Self organization of tilts in relay enhanced networks: a distributed solution

Despite years of physical-layer research, the capacity enhancement potential of relays is limited by the additional spectrum required for Base Station (BS)-Relay Station (RS) links. This paper presents a novel distributed solution by exploiting a system level perspective instead. Building on a realistic system model with impromptu RS deployments, we develop an analytical framework for tilt optimization that can dynamically maximize spectral efficiency of both the BS-RS and BS-user links in an online manner. To obtain a distributed self-organizing solution, the large scale system-wide optimization problem is decomposed into local small scale subproblems by applying the design principles of self-organization in biological systems. The local subproblems are non-convex, but having a very small scale, can be solved via standard nonlinear optimization techniques such as sequential quadratic programming. The performance of the developed solution is evaluated through extensive simulations for an LTE-A type system and compared against a number of benchmarks including a centralized solution obtained via brute force, that also gives an upper bound to assess the optimality gap. Results show that the proposed solution can enhance average spectral efficiency by up to 50% compared to fixed tilting, with negligible signaling overheads. The key advantage of the proposed solution is its potential for autonomous and distributed implementation

Cooperative control of relay based cellular networks

PhDThe increasing popularity of wireless communications and the higher data requirements of new types of service lead to higher demands on wireless networks. Relay based cellular networks have been seen as an effective way to meet users’ increased data rate requirements while still retaining the benefits of a cellular structure. However, maximizing the probability of providing service and spectrum efficiency are still major challenges for network operators and engineers because of the heterogeneous traffic demands, hard-to-predict user movements and complex traffic models. In a mobile network, load balancing is recognised as an efficient way to increase the utilization of limited frequency spectrum at reasonable costs. Cooperative control based on geographic load balancing is employed to provide flexibility for relay based cellular networks and to respond to changes in the environment. According to the potential capability of existing antenna systems, adaptive radio frequency domain control in the physical layer is explored to provide coverage at the right place at the right time. This thesis proposes several effective and efficient approaches to improve spectrum efficiency using network wide optimization to coordinate the coverage offered by different network components according to the antenna models and relay station capability. The approaches include tilting of antenna sectors, changing the power of omni-directional antennas, and changing the assignment of relay stations to different base stations. Experiments show that the proposed approaches offer significant improvements and robustness in heterogeneous traffic scenarios and when the propagation environment changes. The issue of predicting the consequence of cooperative decisions regarding antenna configurations when applied in a realistic environment is described, and a coverage prediction model is proposed. The consequences of applying changes to the antenna configuration on handovers are analysed in detail. The performance evaluations are based on a system level simulator in the context of Mobile WiMAX technology, but the concepts apply more generally

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

Planning Wireless Cellular Networks of Future: Outlook, Challenges and Opportunities

Cell planning (CP) is the most important phase in the life cycle of a cellular system as it determines the operational expenditure, capital expenditure, as well as the long-term performance of the system. Therefore, it is not surprising that CP problems have been studied extensively for the past three decades for all four generations of cellular systems. However, the fact that small cells, a major component of future networks, are anticipated to be deployed in an impromptu fashion makes CP for future networks vis-a-vis 5G a conundrum. Furthermore, in emerging cellular systems that incorporate a variety of different cell sizes and types, heterogeneous networks (HetNets), energy efficiency, self-organizing network features, control and data plane split architectures (CDSA), massive multiple input multiple out (MIMO), coordinated multipoint (CoMP), cloud radio access network, and millimetre-wave-based cells plus the need to support Internet of Things (IoT) and device-to-device (D2D) communication require a major paradigm shift in the way cellular networks have been planned in the past. The objective of this paper is to characterize this paradigm shift by concisely reviewing past developments, analyzing the state-of-the-art challenges, and identifying future trends, challenges, and opportunities in CP in the wake of 5G. More specifically, in this paper, we investigate the problem of planning future cellular networks in detail. To this end, we first provide a brief tutorial on the CP process to identify the peculiarities that make CP one of the most challenging problems in wireless communications. This tutorial is followed by a concise recap of past research in CP. We then review key findings from recent studies that have attempted to address the aforementioned challenges in planning emerging networks. Finally, we discuss the range of technical factors that need to be taken into account while planning future networks and the promising research directions that necessitates the paradigm shift to do so

Tilt Angle Optimization in Two-Tier Cellular Networks - A Stochastic Geometry Approach

In this work, we address the antenna tilt optimization problem for a two tier cellular network consisting of macrocells and femtocells, where both tiers share the same spectrum and their positions are modelled via two independent Poisson Point Processes (PPPs). First, we derive the coverage probability for a traditional cellular network consisting only of macrocells and obtain the optimum tilt angle that maximises the overall energy efficiency (EE). Gains of up to 400% in EE were found for a scenario (approximately) equivalent to a hexagonal cell deployment with cell radius of 200 m when the optimum tilt was selected. We then proceed to model the Heterogeneous Network (HetNet) scenario where femtocells are also deployed in the network’s area. We observe that the macro users performance is highly sensitive to the interference emanating from the femtocell tier. In order to circumvent this issue, interference coordination by employing a guard zone for the macrocell user is proposed. Subsequently, we formulate a joint optimization problem where we derive both, the radius of a guard zone protecting the macro user and the tilt angle that maximize the EE of the network

