Physical and medium access control layer advances in 5G wireless networks

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Self-organised multi-objective network clustering for coordinated communications in future wireless networks

The fifth generation (5G) cellular system is being developed with a vision of 1000 times more capacity than the fourth generation (4G) systems to cope with ever increasing mobile data traffic. Interference mitigation plays an important role in improving the much needed overall capacity especially in highly interference-limited dense deployment scenarios envisioned for 5G. Coordinated multi-point (CoMP) is identified as a promising interference mitigation technique where multiple base stations (BS) can cooperate for joint transmission/reception by exchanging user/control data and perform joint signal processing to mitigate inter-cell interference and even exploit it as a useful signal. CoMP is already a key feature of long term evolution-advanced (LTE-A) and envisioned as an essential function for 5G. However, CoMP cannot be realized for the whole network due to its computational complexity, synchronization requirement between coordinating BSs and high backhaul capacity requirement. BSs need to be clustered into smaller groups and CoMP can be activated within these smaller clusters. This PhD thesis aims to investigate optimum dynamic CoMP clustering solutions in 5G and beyond wireless networks with massive small cell (SC) deployment. Truly self-organised CoMP clustering algorithms are investigated, aiming to improve much needed spectral efficiency and other network objectives especially load balancing in future wireless networks. Low complexity, scalable, stable and efficient CoMP clustering algorithms are designed to jointly optimize spectral efficiency, load balancing and limited backhaul availability. Firstly, we provide a self organizing, load aware, user-centric CoMP clustering algorithm in a control and data plane separation architecture (CDSA) proposed for 5G to maximize spectral efficiency and improve load balancing. We introduce a novel re-clustering algorithm for user equipment (UE) served by highly loaded cells and show that unsatisfied UEs due to high load can be significantly reduced with minimal impact on spectral efficiency. Clustering with load balancing algorithm exploits the capacity gain from increase in cluster size and also the traffic shift from highly loaded cells to lightly loaded neighbours. Secondly, we develop a novel, low complexity, stable, network-centric clustering model to jointly optimize load balancing and spectral efficiency objectives and tackle the complexity and scalability issues of user-centric clustering. We show that our clustering model provide high spectral efficiency in low-load scenario and better load distribution in high-load scenario resulting in lower number of unsatisfied users while keeping spectral efficiency at comparably high levels. Unsatisfied UEs due to high load are reduced by $68.5\%$ with our algorithm when compared to greedy clustering model. In this context, the unique contribution of this work that it is the first attempt to fill the gap in literature for multi-objective, network-centric CoMP clustering, jointly optimizing load balancing and spectral efficiency. Thirdly, we design a novel multi-objective CoMP clustering algorithm to include backhaul-load awareness and tackle one of the biggest challenges for the realization of CoMP in future networks i.e. the demand for high backhaul bandwidth and very low latency. We fill the gap in literature as the first attempt to design a clustering algorithm to jointly optimize backhaul/radio access load and spectral efficiency and analyze the trade-off between them. We employ 2 novel coalitional game theoretic clustering methods, 1-a novel merge/split/transfer coalitional game theoretic clustering algorithm to form backhaul and load aware BS clusters where spectral efficiency is still kept at high level, 2-a novel user transfer game model to move users between clusters to improve load balancing further. Stability and complexity analysis is provided and simulation results are presented to show the performance of the proposed method under different backhaul availability scenarios. We show that average system throughout is increased by 49.9% with our backhaul-load aware model in high load scenario when compared to a greedy model. Finally, we provide an operator's perspective on deployment of CoMP. Firstly, we present the main motivation and benefits of CoMP from an operator's viewpoint. Next, we present operational requirements for CoMP implementation and discuss practical considerations and challenges of such deployment. Possible solutions for these experienced challenges are reviewed. We then present initial results from a UL CoMP trial and discuss changes in key network performance indicators (KPI) during the trial. Additionally, we propose further improvements to the trialed CoMP scheme for better potential gains and give our perspective on how CoMP will fit into the future wireless networks

