Optimizing sensing in spectrally crowded RF enrivonments

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This proposal lays out a research program to develop the required theory, and approaches, for effective sensing and communications in spectrally-crowded environments. The motivation for pursuing this work comes from the growing allocation of spectrum for wireless communications, a trend that is making radar sensing increasingly difficult. The proposal focuses on four themes to be explored within the next 5 years. 1. Semi-distributed Detection in Spectrally-Crowded Environments: In spectrally-crowded environments, a single sensor is unreliable and multiple, geographically distributed sensors must coordinate their decisions. An important issue is how to incorporate the challenging communication environment with the sensing. The proposal lays out plans in both theoretical (e.g., how to process data received) and algorithmic (e.g., how to compress data to match the uncertain communication channel). A key feature of our model will be the receiving of correlated data arising from the practical case of sensors surveying the same region. 2. Waveform design: Our second theme is the optimal design of waveforms to fit within spectral holes - while meeting amplitude and peak-to-average power constraints. The key is designing the waveforms in real-time to avoid dynamic spectral usage. These waveforms coupled with orthogonal frequency division multiple access would allow for simultaneous sensing and communications. The theoretical questions will be that of feasibility and the design of multiple, orthogonal, waveforms. 3. Joint sensing and communication: In spectrally-denied environments, parameters of the radar signal can be modulated to enable communications. However, this impacts the sensing capabilities; we will explore the optimal trade-off between the two. Another aspect of this problem is the use of ambient signals as illuminators of opportunity to execute the radar task without active transmissions. We will explore the crucial issue of synchronization and develop reduced-rank adaptive signal processing techniques to exploit all available illumination sources. 4. Application specific designs: Applications have specific constraints that must be met. The best approach to use is heavily dependent on what is known of how the spectrum is being used; we will focus on two applications of theoretical and practical importance: urban sensing and sensing in littoral environments. This proposal represents a new thrust in my research agenda; it combines the fields in which I have made my most significant research contributions: my work in developing theory and algorithms for wireless networks and radar systems provides me with a unique background to address a problem of growing importance.