Publications

Journal Papers

  1. Y. Wang, K. Gu, Y. Wu, W. Dai, and Y. Shen, “NLOS effect mitigation via spatial geometry exploitation in cooperative localization”, IEEE Trans. Wireless Comm., 2020.
  2. Q. Yu, W. Dai, Z. Cvetkovic, and J. Zhu, “Dictionary learning with BLOTLESS update”, IEEE Trans. Signal Proc., vol. 68, 2020.
  3. Y. Lu, W. Dai, and Y. Eldar, “Optimal number of measurements in a linear system with quadratically decreasing SNR”, IEEE Trans. Signal Proc., vol. 67, no. 11, pp. 2947-2959, 2019.
  4. Z. Ma, W. Dai, Y. Liu, and X. Wang, “Group sparse Bayesian learning via exact and fast marginal likelihood maximization”, IEEE Trans. Sign. Proc., vol. 65, no. 10, pp. 2741-2753, 2017.
  5. A. Liu, V. Lau, and W. Dai, “Exploiting burst-sparsity in massive MIMO with partial channel support information,” IEEE Trans. Wireless Commun., vol. 15, no. 11, pp. 7820-7830, 2016.
  6. W. Ding, Y. Lu, F. Yang, W. Dai, P. Li, S Liu, and J. Song, “Spectrally efficient CSI acquisition for power line communications: A Bayesian compressive sensing perspective,” IEEE Journal on Selected Areas in Communications, vol. 34, no. 7, pp. 2022-2032, 2016.
  7. Z. Gao, L. Dai, W. Dai, B. Shim, and Z. Wang, “Structured compressive sensing-based spatio-temporal joint channel estimation for FDD massive MIMO,” IEEE Trans. Comm., vol. 64, no. 2, pp. 601-617, 2016.
  8. J. Dong, W. Wang, W. Dai, M. Plumbley, Z. Han, and J. Chambers, “Analysis SimCO algorithms for sparse analysis model based dictionary learning,” IEEE Trans. Signal Processing, vol. 64, no. 2, pp. 417-431, 2015.
  9. R.-A. Pitaval, W. Dai, and O. Tirkkonen, “Convergence of gradient descent for low-rank matrix approximation,” IEEE Trans. Inform. Theory, vol. 61, no. 8, pp. 4451-4457, 2015.
  10. W. Dai and S. Yüksel, “Observability of a Linear System under Sparsity Constraints,” IEEE Trans. Automatic Control, in press, 2013.
  11. T. Xu, W. Wang, and W. Dai, "Sparse coding with adaptive dictionary learning for underdetermined blind speech separation", Speech Communication, vol. 55, no. 3, pp. 432-450, 2013.
  12. W. Dai, T. Xu, and W. Wang, "Simultaneous Codeword Optimisation (SimCO) for Dictionary Update and Learning", IEEE Trans. Signal Processing, vol. 60, no. 12, pp. 6340-6353, 2012.
  13. W. Dai, E. Kerman, and O. Milenkovic, “A geometric approach to low-rank matrix completion,” IEEE Trans. Inform. Theory, vol. 58, no. 1, pp. 237-247, 2012.
  14. W. Dai, O. Milenkovic and H.V. Pham, “Structured sublinear compressive sensing via belief propagation,” Physical Communication, vol. 5, no. 2, pp. 76-90, 2011.
  15. W. Dai, O. Milenkovic, and E. Kerman, “Subspace evolution and transfer (SET) for low-rank matrix completion,” IEEE Trans. Signal Processing, vol. 59, no. 7, pp. 3120-3132, 2011.
  16. W. Dai, H. V. Pham, and O. Milenkovic, “Information theoretical and algorithmic approaches to quantized compressive sensing,” IEEE Trans. Communications, vol. 59, no. 7, pp. 1857-1866, 2011.
  17. W. Dai and O. Milenkovic, “Subspace pursuit for compressive sensing reconstruction,” IEEE Trans. Inform. Theory, vol. 55, no. 5, pp. 2230-2249, 2009.
  18. W. Dai, M. A. Sheikh, O. Milenkovic, and R. G. Baraniuk, “Compressive sensing DNA microarrays,” Eurosip Journal on Bioinformatics and Systems Biology, vol. 2009, 2009.
