Path signalling in a wireless back-haul network integrating unidirectional broadcast technologies

The black-haul infrastructures of today's wireless operators must support the triple-play services demanded by the market or regulatory bodies. To cope with increasing capacity demand, in our previous work, we have developed a cost-effective heterogeneous layer 2.5 wireless back-haul (WiBACK) architecture, which leverages the native multicast capabilities of broadcast technologies such as DVB to off-load high-bandwidth broadcast content delivery. Furthermore, our architecture provides support for unidirectional technologies on the data and the control plane. This adopts a centralized coordinator approach, in which coordinator nodes install so-called management and data pipes. No routing state is kept at plain WiBACK nodes, which merely store QoS-aware pipe forwarding state. Consequently, the architecture requires a reliable protocol to push resource allocation and pipe forwarding state into the network, considering possibly unidirectional connectivity. Such a protocol, whose task is related to MPLS label distribution, is essential during the initial forming of WiBACK topologies and during regular network operations to reliably manage the data pipes. In this paper, we present a novel approach to extend our IEEE 802.21-inspired WiBACK TransportService and, based upon this, the design of an RSVP-TE-style pipe signalling protocol using nested hop-by-hop request/response MIH transactions that supports signalling over unidirectional technologies. A thorough evaluation and successful testbed deployments show that this protocol reliably signals pipe state even under high loss conditions

Integration of unidirectional technologies into wireless back-haul architecture

This thesis was submitted for the degree of Docter of Philosophy and awarded by Brunel University.Back-haul infrastructures of today's wireless operators must support the triple-play services demanded by the market or regulatory bodies. To cope with increasing capacity demand, the EU FP7 project CARMEN has developed a cost-effective heterogeneous multi-radio wireless back-haul architecture, which may also leverage the native multicast capabilities of broadcast technologies such as DVB-T to off-load high-bandwidth broadcast content delivery. However, the integration of such unidirectional technologies into a packet-switched architecture requires careful considerations. The contribution of this thesis is the investigation, design and evaluation of protocols and mechanisms facilitating the integration of such unidirectional technologies into the wireless back-haul architecture so that they can be configured and utilized by the spectrum and capacity optimization modules. This integration mainly concerns the control plane and, in particular, the aspects related to resource and capability descriptions, neighborhood, link and Multi Protocol Label Switching (MPLS) Label-Switched Path (LSP) monitoring, unicast and multicast LSP signalling as well as topology forming and maintenance. During the course of this study we have analyzed the problem space, proposed solutions to the resulting research questions and evaluated our approach. Our results show that the now Unidirectional Technology (UDT)-aware architecture can readily consider Unidirectional Technologies (UDTs) to distribute, for example, broadcast content

Traffic Engineering And Supporting QoS

ABSTRACT Traffic Engineering describes techniques for optimising network performance by measuring, modelling, characterizing and controlling Internet traffic for specific performance goals [11]. This is a comprehensive definition. Traffic engineering performance goals typically fall into one of two categories. The first one is traffic related performance objectives such as minimizing packet loss, lowering end-to-end delay, or supporting a contracted Service Level Agreement (SLA). The second category is efficiency related objectives, such as balancing the distribution of traffic across available bandwidth resources. Traffic related performance goals are set in order to meet contracted service levels and offer competitive services to customers. Efficiency related goals, are required by the service provider to minimize the cost of delivering services, especially the cost of utilizing expensive network resources. The objective of this thesis is to present a description of Multi Protocol Label Switching (MPLS) architecture and its functionality to achieve a tool for performing traffic engineering and QoS support. We simulate traffic engineering with MPLS on a simple network and measure its performance. We analyse measurements related to queuing delay, throughput and other traffic related issues. We then move on fine-tuning the MPLS-TE network to also take into consideration QoS support when aggregating flows through a single label- switching path. We combine differentiated services with MPLS architecture in order to support QoS requirements. The simulation tool used in this thesis is called OPNET Modeler version 8.11

A Cognitive Routing framework for Self-Organised Knowledge Defined Networks

This study investigates the applicability of machine learning methods to the routing protocols for achieving rapid convergence in self-organized knowledge-defined networks. The research explores the constituents of the Self-Organized Networking (SON) paradigm for 5G and beyond, aiming to design a routing protocol that complies with the SON requirements. Further, it also exploits a contemporary discipline called Knowledge-Defined Networking (KDN) to extend the routing capability by calculating the “Most Reliable” path than the shortest one. The research identifies the potential key areas and possible techniques to meet the objectives by surveying the state-of-the-art of the relevant fields, such as QoS aware routing, Hybrid SDN architectures, intelligent routing models, and service migration techniques. The design phase focuses primarily on the mathematical modelling of the routing problem and approaches the solution by optimizing at the structural level. The work contributes Stochastic Temporal Edge Normalization (STEN) technique which fuses link and node utilization for cost calculation; MRoute, a hybrid routing algorithm for SDN that leverages STEN to provide constant-time convergence; Most Reliable Route First (MRRF) that uses a Recurrent Neural Network (RNN) to approximate route-reliability as the metric of MRRF. Additionally, the research outcomes include a cross-platform SDN Integration framework (SDN-SIM) and a secure migration technique for containerized services in a Multi-access Edge Computing environment using Distributed Ledger Technology. The research work now eyes the development of 6G standards and its compliance with Industry-5.0 for enhancing the abilities of the present outcomes in the light of Deep Reinforcement Learning and Quantum Computing

