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School Of Engineering

Study group of the Ministry of Communications. Masataka Nakazawa President [2]. This article about an organization or organization-related topic in Japan is a stub. You can help Wikipedia by expanding it. This article related to telecommunications is a stub. This electronics-related article is a stub. The research group also exhibits a strong track-record in attracting funding from various competitive sources both at National, European and International level. Academics, who currently lead their respective fields, are actively engaged in research as well as consultancy, policy and standardization activities.

Researchers and students experience a unique vibrant and stimulating environment to grow in their fields, that paves their way to a very successful career in academia or industry. ICCS brings together world-leading academic experts and state-of-the-art facilities from across UCL that work in the broad profile of connected Systems, from technical to social studies, development and innovation.

M.Eng. in Information and Communication Engineering

Key to the work of ICCS is engaging with industry and the engineers of the future to develop this dynamic field of research. Research in this area covers a range of applications to communications systems.

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Examples range from high-speed circuits for next-generation optical systems, to system demonstrations and validation test-beds for wireless sensor networks. This research theme studies the fundamental principles of information compression, transmission and processing by leveraging tools from information theory, communications theory and machine learning.

This work is unveiling the fundamental limits of data transmission and security in current and upcoming communications systems. Further research from the group has contributed to the foundations and applications of information-theoretic and physical-layer security [Bloch,; Reboredo,] — this contribution was honoured with the IEEE Information Theory and Communications Societies Joint Paper Award Research in this area focuses on the foundations and applications of compressive information processing for future information processing systems.

This foundational work in compressive information processing has also been complemented by practical-oriented work in various emerging applications ranging from compressive signal and image processing [Carson,; Chen,; Renna,] to compressive topic modelling [Wang,]. Capitalising on compressive sensing and distributed compressive sensing this research looks to unlock energy neutrality in energy harvesting wireless sensor networks [Chen,].

It is widely recognized that future deployments of wireless sensor networks infrastructures are expected to be equipped with energy harvesters to substantially increase their autonomy and lifetime.

  • Master of Science in Information and Communications Engineering.
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  • With the continuous improvement of energy efficiency representing a major drive in wireless sensor networks research, the major objective of this work is to develop transformative sensing mechanisms, which can be used in conjunction with current or upcoming energy harvesting capabilities, in order to enable the deployment of energy neutral wireless sensor networks with data gathering rates that are substantially higher than the current state-of-the-art. Members of the group envision a typical centralised wireless sensor network architecture where a set of sensor nodes periodically convey data to one or more base stations; in addition, the sensor nodes would be active during a certain period to capture and transmit data and inactive during the remaining period of time to harvest energy from the environment.

    By leveraging the emerging paradigms of compressive sensing and distributed compressive sensing as well as energy- and information-optimal data acquisition and transmission protocols [Buranapanichkit,; Vittorioso,], it will be possible to tightly couple energy demand to the energy supply in wireless sensor networks in order to achieve the proposed breakthroughs [Chen,]. Video has been one of the most pervasive forms of online media for some time. Several statistics show that video traffic will dominate IP networks within the next five years.

    Yet, video remains one of the least-manageable elements of the big data ecosystem. We believe that this difficulty stems primarily from the fact that all advanced computer vision and machine learning algorithms view video as a stream of frames of picture elements. This is despite the fact that pixel-domain representations are known to be notoriously difficult to manage in machine learning systems, mainly due to: their high volume, high redundancy between successive frames, and artifacts stemming from camera calibration under varying illumination.

    In our work, we focus on video representations that go beyond conventional pixel streams and consider spatio-temporal activity information that is directly extractable from compressed video bitstreams or neuromorphic vision sensing NVS hardware.

    Introduction to the Program

    In order to understand and optimize video representations, we design deep neural networks DNNs that ingest such activity information in order to derive state-of-the-art classification, action recognition and retrieval results within large video datasets. These outcomes will pave the way for exabyte-scale video datasets to be newly-discovered and analysed over commodity hardware.

