Karsten Fyhn just sent me the following:
Hi Igor,I have recently had the following two unrelated papers accepted for publication - will you mention them on your blog? The code for both papers is available at www.sparsesampling.com/css and www.sparsesampling.com/scspi, respectively.Thanks in advance!Best,
Thanks Karsten !
With the advent of ubiquitous computing there are two design parameters of wireless communication devices that become very important: power efficiency and production cost. Compressive sensing enables the receiver in such devices to sample below the Shannon-Nyquist sampling rate, which may lead to a decrease in the two design parameters. This paper investigates the use of Compressive Sensing (CS) in a general Code Division Multiple Access (CDMA) receiver. We show that when using spread spectrum codes in the signal domain, the CS measurement matrix may be simplified. This measurement scheme, named Compressive Spread Spectrum (CSS), allows for a simple, effective receiver design. Furthermore, we numerically evaluate the proposed receiver in terms of bit error rate under different signal to noise ratio conditions and compare it with other receiver structures. These numerical experiments show that though the bit error rate performance is degraded by the subsampling in the CS-enabled receivers, this may be remedied by including quantization in the receiver model.We also study the computational complexity of the proposed receiver design under different sparsity and measurement ratios. Our work shows that it is possible to subsample a CDMA signal using CSS and that in one example the CSS receiver outperforms the classical receiver.
Accepted for publication in IEEE Transactions on Wireless Communications 2013. The implementation is available here.
Spectral Compressive Sensing with Polar Interpolation by Karsten Fyhn, Hamid Dadkhahi and Marco Duarte. The abstract:
Existing approaches to compressive sensing of frequency-sparse signals focuses on signal recovery rather than spectral estimation. Furthermore, the recovery performance is limited by the coherence of the required sparsity dictionaries and by the discretization of the frequency parameter space. In this paper, we introduce a greedy recovery algorithm that leverages a band-exclusion function and a polar interpolation function to address these two issues in spectral compressive sensing. Our algorithm is geared towards line spectral estimation from compressive measurements and outperforms most existing approaches in ﬁdelity and tolerance to noise.
Accepted for publication at ICASSP 2013. The implementation is available here.
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