A flexible, abstract network optimisation framework and its application to telecommunications network design and configuration problems

A flexible, generic network optimisation framework is described. The purpose of this framework is to reduce the effort required to solve particular network optimisation problems. The essential idea behind the framework is to develop a generic network optimisation problem to which many network optimisation problems can be mapped. A number of approaches to solve this generic problem can then be developed. To solve some specific network design or configuration problem the specific problem is mapped to the generic problem and one of the problem solvers is used to obtain a solution. This solution is then mapped back to the specific problem domain. Using the framework in this way, a network optimisation problem can be solved using less effort than modelling the problem and developing some algorithm to solve the model. The use of the framework is illustrated in two separate problems: design of an enterprise network to accommodate voice and data traffic and configuration of a core diffserv/MPLS network. In both cases, the framework enabled solutions to be found with less effort than would be required if a more direct approach was used

THE APPLICATION OF REAL-TIME SOFTWARE IN THE IMPLEMENTATION OF LOW-COST SATELLITE RETURN LINKS

Digital Signal Processors (DSPs) have evolved to a level where it is feasible for digital modems with relatively low data rates to be implemented entirely with software algorithms. With current technology it is still necessary for analogue processing between the RF input and a low frequency IF but, as DSP technology advances, it will become possible to shift the interface between analogue and digital domains ever closer towards the RF input. The software radio concept is a long-term goal which aims to realise software-based digital modems which are completely flexible in terms of operating frequency, bandwidth, modulation format and source coding. The ideal software radio cannot be realised until DSP, Analogue to Digital (A/D) and Digital to Analogue (D/A) technology has advanced sufficiently. Until these advances have been made, it is often necessary to sacrifice optimum performance in order to achieve real-time operation. This Thesis investigates practical real-time algorithms for carrier frequency synchronisation, symbol timing synchronisation, modulation, demodulation and FEC. Included in this work are novel software-based transceivers for continuous-mode transmission, burst-mode transmission, frequency modulation, phase modulation and orthogonal frequency division multiplexing (OFDM). Ideal applications for this work combine the requirement for flexible baseband signal processing and a relatively low data rate. Suitable applications for this work were identified in low-cost satellite return links, and specifically in asymmetric satellite Internet delivery systems. These systems employ a high-speed (>>2Mbps) DVB channel from service provider to customer and a low-cost, low-speed (32-128 kbps) return channel. This Thesis also discusses asymmetric satellite Internet delivery systems, practical considerations for their implementation and the techniques that are required to map TCP/IP traffic to low-cost satellite return links

When Stuck, Flip a Coin:New Algorithms for Large-Scale Tasks

Many modern services need to routinely perform tasks on a large scale. This prompts us to consider the following question: How can we design efficient algorithms for large-scale computation? In this thesis, we focus on devising a general strategy to address the above question. Our approaches use tools from graph theory and convex optimization, and prove to be very effective on a number of problems that exhibit locality. A recurring theme in our work is to use randomization to obtain simple and practical algorithms. The techniques we developed enabled us to make progress on the following questions: - Parallel Computation of Approximately Maximum Matchings. We put forth a new approach to computing $O(1)$-approximate maximum matchings in the Massively Parallel Computation (MPC) model. In the regime in which the memory per machine is $\Theta(n)$, i.e., linear in the size of the vertex-set, our algorithm requires only $O((\log \log{n})^2)$ rounds of computations. This is an almost exponential improvement over the barrier of $\Omega(\log {n})$ rounds that all the previous results required in this regime. - Parallel Computation of Maximal Independent Sets. We propose a simple randomized algorithm that constructs maximal independent sets in the MPC model. If the memory per machine is $\Theta(n)$ our algorithm runs in $O(\log \log{n})$ MPC-rounds. In the same regime, all the previously known algorithms required $O(\log{n})$ rounds of computation. - Network Routing under Link Failures. We design a new protocol for stateless message-routing in $k$-connected graphs. Our routing scheme has two important features: (1) each router performs the routing decisions based only on the local information available to it; and, (2) a message is delivered successfully even if arbitrary $k-1$ links have failed. This significantly improves upon the previous work of which the routing schemes tolerate only up to $k/2 - 1$ failed links in $k$-connected graphs. - Streaming Submodular Maximization under Element Removals. We study the problem of maximizing submodular functions subject to cardinality constraint $k$, in the context of streaming algorithms. In a regime in which up to $m$ elements can be removed from the stream, we design an algorithm that provides a constant-factor approximation for this problem. At the same time, the algorithm stores only $O(k \log^2{k} + m \log^3{k})$ elements. Our algorithm improves quadratically upon the prior work, that requires storing $O(k \cdot m)$ many elements to solve the same problem. - Fast Recovery for the Separated Sparsity Model. In the context of compressed sensing, we put forth two recovery algorithms of nearly-linear time for the separated sparsity signals (that naturally model neural spikes). This improves upon the previous algorithm that had a quadratic running time. We also derive a refined version of the natural dynamic programming (DP) approach to the recovery of the separated sparsity signals. This DP approach leads to a recovery algorithm that runs in linear time for an important class of separated sparsity signals. Finally, we consider a generalization of these signals into two dimensions, and we show that computing an exact projection for the two-dimensional model is NP-hard