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Variational Bayes Based Multiuser Detection for On–Off Random Access Channels

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Abstract

This paper considers on–off random access channels where the users transmit either a one or a zero to a base station or fusion center, and it is assumed that only a small fraction of users are active during any channel use. Under these assumptions, the problem of identifying the active users reduces to that of recovering a sparse binary vector from noisy random linear measurements. A hierarchical Bayesian approach is proposed in this paper to recover the set of active users. A fast approximate Bayesian inference based on Variational Bayes (VB) is then developed. Extensive simulation results are then provided to compare the performance of the proposed VB based Bayesian MUD approach to sparse estimation techniques such as OMP and LASSO. It is observed that the proposed approach is robust to variations in noise as well as sparsity levels. Further, for a given BER performance, the proposed approach requires substantially smaller dimensional codes as compared to OMP and LASSO, thus improving the spectral efficiency.

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Notes

  1. Digamma Function is defined as the logarithmic derivative of Gamma Function as \(\psi (x)=\frac{{\text {d}}}{{\text {d}}x}\ln (\Gamma (x)).\)

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Acknowledgements

This work was supported in part by SRIC, IIT Kharagpur under Award IIT/SRIC/GSSST/MUA/2013-14/110.

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Authors and Affiliations

  1. TATA Communications Ltd., Pune, India

    Rituraj Singh Jodha

  2. G. S. Sanyal School of Telecommunications, Indian Institute of Technology, Kharagpur, Kharagpur, India

    Priyadip Ray

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  1. Rituraj Singh Jodha
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  2. Priyadip Ray
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Correspondence to Priyadip Ray.

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Jodha, R.S., Ray, P. Variational Bayes Based Multiuser Detection for On–Off Random Access Channels. Wireless Pers Commun 100, 895–906 (2018). https://doi.org/10.1007/s11277-018-5356-7

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