phil schniter - publications (by topic / by date)

COMPRESSIVE SENSING/IMAGING:

POSTERIOR SAMPLING

1. J. Wen, R. Ahmad, and P. Schniter, ``Posterior Sampling for Accelerated Multicoil MRI Reconstruction using a Conditional Normalizing Flow [pdf] [arxiv],'' Proc. Intl. Conf. Machine Learning (ICML), (Honolulu, HI), 2023. [1827/6538=27.9% acceptance rate]
2. M. Bendel, R. Ahmad, and P. Schniter, ``A Regularized Conditional GAN for Posterior Sampling in Inverse Problems [arxiv].''
3. J. Wen, R. Ahmad, and P. Schniter, ``Posterior Sampling for Accelerated Multicoil MRI Reconstruction using a Conditional Normalizing Flow,'' Proc. Intl. Soc. Magnetic Resonance in Medicine (Toronto), June 2023.
4. M. Pereyra, P. Schniter, E. Chouzenoux, J.-C. Pesquet, J.-Y. Tourneret, A. Hero, and S. McLaughlin, ``A Survey of Stochastic Simulation and Optimization Methods in Signal Processing [pdf] [arxiv],'' IEEE Journal of Selected Topics in Signal Processing: Special Issue on Stochastic Simulation and Optimisation in Signal Processing, vol. 10, no. 2, pp. 224-241, Mar. 2016.

PLUG-AND-PLAY RECOVERY (NON-AMP)

1. S. K. Shastri, R. Ahmad, and P. Schniter, ``Autotuning Plug-and-Play Algorithms for MRI [arxiv],'' Proc. Asilomar Conf. on Signals, Systems, and Computers (virtual), Nov. 2020. [slides]
2. C. Chen, Y. Liu, P. Schniter, M. Tong, K. Zareba, O. Simonetti, L. Potter, R. Ahmad, ``OCMR (v1.0)--Open-Access Multi-Coil k-Space Dataset for Cardiovascular Magnetic Resonance Imaging [arxiv].''
3. R. Ahmad, C. A. Bouman, G. T. Buzzard, S. Chan, E. T. Reehorst, and P. Schniter, ``Plug and play methods for magnetic resonance imaging [arxiv],'' IEEE Signal Processing Magazine, vol. 37, no. 1, pp. 105-116, Jan. 2020
4. E. T. Reehorst and P. Schniter, ``Regularization by Denoising: Clarifications and New Interpretations [pdf] [arxiv],'' IEEE Transactions on Computational Imaging, vol. 5, no. 1, pp. 52-67, Mar 2019.
5. C. A. Metzler, P. Schniter, A. Veeraraghavan, and R. G. Baraniuk, ``prDeep: Robust Phase Retrieval with Flexible Deep Neural Networks [arxiv],'' Proc. Intl. Conf. Machine Learning (ICML), (Stockholm, Sweden), July 2018. [621/2473=25% acceptance rate]

LEARNED REGULARIZATION

1. C. Chen, Y. Liu, P. Schniter, N. Jin, J. Craft, O. Simonetti, R. Ahmad ``Sparsity adaptive reconstruction for highly accelerated cardiac MRI [pdf] [html],'' Magnetic Resonance in Medicine, vol. 81, pp. 3875-3887, Jan 2019.
2. R. Ahmad and P. Schniter, ``Recovering Signals with Unknown Sparsity in Multiple Dictionaries [pdf],'' in Compressed Sensing and its Applications, Volume 2 (G. Kutyniok, G. Caire, H. Boche, R. Calderbank, and R. Mather, eds.), Birkhauser, 2017.
3. R. Ahmad and P. Schniter, ``Iteratively Reweighted L1 Approaches to Sparse Composite Regularization [pdf] [arxiv].'' IEEE Transactions on Computational Imaging, vol. 1, no. 4, pp. 220-235, Dec. 2015. [Matlab]
4. P. Schniter and R. Ahmad, ``Iteratively Reweighted L1 Approaches to Sparse Composite Regularization [pdf],'' presented at the MATHEON Workshop on Compressed Sensing and its Applications (Berlin, Germany), December 2015.
5. R. Ahmad and P. Schniter, ``Iteratively Reweighted L1 Approaches to L2-Constrained Sparse Composite Regularization [arxiv].''
6. P. Schniter and R. Ahmad, ``Iteratively Reweighted L1 Approaches to L2-Constrained Sparse Composite Regularization [slides],'' presented at the Duke Workshop on Sensing and Analysis of High-Dimensional Data (SAHD) (Durham, North Carolina), July 2015.
7. R. Ahmad, P. Schniter, and O. P. Simonetti, ``Parameter-Free Sparsity Adaptive Compressive Recovery,'' Proc. Int. Soc. Magnetic Resonance in Medicine, 2015.

COMPRESSIVE LEARNING

1. R. Gribonval, A. Chatalic, N. Keriven, V. Schellekens, L. Jacques, and P. Schniter, ``Sketching Datasets for Large-Scale Learning [pdf] [arxiv],'' IEEE Signal Processing Magazine, vol. 38, no. 5, pp. 12-36, 2021.
2. E. Byrne, A. Chatalic, R. Gribonval, and P. Schniter, ``Sketched Clustering via Hybrid Approximate Message Passing [pdf] [arxiv],'' IEEE Transactions on Signal Processing, vol. 67, no. 17, pp. 4556-4569, Sep 2019.
3. E. Byrne, R. Gribonval, and P. Schniter, ``Sketched Clustering via Hybrid Approximate Message Passing [arxiv],'' Proc. Asilomar Conf. on Signals, Systems, and Computers (Pacific Grove, CA), Nov. 2017. [poster]

RADAR

1. M. K. Wharton, A. M. Pavy, and P. Schniter, ``Deep Neural Networks for Radar Waveform Classification [arxiv],'' Proc. Asilomar Conf. on Signals, Systems, and Computers (virtual), Nov. 2021. [poster]
2. M. Wharton, E. T. Reehorst, and P. Schniter, ``Compressive SAR Image Recovery and Classification via CNNs [pdf],'' Asilomar Conf. on Signals, Systems, and Computers (Pacific Grove, CA), Nov. 2019. [poster]
3. P. Schniter, L. C. Potter, and J. Ziniel, ``Sparse Reconstruction for Radar [pdf],'' Proc. SPIE, Algorithms for Synthetic Aperture Radar Imagery, (Orlando, FL), Mar. 2008.

LEVERAGING SPARSITY IN COMMUNICATIONS (NON-AMP)

1. P. Schniter and A. Sayeed, ``Channel Estimation and Precoder Design for Millimeter-Wave Communications: The Sparse Way [pdf],'' Proc. Asilomar Conf. on Signals, Systems, and Computers (Pacific Grove, CA), Nov. 2014. [slides]
2. A. P. Kannu and P. Schniter, ``On Communication over Unknown Sparse Frequency-Selective Block-Fading Channels [pdf][arxiv],'' IEEE Transactions on Information Theory, vol. 57, no. 10, pp. 6619-6632, Oct 2011.
3. A. P. Kannu and P. Schniter, ``On Communication over Unknown Sparse Frequency-Selective Block-Fading Channels [pdf],'' in Proc. IEEE Symposium on Information Theory, (Saint-Petersburg, Russia), Aug. 2011.
4. P. Schniter and A. M. Sayeed, ``A Sparseness-Preserving Virtual MIMO Channel Model [pdf],'' Proc. Conf. on Information Sciences and Systems, (Princeton, NJ), pp. 36-41, Mar. 2004.

FAST BAYESIAN MATCHING PURSUIT

1. P. Schniter, L. C. Potter, and J. Ziniel, ``Fast Bayesian Matching Pursuit: Model Uncertainty and Parameter Estimation for Sparse Linear Models [pdf],'' Technical Report No. 09-06, IPS Laboratory, June 2009. [Matlab]
2. P. Schniter, L. C. Potter, and J. Ziniel, ``A Fast Posterior Update for Sparse Undetermined Linear Models [pdf],'' Proc. Asilomar Conf. on Signals, Systems, and Computers, (Pacific Grove, CA), Nov. 2008. (Invited.)
3. P. Schniter, L. C. Potter and J. Ziniel, ``Fast Bayesian Matching Pursuit [pdf],'' Proc. Workshop on Information Theory and Applications (ITA), (La Jolla, CA), Jan. 2008. (Invited.) [slides] [Matlab]

MISCELLANEOUS

1. P. Schniter, ``Sparse Reconstruction as Noncoherent Decoding [slides],'' presented at the Communication Theory Workshop (CTW), (Napa, CA), May 2009. (Invited.)
2. P. Schniter, L. C. Potter and S. Som, ``Sparse Reconstruction via Bayesian Variable Selection and Bayesian Model Averaging [slides],'' presented at the Workshop on Information Theory and Applications (ITA), (La Jolla, CA), Feb. 2009. (Invited.)

APPROXIMATE MESSAGE PASSING (AMP):

AMP AND DEEP LEARNING

1. P. Pandit, M Sahraee-Ardakan, S. Rangan, P. Schniter, and A. K. Fletcher, ``Matrix Inference and Estimation in Multi-Layer Models [pdf] [arxiv],'' Proc. Neural Information Processing Systems Conference (NeurIPS) (Vancouver, Canada), 2020. [1903/9467=20% acceptance rate]
2. P. Pandit, M. Sahraee-Ardakan, S. Rangan, P. Schniter, and A. K. Fletcher, ``Inference with Deep Generative Priors in High Dimensions [pdf] [arxiv],'' IEEE Journal on Selected Areas in Information Theory, vol. 1, no. 1, pp. 336-347, May 2020.
3. P. Pandit, M. Shraee-Ardakan, A. K. Fletcher, P. Schniter, and S. Rangan, ``High-Dimensional Analysis of Learning in Two-Layer Models [poster],'' presented at the Conf. on the Mathematical Theory of Deep Neural Networks (NYC), Oct 2019. [poster]
4. A. K. Fletcher, S. Rangan, and P. Schniter, ``Inference in Deep Networks in High Dimensions [pdf],'' Proc. IEEE Symposium on Information Theory, (Vail, CO), June 2018.

