Continuous monitoring of respiratory rate(cycle) during sleep for diseases such as sleep apnea and sudden infant deathsyndrome (SIDS) can be lifesaving. Wireless radio communications signals areeverywhere and can be harnessed for contactless monitoring of the respiratoryrates. The amplitude of the received signal strength changes periodicallydepending on the exhalation and inhalation of the subject. In this paper,subspace-based multiple signal classification (MUSIC) algorithm is applied toestimate the respiratory rate for better results. The proposed method and theother power spectral density (PSD) methods for respiratory estimations arecompared with the real laboratory measurements. It is demonstrated that theproposed method estimates the respiratory rate with high accuracy andoutperforms the other PSD-based methods which are commonly used in theliterature.