Maximising the Utility of Enterprise Millimetre-Wave Networks

Millimetre-wave (mmWave) technology is a promising candidate for meeting the intensifying demand for ultra fast wireless connectivity, especially in high-end enterprise networks. Very narrow beam forming is mandatory to mitigate the severe attenuation specific to the extremely high frequency (EHF) bands exploited. Simultaneously, this greatly reduces interference, but generates problematic communication blockages. As a consequence, client association control and scheduling in scenarios with densely deployed mmWave access points become particularly challenging, while policies designed for traditional wireless networks remain inappropriate. In this paper we formulate and solve these tasks as utility maximisation problems under different traffic regimes, for the first time in the mmWave context. We specify a set of low-complexity algorithms that capture distinctive terminal deafness and user demand constraints, while providing near-optimal client associations and airtime allocations, despite the problems' inherent NP-completeness. To evaluate our solutions, we develop an NS-3 implementation of the IEEE 802.11ad protocol, which we construct upon preliminary 60GHz channel measurements. Simulation results demonstrate that our schemes provide up to 60% higher throughput as compared to the commonly used signal strength based association policy for mmWave networks, and outperform recently proposed load-balancing oriented solutions, as we accommodate the demand of 33% more clients in both static and mobile scenarios.Comment: 22 pages, 12 figures, accepted for publication in Computer Communication

Maximising system throughput in wireless powered sub-6 GHz and millimetre-wave 5G heterogeneous networks

Millimetre wave (mm-Wave) bands and sub-6 GHz are key technologies in solving the spectrum critical situation in the fifth generation (5G) wireless networks in achieving high throughput with low transmission power. This paper studies the performance of dense small cells that involve a millimetre wave (mm-Wave) band and sub-6 GHz that operate in high frequency to support massive multiple-input-multiple-output systems (MIMO). In this paper, we analyse the propagation path loss and wireless powered transfer for a 5G wireless cellular system from both macro cells and femtocells in the sub-6 GHz (µWave) and mm-Wave tiers. This paper also analyses the tier heterogeneous in downlink for both mm-Wave and sub-6 GHz. It further proposes a novel distributed power to mitigate the inter-beam interference directors and achieve high throughput under game theory-based power constraints across the sub-6 GHz and mm-Wave interfaces. From the simulation results, the proposed distributed powers in femtocell suppresses inter-beam interference by minimising path loss to active users (UEs) and provides substantial power saving by controlling the distributed power algorithm to achieve high throughput

Max-Min Fair Resource Allocation in Millimetre-Wave Backhauls

5G mobile networks are expected to provide pervasive high speed wireless connectivity, to support increasingly resource intensive user applications. Network hyper-densification therefore becomes necessary, though connecting to the Internet tens of thousands of base stations is non-trivial, especially in urban scenarios where optical fibre is difficult and costly to deploy. The millimetre wave (mm-wave) spectrum is a promising candidate for inexpensive multi-Gbps wireless backhauling, but exploiting this band for effective multi-hop data communications is challenging. In particular, resource allocation and scheduling of very narrow transmission/ reception beams requires to overcome terminal deafness and link blockage problems, while managing fairness issues that arise when flows encounter dissimilar competition and traverse different numbers of links with heterogeneous quality. In this paper, we propose WiHaul, an airtime allocation and scheduling mechanism that overcomes these challenges specific to multi-hop mm-wave networks, guarantees max-min fairness among traffic flows, and ensures the overall available backhaul resources are fully utilised. We evaluate the proposed WiHaul scheme over a broad range of practical network conditions, and demonstrate up to 5 times individual throughput gains and a fivefold improvement in terms of measurable fairness, over recent mm-wave scheduling solutions

Resource management in future mobile networks: from millimetre-wave backhauls to airborne access networks

