Variational Bayes Based Multiuser Detection for On–Off Random Access Channels
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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.
KeywordsMultiuser detection On–off random access channels Sparse signal processing Variational Bayes
This work was supported in part by SRIC, IIT Kharagpur under Award IIT/SRIC/GSSST/MUA/2013-14/110.
- 1.Aldosari, S. A., & Moura, J. M. F. (2004). Fusion in sensor networks with communication constraints. In Proceedings of the 3rd international symposium on Information processing in sensor networks, IPSN’04 (pp. 108–115). ACM, New York. https://doi.org/10.1145/984622.984638.
- 2.Applebaum, L., Bajwa, W. U., Duarte, M. F., & Calderbank, R. (2010). Multiuser detection in asynchronous on–off random access channels using lasso. In 48th annual Allerton conference on communication, control, and computing. Monticello, IL. https://doi.org/10.1109/ALLERTON.2010.5706898.
- 5.Fletcher, A., Rangan, S., & Goyal, V. (2009). A sparsity detection framework for on-off random access channels. In IEEE international symposium on information theory (pp. 169–173). https://doi.org/10.1109/ISIT.2009.5205769.
- 6.Fletcher, A.K., Rangan, S., & Goyal, V.K. (2009). On–off random access channels: A compressed sensing framework. CoRR arXiv:0903.1022.
- 7.Griffiths, T. L., & Ghahramani, Z. (2006). Infinite latent feature models and the Indian buffet process. In Y. Weiss, B. Schölkopf & J. C. Platt (Eds.), Advances in Neural Information Processing Systems 18 (pp. 475–482). MIT Press. http://papers.nips.cc/paper/2882-infinite-latent-feature-models-and-the-indian-buffet-process.pdf.
- 9.Paisley, J., & Carin, L. (2009). Nonparametric factor analysis with beta process priors. In Proceedings of the 26th international conference on machine learning (pp. 777–784).Google Scholar
- 10.Pati, Y., Rezaiifar, R., & Krishnaprasad, P. (1993). Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition. In The 27th Asilomar conference on signals, systems and computers, 1993 (Vol. 1, pp. 40–44). https://doi.org/10.1109/ACSSC.1993.342465.
- 12.Seeger, M., & Wipf, D. P. (2010). Variational bayesian inference techniques. IEEE Signal Processing Magazine, 27(6), 81–91.Google Scholar
- 13.Thibaux, R., & Jordan, M. I. (2007) Hierarchical beta processes and the Indian buffet process. In Proceedings of the 11th conference on artificial intelligence and statistic.Google Scholar
- 17.Walsh, B. (2004). Markov chain monte carlo and gibbs sampling. In Lecture Notes for EEB 581, version 26, April.Google Scholar