Power-optimised multi-radio network under varying throughput constraints for rural broadband access

The use of complementary radio access technologies within a network allows the advantages of each technology to be combined to overcome individual limitations. In this paper we show how 5~GHz and ``TV White Space'' overlay networks can be combined to provide fixed wireless access coverage within a rural environment. By creating a model of the whole network we derive the optimum assignment of stations between the two overlay networks to maximise the capacity of individual stations given a desired individual station data rate. Through simulation we show how the power consumption of a base station can be minimised by dynamically adjusting station assignments based on network data rate requirements changing over the course of a day

Multi-radio network optimisation using Bayesian belief propagation

In this paper we show how 5 GHz and “TV White Space” wireless networks can be combined to provide fixed access for a rural community. Using multiple technologies allows the advantages of each to be combined to overcome individual limitations when assigning stations between networks. Specifically, we want to maximise throughput under the constraint of satisfying both the desired individual station data rate and the transmit power within regulatory limits. For this optimisation, we employ Pearl's algorithm, a Bayesian belief propagation implementation, which is informed by statistics drawn from network trials on Isle of Tiree with 100 households. The method confirms results obtained with an earlier deterministic approach

Multi-radio access network assignment using dynamic programming

This paper addresses the formulation of an optimisation problem that assigns a user to a multi-network base station for rural broadband access. The base station in this work is a fully off-grid—powered by renewable energy—system with a wireless backhaul link. The solution proposed in this paper relies on a dynamic programming approach, implementing a cost function that balances power consumption and quality of service. The cost is then aggregated using penalties based on the energy harvested and battery charge. The implemented algorithm is demonstrated (in simulations) able to adapt the user assignment to the network load and energy production

Design and implementation of components for renewably-powered base-stations with heterogeneous access channel