The next generation of mobile networks will connect vast numbers of devices and support services with diverse requirements. Enabling technologies such as millimetre-wave (mm-wave) backhauling and network slicing allow for increased wireless capacities and logical partitioning of physical deployments, yet introduce a number of challenges. These include among others the precise and rapid allocation of network resources among applications, elucidating the interactions between new mobile networking technology and widely used protocols, and the agile control of mobile infrastructure, to provide users with reliable wireless connectivity in extreme scenarios. This thesis presents several original contributions that address these challenges. In particular, I will first describe the design and evaluation of an airtime allocation and scheduling mechanism devised specifically for mm-wave backhauls, explicitly addressing inter-flow fairness and capturing the unique characteristics of mm-wave communications. Simulation results will demonstrate 5x throughput gains and a 5-fold improvement in fairness over recent mm-wave scheduling solutions. Second, I will introduce a utility optimisation framework targeting virtually sliced mm-wave backhauls that are shared by a number of applications with distinct requirements. Based on this framework, I will present a deep learning solution that can be trained within minutes, following which it computes rate allocations that match those obtained with state-of-the-art global optimisation algorithms. The proposed solution outperforms a baseline greedy approach by up to 62%, in terms of network utility, while running orders of magnitude faster. Third, the thesis investigates the behaviour of the Transport Control Protocol (TCP) in Long-Term Evolution (LTE) networks and discusses the implications of employing Radio Link Control (RLC) acknowledgements under different link qualities, on the performance of transport protocols. Fourth, I will introduce a reinforcement learning approach to optimising the performance of airborne cellular networks serving users in emergency settings, demonstrating rapid convergence (approx. 2.5 hours on a desktop machine) and a 5dB improvement of the median Signal-to-Noise-plus-Interference-Ratio (SINR) perceived by users, over a heuristic based benchmark solution. Finally, the thesis discusses promising future research directions that follow from the results obtained throughout this PhD project

The Australian research data infrastructure strategy

Executive summary Data is central to all research. Data, in its raw or processed form, from its original source (such as an ocean sensor) or via an analytical processor (such as the cores of a supercomputer), depends invariably on research infrastructure for its collection, generation, manipulation, characterisation, use and dissemination. Research data infrastructure refers to a range of facilities, equipment or tools that serve research through data generation, manipulation, curation, and access. It includes data itself. The Australian Government has made significant investments in research data infrastructure, guided by principles set out in existing strategies. In light of newly developed sets of principles—in particular the Strategic Framework for Research Infrastructure Investment principles, which appear in the 2011 Strategic Roadmap for Australian Research Infrastructure—the Government established the RDIC. The committee reviewed the national research data landscape to provide advice on how to optimise existing and future investments in research data infrastructure. Developed as the Australian Research Data Infrastructure Strategy, this advice provides a basis for policy makers, investors, developers, operators and users to build and sustain an effective and holistic Australian research data infrastructure system. It is a system that collects data systematically and intentionally, organises data to make it more valuable, and uses data insightfully many times over. The strategy proposes three key requirements for a successful national research data infrastructure framework: sustained infrastructure to support priority research data collections, data generation and management appropriate data governance and access arrangements delivery of enhanced research outcomes from effective data infrastructure arrangement

QoS-aware Adaptive Resource Management in OFDMA Networks

PhDOne important feature of the future communication network is that users in the network are required to experience a guaranteed high quality of service (QoS) due to the popularity of multimedia applications. This thesis studies QoS-aware radio resource management schemes in different OFDMA network scenarios. Motivated by the fact that in current 4G networks, the QoS provisioning is severely constrained by the availability of radio resources, especially the scarce spectrum as well as the unbalanced traffic distribution from cell to cell, a joint antenna and subcarrier management scheme is proposed to maximise user satisfaction with load balancing. Antenna pattern update mechanism is further investigated with moving users. Combining network densi fication with cloud computing technologies, cloud radio access network (C-RAN) has been proposed as the emerging 5G network architecture consisting of baseband unit (BBU) pool, remote radio heads (RRHs) and fronthaul links. With cloud based information sharing through the BBU pool, a joint resource block and power allocation scheme is proposed to maximise the number of satisfi ed users whose required QoS is achieved. In this scenario, users are served by high power nodes only. With spatial reuse of system bandwidth by network densi fication, users' QoS provisioning can be ensured but it introduces energy and operating effciency issue. Therefore two network energy optimisation schemes with QoS guarantee are further studied for C-RANs: an energy-effective network deployment scheme is designed for C-RAN based small cells; a joint RRH selection and user association scheme is investigated in heterogeneous C-RAN. Thorough theoretical analysis is conducted in the development of all proposed algorithms, and the effectiveness of all proposed algorithms is validated via comprehensive simulations.China Scholarship Counci

Efficient radio resource management for future generation heterogeneous wireless networks