A PARADIGM SHIFTING APPROACH IN SON FOR FUTURE CELLULAR NETWORKS

The race to next generation cellular networks is on with a general consensus in academia and industry that massive densification orchestrated by self-organizing networks (SONs) is the cost-effective solution to the impending mobile capacity crunch. While the research on SON commenced a decade ago and is still ongoing, the current form (i.e., the reactive mode of operation, conflict-prone design, limited degree of freedom and lack of intelligence) hinders the current SON paradigm from meeting the requirements of 5G. The ambitious quality of experience (QoE) requirements and the emerging multifarious vision of 5G, along with the associated scale of complexity and cost, demand a significantly different, if not totally new, approach to SONs in order to make 5G technically as well as financially feasible. This dissertation addresses these limitations of state-of-the-art SONs. It first presents a generic low-complexity optimization framework to allow for the agile, on-line, multi-objective optimization of future mobile cellular networks (MCNs) through only top-level policy input that prioritizes otherwise conflicting key performance indicators (KPIs) such as capacity, QoE, and power consumption. The hybrid, semi-analytical approach can be used for a wide range of cellular optimization scenarios with low complexity. The dissertation then presents two novel, user-mobility, prediction-based, proactive self-optimization frameworks (AURORA and OPERA) to transform mobility from a challenge into an advantage. The proposed frameworks leverage mobility to overcome the inherent reactiveness of state-of-the-art self-optimization schemes to meet the extremely low latency and high QoE expected from future cellular networks vis-à-vis 5G and beyond. The proactiveness stems from the proposed frameworks’ novel capability of utilizing past hand-over (HO) traces to determine future cell loads instead of observing changes in cell loads passively and then reacting to them. A semi-Markov renewal process is leveraged to build a model that can predict the cell of the next HO and the time of the HO for the users. A low-complexity algorithm has been developed to transform the predicted mobility attributes to a user-coordinate level resolution. The learned knowledge base is used to predict the user distribution among cells. This prediction is then used to formulate a novel (i) proactive energy saving (ES) optimization problem (AURORA) that proactively schedules cell sleep cycles and (ii) proactive load balancing (LB) optimization problem (OPERA). The proposed frameworks also incorporate the effect of cell individual offset (CIO) for balancing the load among cells, and they thus exploit an additional ultra-dense network (UDN)-specific mechanism to ensure QoE while maximizing ES and/or LB. The frameworks also incorporates capacity and coverage constraints and a load-aware association strategy for ensuring the conflict-free operation of ES, LB, and coverage and capacity optimization (CCO) SON functions. Although the resulting optimization problems are combinatorial and NP-hard, proactive prediction of cell loads instead of reactive measurement allows ample time for combination of heuristics such as genetic programming and pattern search to find solutions with high ES and LB yields compared to the state of the art. To address the challenge of significantly higher cell outage rates in anticipated in 5G and beyond due to higher operational complexity and cell density than legacy networks, the dissertation’s fourth key contribution is a stochastic analytical model to analyze the effects of the arrival of faults on the reliability behavior of a cellular network. Assuming exponential distributions for failures and recovery, a reliability model is developed using the continuous-time Markov chains (CTMC) process. Unlike previous studies on network reliability, the proposed model is not limited to structural aspects of base stations (BSs), and it takes into account diverse potential fault scenarios; it is also capable of predicting the expected time of the first occurrence of the fault and the long-term reliability behavior of the BS. The contributions of this dissertation mark a paradigm shift from the reactive, semi-manual, sub-optimal SON towards a conflict-free, agile, proactive SON. By paving the way for future MCN’s commercial and technical viability, the new SON paradigm presented in this dissertation can act as a key enabler for next-generation MCNs

User Association in 5G Networks: A Survey and an Outlook

26 pages; accepted to appear in IEEE Communications Surveys and Tutorial

DYNAMIC USER-CENTRIC CAPACITY MAXIMIZATION BY OPTIMIZING ANTENNA PARAMETERS

Cellular network has become the primary means of voice as well as data communication. With sophisticated but affordable end user devices e.g. smartphones, tablet PCs etc. and ubiquity of mobile connectivity, users are able to access a range of multimedia services requiring low to high data rate and with desired quality of experience everywhere and all the times. However, mobile network operators (MNOs) always have limited bandwidth resources as compared to users’ demand, as bandwidth is the most expensive resource in the network. Thus MNOs always seek new tools and technologies to optimally utilize the available bandwidth to accommodate maximum number of users and provide high quality of services, maximizing the revenue in return. Especially, in the case of ultra-dense heterogeneous deployment of small cells equipped with massive-MIMO antenna configuration operating over mmWave spectrum in 5G, automated solution for dynamic spectrum optimization with respect to rapidly changing users and network requirement will be of critical importance. This thesis presents a novel scheme for spectral efficiency (SE) optimization through clustering of users. By clustering users with respect to their geographical concentration we propose a solution for dynamic steering of antenna beam by dynamically adjusting antenna azimuth and tilt angles with respect to the most focal point in every cell that would maximize overall SE in the system. The proposed framework thus introduces the notion of elastic cells that can be potential component of 5G networks. The proposed scheme decomposes large-scale system-wide optimization problem into small-scale local sub-problems and thus provides a low complexity solution for dynamic system wide optimization. Every sub-problem involves clustering of users to determine focal point of the cell for given user distribution in time and space, and determining new values of azimuth and tilt that would optimize the overall system SE performance. To this end, we proposed three user clustering algorithms to transform a given user distribution into the focal points that can be used in optimization process: the first is based on received signal to interference ratio (SIR) at the user; the second is based on received signal level (RSL) at the user; the third and final one is based on relative distances of users from the base stations. We also formulate and solve an optimization problem to determine optimal radii of clusters. The performances of proposed algorithms and framework are evaluated through system level simulations. Performance comparison against benchmark where no elastic cell deployed, shows that a gain in spectral efficiency of up to 26% is achievable depending upon user distribution in each cell