Experimental results on wideband spectrum sensing using random sampling ADC in 90nm CMOS

Applications that require wireless wideband spectrum sensing are often limited by energy consumption of the sensing hardware. The power consumption is typically directly related to the amount of data transmitted. The emerging theory of compressed sensing provides a framework for reconstructing the sensed spectrum with fewer samples than are produced from Nyquist rate sampling. We have implemented a compressed sensing analog-to-information converter (AIC) in 90nm CMOS technology that allows complete reconstruction of a sparse spectrum consisting of discrete frequency bands. Typically, ℓ1-minimization based algorithms are used to reconstruct the original signal for compressed sensing. However, these algorithms do not perform well as signal sparsity decreases. This limitation can be mitigated by using ℓ1,2 regularization based algorithms that exploit group sparsity. We present experimental results comparing the performance of both types of algorithms for reconstructing discrete frequency bands sampled with this AIC. These results demonstrate the performance achievable by physical AIC systems that utilize compressed sensing theory.