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    • We are surrounded by large-scale interconnected systems, from the Internet to the power grid and social networks. While essential, the management of such networked systems is exceedingly hard mainly because of their intrinsic and constantly growing complexity.

      Information and Communication Technology (ICT Engineering) - Wikiversity

      Currently available controlling strategies provide suboptimal solutions. Hence, the need for novel methods able to deal with this complexity. Answering to this need is the ambitious goal of our research, that blends together reinforcement learning and graph signal processing. In our group, we focus on developing fundamental learning methodologies targeting data-efficiency in large-scale problems. Some example of use cases of our research are the following:. This research theme develops and analyses the latest communications approaches in the physical layer PHY.

      Members of the group and collaborators have contributed to revealing the fundamental performance limits of a wide class of MIMO fading channels in the very low SNR regime under interference-limited environments [Zhong ; Jin ; Li ; Wen ]. Another important direction is to understand the performance of MIMO under practical settings in the presence of such as rank deficiency due to the phenomenon of keyholes.

      Several major contributions have been made in this regard [Zhong-Wong ; Zhong-Jin ; Jin ]. Finally, recent work has been looking at the performance impacts of deploying high numbers of antennas in fixed physical dimensions [Masouros ], along with practical linear and non-linear precoding schemes for large MIMO systems [Masouros-Sellathurai ],[Masouros-Sellathurai ], [Masouros ].

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      Of particular importance to this line of research, are solutions to address the hardware-efficiency in designing large scale MIMO Transceivers. The group has contributed in these areas with studies of the impact of hardware components [Garcia-Masouros , Garcia , Amadori-Masouros ], studies on hybrid analog-digital techniques [Amadori-Masouros , Li-Masouros , Amadori-Masouros ]. Increasingly, relaying has emerged as a revolutionary technique for cellular networks, particularly for improving the performance of users at the cell edges.

      Future spectrum allocation as proposed by Ofcom and FCC is required to be dynamic, adaptive and self-organised, with a strong need for interference control and management within and across networks. This breaks the rigid boundaries across networks and demands network coordination, giving rise to several fundamental challenges. In the context of primary-secondary link co-existence for cognitive radio, a number of interference-constrained transmission schemes have been introduced [Khan ], [Masouros-Ratnarajah ], [Ratnarajah ], [Masouros-Ratnarajah-Sellathurai ] for the cognitive downlink channel. In addition, several significant contributions in the optimisation of using cooperative relays for beamforming and some in the context of cognitive radio technologies have been made [Huang ; Huang ; Zheng ; Zheng-Wong ; Zheng-Wong-Paulraj ].

      While interference has always been regarded as the main obstacle in wireless systems, recent work has revealed that, on an instantaneous basis, it can contribute constructively to the useful signal energy gleaned at the wireless receiver [Masouros],[Masouros-Ratnarajah-Sellathurai ], www. This important source of useful energy, is currently ignored by the techniques adopted in the communication standards.

      The existing work of Green Communications focuses on energy-efficient network planning, smart duty-cycled BSs, heterogeneous cell deployment, integration with renewable energy sources and use of ICT for energy saving, amongst others. The radical approach of exploiting interference power promises a vast reduction in the power consumption of the network, without the cost of redesigning and redeploying network components as per the solutions above.

      While the signal processing work on wireless communications to date has focused on cancelling or minimizing interference, this work focuses on allowing the utilization of signal power from constructive interference to achieve the required link performance with a much reduced actual transmit power. Recent technological advances and deployments are creating a new landscape in the access networks, with an integration of wireless and fiber technologies a key supporting technology. Our research work considers how the optical fibre access network can be best used to support the next generation of radio access networks.

      The group have been involved in system demonstrations for mm-wave back-haul [Omomukuyo ] as well as architectures to support dynamic allocations of bandwidth [Milosaclievic ], [Attard ]. These use advanced optical components currently only feasible in core networks to provide highly efficient optical access networks [Shea ].

      We have now extended this idea to consider how multiple short-reach PONs could be consolidated into a multi-wavelength long reach architecture.