Providing high-speed broadband services in remote areas can be a challenging task, especially because of the lack of network infrastructure. As typical broadband technologies are often expensive to deploy, they require large investment from the local authorities. Previous studies have shown that a viable alternative is to use wireless base stations with high-throughput point to point (PTP) backhaul links. With base stations comes the problem of powering their systems, it is tackled in this thesis by relying on renewable energy harvesting, such as solar panels or wind turbines. This thesis, in the context of the sustainable cellular network harvesting ambient energy (SCAVENGE) project, aims to contribute to a reliable and energy efficient solution to this problem, by adjusting the design of an existing multi-radio energy harvesting base station. In Western Europe, 49 channels of 8 MHz were used for analogue TV transmissions, ranging from 470 MHz (Channel 21) to 862 MHz (Channel 69); this spectrum, now partially unused due to the digital television (DTV) switch-over, has been opened to alternative uses by the regulatory authorities. Using this newly freed ultra high frequency (UHF) range, also known as TV white space (TVWS), can offer reliable low-cost broadband access to housings and businesses in low-density areas. While UHF transmitters allow long range links, the overcrowding of the TV spectrum limits the achievable throughput; to increase the capacity of such TVWS rural broadband base station the UHF radio has previously been combined with a lower-range higher throughput GHz radio like Wireless Fidelity (WiFi). From the regulatory constraints of TVWS applications arises the need for frequency agile transceivers that observe strict spectral mask requirements, this guided previous works towards discrete Fourier transform (DFT) modulated filter-bank multicarrier (FBMC) systems. These systems are numerically efficient, as they permit the up-and-down conversion of the 40 TV channels at the cost of a single channel transceiver and the modulating transform. Typical implementations rely on power-of two fast Fourier transforms (FFTs); however the smallest transform covering the full 40 channels of the TVWS spectrum is a 64 points wide, thus involving 24 unused channels. In order to attain a more numerically-efficient implemented design, we introduce the use of mixed-radix FFTs modulating transform. Testing various sizes and architectures, this approach provides up to 6.7% of energy saving compared to previous designs. Different from orthogonal frequency-division multiplexing (OFDM), FBMC systems are generally expected to be more robust to synchronisation errors, as oversampled FBMC systems can include a guard band, and even in a doubly-dispersive channel, inter-carrier interference (ICI) can be considered negligible. Even though sub-channels can be treated independently—i.e. without the use of cross-terms—they still require equalisation. We introduce a per-band equalisation, amongst different options, a robust and fast blind approach based on a concurrent constant modulus (CM)/decision directed (DD) fractionally-space equaliser (FSE) is selected. The selected approach is capable of equalising a frequency-selective channel. Furthermore the proposed architecture is advantageous in terms of power consumption and implementation cost. After focussing on the design of the radio for TVWS transmission, we address a multi-radio user assignment problem. Using various power consumption and harvesting models for the base station, we formulate two optimisation problems, the first focuses on the base station power consumption, while the second concentrates on load balancing. We employ a dynamic programming approach to optimise the user assignment. The use of such algorithms could allow a downsizing of the power supply systems (harvesters and batteries), thus reducing the cost of the base station. Furthermore the algorithms provide a better balance between the number of users assigned to each network, resulting in a higher quality of service (QoS) and energy efficiency.Providing high-speed broadband services in remote areas can be a challenging task, especially because of the lack of network infrastructure. As typical broadband technologies are often expensive to deploy, they require large investment from the local authorities. Previous studies have shown that a viable alternative is to use wireless base stations with high-throughput point to point (PTP) backhaul links. With base stations comes the problem of powering their systems, it is tackled in this thesis by relying on renewable energy harvesting, such as solar panels or wind turbines. This thesis, in the context of the sustainable cellular network harvesting ambient energy (SCAVENGE) project, aims to contribute to a reliable and energy efficient solution to this problem, by adjusting the design of an existing multi-radio energy harvesting base station. In Western Europe, 49 channels of 8 MHz were used for analogue TV transmissions, ranging from 470 MHz (Channel 21) to 862 MHz (Channel 69); this spectrum, now partially unused due to the digital television (DTV) switch-over, has been opened to alternative uses by the regulatory authorities. Using this newly freed ultra high frequency (UHF) range, also known as TV white space (TVWS), can offer reliable low-cost broadband access to housings and businesses in low-density areas. While UHF transmitters allow long range links, the overcrowding of the TV spectrum limits the achievable throughput; to increase the capacity of such TVWS rural broadband base station the UHF radio has previously been combined with a lower-range higher throughput GHz radio like Wireless Fidelity (WiFi). From the regulatory constraints of TVWS applications arises the need for frequency agile transceivers that observe strict spectral mask requirements, this guided previous works towards discrete Fourier transform (DFT) modulated filter-bank multicarrier (FBMC) systems. These systems are numerically efficient, as they permit the up-and-down conversion of the 40 TV channels at the cost of a single channel transceiver and the modulating transform. Typical implementations rely on power-of two fast Fourier transforms (FFTs); however the smallest transform covering the full 40 channels of the TVWS spectrum is a 64 points wide, thus involving 24 unused channels. In order to attain a more numerically-efficient implemented design, we introduce the use of mixed-radix FFTs modulating transform. Testing various sizes and architectures, this approach provides up to 6.7% of energy saving compared to previous designs. Different from orthogonal frequency-division multiplexing (OFDM), FBMC systems are generally expected to be more robust to synchronisation errors, as oversampled FBMC systems can include a guard band, and even in a doubly-dispersive channel, inter-carrier interference (ICI) can be considered negligible. Even though sub-channels can be treated independently—i.e. without the use of cross-terms—they still require equalisation. We introduce a per-band equalisation, amongst different options, a robust and fast blind approach based on a concurrent constant modulus (CM)/decision directed (DD) fractionally-space equaliser (FSE) is selected. The selected approach is capable of equalising a frequency-selective channel. Furthermore the proposed architecture is advantageous in terms of power consumption and implementation cost. After focussing on the design of the radio for TVWS transmission, we address a multi-radio user assignment problem. Using various power consumption and harvesting models for the base station, we formulate two optimisation problems, the first focuses on the base station power consumption, while the second concentrates on load balancing. We employ a dynamic programming approach to optimise the user assignment. The use of such algorithms could allow a downsizing of the power supply systems (harvesters and batteries), thus reducing the cost of the base station. Furthermore the algorithms provide a better balance between the number of users assigned to each network, resulting in a higher quality of service (QoS) and energy efficiency

EVEREST IST - 2002 - 00185 : D23 : final report

Deliverable públic del projecte europeu EVERESTThis deliverable constitutes the final report of the project IST-2002-001858 EVEREST. After its successful completion, the project presents this document that firstly summarizes the context, goal and the approach objective of the project. Then it presents a concise summary of the major goals and results, as well as highlights the most valuable lessons derived form the project work. A list of deliverables and publications is included in the annex.Postprint (published version

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

Optimisation of a propagation model for last mile connectivity with low altitude platforms using machine learning

