62 results found
Suliman MA, Dai W, 2022, Blind Two-Dimensional Super-Resolution and Its Performance Guarantee, IEEE TRANSACTIONS ON SIGNAL PROCESSING, Vol: 70, Pages: 2844-2858, ISSN: 1053-587X
Suliman MA, Dai W, 2021, Mathematical Theory of Atomic Norm Denoising in Blind Two-Dimensional Super-Resolution, IEEE TRANSACTIONS ON SIGNAL PROCESSING, Vol: 69, Pages: 1681-1696, ISSN: 1053-587X
Wang Y, Gu K, Wu Y, et al., 2020, NLOS Effect Mitigation via Spatial Geometry Exploitation in Cooperative Localization, IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, Vol: 19, Pages: 6037-6049, ISSN: 1536-1276
Lu Y, Dai W, Eldar YC, 2019, Optimal Number of Measurements in a Linear System With Quadratically Decreasing SNR, IEEE TRANSACTIONS ON SIGNAL PROCESSING, Vol: 67, Pages: 2947-2959, ISSN: 1053-587X
Ma Z, Dai W, Liu Y, et al., 2017, Group Sparse Bayesian Learning Via Exact and Fast Marginal Likelihood Maximization, IEEE TRANSACTIONS ON SIGNAL PROCESSING, Vol: 65, Pages: 2741-2753, ISSN: 1053-587X
Liu A, Lau VKN, Dai W, 2016, Exploiting burst-sparsity in massive MIMO with partial channel support information, IEEE Transactions on Wireless Communications, Vol: 15, Pages: 7820-7830, ISSN: 1536-1276
How to obtain accurate channel state information at the base station (CSIT) is a key implementation challenge behind frequency-division duplex massive MIMO systems. Recently, compressive sensing (CS) has been applied to reduce pilot and CSIT feedback overheads in massive MIMO systems by exploiting the underlying channel sparsity. However, brute-force applications of standard CS may not lead to good performance in massive MIMO systems, because standard sparse recovery algorithms have quite a stringent requirement on the sparsity level for robust recovery and this severely limits the operating regime of the solution. Moreover, since the channel support is usually correlated across time, it is possible to obtain partial channel support information (P-CSPI) from previously estimated channel support. Motivated by the above observations, we propose a P-CSPI aided burst Least Absolute Shrinkage and Selection Operator (LASSO) algorithm to exploit both the P-CSPI and additional structured properties of the sparsity, namely, the burst sparsity in massive MIMO channels. We also accurately characterize the asymptotic channel estimation error of the P-CSPI aided burst LASSO algorithm. Both the analysis and simulations show that the P-CSPI aided burst LASSO algorithm can alleviate the stringent requirement on the sparsity level for robust channel recovery and substantially enhance the channel estimation performance over existing solutions.
Ding W, Lu Y, Yang F, et al., 2016, Spectrally Efficient CSI Acquisition for Power Line Communications: A Bayesian Compressive Sensing Perspective, IEEE Journal on Selected Areas in Communications, Vol: 34, Pages: 2022-2032, ISSN: 1558-0008
Power line communication (PLC) techniques present a no extra wire solution for the communication purpose in a smart grid due to the ubiquity and low cost. Moreover, the through-the-grid property of PLC has naturally extended its possible applications, including but not limited to the automatic meter reading, line quality monitoring, online diagnostics, and network tomography. To guarantee the performance of communications as well as other applications in PLC systems, accurate channel state information (CSI) acquisition should be performed regularly. However, the conventional pilot-based CSI acquisition approaches in PLC systems have not made full use of the channel characteristics and hence suffer from a low spectral efficiency. In this paper, by exploiting the parametric sparsity and discretizing the electrical length in the well-known PLC channel model, we formulate the non-sparse (either time domain or frequency domain) PLC channel into a compressive sensing (CS) applicable problem. Furthermore, we propose a spectrally efficient CSI acquisition scheme under the framework of Bayesian CS and extend it to the multiple-input multiple-output PLC by investigating the channel spatial correlation. Compared with the existing sparse CSI acquisition schemes for PLC, such as the annihilating filter-based and the estimating signal parameters via rotational invariance technique-based ones, the proposed scheme has better mean square error performance and noise robustness.