The heterogeneous deployment of small cells (e.g., femtocells) in the coverage area of the traditional macrocells is a cost-efficient solution to provide network capacity, indoor coverage and green communications towards sustainable environments in the future fifth generation (5G) wireless networks. However, the unplanned and ultra-dense deployment of femtocells with their uncoordinated operations will result in technical challenges such as severe interference, a significant increase in total energy consumption, unfairness in radio resource sharing and inadequate quality of service provisioning. Therefore, there is a need to develop efficient radio resource management algorithms that will address the above-mentioned technical challenges. The aim of this thesis is to develop and evaluate new efficient radio resource management algorithms that will be implemented in cognitive radio enabled femtocells to guarantee the economical sustainability of broadband wireless communications and users' quality of service in terms of throughput and fairness. Cognitive Radio (CR) technology with the Dynamic Spectrum Access (DSA) and stochastic process are the key technologies utilized in this research to increase the spectrum efficiency and energy efficiency at limited interference. This thesis essentially investigates three research issues relating to the efficient radio resource management: Firstly, a self-organizing radio resource management algorithm for radio resource allocation and interference management is proposed. The algorithm considers the effect of imperfect spectrum sensing in detecting the available transmission opportunities to maximize the throughput of femtocell users while keeping interference below pre-determined thresholds and ensuring fairness in radio resource sharing among users. Secondly, the effect of maximizing the energy efficiency and the spectrum efficiency individually on radio resource management is investigated. Then, an energy-efficient radio resource management algorithm and a spectrum-efficient radio resource management algorithm are proposed for green communication, to improve the probabilities of spectrum access and further increase the network capacity for sustainable environments. Also, a joint maximization of the energy efficiency and spectrum efficiency of the overall networks is considered since joint optimization of energy efficiency and spectrum efficiency is one of the goals of 5G wireless networks. Unfortunately, maximizing the energy efficiency results in low performance of the spectrum efficiency and vice versa. Therefore, there is an investigation on how to balance the trade-off that arises when maximizing both the energy efficiency and the spectrum efficiency simultaneously. Hence, a joint energy efficiency and spectrum efficiency trade-off algorithm is proposed for radio resource allocation in ultra-dense heterogeneous networks based on orthogonal frequency division multiple access. Lastly, a joint radio resource allocation with adaptive modulation and coding scheme is proposed to minimize the total transmit power across femtocells by considering the location and the service requirements of each user in the network. The performance of the proposed algorithms is evaluated by simulation and numerical analysis to demonstrate the impact of ultra-dense deployment of femtocells on the macrocell networks. The results show that the proposed algorithms offer improved performance in terms of throughput, fairness, power control, spectrum efficiency and energy efficiency. Also, the proposed algorithms display excellent performance in dynamic wireless environments

An Examination of the Evolution of Broadband Technologies in the UK

The aim of this thesis is to examine the reasons due to which Digital Subscriber Line (DSL) became the most widely used technology to deliver broadband connectivity in the United Kingdom (UK). The research examines the outcome starting with events in 1960s when broadband as it is defined today did not exist. The research shows that a combination of factors involving regulatory decisions, changing market conditions, and unexpected technological breakthroughs contributed to the current day mix of broadband technologies in the last mile access in the UK. To interpret the events that have shaped the development, deployment, and adoption of broadband technologies in the UK, the thesis draws from various theoretical ideas related to Science and Technology Studies (STS) to understand and analyse the events. In order to discover and establish the historical context, the thesis employs original, unpublished interviews along with the extensive use of archival material and secondary sources. Influenced by some of the core ideas of social constructionist studies, this research combines concepts from economic studies of technological change along with themes involving maintenance of technology, path dependence, and the role of bandwagon effect. These research threads are combined to understand the way development, deployment, and adoption of broadband technologies took place in the UK. The research is intended to contribute to the understanding of technology in a constantly changing regulatory and socio-economic environment and how it is shaped by multiple factors. The targeted readership is researchers, analysts, and decision makers working with broadband technology, telecommunications policy, and STS. Further research is suggested in the form of studies of wireless broadband technologies and the role of regulatory policies in the development of the UK communications market