  19. W. Dai and O. Milenkovic, “Weighted superimposed codes and constrained integer compressed sensing,” IEEE Trans. Inform. Theory, vol. 55, no. 5, pp. 2215–2229, May, 2009.
  20. W. Dai, Y. Liu, V. K. N. Lau, and B. Rider, “On the information rate of MIMO systems with finite rate channel state feedback using beamforming and power on/off strategy,” IEEE Trans. Inform. Theory, vol. 55, no. 11, pp. 5032-5047, Nov., 2009.
  21. W. Dai, Y. Liu, and B. Rider, “Effect of finite rate feedback on CDMA signature optimization and MIMO beamforming vector selection,” IEEE Trans. Inform. Theory, vol. 55, no. 8, pp. 3651 - 3669, August 2009.
  22. W. Dai, B. C. Rider, and Y. Liu, “Joint beamforming for multiaccess MIMO systems with finite rate feedback,” IEEE Trans. Wireless Commun., accepted, 2009.
  23. W. Dai, Y. Liu, and B. Rider, “How many users should be turned on in a multi-antenna broadcast channel?” IEEE J. Select. Areas Commun., vol. 26, no. 8, pp. 1526–1535, October, 2008.
  24. W. Dai, Y. Liu, and B. Rider, “Quantization bounds on Grassmann manifolds and applications to MIMO systems,” IEEE Trans. Inform. Theory, vol. 54, no. 3, pp. 1108–1123, March 2008.
  25. W. Dai, Y. Wang, and J. Wang, “A blind modulation recognition method based on AR model,” Acta Electronica Sinica, vol. 29, pp. 1883 - 1885, 2001.

Conference Papers

  1. M. A. Suliman and W. Dai, ``Atomic Norm Denoising in Blind Two-Dimensional Super-Resolution'', IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2020.
  2. Y. Wang, K. Gu, Y. Wu, W. Dai, and Y. Shen, ``Exploiting NLOS Bias Correlation in Cooperative Localization'', IEEE Int. Conf. Comm. (ICC), Shanghai, China, 20-24 May, 2019.
  3. M. A. Suliman, and W. Dai, ``Blind super-resolution in two-dimensional parameter space'', IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Briton, UK, 12-17 May, 2019.
  4. Q. Yu, W. Dai, Z. Cvetkovic, and J. Zhu, ``Bilinear dictionary update via linear least squares'', IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Briton, UK, 12-17 May, 2019.
  5. M. A. Suliman, and W. Dai, ``Exact three-dimensional estimation in blind super-resolution via convex optimization'', Conference on Information Sciences and Systems (CISS), Baltimore, MD, USA, 20-22 March, 2019.
  6. J. Xia, W. Dai, J. Polak, M. Bierlaire, “Blind Estimation of Origin-Destination Flows in Networks”, Allerton Conference, Monticello, IL, 2-4 Oct. 2018.
  7. M. Ferreira Da Costa, and W. Dai, “A tight converse to the spectral resolution limit via convex programming”, IEEE International Symposium on Information Theory (ISIT), Vail, CO, USA, 17-22 June, 2018
  8. Y. Xu, Q. Yu, W. Dai, Z. Cvetkovic, and V. Mcclelland, “Cortico-muscular coherence enhancement via sparse signal representation”, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Calgary, Alberta, Canada, 15–20 April 2018.
  9. Y. Lu, W. Dai, and Y. Eldar, “Optimal number of measurements for compressed sensing with quadratically decreasing SNR”, European Signal Processing Conference (EUSIPCO), Kos island, Greece, 28 Aug. - 2 Sept., 2017. 
  10. M. Ferreira Da Costa and W. Dai, “Low dimensional atomic norm representations in line spectral estimation”, IEEE International Symposium on Information Theory (ISIT), Aachen, Germany, 25-30 June, 2017.
  11. M. Ferreira Da Costa and W. Dai, “A guaranteed poly-logarithmic time relaxation for the line spectral estimation problem”, Sign. Proc. with Adaptive Sparse Structured Represent. (SPARS), Lisbon, Portugal, 5-8 June, 2017.