Gao Z, Dai L, Dai W, et al., 2016, Structured compressive sensing-based spatio-temporal joint channel estimation for FDD massive MIMO, IEEE Transactions on Communications, Vol: 64, Pages: 601-617, ISSN: 0090-6778
Massive MIMO is a promising technique for future 5G communications due to its high spectrum and energy efficiency. To realize its potential performance gain, accurate channel estimation is essential. However, due to massive number of antennas at the base station (BS), the pilot overhead required by conventional channel estimation schemes will be unaffordable, especially for frequency division duplex (FDD) massive MIMO. To overcome this problem, we propose a structured compressive sensing (SCS)-based spatio-temporal joint channel estimation scheme to reduce the required pilot overhead, whereby the spatio-temporal common sparsity of delay-domain MIMO channels is leveraged. Particularly, we first propose the nonorthogonal pilots at the BS under the framework of CS theory to reduce the required pilot overhead. Then, an adaptive structured subspace pursuit (ASSP) algorithm at the user is proposed to jointly estimate channels associated with multiple OFDM symbols from the limited number of pilots, whereby the spatio-temporal common sparsity of MIMO channels is exploited to improve the channel estimation accuracy. Moreover, by exploiting the temporal channel correlation, we propose a space-time adaptive pilot scheme to further reduce the pilot overhead. Additionally, we discuss the proposed channel estimation scheme in multicell scenario. Simulation results demonstrate that the proposed scheme can accurately estimate channels with the reduced pilot overhead, and it is capable of approaching the optimal oracle least squares estimator.
Dong J, Wang W, Dai W, et al., 2015, Analysis SimCO Algorithms for Sparse Analysis Model Based Dictionary Learning, IEEE Transactions on Signal Processing, Vol: 64, Pages: 417-431, ISSN: 1941-0476
In this paper, we consider the dictionary learning problem for the sparse analysis model. A novel algorithm is proposed by adapting the simultaneous codeword optimization (SimCO) algorithm, based on the sparse synthesis model, to the sparse analysis model. This algorithm assumes that the analysis dictionary contains unit l2-norm atoms and learns the dictionary by optimization on manifolds. This framework allows multiple dictionary atoms to be updated simultaneously in each iteration. However, similar to several existing analysis dictionary learning algorithms, dictionaries learned by the proposed algorithm may contain similar atoms, leading to a degenerate (coherent) dictionary. To address this problem, we also consider restricting the coherence of the learned dictionary and propose Incoherent Analysis SimCO by introducing an atom decorrelation step following the update of the dictionary. We demonstrate the competitive performance of the proposed algorithms using experiments with synthetic data and image denoising as compared with existing algorithms.
Pitaval R-A, Dai W, Tirkkonen O, 2015, Convergence of Gradient Descent for Low-Rank Matrix Approximation, IEEE Transactions on Information Theory, Vol: 61, Pages: 4451-4457, ISSN: 1557-9654
This paper provides a proof of global convergence of gradient search for low-rank matrix approximation. Such approximations have recently been of interest for large-scale problems, as well as for dictionary learning for sparse signal representations and matrix completion. The proof is based on the interpretation of the problem as an optimization on the Grassmann manifold and Fubiny-Study distance on this space.
