de manera simultánea el balanceo de carga en la intefaz radio y el backhaul.Postprint (published version

A spatio-temporal approach to individual mobility modeling in on-device cognitive computing platforms

The increased availability of GPS-enabled devices makes possible to collect location data for mining purposes and to develop mobility-based services (MBS). For most of the MBSs, determining interesting locations and frequent Points of Interest (POIs) is of paramount importance to study the semantic of places visited by an individual and the mobility patterns as a spatio-temporal phenomenon. In this paper, we propose a novel approach that uses mobility-based services for on-device and individual-centered mobility understanding. Unlike existing approaches that use crowd data for cloud-assisted POI extraction, the proposed solution autonomously detects POIs and mobility events to incrementally construct a cognitive map (spatio-temporal model) of individual mobility suitable to constrained mobile platforms. In particular, we focus on detecting POIs and enter-exits events as the key to derive statistical properties for characterizing the dynamics of an individual’s mobility. We show that the proposed spatio-temporal map effectively extracts core features from the user-POI interaction that are relevant for analytics such as mobility prediction. We also demonstrate how the obtained spatio-temporal model can be exploited to assess the relevance of daily mobility routines. This novel cognitive and on-line mobility modeling contributes toward the distributed intelligence of IoT connected devices without strongly compromising energy

Spectrum availability in indoor locations for opportunistic spectrum access in dense urban scenarios

This paper analyses the possibility to exploit opportunistic spectrum access (OSA) for short-range radio communication systems within indoor locations in dense urban areas. In particular, considering the service area of a primary system devoted to providing outdoor coverage in a dense urban scenario, the percentage of indoor locations where the secondary users can reuse the primary frequency band without disturbing the primary system or being disturbed is estimated. The analysis considers heterogeneous path loss models for the primary and secondary systems encompassing the characterization of outdoor, indoor and building penetration losses. Obtained results quantify how aspects like the location of the primary network elements and the considered interference margins to protect primary transmissions impact on the spatial availability of the primary band within the interior of the buildings.Postprint (published version

Reliable Multihop Broadcast Protocol with a Low-Overhead Link Quality Assessment for ITS Based on VANETs in Highway Scenarios

Vehicular ad hoc networks (VANETs) have been identified as a key technology to enable intelligent transport systems (ITS), which are aimed to radically improve the safety, comfort, and greenness of the vehicles in the road. However, in order to fully exploit VANETs potential, several issues must be addressed. Because of the high dynamic of VANETs and the impairments in the wireless channel, one key issue arising when working with VANETs is the multihop dissemination of broadcast packets for safety and infotainment applications. In this paper a reliable low-overhead multihop broadcast (RLMB) protocol is proposed to address the well-known broadcast storm problem. The proposed RLMB takes advantage of the hello messages exchanged between the vehicles and it processes such information to intelligently select a relay set and reduce the redundant broadcast. Additionally, to reduce the hello messages rate dependency, RLMB uses a point-to-zone link evaluation approach. RLMB performance is compared with one of the leading multihop broadcast protocols existing to date. Performance metrics show that our RLMB solution outperforms the leading protocol in terms of important metrics such as packet dissemination ratio, overhead, and delay

Enhanced base station assignment approach for coping with backhaul constraints in OFDMA-based cellular networks

In this paper we extend the base station (BS) assignment problem to incorporate backhaul related constraints into the assignment decision. This is motivated by the fact that the deployment of more spectral efficient radio access technologies are currently imposing stringent bandwidth requirements at cell sites, and there is a growing concern that backhaul network can become a new network bottleneck in certain deployment scenarios. Unlike existing assignment approaches, we propose a BS assignment algorithm envisioned as a suitable technique capable to cope, at some extent, with possible backhaul congestion situations in OFDMA-based systems. Simulation results demonstrate that the proposed algorithm can provide the same system capacity with less backhaul resources so that, under backhaul bottleneck situations, a better overall network performance is effectively achieved

Enhanced base station assignment approach for coping with backhaul constraints in OFDMA-based cellular networks

In this paper we extend the base station (BS) assignment problem to incorporate backhaul related constraints into the assignment decision. This is motivated by the fact that the deployment of more spectral efficient radio access technologies are currently imposing stringent bandwidth requirements at cell sites, and there is a growing concern that backhaul network can become a new network bottleneck in certain deployment scenarios. Unlike existing assignment approaches, we propose a BS assignment algorithm envisioned as a suitable technique capable to cope, at some extent, with possible backhaul congestion situations in OFDMA-based systems. Simulation results demonstrate that the proposed algorithm can provide the same system capacity with less backhaul resources so that, under backhaul bottleneck situations, a better overall network performance is effectively achieved

A Cognitive-Inspired Event-Based Control for Power-Aware Human Mobility Analysis in IoT Devices

Mobile Edge Computing (MEC) relates to the deployment of decision-making processes at the network edge or mobile devices rather than in a centralized network entity like the cloud. This paradigm shift is acknowledged as one key pillar to enable autonomous operation and self-awareness in mobile devices in IoT. Under this paradigm, we focus on mobility-based services (MBSs), where mobile devices are expected to perform energy-efficient GPS data acquisition while also providing location accuracy. We rely on a fully on-device Cognitive Dynamic Systems (CDS) platform to propose and evaluate a cognitive controller aimed at both tackling the presence of uncertainties and exploiting the mobility information learned by such CDS toward energy-efficient and accurate location tracking via mobility-aware sampling policies. We performed a set of experiments and validated that the proposed control strategy outperformed similar approaches in terms of energy savings and spatio-temporal accuracy in LBS and MBS for smartphone devices

Full On-Device Stay Points Detection in Smartphones for Location-Based Mobile Applications

The tracking of frequently visited places, also known as stay points, is a critical feature in location-aware mobile applications as a way to adapt the information and services provided to smartphones users according to their moving patterns. Location based applications usually employ the GPS receiver along with Wi-Fi hot-spots and cellular cell tower mechanisms for estimating user location. Typically, fine-grained GPS location data are collected by the smartphone and transferred to dedicated servers for trajectory analysis and stay points detection. Such Mobile Cloud Computing approach has been successfully employed for extending smartphone’s battery lifetime by exchanging computation costs, assuming that on-device stay points detection is prohibitive. In this article, we propose and validate the feasibility of having an alternative event-driven mechanism for stay points detection that is executed fully on-device, and that provides higher energy savings by avoiding communication costs. Our solution is encapsulated in a sensing middleware for Android smartphones, where a stream of GPS location updates is collected in the background, supporting duty cycling schemes, and incrementally analyzed following an event-driven paradigm for stay points detection. To evaluate the performance of the proposed middleware, real world experiments were conducted under different stress levels, validating its power efficiency when compared against a Mobile Cloud Computing oriented solution

On permutation modules corresponding to the action of finite classical groups on their conjugacy classes

SIGLEAvailable from British Library Document Supply Centre-DSC:DXN044466 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

User allocation algorithm with rate guarantees for multi-rate mobile networks with Backhaul Constraints

The optimization of the base station (BS) assignment problem in mobile access networks is a primary task towards enabling efficient utilization of network resources. So far, this problem has been mainly studied in terms of air interface optimization. In this paper, we present a novel BS assignment strategy that integrates backhaul constraints in the user assignment criterion. The motivation of this strategy is the fact that in some scenarios the backhaul can become the network bottleneck. The BS assignment problem or user allocation problem is formulated using a utility-based framework. We take into consideration key aspects such as the revenue associated to each type of service along with the resource consumption in terms of both radio and backhaul resources. Results are given in a multi-service scenario for guaranteed rate services