Experimental demonstration and analysis of cognitive spectrum sensing and notching for radar

Abstract

Spectrum sensing and transmit notching is a form of cognitive radar that seeks to reduce mutual interference with other spectrum users in the same band. This concept is examined for the case where another spectrum user moves in frequency during the radar's CPI. The physical radar emission is based on a recent FM noise waveform possessing attributes that are inherently robust to sidelobes that otherwise arise for spectral notching. Due to increasing spectrum sharing with cellular communications, the interference considered takes the form of in-band OFDM signals that hop around the band. The interference is measured each PRI and a fast spectrum sensing algorithm determines where notches are required, thus facilitating a rapid response to dynamic interference. To demonstrate the practical feasibility and to understand the tradespace such a scheme entails, free-space experimental measurements based on notched radar waveforms are collected and synthetically combined with separately measured hopping interference under a variety of conditions to assess the efficacy of such an approach, including the impact of interference hopping during the radar CPI, latency in the spectrum sensing/waveform design process, notch tapering to reduce sidelobes, notch width modulation due to spectrum sensing, and the impact of digital up-sampling on notch depth.

5G Technology: Towards Dynamic Spectrum Sharing Using Cognitive Radio Networks

“…The mutual interference for spectrum users in the same band can be reduced by using a cognitive radar with spectrum sensing and transmission notching abilities. Ravenscroft et al [124] conducted a study on the case where another spectrum user moves in the frequency during the radar's coherent processing interval, and the radar emission is inherently robust to sidelobes that otherwise arise for spectral notching. The interference considered was in-band OFDM signals that hop around the band, and the fast spectrum sensing algorithm determined where notches are required.…”

“…On the one hand, underestimating (missed detection) or overestimating (dales alarm) interference leads to SINR degradation. On the other hand, correctly estimating radio frequency interference may degrade SINR when the bandwidth is varied from pulse to pulse [124]. Crowd sensing has elicited attention in recent years despite its issues, such as manifestation of abnormal data in crowd sensors [133], data incompleteness and inaccuracy [134], need for scalable radio-frequency spectrum monitoring, lowpower and low-cost sensors [135], fading, shadowing and noise uncertainty effects [127].…”

Designing Random FM Radar Waveforms with Compact Spectrum

“…1, the spectral efficiencies are approximately 0.23, 0.43, 0.69, and 0.80, respectively. These values are also the inverse of "oversampling" factor K when performing optimization of discretized FM waveforms [12][13][14][15][16][17].…”

“…It has recently been shown [10,11] that imposing structure to RFM can provide a Gaussian spectral density in the expectation (over the set of unique waveforms), yielding a per-waveform peak sidelobe level (PSL) of ~10 log10 (B3dBT) after expectation, for 3-dB bandwidth B3dB and pulse width T. Better sidelobe performance can be achieved by optimizing each waveform to match the desired spectrum density (e.g. [12][13][14][15][16][17]).…”

Radar Waveform Optimization for Joint Radar Communications Performance

“…Finally, research has also been done into radar waveform design in the presence of signal-dependent noise to maximize various other detection and information theoretic metrics such as the mutual information, and Kullback-Liebler divergence [30,31]. Cognitive radar is another emrging technology that researchers have begun to look at as a solution to the spectral scarcity problem via radar scheduling [32] or employing cognitive radio spectrum sensing techniques, emitter localization, and power allocation to avoid interference [33][34][35][36]. Other works have also investigated cognitive radar as a solution to the spectral congestion problem [37][38][39][40].…”