Abstract
With the ever-increasing demand for high-speed wireless data transmission, ultra-wideband spectrum sensing is critical to support the cognitive communication over an ultra-wide frequency band for ultra-wideband communication systems. However, it is challenging for the analog-to-digital converter design to fulfill the Nyquist rate for an ultra-wideband frequency band. Therefore, we explore the spectrum sensing mechanism based on the sub-Nyquist sampling and conduct extensive experiments to investigate the influence of sampling rate, bandwidth resolution and the signal-to-noise ratio on the accuracy of sub-Nyquist spectrum sensing. Afterward, an adaptive policy is proposed to determine the optimal sampling rate, and bandwidth resolution when the spectrum occupation or the strength of the existing signals is changed. The performance of the policy is verified by simulations.
Highlights
After decades of rapid development, wireless technologies have been intensively applied to almost every field from daily communications to mobile medical and battlefield communications.Whether you admit it or not, wireless communication technologies have become an essential part of our daily life
Extensive studies are conducted to explore the influence of some critical parameters, for example, the sampling rate, bandwidth resolution and the signal signal-to-noise ratio (SNR), for the sake of understanding the performance of sub-Nyquist spectrum sensing under various conditions
Ultra-wideband spectrum sensing is quite important to the future high speed cognitive communication
Summary
After decades of rapid development, wireless technologies have been intensively applied to almost every field from daily communications to mobile medical and battlefield communications. Can we just deal with the ultra-wideband spectrum sensing using undersampled data whose sampling rate is less than the Nyquist rate? The main challenge to handle ultra-wideband spectrum sensing with sub-Nyquist sampling rates is the information loss resulting from the sub-Nyquist sampling, which makes us unable to recover the frequency information from the sampled data. We only need to reconstruct the frequencies we are interested in Based on these considerations, this paper introduces SNSS (Sub-Nyquist Spectrum Sensing) to address this problem. SNSS makes full use of the Chinese Remainder Theorem (CRT) to recover all the frequency information that we are interested in from the undersampled data, making it possible to sense the ultra-wideband spectrum with relatively low sampling rates and computing resources.
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