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We are surrounded by electronic devices that take advantage of wireless technologies, from our computer mice, which require little amounts of information, to our cellphones, which demand increasingly higher data rates. Until today, the coexistence of such a variety of services has been guaranteed by a fixed assignment of spectrum resources by regulatory agencies. This has resulted into a blind alley, as current wireless spectrum has become an expensive and a scarce resource. However, recent measurements in dense areas paint a very different picture: there is an actual underutilization of the spectrum by legacy sys-tems. Cognitive radio exhibits a tremendous promise for increasing the spectral efficiency for future wireless systems. Ideally, new secondary users would have a perfect panorama of the spectrum usage, and would opportunistically communicate over the available re-sources without degrading the primary systems. Yet in practice, monitoring the spectrum resources, detecting available resources for opportunistic communication, and transmit-ting over the resources are hard tasks. This thesis addresses the tasks of monitoring, de-

We explore two fundamental questions at the intersection of sampling theory and information theory: how is channel capacity affected by sampling below the channel’s Nyquist rate, and what sub-Nyquist sampling strategy should be employed to maximize capacity. In particular, we first derive

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in the application to RFID technology where it is only the identity of the active users that is required. The coherent detector is also able to recover the transmitted symbols. It is shown that compressive demodulation requires O(K logN(τ + 1)) samples to recover K active users whereas standard MUD requires N(τ +1

pages 156–175, 2013. [2] Johannes Buchmann, Denise Demirel, and Jeroen van de Graaf. Towards a publicly-verifiable mix-net providing everlasting privacy. In Financial Cryptog-raphy, pages 197–204, 2013. [3] Denise Demirel, Jeroen van de Graaf, and Roberto Araùjo. Improving helios

. We develop a new theoretical framework to analyze this extreme case and develop new algorithms for signal reconstruction from such coarsely quantized measurements. The 1-bit CS framework leads us to scenarios where it may be more appropriate to reduce bit-depth instead of sampling rate. We find

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