Performance analysis of 4G wireless networks using system level simulator

Doutoramento em Engenharia ElectrotécnicaIn the last decade, mobile wireless communications have witnessed an explosive growth in the user’s penetration rate and their widespread deployment around the globe. In particular, a research topic of particular relevance in telecommunications nowadays is related to the design and implementation of mobile communication systems of 4th generation (4G). 4G networks will be characterized by the support of multiple radio access technologies in a core network fully compliant with the Internet Protocol (all IP paradigms). Such networks will sustain the stringent quality of service (QoS) requirements and the expected high data rates from the type of multimedia applications (i.e. YouTube and Skype) to be available in the near future. Therefore, 4G wireless communications system will be of paramount importance on the development of the information society in the near future. As 4G wireless services will continue to increase, this will put more and more pressure on the spectrum availability. There is a worldwide recognition that methods of spectrum managements have reached their limit and are no longer optimal, therefore new paradigms must be sought. Studies show that most of the assigned spectrum is under-utilized, thus the problem in most cases is inefficient spectrum management rather spectrum shortage. There are currently trends towards a more liberalized approach of spectrum management, which are tightly linked to what is commonly termed as Cognitive Radio (CR). Furthermore, conventional deployment of 4G wireless systems (one BS in cell and mobile deploy around it) are known to have problems in providing fairness (users closer to the BS are more benefited relatively to the cell edge users) and in covering some zones affected by shadowing, therefore the use of relays has been proposed as a solution. To evaluate and analyse the performances of 4G wireless systems software tools are normally used. Software tools have become more and more mature in recent years and their need to provide a high level evaluation of proposed algorithms and protocols is now more important. The system level simulation (SLS) tools provide a fundamental and flexible way to test all the envisioned algorithms and protocols under realistic conditions, without the need to deal with the problems of live networks or reduced scope prototypes. Furthermore, the tools allow network designers a rapid collection of a wide range of performance metrics that are useful for the analysis and optimization of different algorithms. This dissertation proposes the design and implementation of conventional system level simulator (SLS), which afterwards enhances for the 4G wireless technologies namely cognitive Radios (IEEE802.22) and Relays (IEEE802.16j). SLS is then used for the analysis of proposed algorithms and protocols.FC

Machine Learning-Enabled Resource Allocation for Underlay Cognitive Radio Networks

Due to the rapid growth of new wireless communication services and applications, much attention has been directed to frequency spectrum resources and the way they are regulated. Considering that the radio spectrum is a natural limited resource, supporting the ever increasing demands for higher capacity and higher data rates for diverse sets of users, services and applications is a challenging task which requires innovative technologies capable of providing new ways of efficiently exploiting the available radio spectrum. Consequently, dynamic spectrum access (DSA) has been proposed as a replacement for static spectrum allocation policies. The DSA is implemented in three modes including interweave, overlay and underlay mode [1]. The key enabling technology for DSA is cognitive radio (CR), which is among the core prominent technologies for the next generation of wireless communication systems. Unlike conventional radio which is restricted to only operate in designated spectrum bands, a CR has the capability to operate in different spectrum bands owing to its ability in sensing, understanding its wireless environment, learning from past experiences and proactively changing the transmission parameters as needed. These features for CR are provided by an intelligent software package called the cognitive engine (CE). In general, the CE manages radio resources to accomplish cognitive functionalities and allocates and adapts the radio resources to optimize the performance of the network. Cognitive functionality of the CE can be achieved by leveraging machine learning techniques. Therefore, this thesis explores the application of two machine learning techniques in enabling the cognition capability of CE. The two considered machine learning techniques are neural network-based supervised learning and reinforcement learning. Specifically, this thesis develops resource allocation algorithms that leverage the use of machine learning techniques to find the solution to the resource allocation problem for heterogeneous underlay cognitive radio networks (CRNs). The proposed algorithms are evaluated under extensive simulation runs. The first resource allocation algorithm uses a neural network-based learning paradigm to present a fully autonomous and distributed underlay DSA scheme where each CR operates based on predicting its transmission effect on a primary network (PN). The scheme is based on a CE with an artificial neural network that predicts the adaptive modulation and coding configuration for the primary link nearest to a transmitting CR, without exchanging information between primary and secondary networks. By managing the effect of the secondary network (SN) on the primary network, the presented technique maintains the relative average throughput change in the primary network within a prescribed maximum value, while also finding transmit settings for the CRs that result in throughput as large as allowed by the primary network interference limit. The second resource allocation algorithm uses reinforcement learning and aims at distributively maximizing the average quality of experience (QoE) across transmission of CRs with different types of traffic while satisfying a primary network interference constraint. To best satisfy the QoE requirements of the delay-sensitive type of traffics, a cross-layer resource allocation algorithm is derived and its performance is compared against a physical-layer algorithm in terms of meeting end-to-end traffic delay constraints. Moreover, to accelerate the learning performance of the presented algorithms, the idea of transfer learning is integrated. The philosophy behind transfer learning is to allow well-established and expert cognitive agents (i.e. base stations or mobile stations in the context of wireless communications) to teach newly activated and naive agents. Exchange of learned information is used to improve the learning performance of a distributed CR network. This thesis further identifies the best practices to transfer knowledge between CRs so as to reduce the communication overhead. The investigations in this thesis propose a novel technique which is able to accurately predict the modulation scheme and channel coding rate used in a primary link without the need to exchange information between the two networks (e.g. access to feedback channels), while succeeding in the main goal of determining the transmit power of the CRs such that the interference they create remains below the maximum threshold that the primary network can sustain with minimal effect on the average throughput. The investigations in this thesis also provide a physical-layer as well as a cross-layer machine learning-based algorithms to address the challenge of resource allocation in underlay cognitive radio networks, resulting in better learning performance and reduced communication overhead