UNROLLED AMP

1. M. Borgerding, P. Schniter, and S. Rangan, ``AMP-Inspired Deep Networks for Sparse Linear Inverse Problems [pdf] [arxiv]," IEEE Transactions on Signal Processing, vol. 65, no. 16, pp. 4293-4308, Aug. 2017.
2. P. Schniter, M. Borgerding, S. Rangan, and A. K. Fletcher, ``AMP-inspired Deep Networks [slides],'' presented at the Workshop on Information Theory and Applications (ITA), (San Diego, CA), Feb. 2017. (Invited.)
3. M. Borgerding and P. Schniter, ``Onsager-corrected deep learning for sparse linear inverse problems [arxiv],'' Proc. IEEE Global Conference on Signal and Information Processing, (Washington, DC) Dec. 2016.
4. P. Schniter, S. Rangan, A. K. Fletcher, and M. Borgerding, ``Vector AMP and its Connections to Deep Learning [slides],'' presented at the IEEE Info. Thy. Workshop (Cambridge, UK), Sept. 2016.

AMP FOR COMPRESSIVE IMAGING

1. S. K. Shastri, R. Ahmad, C. A. Metzler, and P. Schniter, ``Denoising Generalized Expectation-Consistent Approximation for MR Image Recovery [pdf] [arxiv],'' IEEE Journal on Selected Areas in Information Theory, vol. 3, no. 3, pp. 528-542, Sep. 2022.
2. S.K. Shastri, R. Ahmad, C. Metzler, and P. Schniter, ``Expectation Consistent Plug-and-Play for MRI [arxiv],'' Proc. IEEE Internat. Conf. on Acoust., Speech, and Signal Processing (ICASSP) (Singapore), May 2022.
3. S.K. Shastri, R. Ahmad, C. Metzler, and P. Schniter, ``Matching Plug-and-Play Algorithms to the Denoiser [openreview],'' NeurIPS Workshop on Deep Inverse Problems (virtual), Dec. 2021. [poster]
4. S. Sarkar, R. Ahmad, and P. Schniter, ``MRI Image Recovery using Damped Denoising Vector AMP [arxiv],'' Proc. IEEE Internat. Conf. on Acoust., Speech, and Signal Processing (ICASSP) (virtual), May 2021. [slides] [poster]
5. A. K. Fletcher, P. Pandit, S. Rangan, S. Sarkar, and P. Schniter, ``Plug-in Estimation in High-Dimensional Linear Inverse Problems: A Rigorous Analysis [pdf] [arxiv],'' Proc. Neural Information Processing Systems Conference (NeurIPS) (Montreal, Canada), pp. 7440-7449, Dec. 2018. [1011/4865=21% acceptance rate]
6. P. Schniter, S. Rangan, and A. K. Fletcher, ``Denoising-based Vector Approximate Message Passing [pdf] [arxiv],'' Proc. Intl. Biomedical and Astronomical Signal Processing (BASP) Frontiers Workshop (Villars-sur-Ollon, Switzerland), Jan. 2017. [poster]
7. P. Schniter, S. Rangan, and A. Fletcher, ``Statistical Image Recovery: A Message-Passing Perspective [pdf],'' Proc. Intl. Biomedical and Astronomical Signal Processing (BASP) Frontiers Workshop (Villars-sur-Ollon, Switzerland), Jan. 2015. (Invited.) [slides]
8. M. A. Borgerding, P. Schniter, J. Vila, and R. Rangan ``Generalized Approximate Message Passing for Cosparse Analysis Compressive Sensing [arxiv].'' [Matlab]
9. S. Som and P. Schniter, ``Compressive Imaging using Approximate Message Passing and a Markov-Tree Prior [pdf] [arxiv],'' IEEE Transactions on Signal Processing, vol. 60, no. 7, pp. 3439-3448, July 2012. [Matlab]
10. S. Som and P. Schniter, ``Approximate Message Passing for Recovery of Sparse Signals with Markov-Random-Field Support Structure [pdf],'' presented at the Intl. Conf. on Machine Learning (ICML) Workshop on Structured Sparsity: Learning and Inference, (Bellevue, Washington), July 2011. [slides]
11. S. Som, L. C. Potter, and P. Schniter, ``Compressive Imaging using Approximate Message Passing and a Markov-Tree Prior [pdf],'' Proc. Asilomar Conf. on Signals, Systems, and Computers (Pacific Grove, CA), Nov. 2010. [slides]

AMP FOR SPARSE BAYESIAN LEARNING

1. M. Al-Shoukairi, P. Schniter, and B. D. Rao, ``A GAMP Based Low Complexity Sparse Bayesian Learning Algorithm [pdf] [arxiv]," IEEE Transactions on Signal Processing, vol. 66, no. 2, pp. 294-308, Jan. 2018.

AMP FOR BLIND DECONVOLUTION / SELF CALIBRATION / MATRIX COMPRESSIVE SENSING

1. S. Sarkar, A. K. Fletcher, S. Rangan, and P. Schniter, ``Bilinear Recovery using Adaptive Vector-AMP [pdf] [arxiv],'' IEEE Transactions on Signal Processing, vol. 67, no. 13, pp. 3383-3396, July 2019.
2. A. K. Fletcher, P. Pandit, S. Rangan, S. Sarkar, and P. Schniter, ``Plug-in Estimation in High-Dimensional Linear Inverse Problems: A Rigorous Analysis [pdf] [arxiv],'' Proc. Neural Information Processing Systems Conference (NeurIPS) (Montreal, Canada), pp. 7440-7449, Dec. 2018. [1011/4865=21% acceptance rate]
3. P. Schniter, S. Sarkar, S. Rangan, and A. K. Fletcher, ``Bilinear Recovery using EM Vector-AMP [slides],'' presented at the Workshop on Information Theory and Applications (ITA), (San Diego, CA), Feb. 2018. (Invited.)
4. C. Schulke, P. Schniter, and L. Zdeborova, ``Phase diagram of matrix compressed sensing [pdf] [arxiv]" Physical Review E, vol. 94, no. 6, pp. 062136(1-16), Dec. 2016.
5. J. T. Parker and P. Schniter, ``Parametric Bilinear Generalized Approximate Message Passing [pdf],'' IEEE Journal of Selected Topics in Signal Processing: Special Issue on Structured Matrices in Signal and Data Processing, vol. 10, no. 4, pp. 795-808, June 2016. (Longer version on [arxiv])
6. P. Schniter and J. T. Parker, ``Structured Matrix Estimation via Approximate Message Passing [slides],'' presented at the Workshop on Information Theory and Applications (ITA), (San Diego, CA), Feb. 2016. (Invited.)
7. P. Schniter and J. T. Parker, ``Parametric Bilinear Generalized Approximate Message Passing [slides],'' presented at the Workshop on Information Theory and Applications (ITA), (San Diego, CA), Feb. 2015. (Invited.)

AMP FOR HYPERSPECTRAL UNMIXING / NONNEGATIVE MATRIX FACTORIZATION:

1. J. Vila, P. Schniter, and J. Meola, ``Hyperspectral Unmixing via Turbo Bilinear Approximate Message Passing [pdf] [arxiv],'' [Matlab] IEEE Transactions on Computational Imaging, vol. 1, no. 3, pp. 143-158, Sep. 2015. J. Vila and P. Schniter, ``An Empirical-Bayes Approach to Recovering Linearly Constrained Non-Negative Sparse Signals [pdf] [arxiv],'' IEEE Transactions on Signal Processing, vol. 62, no. 18, pp. 4689-4703, Sep. 2014. [Matlab]
2. J. Vila, P. Schniter, and J. Meola, ``Hyperspectral Image Unmixing via Bilinear Generalized Approximate Message Passing [pdf],'' Proc. SPIE Defense, Security, and Sensing, (Baltimore, MD), Apr. 2013. [slides]

AMP FOR MATRIX COMPLETION / ROBUST PCA / DICTIONARY LEARNING:

1. S. Sarkar, A. K. Fletcher, S. Rangan, and P. Schniter, ``Bilinear Recovery using Adaptive Vector-AMP [pdf] [arxiv],'' IEEE Transactions on Signal Processing, vol. 67, no. 13, pp. 3383-3396, July 2019.
2. A. K. Fletcher, P. Pandit, S. Rangan, S. Sarkar, and P. Schniter, ``Plug-in Estimation in High-Dimensional Linear Inverse Problems: A Rigorous Analysis [pdf] [arxiv],'' Proc. Neural Information Processing Systems Conference (NeurIPS) (Montreal, Canada), pp. 7440-7449, Dec. 2018. [1011/4865=21% acceptance rate]
3. P. Schniter, S. Sarkar, S. Rangan, and A. K. Fletcher, ``Bilinear Recovery using EM Vector-AMP [slides],'' presented at the Workshop on Information Theory and Applications (ITA), (San Diego, CA), Feb. 2018. (Invited.)
4. J. T. Parker and P. Schniter, ``Parametric Bilinear Generalized Approximate Message Passing [pdf],'' IEEE Journal of Selected Topics in Signal Processing: Special Issue on Structured Matrices in Signal and Data Processing, vol. 10, no. 4, pp. 795-808, June 2016. (Longer version on [arxiv])
5. P. Schniter and J. T. Parker, ``Parametric Bilinear Generalized Approximate Message Passing [slides],'' presented at the Workshop on Information Theory and Applications (ITA), (San Diego, CA), Feb. 2015. (Invited.)
6. J. T. Parker, P. Schniter, and V. Cevher, ``Bilinear Generalized Approximate Message Passing---Part I: Derivation [pdf] [arxiv].'' [Matlab] IEEE Transactions on Signal Processing, vol. 62, no. 22, pp. 5839-5853, Nov. 2014. [2016 IEEE Signal Processing Society Best Paper Award]
7. J. T. Parker, P. Schniter, and V. Cevher, ``Bilinear Generalized Approximate Message Passing---Part II: Applications [pdf] [arxiv].'' [Matlab] IEEE Transactions on Signal Processing, vol. 62, no. 22, pp. 5854-5867, Nov. 2014. [2016 IEEE Signal Processing Society Best Paper Award]
8. J. T. Parker and P. Schniter, ``Bilinear Generalized Approximate Message Passing (BiG-AMP) for Matrix Completion [slides],'' presented at the Asilomar Conf. on Signals, Systems, and Computers (Pacific Grove, CA), Nov. 2012. (Invited.) [Matlab]
9. P. Schniter, J. T. Parker, and V. Cevher, ``Bilinear Generalized Approximate Message Passing (BiG-AMP) for Matrix Recovery Problems [slides],'' presented at the Workshop on Information Theory and Applications (ITA), (San Diego, CA), Feb. 2012. (Invited.)
10. P. Schniter and V. Cevher, ``Approximate Message Passing for Bilinear Models [pdf],'' Proc. Workshop on Signal Processing with Adaptive Sparse Structured Representations (SPARS), (Edinburgh, Scotland), June 2011. [slides]

