Information and Communications Theory, and Signal Processing
This research theme studies the fundamental principles of information compression, transmission, and processing, by leveraging tools from information theory, communications theory and signal processing. Current research lines include:
1. Foundations of Information Compression, Transmission, and Security
One of the research topics is associated with unveiling the fundamental limits of data transmission and security in current and upcoming communications systems. Members of the group and collaborators have contributed to the information-theoretic characterization of such limits as well as to information-theoretic inspired transceiver designs that achieve such limits [Pérez-Cruz,2010; Rodrigues,2013; Ramos,2013]. Members of the group and collaborators have also contributed to the foundations and applications of information-theoretic and physical-layer security [Bloch,2008; Reboredo,2013] – this contribution that has been honoured with the IEEE Information Theory and Communications Societies Joint Paper Award 2011.
2. Foundations and Applications of Compressive Information Processing
Another research topic is associated with the foundations and applications of compressive information processing for future information processing systems. Members of the group and collaborators have contributed to information-theoretic inspired kernel design for reconstruction and classification applications [Carson,2012; Chen,2012; Chen,2013; Wang,2013], the foundations of compressive classification [Reboredo,2013] and reconstruction [Renna,2013]. 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,2012; Chen,2012; Renna,2013] to compressive topic modelling [Wang,2013].
This line of research is also being funded in part by a Royal Society International Exchange Scheme involving both UCL and Duke University in the USA.
3. Emergent Applications: Unlocking Energy Neutrality in Energy Harvesting Wireless Sensor Networks
Another emerging line of research capitalizes on compressive sensing and distributed compressive sensing to unlock energy neutrality in energy harvesting wireless sensor networks [Chen,2013].
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. However, it is also recognized that the existing gap between the sensors’ energy harvesting supply and the sensors’ energy demand is not likely to close in the near future due to limitations in current energy harvesting technology, together with the surge in demand for more data-intensive applications.
With the continuous improvement of energy efficiency representing a major drive in wireless sensor networks research, the major objective of this line 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 (centralized) wireless sensor network architecture where a set of sensor nodes periodically convey data to one or more base stations; in addition, the sensor nodes are also 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,2012; Vittorioso,2012], 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,2013].
This work is being funded by the Engineering and Physical Sciences Research Council, involving both UCL, the University of Cambridge and various industrial partners.
Academics involved in the theme:
- Efficient Energy Management in Energy Harvesting Wireless Sensor Networks: An Approach Based on Distributed Compressive Sensing. EPSRC Standard Research, 2013-2016.
- Adaptive Compressive Sensing: Foundations and Applications. Royal Society International Exchange Scheme, 2013-2015.
[Rodrigues,2013] M. R. D. Rodrigues. Multiple-antenna fading coherent channels with arbitrary inputs: Characterization and optimization of the reliable information transmission rate. IEEE Transactions on Information Theory, second round of reviews. (arxiv version)
[Ramos,2013] A. G. C. P. Ramos and M. R. D. Rodrigues. Coherent fading channels driven by arbitrary inputs: Asymptotic characterization of the constrained capacity and related information- and estimation-theoretic quantities. IEEE Transactions on Information Theory, second round of reviews. (arxiv version)
[Pérez-Cruz,2010] F. Pérez-Cruz, M. R. D. Rodrigues and S. Verdú. MIMO Gaussian channels with arbitrary inputs: Optimal precoding and power allocation. IEEE Transactions on Information Theory, vol. 56, pp. 1070-1084, March 2010. (ieeexplore version)
[Bloch,2008] M. Bloch, J. Barros, M. R. D. Rodrigues and S. W. McLaughlin. Wireless information-theoretic security. IEEE Transactions on Information Theory - Special Issue on Information-Theoretic Security, vol. 54, pp. 2515-2534, June 2008. (ieeexplore version)
[Reboredo,2013] H. Reboredo, J. Xavier, and M. R. D. Rodrigues. Filter design with secrecy constraints: The MIMO Gaussian wiretap channel. IEEE Transactions on Signal Processing, to appear. (arxiv version)
[Carson,2012] W. R. Carson, M. Chen, M. R. D. Rodrigues, A. R. Calderbank, and L. Carin. Communications-inspired projection design with application to compressive sensing. SIAM Journal on Imaging Sciences, vol. 5, pp. 1185–1212, October 2012. (arxiv version)
[Chen,2012] M. Chen, W. R. Carson, M. R. D. Rodrigues, A. R. Calderbank, and L. Carin. Communications inspired linear discriminant analysis. Proceedings of the International Conference on Machine Learning, Edinburgh, U.K., June 2012. (arxiv version)
[Chen,2013] W. Chen, M. R. D. Rodrigues, and I. J. Wassell. Projections design for statistical compressive sensing: A tight frame based approach. IEEE Transactions on Signal Processing, to appear. (arxiv version)
[Wang,2013] L. Wang, M. R. D. Rodrigues, and L. Carin. Generalized Bregman divergence and gradient of mutual information for vector Poisson channels. Submitted to the IEEE Transactions on Information Theory, 2013. (conference version available in arxiv).
[Reboredo,2013] H. Reboredo, F. Renna, A. R. Calderbank, and M. R. D. Rodrigues. Compressive classification: Fundamental limits, designs and applications. Submitted to the IEEE Transactions on Information, 2013. (conference version available in arxiv).
[Renna,2013] F. Renna, A. R. Calderbank, L. Carin and M. R. D. Rodrigues. Reconstruction of signals drawn from a Gaussian mixture from noisy compressive measurements: MMSE phase transitions and beyond. Submitted to the IEEE Transactions on Signal Processing, 2013. (arxiv version).
[Chen,2013] W. Chen, I. Andreopoulos, I. J. Wassell, and M. R. D. Rodrigues. Towards Energy Neutrality in Energy Harvesting Wireless Sensor Networks: A Case for Distributed Compressive Sensing? (arxiv version)
[Buranapanichkit,2012] D. Buranapanichkit and Y. Andreopoulos. Distributed time-frequency division multiple access protocol for wireless sensor networks. IEEE Wireless Communications Letters, vol. 1, pp. 440-443, October 2012.
[Vittorioso,2012] A. Vittorioso, D. Buranapanichkit, G. Fortino, and Y. Andreopoulos. Coordination for TDMA operation in WSNs: Comparison between centralized and distributed mechanisms. 9th European Conference on Wireless Sensor Networks, 2012.