The spectrum crunch is a global phenomenon, where wireless networks constrained by scarce spectrum resource cannot keep pace with the explosion in mobile broadband use, particularly at a time when smartphones and tablets are becoming even more prevalent and heavily used. Every new opportunity has to be maximally exploited to cope with this spectrum deficit and meet the demands of explosive broadband usage by pushing more data through existing spectrum. Massive multiple-input multiple-output (MIMO), an advanced antenna technology only developed in 2010 offers one such opportunity. Massive MIMO enables a sparse infrastructure network, whereby a single base station (BS) is powerful enough to eliminate inter-cell interference through highly directional beamforming, and hence avoid the need for any cell-to-cell coordination. Initial work, particularly the experiments in have demonstrated the feasibility of massive MIMO. However, there is still lack of insightful understanding of the fundamental limits of massive MIMO, and also there is a large gap in the performance evaluation of massive MIMO under ideal and non-ideal practical conditions. The aim of this project is to establish a unified theoretical framework for the fundamental limits of massive MIMO with various practical constraints, and develop sophisticated signal processing algorithms to realize the concept of massive MIMO in realistic environments. The novelty of this project lies in the fact that advanced mathematical tools, such as random matrix theory and stochastic geometry, will be used to capture the dynamic nature of multi-user wireless channels. Sophisticated signal processing methods, such as game theoretic algorithms and compressed sensing, will be applied to massive MIMO in order to combat the practical constraints, such as frequency selective channel fading and limited channel feedback.

Wireless communications has been shaping the planet in an unprecedented way as we live in an increasingly connected, automated, and globalised society of smart environments where the physical world is connected with the information world. Looking 10-20 years ahead, multi-gigabit wireless communications will play an even more prominent role in the evolution and development of our unwired networked society. This project is proposed at a time when gigabit per second wireless communications is envisioned to bring a fundamental shift to the design of future smart environments. The results of this project will trigger the emerging concept of smart environments, ranging from smart materials controlled or manipulated at the nanoscale, to smart cities with massive deployment of sensors and monitoring systems. In particular, the widespread availability and demand for multimedia capable devices and multimedia content have fuelled the need for high-speed wireless connectivity beyond the capabilities of existing commercial standards. The technologies developed in this project will address practical issues concerning the design and implementation of next generation multi-gigabit wireless applications enabling low cost fibre replacement mobile backhauls, last mile wireless broadband access, ultra-dense small cells, low latency uncompressed high-definition media transfers, and wireless access to the cloud. The challenges and fundamental limits of future networked societies can only be mastered by exploring the disruptive potential of low-interference high-speed wireless links for smart and sustainable environments. The results of this project will have immediate impact on advancing the state-of-the-art in mobile and ubiquitous computing for multi-gigabit-per-second data rates, supporting new wireless platforms such as cloud computing and tactile Internet to handle large quantities of data and thus to underpin the Internet of Everything (IoE) as a truly networked society connecting hundreds of billions of people, objects, and services. In particular, the concepts, algorithms, and theory developed in this project will address practical issues concerning the unbalanced temporal and geographical variations of the spectrum, along with the rapid proliferation of bandwidth-hungry mobile applications, such as video streaming with high definition television (HDTV) and ultra-high definition video (UHDV). Even though wireless channel impairments greatly impact the bandwidth efficiency of wireless networks, their effects have not been taken into consideration in the recent research carried out in this discipline, especially in the microwave and millimetre-wave bands for fifth generation (5G) cellular. The objective of this project is to improve the bandwidth efficiency of next generation 5G operating in the microwave and millimetre-wave bands through effective transmitter and receiver designs that exploit massive multiple-input multiple-output (MIMO) and heterogeneous small cell deployment, while taking into account the effects of impairments, such as channel estimation error, phase noise, and carrier frequency offset. As a result, this project is not based on any idealistic assumptions regarding the wireless channel, which compared to existing work in this field is unique. The proposed research certainly raises several fundamental design challenges far from trivial, that have their roots in diverse disciplines, including information theory, stochastic control theory, sequential statistics, large system analysis, automated decision making, and pervasive computing. Industrial partners will be engaged throughout the project to ensure industrial relevance of our work.

Information and energy are two fundamental notions in nature with critical impact on all aspects of life. All living and machine entities rely on both information and energy for their existence. Most, if not all, processes in life involve transforming, storing or transferring energy or information in one form or the other. Although these concepts are in harmony in nature, in traditional engineering design, information and energy are handled by two separate systems with limited interaction. In wireless communications, the relationship between information and energy is even more apparent as radio waves that carry information also transfer energy. Indeed, the first use of radio waves was for energy transfer rather than information transmission. However, despite the pioneering work of Tesla, who experimentally demonstrated wireless energy transfer (WET) in the late 19th century, modern wireless communication systems mainly focus on the information content of the radio-frequency (RF) radiation, neglecting the energy transported by the signal. This project is the first interdisciplinary initiative to promote innovation and technology transfer between academia and industry in the UK for one of the most challenging and most important problems in future communication networks: The simultaneous transfer of both energy and information. The aim of this project is to develop a new theoretical framework for the design and operation of next-generation networks with simultaneously wireless information and energy transfer (SWIFT) capabilities. The research efforts are interdisciplinary and bring together researchers with strong and complementary backgrounds in the domain of wireless communications such as electronics/microwave engineering, information theory, game theory, control theory, and communication theory to bridge the gap between theory and practice of future WET-based communication systems.