  12. J. Wu, Y. Lu, and W. Dai, “Off-grid compressed sensing for WiFi-based passive radar”, IEEE Int. Symp. on Signal Proc. and Inf. Tech. (ISSPIT), Limassol, Cypress, 12-14 Dec., 2016.
  13. Y. Lu and W. Dai, “Extended AMP algorithm for correlated distributed compressed sensing model”, IEEE Int. Conf. Digit. Sign. Proc. (DSP), Beijing, China, 16-18 Oct., 2016.
  14. M. Ferreira Da Costa and W. Dai, “Achieving super-resolution in multi-rate sampling systems via efficient semidefinite programming”, IEEE Inf. Theory Workshop, Cambridge, UK, 11-14 Sept. 2016.
  15. G. Zhou and W. Dai, “An approximate message passing based algorithm for robust face recognition”, European Signal Processing Conference (EUSIPCO), 29 Aug. - 2. Sept., Hungary, 2016.
  16. Y. Lu and W. Dai, “Independent versus repeated measurements: a performance quantification via state evolution”, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Shanghai, China, 20-25 March, 2016.
  17. Z. Ma, W. Dai, Y. Liu, and X. Wang, “Group sparse Bayesian learning via exact and fast marginal likelihood maximization”, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Shanghai, China, 20-25 March, 2016.
  18. E. Karseras and W. Dai, “Fast variational Bayesian learning for channel estimation with prior statistical information”, IEEE Intern. Workshop on Sign. Proc. Advances in Wireless Comm. (SPAWC), 2015.
  19. Y. Lu and W. Dai, “Improved AMP (IAMP) for non-ideal measurement matrices”, European Signal Processing Conference (EUSIPCO), 2015.
  20. X. Zhao and W. Dai, “On joint recovery of sparse signals with common supports”, IEEE International Symposium on Information Theory (ISIT), 2015.
  21. P. Li, W. Dai, H. Meng, and X. Wang, “On recovery of sparse signals with block structures”, IEEE International Symposium on Information Theory (ISIT), 2015.
  22. X. Zhu, L. Dai, G. Gui, W. Dai, Z. Wang, F. Adachi, “Structured matching pursuit for reconstruction of dynamic sparse channels”, IEEE Global Comm. Conf. (GLOBECOM), 2015.
  23. X. Zhu, L. Dai, W. Dai, Z. Wang, M. Moonen, “Tracking a dynamic sparse channel via differential orthogonal matching pursuit”, IEEE Military Comm. Conf., 2015.
  24. E. Karseras and W. Dai, “Fast variational Bayesian learning for channel estimation with prior statistical information”, IEEE Intern. Workshop on Sign. Proc. Advances in Wireless Comm. (SPAWC), 2015.
  25. X. Zhao and W. Dai, “Power Allocation in Compressed Sensing of Non-uniformly Sparse Signals”, IEEE International Symposium on Information Theory (ISIT), Honolulu, HI, 29 June - 4 July, 2014. 
  26. E. Karseras and W. Dai, “A fast variational approach for Bayesian compressive sensing with informative priors”, International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Florence, Italy, 4-9 May, 2014. 
  27. J. Dong, W. Wang, and W. Dai, “Analysis SIMCO: A new algorithm for analysis dictionary learning”, International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Florence, Italy, 4-9 May, 2014. 
  28. B. I. Ahmad, W. Dai, and C. Ling, “Model-based compressive harmonic-aware matching pursuit: An evaluation”, Asilomar Conference on Signals, Systems & Computers, Pacific Grove, CA, 3-6 Nov., 2013.
  29. B. I. Ahmad, M. Al-Ani, A. Tarczynski, W. Dai, and C. Ling, “Compressive andnNon-compressive reliable wideband spectrum sensing at sub-Nyquist rates”, European Signal Processing Conference (EUSIPCO), Marrakech, Morocco, 9-13 Sept., 2013.
  30. E. Karseras, K. Leung, and W. Dai, “Tracking dynamic sparse signals using hierarchical Bayesian Kalman filters”, European Signal Processing Conference (EUSIPCO), Marrakech, Morocco, 9-13 Sept., 2013.