Radio resource allocation algorithms for multicast OFDM systems

Mención Internacional en el título de doctorVideo services have become highly demanded in mobile networks leading to an unprecedented traffic growth. It is expected that traffic from wireless and mobile devices will account for nearly 70 percent of total IP traffic by the year 2020, and the video services will account for nearly 75 percent of mobile data traffic by 2022. Multicast transmission is one of the key enablers towards a more spectral and energy efficient distribution of multimedia content in current and envisaged mobile networks. It is worth noting that multicast is a mechanism that efficiently delivers the same content to many users, not only focusing on video broadcasting, but also distributing many other media, such as software updates, weather forecast or breaking news. Although multicast services are available in Long Term Evolution (LTE) and LTE-Advanced (LTE-A) networks, new improvements are needed in some areas to handle the demands expected in the near future. Resource allocation techniques for multicast services are one of the main challenging issues, since it is required the development of novel schemes to meet the demands of their evolution towards the next generation. Most multicast techniques adopt rather conservative strategies that select a very robust modulation and coding scheme (MCS), whose characteristics are determined by the propagation conditions experienced by the worst user in the group in order to ensure that all users in a multicast group are able to correctly decode the received data. Obviously, this robustness comes at the prize of a low spectral efficiency. This thesis presents an exhaustive study of broadcast/multicast technology for current mobile networks, especially focusing on the scheduling and resource allocation (SRA) strategies to maximize the potential benefits that multicast transmissions imply on the spectral efficiency. Based on that issue, some contributions have been made to the state of the art in the radio resource management (RRM) for current and beyond mobile multicast services. • In the frame of LTE/LTE-A, the evolved multimedia broadcast and multicast service (eMBMS) shares the physical layer resources with the unicast transmission mode (at least up to Release 12). Consequently, the time allocation to multicast transmission is limited to a maximum of a 60 percent, and the remaining subframes (at least 40 percent) are reserved for unicast transmissions. With the aim of achieving the maximum aggregated data rate (ADR) among the multicast users, we have implemented several innovative SRA schemes that combine the allocation of multicast and unicast resources in the LTE/LTE-A frame, guaranteeing the prescribed quality of service (QoS) requirements for every user. • In the specific context of wideband communication systems, the selection of the multicast MCS has often relied on the use of wideband channel quality indicators (CQIs), providing rather imprecise information regarding the potential capacity of the multicast channel. Only recently has the per-subband CQI been used to improve the spectral efficiency of the system without compromising the link robustness. We have proposed novel subband CQI-based multicast SRA strategies that, relying on the selection of more spectrally efficient transmission modes, lead to increased data rates while still being able to fulfill prescribed QoS metrics. • Mobile broadcast/multicast video services require effective and low complexity SRA strategies. We have proposed an SRA strategy based on multicast subgrouping and the scalable video coding (SVC) technique for multicast video delivery. This scheme focuses on reducing the search space of solutions and optimizes the ADR. The results in terms of ADR, spectral efficiency, and fairness among multicast users, along with the low complexity of the algorithm, show that this new scheme is adequate for real systems. These contributions are intended to serve as a reference that motivate ongoing and future investigation in the challenging field of RRM for broadcast/ multicast services in next generation mobile networks.La demanda de servicios de vídeo en las redes móviles ha sufrido un incremento exponencial en los últimos años, lo que a su vez ha desembocado en un aumento sin precedentes del tráfico de datos. Se espera que antes del año 2020, el trafico debido a dispositivos móviles alcance cerca del 70 por ciento del tráfico IP total, mientras que se prevé que los servicios de vídeo sean prácticamente el 75 por ciento del tráfico de datos en las redes móviles hacia el 2022. Las transmisiones multicast son una de las tecnologías clave para conseguir una distribución más eficiente, tanto espectral como energéticamente, del contenido multimedia en las redes móviles actuales y futuras. Merece la pena reseñar que el multicast es un mecanismo de entrega del mismo contenido a muchos usuarios, que no se enfoca exclusivamente en la distribución de