The recent advent of killer applications such as content distribution, cloud computing and Internet of things (IoT), all require for the underlying network to be able to understand specific service contexts. In this project we propose the Knowledge Centric Networking (KCN) paradigm, in which knowledge is positioned at the centre of the networking landscape. The objective is to enable in-network knowledge generation and distribution in order to develop necessary network control intelligence for handling complexity and uncertainty. In order to achieve this, specific algorithms and mechanisms/protocols will be developed for knowledge acquisition, processing, dissemination and organisation both within single and across homogeneous/heterogeneous administrative domains in the Internet. The project will investigate three styles of knowledge exchange based on Software Defined Networking (SDN) principles: Knowledge as a Tool (KaaT), Knowledge as a Service (KaaS) and Knowledge as a Cloud (KaaC). KaaT will enable intelligent network operations in dynamic network environments driven by knowledge gathered at different vantage points. We advocate a hierarchical knowledge framework in which knowledge and control functions are distributed at the right places within the network for fulfilling specific control tasks. In addition, we will invetigate knowledge sharing between different players in the Internet marketplace. This can be achieved either through explicit knowledge transfer from a knowledge provider to a knowledge consumer (KaaS), or based on open knowledge clouds where knowledge prosumers may publish or subscribe to information through an open but controlled knowledge ecosystem (KaaS). The proposed KCN architecture will be validated through two complementary use cases. KCN-driven content traffic offloading between heterogeneous radio access technologies for the future mobile Internet aims to achieve adaptive resource control by taking into account a wide variety of knowledge associated with content, users and network conditions. In addition, KCN-driven energy management targets cross-layer energy saving techniques at both the IP and the physical optical layer according to the derived knowledge and dynamically changing context information. The project provides direct contributions to the TI3 sub-challenges 1, 2, 3 and 4. First of all, the KCN-based knowledge ecosystem will equip the next generation Internet with necessary intelligence for handling complex requirements under dynamic conditions. Such an ecosystem, seamlessly coupled with the SDN architecture, will be able to gracefully support the ever increasing complexity and heterogeneity of future networked services and multitude of users. The two complementary use cases demonstrate how the proposed KCN framework will be instantiated in two different application domains, content traffic offloading in mobile/wireless access networks and energy efficiency in IP/optical transport networks. Use case 1 contributes to the 3rd sub-challenge, with knowledge-based content caching and traffic offloading techniques for the future content-oriented mobile Internet. Use case 2, on the other hand, contributes to the 2nd sub-challenge with intelligent energy saving mechanisms at both the IP and optical layer. Finally, with in-network knowledge inference and learning based on raw context information, the project also addresses the 4th sub-challenge of extracting understanding from data. In summary, context information captured during network/service operation will be used to derive systematic in-network knowledge and intelligence in order to deal adaptively with both complexity and uncertainty and enable near-optimal network operation.

The complexity of wireless communication networks has grown considerably in recent years. This has been driven in part by academic research that has started to define the information theoretic boundaries and advantages of certain complex networking topologies and protocols. On the other hand, the demands from consumers and industry have pushed wireless networks towards more sophisticated architectures and solutions, primarily in order to ensure a broad range of services can be delivered using a common infrastructure. This is particularly true of 4/5G technologies, which many believe should support all things for all people, including voice, data, public safety, distributed sensing and monitoring, etc. However, similar beliefs and trends can be found in other sectors, such as smart grid networks and even satellite networks. It is important that engineers understand the global properties of complex networks, and how these properties arise from local structure. Such information can be fed into models and optimisation routines so that practical networks can be designed to perform as well as possible. A common approach to tackling complex problems is to exploit randomness and statistical properties of the underlying system. Probabilistic approaches to network modelling are not without their difficulties, and some of the main problems that researchers have struggled with over the years arise from the fact that networks are finite entities with physical boundaries. Recent research by the investigators has focused on the effects that boundaries have on connectivity when networks are embedded in some finite spatial domain. Analytic expressions for the overall connection probability have been obtained. These formulae quantify the intuitive phenomenon that nodes near the boundary are more likely to disconnect, and thus they explain how the network outage probability behaves at high node densities. This work has been extended considerably to explore notions of resilience (k-connectivity), the effects of node directivity, diversity and power scaling laws, complicated geometric bounding domains (both convex and non-convex), and even the interplay between higher layer trust protocols and the physical network set-up and spatial domain. In this project, the probabilistic formalism alluded to above will be exploited further to study several key concepts that influence the structure of spatially embedded networks. The following four topics will be treated: - continuum models of spatially embedded networks, including the investigation of spectral and centrality properties of random networks; - mobility models in spatially embedded networks, including random waypoint and Levy flight processes; - trust models in spatially embedded networks, including trust dynamics and protocol design; - temporal models of spatially embedded networks, including dynamical node and link (edge) models. The work will take a mathematical approach, but will always maintain a focus on practical implications and designs.