A statistical-mechanics approach to large-system analysis of CDMA multiuser detectors

By Toshiyuki TANAKA. IEEE Transactions on Information Theory, 2002

533 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/107be97b7c9ebbfc558a187f332d386e7f445cfe

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Citations in the corpus

Random Matrix Theory and Wireless Communications (2004, methodology): “For example, the analysis of single-user matched filter receivers [275] and the analysis of the optimum asymptotic multiuser efficiency [258] have used various versions of the centrallimit theorem; the analysis of the asymptotic uncoded error probability as well as the rates achievable with suboptim”
Hypothesis Testing in High-Dimensional Regression Under the Gaussian Random Design Model: Asymptotic Theory (2013, methodology): “We limit ourselves to the main steps, since analogous calculations can be found in several earlier works [40], [41], [48].”
Polar Codes for Channel and Source Coding (2009, methodology): “The work of Tanaka [98] is a breakthrough in the analysis of binary input CDMA system.”
Generalized approximate message passing for estimation with random linear mixing (2010, result): “The fixed point of the updates (28) are precisely the equations given by Guo and Verdú in their replica analysis of MMSE estimation with AWGN outputs and non-Gaussian priors [18], which generalized several earlier analyses in [19]–[21].”
A perspective on massive random-access (2017, background): “Optimal MUD results in each user seeing an effective single-user BI-AWGN channel with SNR found from Tanaka’s formula [29], [30].”
Mutual information and minimum mean-square error in Gaussian channels (2004, background): “(72) The spectral efficiencies under joint and separate decoding were first derived for Gaussian inputs in [42], and then found implicitly in [43] and later explicitly [44] for equal-power users with binary inputs.”
Statistical physics-based reconstruction in compressed sensing (2011, methodology): “The replica method is a standard method in statistical physics [7], which has been applied successfully to several problems of information theory [17,28,36,37], including compressed sensing [12–14].”
MIMO capacity through correlated channels in the presence of correlated interferers and noise: a (not so) large N analysis (2003, methodology): “More recently, this method has been seeing increased application in the field of information theory [17]–[19].”
Optimal errors and phase transitions in high-dimensional generalized linear models (2017, methodology): “Random instances of GLMs describe both the setting of code-division multiple access—a multiuser access method used in communication technologies (34, 35)—and an error correction scheme called sparse superposition codes, which have been shown to achieve the Shannon capacity for any type of noisy…”
Bayes-Optimal Joint Channel-and-Data Estimation for Massive MIMO With Low-Precision ADCs (2015, background): “In particular, AMP is a variant of the sum-product algorithm, which was initially proposed by Donoho et al. [27] in order to solve a linear inverse problem in the context of compressive sensing.”