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonOur related research review on propagation models reveals six factors that are significant in last mile connectivity via LAP: path loss, elevation angle, LAP altitude, coverage area, power consumption, operation frequency, interference, and antenna type. These factors can help with monitoring system performance, network planning, coverage footprint, receivers’ line-of-sight, quality of service requirements, and data rates which may all vary in response to geomorphology characteristics. Several competing propagation models have been proposed over the years but whilst they collectively raise many shortcomings such as limited altitude up to few tens of meters, lack of cover across different environments, low perdition accuracy they also exhibit several advantages. Four propagation models, which are representatives of their types, have been selected since they exhibit advantages in relation to high altitude, wide coverage range, adaption across different terrains. In addition, all four have been extensively deployed in the past and as a result their correction factors have evolved over the years to yield extremely accurate results which makes the development and evaluation aspects of this research very precise. The four models are: ITU-R P.529-3, Okumura, Hata-Davidson, and ATG. The aim of this doctoral research is to design a new propagation model for last-mile connectivity using LAPs technology as an alternative to aerial base station that includes all six factors but does not exhibit any of the shortcomings of existing models. The new propagation model evolves from existing models using machine learning. The four models are first adapted to include the elevation angle alongside the multiple-input multiple-output diversity gain, our first novelty in propagation modelling. The four adapted models are then used as input in a Neural Network framework and their parameters are clustered in a Self-Organizing-Map using a minimax technique. The framework evolves an optimal propagation model that represents the main research contribution of this research. The optimal propagation model is deployed in two proof-of-concept applications, a wireless sensor network, and a cellular structure. The performance of the optimal model is evaluated and then validated against that of the four adapted models first in relation to predictions reported in the literature and then in the context of the two proof-of-concept applications. The predictions of the optimised model are significantly improved in comparison to those of the four adapted propagation models. Each of the two proof-of-concept applications also represent a research novelty.The Royal Saudi Embassy and the Saudi Cultural Bureau in London, and Taif University in the Kingdom of Saudi Arabia

An intelligent call admission control algorithm for load balancing in 5G-satellite networks

A thesis submitted in partial fulfilment of the requirements of the University of Wolverhampton for the degree of Doctor of Philosophy.Cellular networks are projected to deal with an immense rise in data traffic, as well as an enormous and diverse device, plus advanced use cases, in the nearest future; hence, future 5G networks are being developed to consist of not only 5G but also different RATs integrated. In addition to 5G, the user’s device (UD) will be able to connect to the network via LTE, WiMAX, Wi-Fi, Satellite, and other technologies. On the other hand, Satellite has been suggested as a preferred network to support 5G use cases. Satellite networks are among the most sophisticated communication technologies which offer specific benefits in geographically dispersed and dynamic networks. Utilising their inherent advantages in broadcasting capabilities, global coverage, decreased dependency on terrestrial infrastructure, and high security, they offer highly efficient, effective, and rapid network deployments. Satellites are more suited for large-scale communications than terrestrial communication networks. Due to their extensive service coverage and strong multilink transmission capabilities, satellites offer global high-speed connectivity and adaptable access systems. The convergence of 5G technology and satellite networks therefore marks a significant milestone in the evolution of global connectivity. However, this integration introduces a complex problem related to resource management, particularly in Satellite – Terrestrial Integrated Networks (STINs). The key issue at hand is the efficient allocation of resources in STINs to enhance Quality of Service (QoS) for users. The root cause of this issue originates from a vast quantity of users sharing these resources, the dynamic nature of generated traffic, the scarcity of wireless spectrum resources, and the random allocation of wireless channels. Hence, resource allocation is critical to ensure user satisfaction, fair traffic distribution, maximised throughput, and minimised congestion. Achieving load balancing is essential to guarantee an equal amount of traffic distributed between different RATs in a heterogeneous wireless network; this would enable optimal utilisation of the radio resources and lower the likelihood of call blocking/dropping. This research endeavours to address this challenge through the development and evaluation of an intelligent call admission control (CAC) algorithm based on Enhanced Particle Swarm Optimization (EPSO). The primary aim of this research is to design an EPSO-based CAC algorithm tailored specifically for 5G-satellite heterogeneous wireless networks. The algorithm's objectives include maximising the number of admitted calls while maintaining Quality of Service (QoS) for existing users, improving network resource utilization, reducing congestion, ensuring fairness, and enhancing user satisfaction. To achieve these objectives, a detailed research methodology is outlined, encompassing algorithm development, numerical simulations, and comparative analysis. The proposed EPSO algorithm is benchmarked against alternative artificial intelligence and machine learning algorithms, including the Artificial Bee Colony algorithm, Simulated Annealing algorithm, and Q-Learning algorithm. Performance metrics such as throughput, call blocking rates, and fairness are employed to evaluate the algorithms' efficacy in achieving load-balancing objectives. The experimental findings yield insights into the performance of the EPSO-based CAC algorithm and its comparative advantages over alternative techniques. Through rigorous analysis, this research elucidates the EPSO algorithm's strengths in dynamically adapting to changing network conditions, optimising resource allocation, and ensuring equitable distribution of traffic among different RATs. The result shows the EPSO algorithm outperforms the other 3 algorithms in all the scenarios. The contributions of this thesis extend beyond academic research, with potential societal implications including enhanced connectivity, efficiency, and user experiences in 5G-Satellite heterogeneous wireless networks. By advancing intelligent resource management techniques, this research paves the way for improved network performance and reliability in the evolving landscape of wireless communication

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