  Influence of the finite-length registers and quantization effects on the
reconstruction of sparse and approximately sparse signals is analyzed in this
paper. For the nonquantized measurements, the compressive sensing (CS)
framework provides highly accurate reconstruction algorithms that produce
negligible errors when the reconstruction conditions are met. However, hardware
implementations of signal processing algorithms involve the finite-length
registers and quantization of the measurements. An analysis of the effects
related to the measurements quantization with an arbitrary number of bits is
the topic of this paper. A unified mathematical model for the analysis of the
quantization noise and the signal nonsparsity on the CS reconstruction is
presented. An exact formula for the expected energy of error in the CS-based
reconstructed signal is derived. The theory is validated through various
numerical examples with quantized measurements, including the cases of
approximately sparse signals, noise folding, and floating-point arithmetics.
