Compressed Channel Sensing: A New Approach to Estimating Sparse Multipath Channels

By Waheed Uz Zaman Bajwa, Jarvis D. Haupt, Akbar M. Sayeed, Robert D. Nowak. Proceedings of the IEEE, 2010

1,148 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computational Mechanics, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/eab7442efb18e52f1c81ab1719ef3162802a3420

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Citations in the corpus

Channel Estimation via Orthogonal Matching Pursuit for Hybrid MIMO Systems in Millimeter Wave Communications (2016, methodology): “An alternative approach to AoD/AoA estimation of mmwave channels is to apply the compressed sensing (CS) techniques in [12]–[14], which have been proposed for sparse multipath estimation in angular domain of massive MIMO channels.”
Compressive sensing: From theory to applications, a survey (2013, background): “CCS based sparse channel estimation has been shown to achieve much less reconstruction error while utilizing significantly less energy and, in some cases, less latency and bandwidth as well [84].”
Enabling Joint Communication and Radar Sensing in Mobile Networks—A Survey (2020, background): “3) On-Grid Compressive Sensing Algorithms: Compressive Sensing (CS) techniques [182] have been widely used in communication systems for channel estimation [183]–[185] and in radar systems [186].”
Channel Estimation for Extremely Large-Scale MIMO: Far-Field or Near-Field? (2021, methodology): “Following the CS framework [27], to achieve the satisfying channel recovery accuracy, the sampling on angle and distance should make the column coherence µ = max p (cid:54) = q (cid:12)(cid:12) b ( θ p , r p ) H b ( θ q , r q ) (cid:12)(cid:12) of the polar-domain transform matrix W as small as…”
Sensitivity to Basis Mismatch in Compressed Sensing (2010, background): “Grid misalignment problems also arise in many other applications including channel estimation [38]–[41].”
Channel Estimation in Broadband Millimeter Wave MIMO Systems With Few-Bit ADCs (2016, methodology): “.25 14.27 19.38 24.55 29.85 35.23 40.68 A. Clustered MIMO Channel Model The mmWave channel can be modeled using a clusterbased model with N cl clusters, each of which is composed of Nn ray rays [31], [34], [35]. Denote i;m, ˝ , ’r i;m (orr i;m ), ’ t i;m (or i;m ) as the complex path gain, delay, az”
Millimeter Wave Communications for Future Mobile Networks (2017, methodology): “In [154], several design methods have been proposed to achieve this goal.”
Beamforming for Millimeter Wave Communications: An Inclusive Survey (2016, methodology): “Compressed channel sensing offers a promising method to estimate the sparse multipath channels [260] [101], and has been used extensively to reduce the signal processing complexity in hybrid beamforming systems [175]–[181].”
Decimeter-Level Localization with a Single WiFi Access Point (2016, background): “Intuitively, this stems from the fact that while signals in indoor environments traverse several paths, a few paths tend to dominate as they suffer minimal attenuation [10].”
Off-grid Channel Estimation with Sparse Bayesian Learning for OTFS Systems (2021, methodology): “Following the principle of compressed channel sensing [11], we formulate an SSR problem via a virtual representation based on a virtual sampling grid in the DD space.”
Distributed Compressive CSIT Estimation and Feedback for FDD Multi-User Massive MIMO Systems (2014, background): “There is one important observation for massive MIMO systems, which can help to address the above issues.”
Off-Grid Direction of Arrival Estimation Using Sparse Bayesian Inference (2011, background): “ve possible higher precision. Our framework of off-grid DOA estimation is quite general. Its potential applications include near-ﬁeld source localization, radar imaging [42,43] and channel estimation [44]. Appendix: Derivation of (28) I = ky (A+ B) k2 2 = k(y A) Bdiag() k2 2 = TdiagH()BHBdiag() 2&lt”
Compressed Sensing (2014, neutral): “Estas abarcan diversas áreas como: imagenoloǵıa medica [49, 45], conversión analógico/digital [68], detección mediante RADAR [4, 42], corrección de errores [18], cámaras digitales [34, 21, 51], Sismoloǵıa [41], Radioastronomı́a [71], comunicación inalámbrica [63, 3, 65] y redes de sensores inalámbri”
Application of compressive sensing to sparse channel estimation (2010, background): “• Considering mobile radio channels, each path is characterized by a delay and a relative Doppler speed [7], [8].”
Channel Estimation for Orthogonal Time Frequency Space (OTFS) Massive MIMO (2019, background): “Moreover, a general framework of compressed channel sensing was provided in [32].”