EARS
Enhancing Access to the Radio Spectrum (EARS)
NSF Proposal No. 1247940
Enhancing Spectral Access via Directional Spectrum Sensing Employing 3D Cone Filterbanks: Interdisciplinary Algorithms and Prototypes
Dr. A. Madanayake (PI)
Intellectual Merit
This NSF EARS project proposes a new spectrum sensing architecture combined with joint link scheduling and routing to significantly enhance access to the radio spectrum. Traditional non-directional sensing algorithms do not offer information about the direction of primary and secondary signals, directional information on interference, and information on network node location, and hence significantly limit the potential of cognitive radio technology in terms of spectrum utilization. This project envisions a generalized framework leading to the determination and subsequent utilization of spatio-temporal vacancies in time, frequency, position and direction. New mathematical, hardware, and software algorithms and techniques will be pioneered toward enabling low-complexity digital radios. Multi-dimensional sensed information will drive the innovation of cross-layer link scheduling and routing schemes aimed at boosting the cognitive radio network performance. The proposed innovations will be accomplished through mathematical formulation and modeling of directional sensing algorithms based on multi-dimensional signal processing concepts. The project will also investigate low-complexity fast algorithms for enabling real-time realization leading to new types of (i) digital integrated circuits, (ii) new design techniques for cognitive radios, and (iii) highly agile radio frequency component models all leading to an integrated directional spectrum sensor.
Broader Impacts
This proposal entails tightly integrated research and educational activities at four universities including an HBCU and an undergraduate institution. Spectrum-aware education is pursued as one of the key components of the project because wireless system designers and policy makers alike urgently need this knowledge for pioneering new innovations in this upcoming area of technology. Scientific findings enabled by the proposed research in the cognitive radio networks will serve as a tangible tool-box for engineering transformational technologies such technologies could, in turn, lead to mushrooming of businesses and services that directly benefit from intellectual property (e.g. patents). This research will foster startup firms manufacturing new devices that will potentially improve today's wireless infrastructure. Distinct and diverse applications in education, energy, environment, healthcare, infrastructure, and public-safety will be studied from a unified perspective, i.e. spectrum scarcity, with the objective of maximizing the untapped economic potential of such scientific findings. The project will involve minorities, underrepresented groups, and women in research, while inspiring spectrum-aware educational concepts through new laboratory modules. Participation of underrepresented groups and women will be encouraged and promoted through mentorship and outreach, aimed at inspiring them to take up graduate studies in engineering and computer science.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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A. Madanayake, C. Wijenayake, D. Dansereau, T. K. Gunaratne, L. T. Bruton, and S. Williams. "Multidimensional (MD) Circuits and Systems For Emerging Applications Including Cognitive Radio, Radio Astronomy, Robot Vision and Imaging," IEEE Circuits and Systems (CAS) Magazine, v.13, 2013.
A. Madanayake, C. Wijenayake, J. S. Kota and L. T. Bruton. "Space-Time Spectral White Spaces in Cognitive Radio: Theory, Algorithms and Circuits," IEEE Journal of Emerging and Selected Topics in Circuits and Systems (JETCAS) Special Issue on Cognitive Radio and Software Defined Radio (SDR), 2013.
A. Madanayake, C. Wijenayake, R. M. Joshi, L. Belostotski, M. Almalkawi, L. T. Bruton and V. Devabhaktuni. "Electronically Scanned RF-to-Bits Beam Aperture Arrays Using 2-D IIR Spatially Bandpass Digital Filters," Multi-Dimensional Systems and Signal Processing (MSSP), 2013.
Chamith Wijenayake, Arjuna Madanayake, Leonid Belostotski, Yonghseng Xu and Len Bruton. "All-Pass Filter-Based 2-D IIR Filter-Enhanced Beamformers for AESA Receivers," IEEE Trans. on Circuits and Systems (TCAS-I):Regular Papers, v.61, 2014.
J. Adams, A. Madanayake and L. T. Bruton. "Approximate Realization of Fractional-Order 2-D IIR Frequency-Planar Filters," IEEE Journal of Emerging and Selected Topics in Circuits and Systems (JETCAS) Special Issue on Fractional Order Systems, 2013.
J. Kota, C. Wijenayake, A. Madanayake, L. Belostotski and L. T. Bruton. "A 2-D Signal Processing Model to Predict the Effect of Mutual Coupling on Array Factor," IEEE Antennas and Wireless Communication Letters (AWPL), 2013.
N. Rajapaksha, A. Madanayake, and L.T. Bruton. "Systolic array architecture for steerable multibeam VHF wave-digital RF apertures," IEEE Aerospace and Electronic Systems, v.51, 2015.
A. Madanayake, T. Randeny, N. Udayanga, A. Sengupta, G. Jones, C. Wijenayake and L.T. Bruton. "Applications of RF Aperture-Array Spatially-Bandpass 2-D IIR Filters in Sub-Nyquist Spectrum Sensing, Wideband Doppler Radar and Radio Astronomy Beamforming," Multidimensional Systems and Signal Processing, 2015.
A. Sengupta, A. Madanayake, R. Gómez-García and L. Belostotski. "Wide-Band Aperture Array Using a Four-channel Manifold-Type Planar Multiplexer and Digital 2-D IIR Filterbank," Circuit Theory and Applications (CTA), 2015.
L. Belostotski, K. Warnick, B. Veidt, and A. Madanayake. "Low Noise Amplifier Design Considerations For Use in Antenna Arrays," IEEE Trans. on Antennas and Propagation, 2015.
N. Rajapaksha, A. Madanayake and L.T Bruton. "VLSI Systolic Array Multi-beam 3-D IIR Wave-Digital Aperture Antennas," IEEE Transactions on Aerospace and Electronic Systems, 2015.
S. Wijeratna, A. Madanayake, C. Wijenayake, and L. T. Bruton. "VLSI architectures for direct conversation receiver beamforming using 2-D IIR beam filter," IEEE Aerospace and Electronic Systems, 2015.
Gihan Mendis, Jin Wei and Arjuna Madanayake. "Deep Learning Based Doppler-Radar for Micro-UAS Detection and Classification," IEEE MILCOM 2016, 2016.
Gihan Mendis, Jin Wei and Arjuna Madanayake. "Deep-Learning based Automated Modulation Classification for Cognitive Radio," IEEE International Conference on Communication Systems (ICCS), 2016.
V. Devabhaktuni, K. Alshamaileh and A. Madanayake. "Multi-way Impedance-varying Power Dividers for Wideband Applications," International Journal of RF and Microwave Computer Aided Engineering, 2015.