AMP FOR PHASE RETRIEVAL

1. C. A. Metzler, P. Schniter, and R. G. Baraniuk, ``An Expectation-Maximization Approach to Tuning Generalized Vector Approximate Message Passing [arxiv],'' Proc. Intl. Conf. on Latent Variable Analysis and Signal Separation (LVA/ICA), (Guildford, UK), July 2018.
2. P. Schniter and S. Rangan, ``Compressive Phase Retrieval via Bethe Free Energy Minimization,'' presented at the AMS Central Spring Sectional Meeting --- Special Session on Phase Retrieval in Theory and Practice, (East Lansing, MI), 3.14.15 (Invited.) [slides]
3. P. Schniter and S. Rangan, ``Compressive Phase Retrieval via Generalized Approximate Message Passing [pdf] [arxiv].'' IEEE Transactions on Signal Processing, vol. 63, no. 4, pp. 1043-1055, Feb. 2015.
4. P. Schniter and S. Rangan, ``A Message-Passing Approach to Phase Retrieval of Sparse Signals [pdf],'' Excursions in Harmonic Analysis, Volume 4, (Radu Balan, Matthew Begue, John J. Benedetto, Wojciech Czaja, and Kasso Okoudjou, eds.), pp. 177-204, Birkhauser, 2015.
5. P. Schniter, ``Compressive Phase Retrieval via Generalized Approximate Message Passing [slides],'' presented at the February Fourier Talks (FFT) Workshop on Phaseless Reconstruction, (College Park, MD), 23 Feb. 2013. (Invited.)
6. P. Schniter and S. Rangan, ``Compressive Phase Retrieval via Generalized Approximate Message Passing [pdf],'' Proc. Allerton Conf. on Communication, Control, and Computing, (Monticello, IL), Oct. 2012. [slides]

AMP FOR CLASSIFICATION & FEATURE SELECTION / ONE-BIT COMPRESSIVE SENSING

1. P. Sun, Z. Wang, R. W. Heath Jr., and P. Schniter, ``Joint Channel-Estimation/Decoding with Frequency-Selective Channels and Few-Bit ADCs [pdf] [arxiv],'' IEEE Transactions on Signal Processing, vol. 67, no. 4, pp. 899-914, Feb 2019.
2. P. Sun, Z. Wang, R. W. Heath Jr., and P. Schniter, ``Joint Channel-Estimation/Decoding with Frequency-Selective Channels and Few-Bit ADCs [arxiv],'' Proc. Asilomar Conf. on Signals, Systems, and Computers (Pacific Grove, CA), Nov. 2017.
3. E. Byrne and P. Schniter, ``Sparse Multinomial Logistic Regression via Approximate Message Passing [pdf] [arxiv]," IEEE Transactions on Signal Processing, vol. 64, no. 21, pp. 5485-5498, Nov. 2016.
4. J. Mo, P. Schniter, N. Gonzalez-Prelcic, and R. W. Heath Jr., ``Channel Estimation in Millimeter Wave MIMO Systems with One-Bit Quantization,'' Proc. Asilomar Conf. on Signals, Systems, and Computers (Pacific Grove, CA), Nov. 2014.
5. J. Ziniel, P. Schniter, and P. Sederberg, ``Binary Linear Classification and Feature Selection via Generalized Approximate Message Passing [pdf] [arxiv].'' IEEE Transactions on Signal Processing. vol. 63, no. 8, pp. 2020-2032, Apr. 2015.
6. J. Ziniel, P. Schniter, and P. Sederberg, ``Binary Linear Classification and Feature Selection via Generalized Approximate Message Passing [arxiv],'' Proc. Conf. on Information Sciences and Systems, (Princeton, NJ), Mar. 2014. (Invited.) [slides]
7. P. Schniter and J. Ziniel, ``Binary Linear Classification and Feature Selection via Generalized AMP [slides],'' presented at the Workshop on Information Theory and Applications (ITA), (San Diego, CA), Feb. 2013. (Invited.)

AMP FOR ADAPTIVE COMPRESSIVE SENSING

1. P. Schniter, ``Adaptive compressive noncoherent change detection: An AMP-based approach [pdf],'' Proc. Intl. Biomedical and Astronomical Signal Processing (BASP) Frontiers Workshop (Villars-sur-Ollon, Switzerland), Jan. 2013. (Invited.) [planned slides] [presented slides]
2. P. Schniter, ``Exploiting Structured Sparsity in Bayesian Experimental Design [pdf],'' Proc. Intl. Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) (San Juan, Puerto Rico), Dec. 2011. (Invited.) [slides]

AMP: THEORY AND METHODS TO HANDLE NON-IID TRANSFORMS

1. S. Rangan, P. Schniter, and A. K. Fletcher, ``Vector Approximate Message Passing [pdf] [arxiv],'' IEEE Transactions on Information Theory, vol. 65, no. 10, pp. 6664-6684, Oct 2019.
2. A. K. Fletcher, P. Pandit, S. Rangan, S. Sarkar, and P. Schniter, ``Plug-in Estimation in High-Dimensional Linear Inverse Problems: A Rigorous Analysis [pdf] [arxiv],'' Proc. Neural Information Processing Systems Conference (NeurIPS) (Montreal, Canada), pp. 7440-7449, Dec. 2018. [1011/4865=21% acceptance rate]
3. S. Rangan, P. Schniter, and A. K. Fletcher, ``Vector Approximate Message Passing [arxiv].'' Proc. IEEE Symposium on Information Theory, (Aachen, Germany), June 2017.
4. P. Schniter, S. Rangan, and A. K. Fletcher, ``Vector AMP for the Generalized Linear Model [arxiv],'' Proc. Asilomar Conf. on Signals, Systems, and Computers (Pacific Grove, CA), Nov. 2016. [slides]
5. A. K. Fletcher, M. Saharee, S. Rangan, and P. Schniter, ``Expectation Consistent Approximate Inference: Generalizations and Convergence [arxiv].'' Proc. IEEE Symposium on Information Theory, (Barcelona), July 2016.
6. S. Rangan, A. K. Fletcher, P. Schniter, and U. Kamilov, ``Inference for Generalized Linear Models via Alternating Directions and Bethe Free Energy Minimization [pdf] [arxiv]" IEEE Transactions on Information Theory, vol. 63, no. 1, pp. 676-697, Jan. 2017.
7. S. Rangan, A. K. Fletcher, P. Schniter, and U. Kamilov, ``Inference for Generalized Linear Models via Alternating Directions and Bethe Free Energy Minimization [arxiv],'' Proc. IEEE Symposium on Information Theory, (Hong Kong), June 2015.
8. J. Vila, P. Schniter, S. Rangan, F. Krzakala, and L. Zdeborova, ``Adaptive Damping and Mean Removal for the Generalized Approximate Message Passing Algorithm [arxiv],'' Proc. IEEE Conf. on Acoustics Speech and Signal Processing, (Brisbane, Australia), April 2015.
9. S. Rangan, P. Schniter, A. K. Fletcher, and S. Sarkar, ``On the Convergence of Approximate Message Passing with Arbitrary Matrices [pdf] [arxiv],'' IEEE Transactions on Information Theory, vol. 65, no. 9, pp. 5339-5351, Sep 2019.
10. S. Rangan, P. Schniter, and A. Fletcher, ``On the Convergence of Approximate Message Passing with Arbitrary Matrices [arxiv],'' Proc. IEEE Symposium on Information Theory (Honolulu, Hawaii), July 2014.
11. S. Rangan, P. Schniter, E. Riegler, A. K. Fletcher, and V. Cevher ``Fixed Points of Generalized Approximate Message Passing with Arbitrary Matrices [pdf] [arxiv]" IEEE Transactions on Information Theory, vol. 62, no. 12, pp. 7464-7474, Dec. 2016.
12. S. Rangan, P. Schniter, E. Riegler, A. Fletcher, V. Cevher ``Fixed Points of Generalized Approximate Message Passing with Arbitrary Matrices [arxiv],'' Proc. IEEE Symposium on Information Theory (Istanbul, Turkey), July 2013.
13. J. T. Parker, V. Cevher, and P. Schniter, ``Compressive Sensing under Matrix Uncertainties: An Approximate Message Passing Approach [pdf],'' Proc. Asilomar Conf. on Signals, Systems, and Computers (Pacific Grove, CA), Nov. 2011. [slides]