  31. E. Karseras, K. Leung, and W. Dai, “Bayesian compressed sensing: improved inference”, IEEE China Summit and International Conference on Signal and Information Processing (ChinaSIP), Beijing, China, 6-10 July, 2013.
  32. X. Zhao, T. Xu, G. Zhou, W. Dai, and W. Wang, “Joint image separation and dictionary learning”, International Conference on Digital Signal Processing (DSP), Santorini, Greece, 1-3 July, 2013.
  33. J. Filos, E. Karseras, W. Dai and S. Yan, “Tracking dynamic sparse signals with hierarchical Kalman filters: a case study”, International Conference on Digital Signal Processing (DSP), Santorini, Greece, 1-3 July, 2013.
  34. E. Karseras, K. Leung, and W. Dai, “Tracking dynamic sparse signals with Kalman filters: framework and improved inference”, International Conference on Sampling Theory and Applications (SampTA), Bremen, Germany, 1-5 July, 2013.
  35. X. Zhao, G. Zhou, and W. Dai, "Smoothed SimCO for dictionary learning: handling the singularity issue", International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Vancouver, Canada, 26-31 May, 2013.
  36. E. Karseras, K. Leung, and W. Dai, "Tracking dynamic sparse signals using hierarchical Bayesian Kalman filters", International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Vancouver, Canada, 26-31 May, 2013.
  37. S. Zubair, W. Dai, and W. Wang, "Sparseness Constrained Tensor Factorization Algorithm for Dictionary Learning over High-Dimensional Space", in Proc. 9th IMA International Conference on Mathematics in Signal Processing (IMA 2012), Birmingham, UK, 17-20 December, 2012.
  38. T. Xu, W. Dai, and W. Wang, "Fast Dictionary Learning Algorithm via Codeword Clustering and Hierarchical Sparse Coding", in Proc. 9th IMA International Conference on Mathematics in Signal Processing (IMA 2012), Birmingham, UK, 17-20 December, 2012.
  39. G. Zhou, X. Zhao, and W. Dai, “Low rank matrix completion: a smoothed \ell_{0}-search”, Allerton Conference, Monticello, IL, 2012.
  40. X. Zhao, G. Zhou, W. Wang, W. Dai, “Weighted SimCO: a novel algorithm for dictionary update”, Sensor Signal Processing for Defence, London, UK, 2012.
  41. X. Gao, J. Filos, and W. Dai, “DOA tracking via simultaneous angle-source update (SASU)”, Sensor Signal Processing for Defence, London, UK, 2012.
  42. S. Yan, C. Wu, W. Dai, M. Ghanem, and Y. Guo, “Environmental monitoring via compressive sensing”, SensorKDD, Beijing, 2012.
  43. B. Ahmad, W. Dai, and C. Ling, “Reliable sub-Nyquist wideband spectrum sensing based on randomised sampling”, International Workshop on Compressed Sensing applied to Radar, Bonn, Germany, 2012.
  44. W. Dai, T. Xu, and W. Wang, “Dictionary learning and update based on simultaneous codeword optimization (SimCO)”, in International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2012.
  45. W. Dai, T. Xu and W. Wang, “Simultaneous codeword optimization (SimCO) for dictionary learning”, Allerton Conference, Monticello, IL, 2011.
  46. X. Zhao, T. Lu, and W. Dai, “Compressive Sensing Reconstruction Techniques with Magnitude Prior Information”, Sensor Signal Processing for Defence, London, UK, 2011.
  47. W. Dai, D. Sejdinovic, and O. Milenkovic, “Compressive Sensing for Gaussian Dynamic Signals”, in Signal Processing with Adaptive Sparse Structured Representations Workshop, Edinburgh, UK, 2011.
  48. W. Dai, E. Kerman, and O. Milenkovic, “Low-rank matrix completion with geometric performance guarantees”, in International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Prague, Czech Republic, 2011.
  49. W. Dai, D. Sejdinovic, and O. Milenkovic, “Gaussian dynamic compressive sensing”, in International Conference on Sampling Theory and Applications (SampTA), Singapore, 2011.
  50. W. Dai, and O. Milenkovic, “Algorithmic solutions for quantized compressive sensing”, in International Conference on Sampling Theory and Applications (SampTA), Singapore, 2011.