Admission Control Optimisation for QoS and QoE Enhancement in Future Networks

Recent exponential growth in demand for traffic heterogeneity support and the number of associated devices has considerably increased demand for network resources and induced numerous challenges for the networks, such as bottleneck congestion, and inefficient admission control and resource allocation. Challenges such as these degrade network Quality of Service (QoS) and user-perceived Quality of Experience (QoE). This work studies admission control from various perspectives. For example, two novel single-objective optimisation-based admission control models, Dynamica Slice Allocation and Admission Control (DSAAC) and Signalling and Admission Control (SAC), are presented to enhance future limited-capacity network Grade of Service (GoS), and for control signalling optimisation, respectively. DSAAC is an integrated model whereby a cost-estimation function based on user demand and network capacity quantifies resource allocation among users. Moreover, to maximise resource utility, adjustable minimum and maximum slice resource bounds have also been derived. In the case of user blocking from the primary slice due to congestion or resource scarcity, a set of optimisation algorithms on inter-slice admission control and resource allocation and adaptability of slice elasticity have been proposed. A novel SAC model uses an unsupervised learning technique (i.e. Ranking-based clustering) for optimal clustering based on users’ homogeneous demand characteristics to minimise signalling redundancy in the access network. The redundant signalling reduction reduces the additional burden on the network in terms of unnecessary resource utilisation and computational time. Moreover, dynamically reconfigurable QoE-based slice performance bounds are also derived in the SAC model from multiple demand characteristics for clustered user admission to the optimal network. A set of optimisation algorithms are also proposed to attain efficient slice allocation and users’ QoE enhancement via assessing the capability of slice QoE elasticity. An enhancement of the SAC model is proposed through a novel multi-objective optimisation model named Edge Redundancy Minimisation and Admission Control (E-RMAC). A novel E-RMAC model for the first time considers the issue of redundant signalling between the edge and core networks. This model minimises redundant signalling using two classical unsupervised learning algorithms, K-mean and Ranking-based clustering, and maximises the efficiency of the link (bandwidth resources) between the edge and core networks. For multi-operator environments such as Open-RAN, a novel Forecasting and Admission Control (FAC) model for tenant-aware network selection and configuration is proposed. The model features a dynamic demand-estimation scheme embedded with fuzzy-logic-based optimisation for optimal network selection and admission control. FAC for the first time considers the coexistence of the various heterogeneous cellular technologies (2G, 3G,4G, and 5G) and their integration to enhance overall network throughput by efficient resource allocation and utilisation within a multi-operator environment. A QoS/QoE-based service monitoring feature is also presented to update the demand estimates with the support of a forecasting modifier. he provided service monitoring feature helps resource allocation to tenants, approximately closer to the actual demand of the tenants, to improve tenant-acquired QoE and overall network performance. Foremost, a novel and dynamic admission control model named Slice Congestion and Admission Control (SCAC) is also presented in this thesis. SCAC employs machine learning (i.e. unsupervised, reinforcement, and transfer learning) and multi-objective optimisation techniques (i.e. Non-dominated Sorting Genetic Algorithm II ) to minimise bottleneck and intra-slice congestion. Knowledge transfer among requests in form of coefficients has been employed for the first time for optimal slice requests queuing. A unified cost estimation function is also derived in this model for slice selection to ensure fairness among slice request admission. In view of instantaneous network circumstances and load, a reinforcement learning-based admission control policy is established for taking appropriate action on guaranteed soft and best-effort slice requests admissions. Intra-slice, as well as inter-slice resource allocation, along with the adaptability of slice elasticity, are also proposed for maximising slice acceptance ratio and resource utilisation. Extensive simulation results are obtained and compared with similar models found in the literature. The proposed E-RMAC model is 35% superior at reducing redundant signalling between the edge and core networks compared to recent work. The E-RMAC model reduces the complexity from O(U) to O(R) for service signalling and O(N) for resource signalling. This represents a significant saving in the uplink control plane signalling and link capacity compared to the results found in the existing literature. Similarly, the SCAC model reduces bottleneck congestion by approximately 56% over the entire load compared to ground truth and increases the slice acceptance ratio. Inter-slice admission and resource allocation offer admission gain of 25% and 51% over cooperative slice- and intra-slice-based admission control and resource allocation, respectively. Detailed analysis of the results obtained suggests that the proposed models can efficiently manage future heterogeneous traffic flow in terms of enhanced throughput, maximum network resources utilisation, better admission gain, and congestion control