AMP: METHODS FOR NON-IID SIGNAL PRIORS

1. A. K. Fletcher, P. Pandit, S. Rangan, S. Sarkar, and P. Schniter, ``Plug-in Estimation in High-Dimensional Linear Inverse Problems: A Rigorous Analysis [pdf] [arxiv],'' Proc. Neural Information Processing Systems Conference (NeurIPS) (Montreal, Canada), pp. 7440-7449, Dec. 2018. [1011/4865=21% acceptance rate]
2. S. Rangan, A. K. Fletcher, V. K. Goyal, E. Byrne, and P. Schniter, ``Hybrid Generalized Approximate Message Passing [arxiv],'' IEEE Transactions on Signal Processing, vol. 65, no. 17, pp. 4577-4592, Sep. 2017.
3. J. Ziniel and P. Schniter, ``Dynamic Compressive Sensing of Time-Varying Signals via Approximate Message Passing [pdf][arxiv].'' IEEE Transactions on Signal Processing, vol. 61, no. 21, pp. 5270-5284, Nov. 2013. [Matlab]
4. J. Ziniel and P. Schniter, ``Efficient High-Dimensional Inference in the Multiple Measurement Vector Problem [pdf] [arxiv],'' IEEE Transactions on Signal Processing, vol. 61, no. 2, pp. 340-354, Jan. 2013. [Matlab]
5. J. Ziniel, S. Rangan, and P. Schniter, ``A Generalized Framework for Learning and Recovery of Structured Sparse Signals [pdf],'' Proc. Statistical Signal Processing Workshop, (Ann Arbor, MI), Aug. 2012. [Matlab]
6. S. Rangan, A. K. Fletcher, V. K. Goyal, and P. Schniter, ``Hybrid Generalized Approximate Message Passing with Applications to Structured Sparsity [pdf][arxiv],'' Proc. IEEE Symposium on Information Theory, (Cambridge, MA), July 2012.
7. J. Ziniel and P. Schniter, ``Efficient Message Passing-Based Inference in the Multiple Measurement Vector Problem [pdf],'' Proc. Asilomar Conf. on Signals, Systems, and Computers (Pacific Grove, CA), Nov. 2011. [slides] [Matlab]
8. J. Ziniel, L. C. Potter, and P. Schniter, ``Tracking and Smoothing of Time-Varying Sparse Signals via Approximate Belief Propagation [pdf],'' Proc. Asilomar Conf. on Signals, Systems, and Computers (Pacific Grove, CA), Nov. 2010. [slides] [Matlab]
9. S. Som, L. C. Potter, and P. Schniter, ``On Approximate Message Passing for Reconstruction of Non-Uniformly Sparse Signals [pdf],'' Proc. National Aerospace and Electronics Conf., (Dayton, OH), July 2010.
10. P. Schniter, ``Turbo Reconstruction of Structured Sparse Signals [pdf with typos corrected],'' Proc. Conf. on Information Sciences and Systems, (Princeton, NJ), Mar. 2010. [slides]

AMP: EXPECTATION-MAXIMIZATION METHODS FOR UNKNOWN PRIORS

1. C. A. Metzler, P. Schniter, and R. G. Baraniuk, ``An Expectation-Maximization Approach to Tuning Generalized Vector Approximate Message Passing [arxiv],'' Proc. Intl. Conf. on Latent Variable Analysis and Signal Separation (LVA/ICA), (Guildford, UK), July 2018.
2. A. K. Fletcher, M. Sahraee-Ardakan, S. Rangan, and P. Schniter, ``Rigorous Dynamics and Consistent Estimation in Arbitrarily Conditioned Linear Systems [pdf] [arxiv],'' Proc. Neural Information Processing Systems Conference (NeurIPS) (Long Beach, CA), Dec. 2017. [679/3240=21% acceptance rate]
3. A. K. Fletcher and P. Schniter, ``Learning and Free Energies for Vector Approximate Message Passing [pdf] [arxiv],'' Proc. IEEE Conf. on Acoustics Speech and Signal Processing (New Orleans, LA), Mar. 2017.
4. J. Vila and P. Schniter, ``An Empirical-Bayes Approach to Recovering Linearly Constrained Non-Negative Sparse Signals [pdf] [arxiv],'' IEEE Transactions on Signal Processing, vol. 62, no. 18, pp. 4689-4703, Sep. 2014. [Matlab]
5. J. Vila and P. Schniter, ``An Empirical-Bayes Approach to Recovering Linearly Constrained Non-Negative Sparse Signals [pdf],'' Proc. IEEE Intl. Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) (Saint Martin Island), Dec. 2013. [Matlab]
6. J. P. Vila and P. Schniter, ``Expectation-Maximization Gaussian-Mixture Approximate Message Passing [pdf][arxiv],'' IEEE Transactions on Signal Processing, vol. 61, no. 19, pp. 4658-4672, Oct. 2013. [Matlab]
7. J. P. Vila and P. Schniter, ``Expectation-Maximization Gaussian-Mixture Approximate Message Passing [pdf],'' Proc. Conf. on Information Sciences and Systems, (Princeton, NJ), Mar. 2012. (Invited.) [slides] [Matlab]
8. J. P. Vila and P. Schniter, ``Expectation-Maximization Bernoulli-Gaussian Approximate Message Passing [pdf],'' Proc. Asilomar Conf. on Signals, Systems, and Computers (Pacific Grove, CA), Nov. 2011. [slides] [Matlab]
9. J. P. Vila and P. Schniter, ``An Empirical-Bayes Approach to Compressive Sensing via Approximate Message Passing [pdf],'' presented at the Duke Workshop on Sensing and Analysis of High-Dimensional Data (SAHD) (Durham, North Carolina), July 2011. [poster]

AMP FOR COMMUNICATIONS

1. P. Sun, Z. Wang, R. W. Heath Jr., and P. Schniter, ``Joint Channel-Estimation/Decoding with Frequency-Selective Channels and Few-Bit ADCs [pdf] [arxiv],'' IEEE Transactions on Signal Processing, vol. 67, no. 4, pp. 899-914, Feb 2019.
2. P. Sun, Z. Wang, and P. Schniter, ``Joint Channel-Estimation and Equalization of Single-Carrier Systems via Bilinear AMP [pdf],'' IEEE Transactions on Signal Processing, vol. 66, no. 10, pp. 2772-2785, May 2018.
3. P. Sun, R. W. Heath Jr., and P. Schniter, ``Joint Channel-Estimation/Decoding with Frequency-Selective Channels and Few-Bit ADCs [arxiv],'' Proc. Asilomar Conf. on Signals, Systems, and Computers (Pacific Grove, CA), Nov. 2017.
4. J. Mo, P. Schniter, and R. W. Heath Jr., ``Channel Estimation in Broadband Millimeter Wave MIMO Systems with Few-Bit ADCs [pdf] [arxiv],'' IEEE Transactions on Signal Processing, vol. 66, no. 5, pp. 1141-1154, Mar. 2018.
5. J. Mo, P. Schniter, N. Gonzalez-Prelcic, and R. W. Heath Jr., ``Channel Estimation in Millimeter Wave MIMO Systems with One-Bit Quantization [pdf],'' Proc. Asilomar Conf. on Signals, Systems, and Computers (Pacific Grove, CA), Nov. 2014.
6. P. Schniter, ``Communications over Sparse Channels: Fundamental Limits and Practical Design [pdf],'' presented at the Internation Zurich Seminar on Communications, (Zurich, Switzerland), Feb. 2014. (Invited.) [slides]
7. P. Schniter, ``Approximate Message Passing: Applications to Communications Receivers [slides],'' Trellisware (San Diego, CA), Feb. 2014. (Invited.)
8. M. Nassar, P. Schniter, and B. Evans, ``A Factor-Graph Approach to Joint OFDM Channel Estimation and Decoding in Impulsive Noise Environments [pdf] [arxiv],'' IEEE Transactions on Signal Processing, vol. 62, no. 6, pp. 1576-1589, Mar. 2014.
9. M. Nassar, P. Schniter, and B. Evans, ``A Factor-Graph Approach to Joint OFDM Channel Estimation and Decoding in Impulsive Noise Channels [pdf][arxiv],'' Proc. Asilomar Conf. on Signals, Systems, and Computers (Pacific Grove, CA), Nov. 2013. [slides]
10. P. Schniter, ``A Message-Passing Receiver for BICM-OFDM over Unknown Clustered-Sparse Channels [pdf][arxiv],'' IEEE Journal of Selected Topics in Signal Processing: Special Issue on Soft Detection for Wireless Transmission, vol. 5, no. 8, pp. 1462-1474, Dec. 2011. [Matlab]
11. P. Schniter, ``Belief-propagation-based joint channel estimation and decoding for spectrally efficient communication over unknown sparse channels [pdf][arxiv].'' Physical Communication: Special Issue in Compressive Sensing in Communications (Elsevier), vol. 5, no. 3, pp. 91-101, Mar. 2012.
12. P. Schniter and D. Meng, ``A Message-Passing Receiver for BICM-OFDM over Unknown Time-Varying Sparse Channels [slides],'' presented at the Allerton Conf. on Communication, Control, and Computing, (Monticello, IL), Sep. 2011. (Invited.)
13. P. Schniter, ``A Message-Passing Receiver for BICM-OFDM over Unknown Clustered-Sparse Channels [pdf],'' in Proc. IEEE Workshop on Signal Processing Advances in Wireless Communications, (San Francisco, CA), June 2011. [slides] [Matlab]
14. P. Schniter, ``Achieving the Noncoherent Sparse-Channel's Capacity Prelog Factor: A Graphical-Model Approach [slides],'' Proc. Workshop on Information Theory and Applications (ITA), (La Jolla, CA), Feb. 2011. (Invited.)
15. P. Schniter, ``Joint Estimation and Decoding for Sparse Channels via Relaxed Belief Propagation [pdf],'' Proc. Asilomar Conf. on Signals, Systems, and Computers (Pacific Grove, CA), Nov. 2010. (Invited.) [slides]

MISCELLANEOUS AMP

1. P. Schniter, ``A Simple Derivation of AMP and its State Evolution via First-Order Cancellation [pdf] [arxiv],'' IEEE Transactions on Signal Processing, vol. 68, pp. 4283-4292, 2020.