  51. W. Dai and O. Milenkovic, “SET: an algorithm for consistent matrix completion”, in International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2010.
  52. H. V. Pham, W. Dai, and O. Milenkovic, “Compressive list-support recovery for colluder identification”, in International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2010.
  53. W. Dai, H. V. Pham, and O. Milenkovic, “A comparative study of quantized compressive sensing schemes,” in IEEE International Symposium on Information Theory (ISIT), pp. 11-15, Seoul, Korea, June-July 2009.
  54. H. V. Pham, W. Dai, and O. Milenkovic, “Sublinear compressive sensing reconstruction via belief propagation decoding,” in IEEE International Symposium on Information Theory (ISIT), pp. 674 - 678, Seoul, Korea, June-July 2009.
  55. W. Dai, H. V. Pham, and O. Milenkovic, “Distortion-rate functions for quantized compressive sensing,” in IEEE Information Theory Workshop (ITW), pp. 171-175, Volos, Greece, June 2009.
  56. X. Hang, W. Dai, and F.-X. Wu, “Subspace pursuit for gene profile classification,” in IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS), pp. 1-4, Minneapolis, MN, May 2009.
  57. W. Dai, O. Milenkovic, M. A. Sheikh, and R. G. Baraniuk, “Probe design for compressive sensing microarrays,” in IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 163-169, Philadelphia, PA, November 2008.
  58. W. Dai and O. Milenkovic, “Iterative subspace pursuit decoding of Euclidean superimposed codes,” in International Symposium on Turbo Codes and Related Topics, pp. 402-407, Lausanne, Switzerland, September 2008.
  59. W. Dai and O. Milenkovic, “Weighted superimposed codes,” in Proc. in Coding Theory, Saint Petersburg, Russia, October 2008.
  60. W. Dai and O. Milenkovic, “Sparse weighted Euclidean superimposed coding for integer compressed sensing,” in Conference on Information Sciences and Systems (CISS), pp. 480-485, Princeton, NJ, March 2008. 
  61. W. Dai and O. Milenkovic, “Weighted Euclidean superimposed codes for integer compressed sensing,” in IEEE Information Theory Workshop (ITW), pp. 124-128, Porto, Portugal, May 2008.
  62. W. Dai, B. C. Rider, and Y. Liu, “Volume growth and general rate quantization on Grassmann manifolds,” in IEEE Global Telecommunications Conference (GLOBECOM), pp. 1441-1445, Washington, DC, November 2007. 
  63. W. Dai, B. Rider, and Y. Liu, “Unequal dimensional quantization and small balls on Grassmann manifolds,” in Proc. IEEE International Symposium on Information Theory (ISIT), pp. 1806-1810, Nice, France, June 2007. 
  64. W. Dai, Y. Liu, and B. Rider, “How many users should be turned on in a multi-antenna broadcast channel?” in Conf. on Info. Sciences and Systems (CISS), pp. 806–811, Baltimore, MD, March 2007. 
  65. W. Dai, B. Rider, and Y. Liu, “Multi-access MIMO systems with finite rate channel state feedback,” in Proc. Allerton Conf. on Commun., Control, and Computing, Monticello, IL, September 2005. 
  66. W. Dai, Y. Liu, and B. Rider, “Performance analysis of CDMA signature optimization with finite rate feedback,” in Conf. on Info. Sciences and Systems (CISS), Princeton, NJ, March 2006. 
  67. W. Dai, Y. Liu, and B. Rider, “Quantization bounds on Grassmann manifolds of arbitrary dimensions and MIMO communications with feedback,” in IEEE Global Telecommunications Conference (GLOBECOM), pp. 1456-1460, St. Louis, MO, Nov.-Dec. 2005. 
  68. W. Dai, Y. Liu, B. Rider, and V. Lau, “On the information rate of MIMO systems with finite rate channel state feedback and power on/off strategy,” in Proc. IEEE International Symposium on Information Theory (ISIT), pp. 1549–1553, Adelaide, SA, September 2005. 
  69. W. Dai, Y. Wang, and J. Wang, “Joint power estimation and modulation classification using second- and higher statistics,” in Proc. IEEE Wireless Communications and Networking Conference (WCNC), 155–158 vol.1, Orlando, FL, March 2002.