Closed form analysis of Poisson cellular networks: a stochastic geometry approach

Ultra dense networks (UDNs) allow for efficient spatial reuse of the spectrum, giving rise to substantial capacity and power gains. In order to exploit those gains, tractable mathematical models need to be derived, allowing for the analysis and optimization of the network operation. In this course, stochastic geometry has emerged as a powerful tool for large-scale analysis and modeling of wireless cellular networks. In particular, the employment of stochastic geometry has been proven instrumental for the characterization of the network performance and for providing significant insights into network densification. Fundamental issues, however, remain open in order to use stochastic geometry tools for the optimization of wireless networks, with the biggest challenge being the lack of tractable closed form expressions for the derived figures of merit. To this end, the present thesis revisits stochastic geometry and provides a novel stochastic geometry framework with a twofold contribution. The first part of the thesis focuses on the derivation of simple, albeit accurate closed form approximations for the ergodic rate of Poisson cellular networks under a noise limited, an interference limited and a general case scenario. The ergodic rate constitutes the most sensible figure of merit for characterizing the system performance, but due to the inherent intractability of the available stochastic geometry frameworks, had not been formulated in closed form hitherto. To demonstrate the potential of the aforementioned tractable expressions with respect to network optimization, the present thesis proposes a flexible connectivity paradigm and employs part of the developed expressions to optimize the network connectivity. The proposed flexible connectivity paradigm exploits the downlink uplink decoupling (DUDe) configuration, which is a promising framework providing substantial capacity and outage gains in UDNs and introduces the DUDe connectivity gains into the 5G era and beyond. Subsequently, the last part of the thesis provides an analytical formulation of the probability density function (PDF) of the aggregate inter-cell interference in Poisson cellular networks. The introduced PDF is an accurate approximation of the exact PDF that could not be analytically formulated hitherto, even though it constituted a crucial tool for the analysis and optimization of cellular networks. The lack of an analytical expression for the PDF of the interference in Poisson cellular networks had imposed the use of intricate formulas, in order to derive sensible figures of merit by employing only the moment generating function (MGF). Hence, the present thesis introduces an innovative framework able to simplify the analysis of Poisson cellular networks to a great extent, while addressing fundamental issues related to network optimization and design.Las redes ultra densas (UDNs) permiten una reutilización espacial del espectro, proporcionando ventajas en términos de mejora de capacidad y ahorro de potencia. Para explotar estas ventajas se necesitan modelos matemáticos simples que permitan el análisis y la optimización de la operación de la red. Por esta razón, la geometría estocástica se ha convertido en una potente herramienta para el análisis de redes celulares. En particular, el empleo de la geometría estocástica ha sido fundamental para la caracterización del rendimiento de la red y para proporcionar información importante sobre la densificación de la misma. Sin embargo, hay problemas fundamentales que deben resolverse para utilizar estas herramientas de geometría estocástica, siendo el mayor desafío la falta de expresiones simples de forma cerrada para las funciones objetivo de interés. Por este motivo, la presente tesis examina la geometría estocástica y proporciona un marco novedoso con una doble contribución. La primera parte de la tesis se centra en la derivación de aproximaciones cerradas simples pero ajustadas para la capacidad ergódica de las redes de Poisson en escenarios limitados por ruido, por interferencia y por ambos. La capacidad ergódica constituye la figura de mérito más apropiada para caracterizar el rendimiento del sistema, pero no se ha formulado en forma cerrada debido a la complejidad inherente de las expresiones de geometría estocástica disponibles. Para demostrar el potencial de las expresiones simples propuestas, la presente tesis propone un paradigma de conectividad flexible y utiliza parte de las expresiones desarrolladas para optimizar la conectividad de la red. El paradigma de conectividad flexible propuesto explota la configuración de "Downlink Uplink Decoupling" (DUDe), que es un marco que proporciona ventajas sustanciales en términos de incremento de capacidad y reducción de la probabilidad de bloqueo en UDNs e introduce mejoras de conectividad DUDe en la era de 5G. Más adelante, la última parte de la tesis proporciona una formulación analítica de la función de densidad de probabilidad (PDF) de la interferencia agregada en las redes celulares de Poisson. La PDF desarrollada es una aproximación precisa de la PDF exacta que hasta ahora no se ha podido formular analíticamente, a pesar de que se trata de una herramienta crucial para el análisis y la optimización de las redes celulares. La falta de una expresión analítica para la PDF de la interferencia en las redes celulares de Poisson había impuesto el uso de fórmulas complejas, a fin de derivar funciones objetivas apropiadas empleando solo la función generadora de momentos (MGF). Por lo tanto, la presente tesis presenta un marco innovador capaz de simplificar el análisis de las redes celulares de Poisson y así resolver problemas fundamentales relacionados con la optimización y el diseño de la red

