Joint SNR and channel estimation for 60 GHz systems using compressed sensing

60 GHz communication supporting multigigabit data rate is a popular choice of industry for next generation short distance wireless communications. However, multi-Gsps ADC becomes a challenge in 60 GHz systems which have ultra wide Nyquist bandwidth. To reduce sampling rate of ADC in the estimation stage, we propose a joint signal-to-noise ratio (SNR) and channel estimation algorithm using compressed sensing (CS) theory. In 60 GHz systems, CS encoding and decoding strategies are optimized to maximize benefits from the design of pilots and estimators. For pilot design, m-sequence rather than conventional Bernoulli random sequence is selected owning to a better average restricted isometry property; for estimator design, a quasi-optimal channel and noise power estimation is put forward underlying signal subspace provided by CS algorithm. Simulation results show that the proposed algorithm reduces the sampling rate of ADC to 9.1% Nyquist bandwidth of 60 GHz communication. Moreover, the algorithm with this compressed sampling efficiently outperforms classical least square algorithm with Nyquist sampling as SNR exceeds 7 dB.