Gao X, Li X, Filos J, et al., 2015, A Sequential Bayesian Algorithm for DOA Tracking in Time-Varying Environments, CHINESE JOURNAL OF ELECTRONICS, Vol: 24, Pages: 140-145, ISSN: 1022-4653
Zhu X, Dai L, Dai W, et al., 2015, Tracking A Dynamic Sparse Channel Via Differential Orthogonal Matching Pursuit, 34th IEEE Annual Military Communications Conference (MILCOM) on Leveraging Technology - The Joint Imperative, Publisher: IEEE, Pages: 792-797, ISSN: 2155-7578
Ding W, Lu Y, Yang F, et al., 2015, Sparse Channel State Information Acquisition for Power Line Communications, IEEE International Conference on Communications (ICC), Publisher: IEEE, Pages: 746-751, ISSN: 1550-3607
Lu Y, Dai W, 2015, Improved AMP (IAMP) for Non-Ideal Measurement Matrices, 23rd European Signal Processing Conference (EUSIPCO), Publisher: IEEE, Pages: 1746-1750, ISSN: 2076-1465
Gao Z, Dai L, Dai W, et al., 2015, Block Compressive Channel Estimation and Feedback for FDD Massive MIMO, 34th IEEE Conference on Computer Communications (INFOCOM), Publisher: IEEE, Pages: 49-50, ISSN: 2159-4228
Karseras E, Dai W, Dai L, et al., 2015, Fast Variational Bayesian Learning for Channel Estimation with Prior Statistical Information, 16th IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Publisher: IEEE, Pages: 470-474, ISSN: 2325-3789
Ding W, Yang F, Dai W, et al., 2015, Time-Frequency Joint Sparse Channel Estimation for MIMO-OFDM Systems, IEEE COMMUNICATIONS LETTERS, Vol: 19, Pages: 58-61, ISSN: 1089-7798
Zhao X, Dai W, 2014, Power Allocation in Compressed Sensing of Non-uniformly Sparse Signals, IEEE International Symposium on Information Theory (ISIT), Publisher: IEEE, Pages: 231-235
Karseras E, Dai W, 2014, A FAST VARIATIONAL APPROACH FOR BAYESIAN COMPRESSIVE SENSING WITH INFORMATIVE PRIORS, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Publisher: IEEE, ISSN: 1520-6149
Dai W, Yueksel S, 2013, Observability of a Linear System Under Sparsity Constraints, IEEE TRANSACTIONS ON AUTOMATIC CONTROL, Vol: 58, Pages: 2372-2376, ISSN: 0018-9286
Xu T, Wang W, Dai W, 2013, Sparse coding with adaptive dictionary learning for underdetermined blind speech separation, SPEECH COMMUNICATION, Vol: 55, Pages: 432-450, ISSN: 0167-6393
Karseras E, Leung K, Dai W, 2013, TRACKING DYNAMIC SPARSE SIGNALS USING HIERARCHICAL BAYESIAN KALMAN FILTERS, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Publisher: IEEE, Pages: 6546-6550, ISSN: 1520-6149
Zhao X, Zhou G, Dai W, 2013, SMOOTHED SIMCO FOR DICTIONARY LEARNING: HANDLING THE SINGULARITY ISSUE, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Publisher: IEEE, Pages: 3292-3296, ISSN: 1520-6149
Ahmad BI, Al-Ani M, Tarczynski A, et al., 2013, COMPRESSIVE AND NON-COMPRESSIVE RELIABLE WIDEBAND SPECTRUM SENSING AT SUB-NYQUIST RATES, 21st European Signal Processing Conference (EUSIPCO), Publisher: IEEE
Zhao X, Zhou G, Dai W, et al., 2013, JOINT IMAGE SEPARATION AND DICTIONARY LEARNING, 18th International Conference on Digital Signal Processing (DSP), Publisher: IEEE, ISSN: 1546-1874
Filos J, Karseras E, Dai W, et al., 2013, Tracking Dynamic Sparse Signals with Hierarchical Kalman Filters: A Case Study, 18th International Conference on Digital Signal Processing (DSP), Publisher: IEEE, ISSN: 1546-1874
Karseras E, Leung K, Dai W, 2013, HIERARCHICAL BAYESIAN KALMAN FILTERS FOR WIRELESS SENSOR NETWORKS, 21st European Signal Processing Conference (EUSIPCO), Publisher: IEEE
Zhou G, Zhao X, Dai W, 2012, Low Rank Matrix Completion: A Smoothed l(0)-Search, 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton), Publisher: IEEE, Pages: 1010-1017, ISSN: 2474-0195
Dai W, Xu T, Wang W, 2012, DICTIONARY LEARNING AND UPDATE BASED ON SIMULTANEOUS CODEWORD OPTIMIZATION (SIMCO), IEEE International Conference on Acoustics, Speech and Signal Processing, Publisher: IEEE, Pages: 2037-2040, ISSN: 1520-6149
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