MULTI-INPUT MULTI-OUTPUT (MIMO) COMMUNICATION:

1. K.-H. Ngo, M. Guillaud, A. Decurninge, S. Yang, and P. Schniter, ``Multi-User Detection Based on Expectation Propagation for the Non-Coherent SIMO Multiple Access Channel [pdf] [arxiv],'' IEEE Transactions on Wireless Communications, vol. 19, no. 9, pp. 6145-6161, 2020.
2. P. Schniter and E. Byrne, ``Adaptive Detection of Structured Signals in Low-Rank Interference [pdf] [arxiv],'' IEEE Transactions on Signal Processing, vol. 67, no. 13, pp. 3439-3454, July 2019.
3. J. Mo, P. Schniter, and R. W. Heath Jr., ``Channel Estimation in Broadband Millimeter Wave MIMO Systems with Few-Bit ADCs [pdf] [arxiv],'' IEEE Transactions on Signal Processing, vol. 66, no. 5, pp. 1141-1154, Mar. 2018.
4. P. Schniter and A. Sayeed, ``Channel Estimation and Precoder Design for Millimeter-Wave Communications: The Sparse Way [pdf],'' Proc. Asilomar Conf. on Signals, Systems, and Computers (Pacific Grove, CA), Nov. 2014. [slides]
5. J. Mo, P. Schniter, N. Gonzalez-Prelcic, and R. W. Heath Jr., ``Channel Estimation in Millimeter Wave MIMO Systems with One-Bit Quantization [pdf],'' Proc. Asilomar Conf. on Signals, Systems, and Computers (Pacific Grove, CA), Nov. 2014.
6. B. P. Day, A. R. Margetts, D. W. Bliss, and P. Schniter, ``Full-Duplex MIMO Relaying: Achievable Rates under Limited Dynamic Range [arxiv],'' IEEE Journal on Selected Areas in Communications, Special Issue on Theories and Methods for Advanced Wireless Relays, vol. 30, no. 8, pp. 1541-1553, Aug. 2012.
7. B. P. Day, A. R. Margetts, D. W. Bliss, and P. Schniter, ``Full-Duplex Bidirectional MIMO: Achievable Rates under Limited Dynamic Range [pdf],'' IEEE Transactions on Signal Processing, vol. 60, no. 7, pp. 3702-3713, July 2012.
8. S. Murugesan, E. Uysal-Biyikoglu and P. Schniter, ``Optimization of Training and Scheduling in the Non-Coherent MIMO Multiple-Access Channel,'' IEEE Journal on Selected Areas in Communication, vol. 25, no. 7, pp. 1446-1456, Sep. 2007.
9. S. Das and P. Schniter, ``A Beamforming and Combining Strategy for MIMO-OFDM over Doubly Selective Channels [pdf],'' Proc. Asilomar Conf. on Signals, Systems, and Computers, (Pacific Grove, CA), Nov. 2006. [slides]
10. S. Murugesan, E. Uysal and P. Schniter, ``Scaling-law Optimal Training and Scheduling in the MIMO Uplink [pdf],'' Proc. Allerton Conf. on Communication, Control, and Computing, (Monticello, IL), Oct. 2006.
11. A. P. Kannu and P. Schniter, ``Minimum Mean-Squared Error Pilot-Aided Transmission for MIMO Doubly Selective Channels [pdf],'' in Proc. Conf. on Information Sciences and Systems, (Princeton, NJ), Mar. 2006.
12. S.-J. Hwang and P. Schniter, ``On the Optimality of MMSE-GDFE Pre-Processed Sphere Decoding [pdf],'' Proc. Allerton Conf. on Communication, Control, and Computing, (Monticello, IL), Oct. 2005.
13. B. Bhukania and P. Schniter, ``On the Robustness of Decision-feedback Detection of DPSK and Differential Unitary Space-Time Modulation in Rayleigh-Fading Channels [pdf],'' IEEE Trans. on Wireless Communications, vol. 3, no. 5, pp. 1481-1489, Sep. 2004.
14. P. Schniter and A. M. Sayeed, ``A Sparseness-Preserving Virtual MIMO Channel Model [pdf],'' Proc. Conf. on Information Sciences and Systems, (Princeton, NJ), pp. 36-41, Mar. 2004.
15. B. Bhukania and P. Schniter, ``On the Robustness of Decision-Feedback Detection of DPSK and Differential Unitary Space-Time Modulation in Rayleigh-Fading Channels [pdf],'' Proc. IEEE Wireless Communications and Networking Conf. (New Orleans, LA), pp. 218-222, Mar. 2003.
16. B. Bhukania and P. Schniter, ``Decision-Feedback Detection of Differential Unitary Space-Time Modulation in Fast Rayleigh-Fading Channels [pdf],'' Proc. Allerton Conf. on Communication, Control, and Computing, (Monticello, IL), Oct. 2002.
17. B. Bhukania and P. Schniter, ``Multiple-Symbol Differential Detection of Differential Unitary Space-Time Modulation in Fast-Fading Channels with Known Correlation [pdf],'' Proc. Conf. on Information Sciences and Systems, (Princeton, NJ), Mar. 2002.

FULL-DUPLEX WIRELESS COMMUNICATION:

1. A. Sabharwal, P. Schniter, D. Guo, D. W. Bliss, S. Rangarajan, and R. Wichman, ``In-band Full-duplex Wireless: Challenges and Opportunities [pdf] [arxiv],'' IEEE Journal on Selected Areas in Communications, Special Issue on In-Band Full-Duplex Wireless Communications and Networks, vol. 31, no. 9, pp. 1637-1652, Sep. 2014.
2. D. W. Bliss, T. Hancock, and P. Schniter, ``Hardware and Environmental Phenomenological Limits on Full-Duplex MIMO Relay Performance [pdf],'' Proc. Asilomar Conf. on Signals, Systems, and Computers (Pacific Grove, CA), Nov. 2012.
3. B. P. Day, A. R. Margetts, D. W. Bliss, and P. Schniter, ``Full-Duplex MIMO Relaying: Achievable Rates under Limited Dynamic Range [pdf] [arxiv],'' IEEE Journal on Selected Areas in Communications, Special Issue on Theories and Methods for Advanced Wireless Relays, vol. 30, no. 8, pp. 1541-1553, Sep. 2012.
4. B. P. Day, A. R. Margetts, D. W. Bliss, and P. Schniter, ``Full-Duplex MIMO Relaying: Achievable Rates under Limited Dynamic Range [pdf],'' Proc. Asilomar Conf. on Signals, Systems, and Computers (Pacific Grove, CA), Nov. 2012. [slides]
5. B. P. Day, A. R. Margetts, D. W. Bliss, and P. Schniter, ``Full-Duplex Bidirectional MIMO: Achievable Rates under Limited Dynamic Range [pdf],'' IEEE Transactions on Signal Processing, vol. 60, no. 7, pp. 3702-3713, July 2012.
6. B. P. Day, D. W. Bliss, A. R. Margetts, and P. Schniter, ``Full-Duplex Bidirectional MIMO: Achievable Rates under Limited Dynamic Range [pdf],'' Proc. Asilomar Conf. on Signals, Systems, and Computers (Pacific Grove, CA), Nov. 2011. [poster]

MULTI-CELL WIRELESS SYSTEMS:

1. R. Aggarwal, C. E. Koksal, and P. Schniter, ``On the Design of Large Scale Wireless Systems [pdf] [arxiv].'' IEEE Journal on Selected Areas in Communications, Special Issue on Large-Scale Multiple Antenna Wireless Systems, vol. 31, no. 2, pp. 215-225, Feb. 2013.
2. R. Aggarwal, C. E. Koksal, and P. Schniter, ``Scaling Laws and Design Principles for Multi-Cellular Wireless OFDMA Systems [arxiv],'' Proc. IEEE International Conference on Computer Communications (INFOCOM), (Orlando, FL), Mar. 2012.

ADAPTIVE COMMUNICATION UNDER LIMITED FEEDBACK:

1. R. Aggarwal, C. E. Koksal, and P. Schniter, ``Joint Scheduling and Resource Allocation in OFDMA Downlink Systems via ACK/NAK Feedback [pdf][arxiv],'' IEEE Transactions on Signal Processing, vol. 60, no. 6, pp. 3217-3227, June 2012.
2. R. Aggarwal, M. Assaad, C. E. Koksal, and P. Schniter, ``Optimal Resource Allocation in OFDMA Downlink Systems With Imperfect CSI [pdf],'' in Proc. IEEE Workshop on Signal Processing Advances in Wireless Communications, (San Francisco, CA), June 2011. [slides]
3. R. Aggarwal, M. Assaad, C. E. Koksal, and P. Schniter, ``Optimal Joint Scheduling and Resource Allocation in OFDMA Downlink Systems with Imperfect Channel-State Information [pdf][arxiv],'' IEEE Transactions on Signal Processing, vol. 59, pp. 5589-5604, Nov. 2011.
4. C. E. Koksal and P. Schniter, ``Robust Rate-Adaptive Wireless Communication Using ACK/NAK-Feedback[pdf][arxiv],'' IEEE Transactions on Signal Processing, vol. 60, no. 4, pp. 1752-1765, Apr. 2012.
5. R. Aggarwal, M. Assaad, C. E. Koksal, and P. Schniter, ``OFDMA Downlink Resource Allocation via ARQ Feedback [pdf],'' in Proc. Asilomar Conf. on Signals, Systems, and Computers (Pacific Grove, CA), Nov. 2009. [slides]
6. R. Aggarwal, P. Schniter, and C. E. Koksal, ``Rate Adaptation via Link-Layer Feedback for Goodput Maximization over a Time-Varying Channel [pdf],'' IEEE Transactions on Wireless Communications, vol. 8, no. 8, pp. 4276-4285, Aug. 2009.
7. R. Aggarwal, P. Schniter, and C. E. Koksal, ``Rate Adaptation via ARQ Feedback for Goodput Maximization over Time-Varying Channels [pdf],'' Proc. Conf. on Information Sciences and Systems, (Princeton, NJ), Mar. 2008.