vídeo, sino que también permite la distribución de otros muchos contenidos, como actualizaciones software, información meteorológica o noticias de última hora. A pesar de que los servicios multicast ya se encuentran disponibles en las redes Long Term Evolution (LTE) y LTE-Advanced (LTE-A), la mejora en algunos ámbitos resulta necesaria para manejar las demandas que se prevén a corto plazo. Las técnicas de asignación de recursos para los servicios multicast suponen uno de los mayores desafíos, ya que es necesario el desarrollo de nuevos esquemas que nos permitan acometer las exigencias que supone su evolución hacia la próxima generación. La mayor parte de las técnicas multicast adoptan estrategias conservadoras, seleccionando esquemas de modulación y codificación (MCS) impuestos por las condiciones de propagación que experimenta el usuario del grupo con peor canal, para así asegurar que todos los usuarios pertenecientes al grupo multicast sean capaces de decodificar correctamente los datos recibidos. Como resulta obvio, la utilización de esquemas tan robustos conlleva el precio de sufrir una baja eficiencia espectral. Esta tesis presenta un exhaustivo estudio de la tecnología broadcast/ multicast para las redes móviles actuales, que se centra especialmente en las estrategias de asignación de recursos (SRA), cuyo objetivo es maximizar los beneficios que la utilización de transmisiones multicast potencialmente implica en términos de eficiencia espectral. A partir de dicho estudio, hemos realizado varias contribuciones al estado del arte en el ámbito de la gestión de recursos radio (RRM) para los servicios multicast, aplicables en las redes móviles actuales y futuras. • En el marco de LTE/LTE-A, el eMBMS comparte los recursos de la capa física con las transmisiones unicast (al menos hasta la revisión 12). Por lo tanto, la disponibilidad temporal de las transmisiones multicast está limitada a un máximo del 60 por ciento, reservándose las subtramas restantes (al menos el 40 por ciento) para las transmisiones unicast. Con el objetivo de alcanzar la máxima tasa total de datos (ADR) entre los usuarios multicast, hemos implementado varios esquemas innovadores de SRA que combinan la asignación de los recursos multicast y unicast de la trama LTE/LTE-A, garantizando los requisitos de QoS a cada usuario. • En los sistemas de comunicaciones de banda ancha, la selección del MCS para transmisiones multicast se basa habitualmente en la utilización de CQIs de banda ancha, lo que proporciona información bastante imprecisa acerca de la capacidad potencial del canal multicast. Recientemente se ha empezado a utilizar el CQI por subbanda para mejorar la eficiencia espectral del sistema sin comprometer la robustez de los enlaces. Hemos propuesto nuevas estrategias para SRA multicast basadas en el CQI por subbanda que, basándose en la selección de los modos de transmisión con mayor eficiencia espectral, conducen a mejores tasas de datos, a la vez que permiten cumplir los requisitos de QoS. • Los servicios móviles de vídeo broadcast/multicast precisan estrategias eficientes de SRA con baja complejidad. Hemos propuesto una estrategia de SRA basada en subgrupos multicast y la técnica de codificación de vídeo escalable (SVC) para la difusión de vídeo multicast, la cual se centra en reducir el espacio de búsqueda de soluciones y optimizar el ADR. Los resultados obtenidos en términos de ADR, eficiencia espectral y equidad entre los usuarios multicast, junto con la baja complejidad del algoritmo, ponen de manifiesto que el esquema propuesto es adecuado para su implantación en sistemas reales. Estas contribuciones pretenden servir de referencia que motive la investigación actual y futura en el interesante ámbito de RRM para los servicios broadcast/multicast en las redes móviles de próxima generación.Programa Oficial de Doctorado en Multimedia y ComunicacionesPresidente: Atilio Manuel Da Silva Gameiro.- Secretario: Víctor Pedro Gil Jiménez.- Vocal: María de Diego Antó

An Overview of Signal Processing Techniques for Joint Communication and Radar Sensing

Joint communication and radar sensing (JCR) represents an emerging research field aiming to integrate the above two functionalities into a single system, by sharing the majority of hardware, signal processing modules and, in a typical case, the transmitted signal. The close cooperation of the communication and sensing functions can enable significant improvement of spectrum efficiency, reduction of device size, cost and power consumption, and improvement of performance of both functions. Advanced signal processing techniques are critical for making the integration efficient, from transmission signal design to receiver processing. This paper provides a comprehensive overview of the state-of-the-art on JCR systems from the signal processing perspective. A balanced coverage on both transmitter and receiver is provided for three types of JCR systems, namely, communication-centric, radar-centric, and joint design and optimization