Interference Mitigation in Multi-Hop Wireless Networks with Advanced Physical-Layer Techniques

In my dissertation, we focus on the wireless network coexistence problem with advanced physical-layer techniques. For the first part, we study the problem of Wireless Body Area Networks (WBAN)s coexisting with cross-technology interference (CTI). WBANs face the RF cross-technology interference (CTI) from non-protocol-compliant wireless devices. Werst experimentally characterize the adverse effect on BAN caused by the CTI sources. Then we formulate a joint routing and power control (JRPC) problem, which aims at minimizing energy consumption while satisfying node reachability and delay constraints. We reformulate our problem into a mixed integer linear programing problem (MILP) and then derive the optimal results. A practical JRPC protocol is then proposed. For the second part, we study the coexistence of heterogeneous multi-hop networks with wireless MIMO. We propose a new paradigm, called cooperative interference mitigation (CIM), which makes it possible for disparate networks to cooperatively mitigate the interference to/from each other to enhance everyone\u27s performance. We establish two tractable models to characterize the CIM behaviors of both networks by using full IC (FIC) and receiver-side IC (RIC) only. We propose two bi-criteria optimization problems aiming at maximizing both networks\u27 throughput, while cooperatively canceling the interference between them based on our two models. In the third and fourth parts, we study the coexistence problem with MIMO from a different point of view: the incentive of cooperation. We propose a novel two-round game framework, based on which we derive two networks\u27 equilibrium strategies and the corresponding closed-form utilities. We then extend our game-theoretical analysis to a general multi-hop case, specifically the coexistence problem between primary network and multi-hop secondary network in the cognitive radio networks domain. In the final part, we study the benefits brought by reconfigurable antennas (RA). We systematically exploit the pattern diversity and fast reconfigurability of RAs to enhance the throughput of MWNs. Werst propose a novel link-layer model that captures the dynamic relations between antenna pattern, link coverage and interference. Based on our model, a throughput optimization framework is proposed by jointly considering pattern selection and link scheduling, which is formulated as a mixed integer non-linear programming problem

Distributed Maximum Likelihood Sensor Network Localization

We propose a class of convex relaxations to solve the sensor network localization problem, based on a maximum likelihood (ML) formulation. This class, as well as the tightness of the relaxations, depends on the noise probability density function (PDF) of the collected measurements. We derive a computational efficient edge-based version of this ML convex relaxation class and we design a distributed algorithm that enables the sensor nodes to solve these edge-based convex programs locally by communicating only with their close neighbors. This algorithm relies on the alternating direction method of multipliers (ADMM), it converges to the centralized solution, it can run asynchronously, and it is computation error-resilient. Finally, we compare our proposed distributed scheme with other available methods, both analytically and numerically, and we argue the added value of ADMM, especially for large-scale networks

A survey of machine learning techniques applied to self organizing cellular networks

In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future

Optimisation of Mobile Communication Networks - OMCO NET

The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University. The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing

Data analytics methods for attack detection and localization in wireless networks

Wireless ad hoc network operates without any fixed infrastructure and centralized administration. It is a group of wirelessly connected nodes having the capability to work as host and router. Due to its features of open communication medium, dynamic changing topology, and cooperative algorithm, security is the primary concern when designing wireless networks. Compared to the traditional wired network, a clean division of layers may be sacrificed for performance in wireless ad hoc networks. As a result, they are vulnerable to various types of attacks at different layers of the protocol stack. In this paper, I present real-time series data analysis solutions to detect various attacks including in- band wormholes attack in the network layer, various MAC layer misbehaviors, and jamming attack in the physical layer. And, I also investigate the problem of node localization in wireless and sensor networks, where a total of n anchor nodes are used to determine the locations of other nodes based on the received signal strengths. A range-based machine learning algorithm is developed to tackle the challenges --Abstract, page iii