Two-stage compressed sensing for millimeter wave channel estimation

Han, Yong-Hee; Lee, Jung Woo

doi:10.1109/isit.2016.7541421

Cited by 63 publications

(42 citation statements)

References 21 publications

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“…• OLOS: For the case withK scatterers and no LOS path, the EXIP could be used, as (18), (20), and (21) describe a mapping η olos = f olos (η olos ). Consequently, the estimatedη olos obtained aŝ…”

Section: Step 3: Conversion To Position and Rotation Angle Estimatesmentioning

confidence: 99%

Position and Orientation Estimation Through Millimeter-Wave MIMO in 5G Systems

Shahmansoori

Garcia

Destino

et al. 2018

IEEE Trans. Wireless Commun.

488

468

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Millimeter wave signals and large antenna arrays are considered enabling technologies for future 5G networks. While their benefits for achieving high-data rate communications are well-known, their potential advantages for accurate positioning are largely undiscovered. We derive the Cramér-Rao bound (CRB) on position and rotation angle estimation uncertainty from millimeter wave signals from a single transmitter, in the presence of scatterers. We also present a novel two-stage algorithm for position and rotation angle estimation that attains the CRB for average to high signal-to-noise ratio. The algorithm is based on multiple measurement vectors matching pursuit for coarse estimation, followed by a refinement stage based on the space-alternating generalized expectation maximization algorithm. We find that accurate position and rotation angle estimation is possible using signals from a single transmitter, in either lineof-sight, non-line-of-sight, or obstructed-line-of-sight conditions.

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Section: Step 3: Conversion To Position and Rotation Angle Estimatesmentioning

confidence: 99%

Position and Orientation Estimation Through Millimeter-Wave MIMO in 5G Systems

Shahmansoori

Garcia

Destino

et al. 2018

IEEE Trans. Wireless Commun.

488

468

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“…Developing beamforming/channel estimation solutions to reduce this training overhead has attracted considerable research interest in the last few years [20]- [33]. This prior research has mainly focused on three directions: (i) beam training [20]- [23], (ii) compressive channel estimation [24]- [28], and (iii) location aided beamforming [29]- [33]. In beam training, the candidate beams at the transmitter and receiver are directly trained using exhaustive or adaptive search to select the ones that optimize the metric of interest, e.g., SNR.…”

Section: A Prior Workmentioning

confidence: 99%

Deep Learning Coordinated Beamforming for Highly-Mobile Millimeter Wave Systems

et al. 2018

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Supporting high mobility in millimeter wave (mmWave) systems enables a wide range of important applications such as vehicular communications and wireless virtual/augmented reality. Realizing this in practice, though, requires overcoming several challenges. First, the use of narrow beams and the sensitivity of mmWave signals to blockage greatly impact the coverage and reliability of highly-mobile links. Second, highly-mobile users in dense mmWave deployments need to frequently hand-off between base stations (BSs), which is associated with critical control and latency overhead. Further, identifying the optimal beamforming vectors in large antenna array mmWave systems requires considerable training overhead, which significantly affects the efficiency of these mobile systems. In this paper, a novel integrated machine learning and coordinated beamforming solution is developed to overcome these challenges and enable highly-mobile mmWave applications. In the proposed solution, a number of distributed yet coordinating BSs simultaneously serve a mobile user. This user ideally needs to transmit only one uplink training pilot sequence that will be jointly received at the coordinating BSs using omni or quasi-omni beam patterns. These received signals draw a defining signature not only for the user location, but also for its interaction with the surrounding environment. The developed solution then leverages a deep learning model that learns how to use these signatures to predict the beamforming vectors at the BSs. This renders a comprehensive solution that supports highly-mobile mmWave applications with reliable coverage, low latency, and negligible training overhead. Extensive simulation results, based on accurate ray-tracing, show that the proposed deep-learning coordinated beamforming strategy approaches the achievable rate of the genie-aided solution that knows the optimal beamforming vectors with no training overhead, and attains higher rates compared to traditional mmWave beamforming techniques.This work was done while the first author was with Facebook. Ahmed Alkhateeb is currently with Arizona State University

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“…These dictionary matrices operate as a sparsifying basis for the channel matrix. Based on that, several channel estimation algorithms that use compressed sensing (CS) tools have been developed for hybrid architectures [15], [14], [16], [17], [18], where the training/measurement matrices are designed using hybrid precoders and combiners. These techniques differ in the way these measurement matrices search for the dominant angles of arrival and departure.…”

Section: A Prior Workmentioning

confidence: 99%

Channel Estimation for Hybrid Architecture-Based Wideband Millimeter Wave Systems

Venugopal

Alkhateeb

González–Prelcic

et al. 2017

IEEE J. Select. Areas Commun.

376

352

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Hybrid analog and digital precoding allows millimeter wave (mmWave) systems to achieve both array and multiplexing gain. The design of the hybrid precoders and combiners, though, is usually based on knowledge of the channel. Prior work on mmWave channel estimation with hybrid architectures focused on narrowband channels. Since mmWave systems will be wideband with frequency selectivity, it is vital to develop channel estimation solutions for hybrid architectures based wideband mmWave systems. In this paper, we develop a sparse formulation and compressed sensing based solutions for the wideband mmWave channel estimation problem for hybrid architectures. First, we leverage the sparse structure of the frequency selective mmWave channels and formulate the channel estimation problem as a sparse recovery in both time and frequency domains. Then, we propose explicit channel estimation techniques for purely time or frequency domains and for combined time/frequency domains.Our solutions are suitable for both SC-FDE and OFDM systems. Simulation results show that the proposed solutions achieve good channel estimation quality, while requiring small training overhead.Leveraging the hybrid architecture at the transceivers gives further improvement in estimation error performance and achievable rates. Index TermsMillimeter wave communications, channel estimation, frequency selective channel, hybrid precodercombiner, compressed sensing, sparse recovery, multi-stream MIMO, IEEE 802.11ad.

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Two-stage compressed sensing for millimeter wave channel estimation

Cited by 63 publications

References 21 publications

Position and Orientation Estimation Through Millimeter-Wave MIMO in 5G Systems

Position and Orientation Estimation Through Millimeter-Wave MIMO in 5G Systems

Deep Learning Coordinated Beamforming for Highly-Mobile Millimeter Wave Systems

Channel Estimation for Hybrid Architecture-Based Wideband Millimeter Wave Systems

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