Compressed sensing based cyclic feature spectrum sensing for cognitive radios

Spectrum sensing is currently one of the most challenging design problems in cognitive radio. A robust spectrum sensing technique is important in allowing implementation of a practical dynamic spectrum access in noisy and interference uncertain environments. In addition, it is desired to minimize the sensing time, while meeting the stringent cognitive radio application requirements. To cope with this challenge, cyclic spectrum sensing techniques have been proposed. However, such techniques require very high sampling rates in the wideband regime and thus are costly in hardware implementation and power consumption. In this thesis the concept of compressed sensing is applied to circumvent this problem by utilizing the sparsity of the two-dimensional cyclic spectrum. Compressive sampling is used to reduce the sampling rate and a recovery method is developed for re- constructing the sparse cyclic spectrum from the compressed samples. The reconstruction solution used, exploits the sparsity structure in the two-dimensional cyclic spectrum do-main which is different from conventional compressed sensing techniques for vector-form sparse signals. The entire wideband cyclic spectrum is reconstructed from sub-Nyquist-rate samples for simultaneous detection of multiple signal sources. After the cyclic spectrum recovery two methods are proposed to make spectral occupancy decisions from the recovered cyclic spectrum: a band-by-band multi-cycle detector which works for all modulation schemes, and a fast and simple thresholding method that works for Binary Phase Shift Keying (BPSK) signals only. In addition a method for recovering the power spectrum of stationary signals is developed as a special case. Simulation results demonstrate that the proposed spectrum sensing algorithms can significantly reduce sampling rate without sacrifcing performance. The robustness of the algorithms to the noise uncertainty of the wireless channel is also shown.

MIMO waveform design using continuous phase modulation

Hybrid MIMO Phased-Array Radar (HMPAR) is an emerging technology that combines MIMO (multiple-in, multiple-out) radar technology with phased-array radar technology. The new technology is in its infancy, but much of the theoretical work for this specific project has already been completed and is explored in great depth in [1]. A brief overview of phased-array radar systems, MIMO radar systems, and the HMPAR paradigm are explored in this paper. This report is the culmination of an effort to support research in MIMO and HMPAR utilizing a concept called intrapulse beamscan. Using intrapulse beamscan, arbitrary spatial coverage can be achieved within one MIMO beam pulse. Therefore, this report focuses on designing waveforms for MIMO radar systems with arbitrary spatial coverage using that phenomenon. With intrapulse beamscan, scanning is done through phase-modulated signal design within one pulse rather than phase-shifters in the phased array over multiple pulses. In addition to using this idea, continuous phase modulation (CPM) signals are considered for their desirable peak-to-average ratio property as well as their low spectral leakage. These MIMO waveforms are designed with three goals in mind. The first goal is to achieve flexible spatial coverage while utilizing intrapulse beamscan. As with almost any radar system, we wish to have flexibility in where we send our signal energy. The second goal is to maintain a peak-to-average ratio close to 1 on the envelope of these waveforms, ensuring a signal that is close to constant modulus. It is desired to have a radar system transmit at the highest available power; not doing so would further diminish the already very small return signals. The third goal is to ensure low spectral leakage using various techniques to limit the bandwidth of the designed signals. Spectral containment is important to avoid interference with systems that utilize nearby frequencies in the electromagnetic spectrum. These three goals are realized allowing for limitations of real radar systems. In addition to flexible spatial coverage, the report examines the spectral properties of utilizing various space-filling techniques for desired spatial areas. The space-filling techniques examined include Hilbert/Peano curves and standard raster scans.