Intelligent and Efficient Ultra-Dense Heterogeneous Networks for 5G and Beyond

Ultra-dense heterogeneous network (HetNet), in which densified small cells overlaying the conventional macro-cells, is a promising technique for the fifth-generation (5G) mobile network. The dense and multi-tier network architecture is able to support the extensive data traffic and diverse quality of service (QoS) but meanwhile arises several challenges especially on the interference coordination and resource management. In this thesis, three novel network schemes are proposed to achieve intelligent and efficient operation based on the deep learning-enabled network awareness. Both optimization and deep learning methods are developed to achieve intelligent and efficient resource allocation in these proposed network schemes. To improve the cost and energy efficiency of ultra-dense HetNets, a hotspot prediction based virtual small cell (VSC) network is proposed. A VSC is formed only when the traffic volume and user density are extremely high. We leverage the feature extraction capabilities of deep learning techniques and exploit a long-short term memory (LSTM) neural network to predict potential hotspots and form VSC. Large-scale antenna array enabled hybrid beamforming is also adaptively adjusted for highly directional transmission to cover these VSCs. Within each VSC, one user equipment (UE) is selected as a cell head (CH), which collects the intra-cell traffic using the unlicensed band and relays the aggregated traffic to the macro-cell base station (MBS) in the licensed band. The inter-cell interference can thus be reduced, and the spectrum efficiency can be improved. Numerical results show that proposed VSCs can reduce $55\%$ power consumption in comparison with traditional small cells. In addition to the smart VSCs deployment, a novel multi-dimensional intelligent multiple access (MD-IMA) scheme is also proposed to achieve stringent and diverse QoS of emerging 5G applications with disparate resource constraints. Multiple access (MA) schemes in multi-dimensional resources are adaptively scheduled to accommodate dynamic QoS requirements and network states. The MD-IMA learns the integrated-quality-of-system-experience (I-QoSE) by monitoring and predicting QoS through the LSTM neural network. The resource allocation in the MD-IMA scheme is formulated as an optimization problem to maximize the I-QoSE as well as minimize the non-orthogonality (NO) in view of implementation constraints. In order to solve this problem, both model-based optimization algorithms and model-free deep reinforcement learning (DRL) approaches are utilized. Simulation results demonstrate that the achievable I-QoSE gain of MD-IMA over traditional MA is $15\%$ - $18\%$. In the final part of the thesis, a Software-Defined Networking (SDN) enabled 5G-vehicle ad hoc networks (VANET) is designed to support the growing vehicle-generated data traffic. In this integrated architecture, to reduce the signaling overhead, vehicles are clustered under the coordination of SDN and one vehicle in each cluster is selected as a gateway to aggregate intra-cluster traffic. To ensure the capacity of the trunk-link between the gateway and macro base station, a Non-orthogonal Multiplexed Modulation (NOMM) scheme is proposed to split aggregated data stream into multi-layers and use sparse spreading code to partially superpose the modulated symbols on several resource blocks. The simulation results show that the energy efficiency performance of proposed NOMM is around 1.5-2 times than that of the typical orthogonal transmission scheme