MULTI-USER SCHEDULING:

1. S. Murugesan, P. Schniter, and N. Shroff, ``Multiuser Scheduling in a Markov-modeled Downlink using Randomly Delayed ARQ Feedback [pdf][arxiv],'' IEEE Transactions on Information Theory, vol. 58, no. 2, pp. 1025-1042, Feb. 2012.
2. S. Murugesan, P. Schniter, and N. B. Shroff, ``Opportunistic Scheduling using ARQ feedback in Multi-Cell Downlink [pdf],'' Proc. Asilomar Conf. on Signals, Systems, and Computers, (Pacific Grove, CA), Nov. 2010.
3. S. Murugesan, P. Schniter, and N. B. Shroff, ``Throughput/Energy Aware Opportunistic Transmission Control in Broadcast Networks [pdf],'' Proc. Allerton Conf. on Communication, Control, and Computing, (Monticello, IL), Oct. 2010. [slides]
4. S. Murugesan and P. Schniter, ``Opportunistic Multiuser Scheduling in Three State Markov-modeled Downlink [pdf],'' OSU Technical Report, 29 Aug 2008.
5. S. Murugesan, P. Schniter, and N. B. Shroff, ``Multiuser Scheduling in a Markov-modeled Downlink Environment [pdf],'' Proc. Allerton Conf. on Communication, Control, and Computing, (Monticello, IL), Sep. 2008. [proofs]
6. S. Murugesan, E. Uysal-Biyikoglu and P. Schniter, ``Asymptotically Optimal Training and Scheduling in the Non-Coherent SIMO Multiple Access Channel [slides],'' Information Theory and Applications, (San Diego, CA), Jan. 2007. (Invited.)
7. S. Murugesan, E. Uysal-Biyikoglu and P. Schniter, ``Optimization of Training and Scheduling in the Non-Coherent MIMO Multiple-Access Channel [pdf],'' IEEE Journal on Selected Areas in Communication vol. 25, no. 7, pp. 1446-1456, Sep. 2007.
8. S. Murugesan, E. Uysal-Biyikoglu and P. Schniter, ``Scaling-law Optimal Training and Scheduling in the MIMO Uplink [pdf],'' Proc. Allerton Conf. on Communication, Control, and Computing, (Monticello, IL), Oct. 2006.

COOPERATIVE COMMUNICATION:

1. K. Azarian, H. El Gamal, and P. Schniter, ``On the Optimality of the ARQ-DDF Protocol [pdf],'' IEEE Transactions on Information Theory, vol. 54, no. 4, pp. 1718-1724, Apr. 2008.
2. K. Azarian, H. El Gamal, and P. Schniter, ``On the Achievable Diversity-Multiplexing Tradeoff in Half-Duplex Cooperative Channels [pdf],'' IEEE Transactions on Information Theory, vol. 51, no. 12, pp. 4152-4172, Dec. 2005.
3. Y.-H. Nam, K. Azarian, H. El Gamal, and P. Schniter, ``Cooperation Through ARQ,'' Proc. IEEE Workshop on Signal Processing Advances in Wireless Communications, (New York, NY), June 2005.
4. K. Azarian, H. El Gamal, and P. Schniter, ``On the Achievable Diversity-Multiplexing Tradeoff in Half Duplex Cooperative Channels [pdf],'' Proc. Allerton Conf. on Communication, Control, and Computing, (Monticello, IL), Oct. 2004.
5. K. Azarian, H. El Gamal, and P. Schniter, ``On the Achievable Diversity-vs-Multiplexing Tradeoff in Cooperative Channels [pdf],'' Proc. Conf. on Information Sciences and Systems, (Princeton, NJ), pp. 956-960, Mar. 2004.
6. K. Azarian, H. El Gamal, and P. Schniter, ``On the Design of Cooperative Transmission Schemes [pdf],'' Proc. Allerton Conf. on Communication, Control, and Computing, (Monticello, IL), pp. 1576-1585, Oct. 2003.

OVERVIEW:
1. P. Schniter, S.-J. Hwang, S. Das, and A. P. Kannu, ``Equalization of Time-Varying Channels [pdf],'' Wireless Communications over Rapidly Time-Varying Channels, (F. Hlawatsch and G. Matz, eds.), Elsevier Academic Press, 2011.

NON-COHERENT COMMUNICATION / JOINT CHANNEL-SYMBOL ESTIMATION:

1. S.-J. Hwang and P. Schniter, ``Soft Noncoherent Equalization of Doubly Selective Channels using Basis Expansion and Tree Search [pdf],'' submitted to IEEE Transactions on Signal Processing.
2. S.-J. Hwang and P. Schniter, ``EM-Based Soft Noncoherent Equalization of Doubly Selective Channels using Tree Search and Basis Expansion [pdf],'' in Proc. IEEE Workshop on Signal Processing Advances in Wireless Communications, (Perugia, Italy), June 2009. [slides]
3. S.-J. Hwang and P. Schniter, ``Efficient Multicarrier Communication for Highly Spread Underwater Acoustic Channels [pdf],'' IEEE Journal on Selected Areas in Communication, vol. 26, no. 9, pp. 1674-1683, Dec. 2008.
4. S.-J. Hwang and P. Schniter, ``Fast Noncoherent Decoding of Block Transmissions Over Doubly Dispersive Channels [pdf],'' Proc. Asilomar Conf. on Signals, Systems, and Computers, (Pacific Grove, CA), Nov. 2007. [slides]
5. S. Das and P. Schniter, ``Noncoherent Communication over the Doubly Selective Channel via Successive Decoding and Channel Re-Estimation [pdf],'' Proc. Allerton Conf. on Communication, Control, and Computing, (Monticello, IL), Oct. 2007. [slides]
6. S.-J. Hwang and P. Schniter, ``Efficient Communication over Highly Spread Underwater Acoustic Channels [pdf],'' The Second ACM Intl. Workshop on Underwater Networks (WUWNet), (Montreal, Quebec), Sep. 2007. [slides]
7. S.-J. Hwang and P. Schniter, ``Maximum Diversity Affine Precoding for the Noncoherent Doubly Dispersive Channel [pdf],'' Proc. IEEE Workshop on Signal Processing Advances in Wireless Communications, (Helsinki, Finland), June 2007. [slides]
8. S.-J. Hwang and P. Schniter, ``Near-Optimal Noncoherent Sequence Detection for Doubly Dispersive Channels [pdf],'' Proc. Asilomar Conf. on Signals, Systems, and Computers, (Pacific Grove, CA), Nov. 2006. (Invited.) [slides]

PILOT DESIGN AND ANALYSIS:

1. A. P. Kannu and P. Schniter, ``On Communication over Unknown Sparse Frequency-Selective Block-Fading Channels [pdf][arxiv],'' IEEE Transactions on Information Theory, vol. 57, no. 10, pp. 6619-6632, Oct. 2011.
2. A. P. Kannu and P. Schniter, ``On the Spectral Efficiency of Noncoherent Doubly Selective Block-Fading Channels [pdf],'' IEEE Transactions on Information Theory, vol. 56, no. 6, pp. 2829-2844, June 2010.
3. A. P. Kannu and P. Schniter, ``On the Spectral Efficiency of Noncoherent Doubly Selective Channels [pdf],'' Proc. Allerton Conf. on Communication, Control, and Computing, (Monticello, IL), Oct. 2006.
4. A. P. Kannu and P. Schniter, ``Minimum Mean-Squared Error Pilot-Aided Transmission for MIMO Doubly Selective Channels [pdf],'' in Proc. Conf. on Information Sciences and Systems, (Princeton, NJ), Mar. 2006.
5. A. P. Kannu and P. Schniter, ``Design and Analysis of MMSE Pilot-Aided Cyclic-Prefixed Block Transmission for Doubly Selective Channels [pdf],'' IEEE Transactions on Signal Processing, vol. 56, no. 3, pp. 1148-1160, Mar. 2008.
6. A. P. Kannu and P. Schniter, ``Capacity Analysis of MMSE Pilot Patterns for Doubly Selective Channels [pdf],'' Proc. IEEE Workshop on Signal Processing Advances in Wireless Communications, (New York, NY), June 2005. [slides]
7. A. P. Kannu and P. Schniter, ``MSE-Optimal Training for Linear Time-Varying Channels [pdf],'' Proc. IEEE Conf. on Acoustics Speech and Signal Processing (Philadelphia, PA), vol. III, pp. 789-792, Mar. 2005. [slides]

CHANNEL ESTIMATION / TRACKING:

1. P. Schniter, ``On Doubly Dispersive Channel Estimation for Pilot-Aided Pulse-Shaped Multicarrier Modulation [pdf],'' Proc. Conf. on Information Sciences and Systems, (Princeton, NJ), Mar. 2006.
2. A. P. Kannu and P. Schniter, ``Reduced-Complexity Decision-Directed Pilot-Aided Tracking of Doubly Selective Channels [pdf],'' Proc. Conf. on Information Sciences and Systems, (Princeton, NJ), pp. 915-920, Mar. 2004. [slides]
3. P. Schniter, ``Low-Complexity Estimation of Doubly Selective Channels [pdf],'' Proc. IEEE Workshop on Signal Processing Advances in Wireless Communications, (Rome, Italy), pp. 200-204, June 2003. [slides]

MULTI-CARRIER COHERENT EQUALIZATION / DECODING:

1. S. Das and P. Schniter, ``A Beamforming and Combining Strategy for MIMO-OFDM over Doubly Selective Channels [pdf],'' Proc. Asilomar Conf. on Signals, Systems, and Computers, (Pacific Grove, CA), Nov. 2006. [slides]
2. S.-J. Hwang and P. Schniter, ``Efficient Sequence Detection of Multi-Carrier Transmissions over Doubly Dispersive Channels [pdf],'' EURASIP Journal on Applied Signal Processing special issue on Reliable Communications over Rapidly Time-Varying Channels, article ID 93638, 17 pages, 2006.
3. S.-J. Hwang and P. Schniter, ``Efficient Sequence Detection of Multi-Carrier Transmissions Over Doubly Dispersive Channels [pdf],'' Proc. IEEE Workshop on Signal Processing Advances in Wireless Communications, (Cannes, France), July 2006. [slides]
4. S. Das and P. Schniter ``Max-SINR ISI/ICI-Shaped Multi-Carrier Communication over the Doubly Dispersive Channel [pdf],'' IEEE Transactions on Signal Processing, vol. 55, no. 12, pp. 5782-5795, Dec. 2007.
5. S. Das and P. Schniter, ``A New Pulse Shaped Frequency Division Multiplexing Technique for Doubly Dispersive Channels [pdf],'' Proc. Asilomar Conf. on Signals, Systems, and Computers, (Pacific Grove, CA), pp. 657-661, Nov. 2004. [slides]
6. P. Schniter, ``Low-Complexity Equalization of OFDM in Doubly Selective Channels [pdf],'' IEEE Transactions on Signal Processing, vol. 52, no. 4, pp. 1002-1011, Apr. 2004.
7. P. Schniter, ``A Low-Complexity Receiver for OFDM in Doubly Selective Channels [pdf] Proc. IEEE Global Communications Conf., (San Francisco, CA), vol. 4, pp. 2285-2289, Dec. 2003. [slides]
8. P. Schniter and S. D'Silva, ``Low-Complexity Detection of OFDM in Doubly Dispersive Channels [pdf] Proc. Asilomar Conf. on Signals, Systems, and Computers, (Pacific Grove, CA), pp. 1799-1803, Nov. 2002.

MULTI-CARRIER WAVEFORM DESIGN:

1. S. Das and P. Schniter, ``A Beamforming and Combining Strategy for MIMO-OFDM over Doubly Selective Channels [pdf],'' Proc. Asilomar Conf. on Signals, Systems, and Computers, (Pacific Grove, CA), Nov. 2006. [slides]
2. S. Das and P. Schniter, ``Design of Multi-Carrier Modulation for Doubly Selective Channels Based on a Complexity-Constrained Achievable Rate Metric [pdf],'' Proc. Asilomar Conf. on Signals, Systems, and Computers, (Pacific Grove, CA), Nov. 2006. [slides]
3. S. Das and P. Schniter, ``Max-SINR ISI/ICI-Shaped Multi-Carrier Communication over the Doubly Dispersive Channel [pdf],'' IEEE Transactions on Signal Processing, vol. 55, no. 12, pp. 5782-5795, Dec. 2007.
4. S. Das and P. Schniter, ``A New Pulse Shaped Frequency Division Multiplexing Technique for Doubly Dispersive Channels [pdf],'' Proc. Asilomar Conf. on Signals, Systems, and Computers, (Pacific Grove, CA), pp. 657-661, Nov. 2004. [slides]
5. P. Schniter, ``On the Design of Non-(Bi)Orthogonal Pulse-Shaped FDM for Doubly Dispersive Channels [pdf],'' IEEE Conf. on Acoustics Speech and Signal Processing (Montreal, Quebec), vol. 3, pp. 817-820, May 2004. [slides]
6. P. Schniter, ``A New Approach to Multicarrier Pulse Design for Doubly Dispersive Channels [pdf],'' Proc. Allerton Conf. on Communication, Control, and Computing, (Monticello, IL), pp. 1012-1021, Oct. 2003. [slides]

SINGLE-CARRIER COHERENT EQUALIZATION / DECODING:

1. P. Schniter and H. Liu, ``Iterative Frequency-Domain Equalization for Single-Carrier Systems in Doubly Dispersive Channels [pdf],'' Proc. Asilomar Conf. on Signals, Systems, and Computers, (Pacific Grove, CA), pp. 667-671, Nov. 2004. [slides]
2. P. Schniter and H. Liu, ``Iterative Equalization for Single-Carrier Cyclic-Prefix in Doubly Dispersive Channels [pdf],'' Proc. Asilomar Conf. on Signals, Systems, and Computers, (Pacific Grove, CA), vol. 1, pp. 502-506, Nov. 2003. [slides]

ULTRA-WIDEBAND (UWB) COMMUNICATION:

1. P. Schniter, ``A Message-Passing Receiver for BICM-OFDM over Unknown Clustered-Sparse Channels [pdf],'' in Proc. IEEE Workshop on Signal Processing Advances in Wireless Communications, (San Francisco, CA), June 2011. [slides] [Matlab]
2. P. Schniter, ``A Message-Passing Receiver for BICM-OFDM over Unknown Clustered-Sparse Channels [pdf][arxiv],'' IEEE Journal of Selected Topics in Signal Processing: Special Issue on Soft Detection for Wireless Transmission, vol. 5, no. 8, pp. 1462-1474, Dec. 2011. [Matlab]
3. A. Margetts and P. Schniter and A. Swami, ``Joint Scale-Lag Diversity in Wideband Mobile Direct Sequence Spread Spectrum Systems [pdf],'' IEEE Transactions on Wireless Communications, vol. 6, no. 12, pp. 4308-4319, Dec. 2007.
4. A. R. Margetts and P. Schniter, ``On the Channel Correlation Structure of Wideband Scale-Lag RAKE Fingers [pdf],'' Proc. IEEE Workshop on Signal Processing Advances in Wireless Communications, (New York, NY), June 2005.
5. A. R. Margetts, P. Schniter, and A. Swami, ``Scale-Lag Diversity Reception in Mobile Wideband Channels [pdf],'' Proc. IEEE Conf. on Acoustics Speech and Signal Processing (Philadelphia, PA), vol. III, pp. 321-324, Mar. 2005.
6. A. R. Margetts and P. Schniter, ``Joint Scale-Lag Diversity in Mobile Ultra-Wideband Systems [pdf],'' Proc. Asilomar Conf. on Signals, Systems, and Computers, (Pacific Grove, CA), pp. 1496-1500, Nov. 2004.

CODE-DIVISION MULTIPLE ACCESS (CDMA) :

1. A. Margetts and P. Schniter and A. Swami, ``Joint Scale-Lag Diversity in Wideband Mobile Direct Sequence Spread Spectrum Systems [pdf],'' IEEE Transactions on Wireless Communications, vol. 6, no. 12, pp. 4308-4319, Dec. 2007.
2. A. R. Margetts and P. Schniter, ``Adaptive Chip-Rate Equalization of Downlink Multirate Wideband CDMA [pdf],'' IEEE Transactions on Signal Processing. vol. 53, no. 6, pp. 2205-2215, June 2005.
3. A.R. Margetts and P. Schniter, ``Chip-Rate Adaptive Two-Stage Receiver for Scrambled Multirate CDMA Downlink [pdf],'' Proc. Asilomar Conf. on Signals, Systems, and Computers, (Pacific Grove, CA), vol. 1, pp. 465-469, Nov. 2003.
4. A.R. Margetts and P. Schniter, ``Adaptive Inter-chip Interference Cancellation of Multirate Scrambled Downlink CDMA [pdf],'' Proc. Allerton Conf. on Communication, Control, and Computing, (Monticello, IL), pp. 1809-1810, Oct. 2003.
5. P. Schniter and A.R. Margetts, ``Adaptive Chip-Rate Equalization of Downlink Multirate Wideband CDMA [pdf],'' Proc. Asilomar Conf. on Signals, Systems, and Computers, (Pacific Grove, CA), pp. 1228-1232, Nov. 2002.
6. W. Chen, U. Mitra, and P. Schniter, ``On the Equivalence of Three Reduced Rank Linear Estimators with Applications to DS-CDMA [pdf],'' IEEE Trans. on Information Theory, vol. 48, no. 9, pp. 2609-2614, Sep. 2002.
7. P. Schniter and C.R. Johnson, Jr., ``On the Robustness of Blind Linear Receivers for Short-Code CDMA [pdf],'' Proc. IEEE Workshop on Signal Processing Advances in Wireless Communications (Annapolis, MD), May 1999.
8. P. Schniter and C.R. Johnson, Jr., ``Minimum-Entropy Blind Acquisition/Equalization for Uplink DS-CDMA [pdf],'' Proc. Allerton Conf. on Communication, Control, and Computing, (Monticello, IL), Oct. 1998.
9. P. Schniter, `` Geometrical Interpretations of the Maximum Output Energy Receiver [pdf],'' Technical Report, Sep. 1998.
10. P. Schniter, `` Linear and Decision Feedback Equalization Structures for Asynchronous DS-CDMA under ICI [pdf],'' Technical Report, Apr. 1998.

BLIND EQUALIZATION / CHANNEL IDENTIFICATION:

1. M. Joho and P. Schniter, ``Frequency Domain Realization of a Multichannel Blind Deconvolution Algorithm Based on the Natural Gradient [pdf],'' Proc. Independent Component Analysis and Blind Signal Separation Conf., (Nara, Japan), pp. 543-548, Apr. 2003.
2. M. Joho and P. Schniter, ``On Frequency-Domain Implementations of Filtered-Gradient Blind Deconvolution Algorithms [pdf],'' Proc. Asilomar Conf. on Signals, Systems, and Computers, (Pacific Grove, CA), pp. 1653-1658, Nov. 2002.
3. P. Schniter and L. Tong, ``Existence and Performance of Shalvi-Weinstein Estimators [pdf],'' IEEE Trans. on Signal Processing, vol. 49, no. 9, pp. 2031-2041, Sep. 2001. [proof details]
4. P. Schniter, R.A. Casas, A. Touzni, and C.R. Johnson, Jr., ``Performance Analysis of Godard-Based Channel Identification [pdf],'' IEEE Trans. on Signal Processing, vol. 49, no. 8, pp. 1757-1767, Aug. 2001. [proof details]
5. C.R. Johnson, Jr., P. Schniter, I. Fijalkow, L. Tong, J.D. Behm, M.G. Larimore, D.R. Brown, R.A. Casas, T.J. Endres, S. Lambotharan, H.H. Zeng, A. Touzni, M. Green, and J.R. Treichler, ``The Core of FSE-CMA Behavior Theory [pdf],'' Unsupervised Adaptive Filtering, Vol.2: Blind Deconvolution, (S. Haykin, ed.), pp. 13-112, Wiley, 2000.
6. P. Schniter and C.R. Johnson, Jr., ``Bounds for the MSE Performance of Constant Modulus Estimators [pdf],'' IEEE Trans. on Information Theory, vol. 46, no. 7, pp. 2544-2560, Nov. 2000.
7. P. Schniter and C.R. Johnson, Jr., ``Sufficient Conditions for the Local Convergence of Constant Modulus Algorithms [pdf],'' IEEE Trans. on Signal Processing, vol. 48, no. 10, pp. 2785-2796, Oct. 2000.
8. P. Schniter, ``Existence and Performance of Shalvi-Weinstein Estimators [pdf],'' Proc. Allerton Conf. on Communication, Control, and Computing, (Monticello, IL), Oct. 2000. (Invited.)
9. P. Schniter, ``Performance Analysis of Godard-Based Channel Identification [pdf],'' Proc. IEEE Adaptive Systems for Signal Processing, Communication and Control Symposium (Lake Louise, Alberta), pp. 390-395, Oct. 2000.
10. P. Schniter, Blind Estimation without Priors: Performance, Convergence, and Efficient Implementation [pdf], PhD Dissertation, Cornell University, May 2000.
11. P. Schniter and C.R. Johnson, Jr., ``Bounds for the MSE Performance of Constant Modulus Estimators [pdf],'' Proc. Conf. on Information Sciences and Systems (Princeton, NJ), Mar. 2000.
12. P. Schniter and C.R. Johnson, Jr., ``SINR-based Sufficient Conditions for CMA Desired-User-Lock [pdf],'' Proc. IEEE Wireless Communications and Networking Conf. (New Orleans, LA), Sep. 1999.
13. P. Schniter and C.R. Johnson, Jr., ``The Dithered Signed-Error Constant Modulus Algorithm [pdf],'' IEEE Trans. on Signal Processing, vol. 47, no. 6, pp. 1592-1603, June 1999.
14. P. Schniter and C.R. Johnson, Jr., ``On the Robustness of Blind Linear Receivers for Short-Code CDMA [pdf],'' Proc. IEEE Workshop on Signal Processing Advances in Wireless Communications (Annapolis, MD), May 1999.
15. P. Schniter and C.R. Johnson, Jr., ``DSE-CMA: The Complex-Valued Case [pdf],'' Proc. Asilomar Conf. on Signals, Systems, and Computers, (Pacific Grove, CA), Nov. 1998.
16. C.R. Johnson, Jr., P. Schniter, T.J. Endres, J. Behm, D.R. Brown, and R.A. Casas, ``Blind Equalization Using the Constant Modulus Criterion: A Review [pdf],'' Proceedings of the IEEE: Special Issue on Blind System Identification and Estimation, vol. 86, no. 10, pp. 1927-50, Oct. 1998.
17. P. Schniter and C.R. Johnson, Jr., ``Minimum-Entropy Blind Acquisition/Equalization for Uplink DS-CDMA [pdf],'' Proc. Allerton Conf. on Communication, Control, and Computing, (Monticello, IL), Oct. 1998.
18. P. Schniter and C.R. Johnson, Jr., ``The Dithered Signed-Error Constant Modulus Algorithm [pdf],'' Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, (Seattle, WA), May 1998.
19. P. Schniter, `` The Effect of Timing Offset on Fractionally-Spaced CMA [pdf],'' Technical Report, Mar. 1998.
20. D.R. Brown, P.B. Schniter, and C.R. Johnson, Jr., ``Computationally Efficient Blind Equalization [pdf],'' Proc. Allerton Conf. on Communication, Control, and Computing, (Monticello, IL), Oct. 1997.
21. P. Schniter, `` CMA Cost and Gradient Expressions: Three Important Cases [pdf],'' Technical Report, June 1997.
22. P. Schniter, `` CMA-FSE Error Surface Examples [pdf],'' Technical Report, July 1996.
    

ADAPTIVE FILTERING:

1. B. D. Rigling and P. Schniter, ``Subspace Leaky LMS [pdf],'' IEEE Signal Processing Letters, vol. 11, no. 2, pp. 136-139, Jan. 2004.
2. M. Joho and P. Schniter, ``Frequency Domain Realization of a Multichannel Blind Deconvolution Algorithm Based on the Natural Gradient [pdf],'' Proc. Independent Component Analysis and Blind Signal Separation Conf., (Nara, Japan), pp. 543-548, Apr. 2003.
3. M. Joho and P. Schniter, ``On Frequency-Domain Implementations of Filtered-Gradient Blind Deconvolution Algorithms [pdf],'' Proc. Asilomar Conf. on Signals, Systems, and Computers, (Pacific Grove, CA), pp. 1653-1658, Nov. 2002.
4. P. Schniter, `` A Derivation of the Steady-State MSE of RLS: Stationary and Non-Stationary Cases [pdf],'' Technical Report, Nov. 2001.
5. P. Schniter and C.R. Johnson, Jr., ``The Dithered Signed-Error Constant Modulus Algorithm [pdf],'' IEEE Trans. on Signal Processing, vol. 47, no. 6, pp. 1592-1603, June 1999.
6. P. Schniter and C.R. Johnson, Jr., ``DSE-CMA: The Complex-Valued Case [pdf],'' Proc. Asilomar Conf. on Signals, Systems, and Computers, (Pacific Grove, CA), Nov. 1998.
7. D.R. Brown, P.B. Schniter, and C.R. Johnson, Jr., ``Computationally Efficient Blind Equalization [pdf],'' Proc. Allerton Conf. on Communication, Control, and Computing, (Monticello, IL), Oct. 1997.

DIGITAL TELEVISION (DTV) RECEIVER DESIGN:

1. H. Liu and P. Schniter, ``Iterative Frequency-Domain Channel Estimation and Equalization for Single-Carrier Transmissions without Cyclic-Prefix [pdf],'' IEEE Transactions on Wireless Communications, vol. 7, no. 10, pp. 3686-3691, Oct. 2008.
2. H. Liu and P. Schniter, ``Iterative Frequency-Domain Channel Estimation and Equalization for Single-Carrier Transmission without Cylic Prefix [pdf],'' Proc. Conf. on Information Sciences and Systems, (Baltimore, MD), Mar. 2007.
3. H. Liu, P. Schniter, H. Fu, and R. A. Casas, ``Frequency Domain Turbo Equalization for Vestigial Sideband Modulation with Punctured Trellis Coding [pdf],'' Proc. IEEE Workshop on Signal Processing Advances in Wireless Communications, (Cannes, France), July 2006.
4. I. M. Garrison, R. K. Martin, W. A. Sethares, B. Hart, W. Chung, J. Balakrishnan, R. A. Casas, T. J. Endres, M. Larimore, P. Schniter, and C. R. Johnson, Jr. ``DTV Channel Characterization [pdf],'' Proc. Conf. on Information Sciences and Systems (Baltimore, MD), Mar. 2001.
5. P. Schniter, `` On VSB Modulation [pdf],'' Technical Report, Feb. 1998.

MISCELLANEOUS:

1. R. Baraniuk, C.S. Burrus, B. Hendricks, G. Henry, A. Hero, D. Johnson, D. Jones, R. Nowak, J. Odegard, L. Potter, R. Reedstrom, P. Schniter, I. Selesnick, D. Williams, and B. Wilson, ``Connexions: DSP Education for a Networked World [pdf],'' Proc. Int. Conf. on Acoustics, Speech, and Signal Processing, (Orlando, FL), vol. 4, pp. 4144-4147, May 2002.
2. P. Schniter, ``The Role of the Receive Filter in BSE/FSE Comparisons [pdf],'' Technical Report, Apr. 1999.
3. R. Casas and P. Schniter, ``A Decision Feedback Equalization (DFE) Tutorial [pdf],'' Technical Report, May 1998.
4. J.D. Behm, T.J. Endres, P. Schniter, C.R. Johnson, Jr., M.L. Alberi, C. Prettie, C.R. Johnson, Jr., and I. Fijalkow, ``Characterization of an Empirically-Derived Database of Time-Varying Microwave Channel Responses [pdf],'' Proc. Asilomar Conf. on Signals, Systems, and Computers, (Pacific Grove, CA), Nov. 1997.
5. R.A. Casas, F. Lopez de Victoria, I. Fijalkow, P. Schniter, T.J. Endres, and C.R. Johnson, Jr., ``On MMSE Fractionally-Spaced Equalizer Design [pdf],'' Proc. 13th Int. Conf. on Digital Signal Processing, (Santorini, Greece), July 1997.
6. P. Schniter, ``Considerations in BSE vs. FSE Comparisons [pdf],'' Technical Report, Feb. 1997.
7. P. Schniter and L. Wozniak, ``Efficiency Based Optimal Control of Kaplan Hydrogenerators [pdf],'' IEEE Trans. Energy Conversion, vol. 10, no. 2, pp. 348-353, June 1995.
8. P. Schniter, An Efficiency Based Technique For The Optimal Control of Kaplan Hydrogenerating Systems M.S. Thesis, The University of Illinois Champaign-Urbana, Aug. 1993.
9. M. Kramer and P. Schniter, ``Active Subwoofer System using Servo Control [pdf],'' B.S. Project, The University of Illinois Champaign-Urbana, Apr. 1992.

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