A compressive channel estimation technique robust to synchronization impairments

Myers, Nitin Jonathan; Heath, Robert W.

doi:10.1109/spawc.2017.8227747

Cited by 17 publications

(21 citation statements)

References 16 publications

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“…Swift-Link's algorithm for the type II case requires solving two CS problems in dimension N 2 with M/2 measurements and one CS problem in dimension N 2 with M measurements. In contrast, the tensor or lifting-based methods in [10] and [31] require solving an N 2 M dimensional CS problem with M measurements. Due to the use of large antenna arrays at mmWave, the number of variables in tensorbased optimization [10] can be in the order of millions for typical mmWave settings.…”

Section: B Swift-link's Type II Trajectory and Shift Correctionmentioning

confidence: 99%

Swift-Link: A Compressive Beam Alignment Algorithm for Practical mmWave Radios

Myers

Mezghani

Heath

2019

IEEE Trans. Signal Process.

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Next generation wireless networks will exploit the large amount of spectrum available at millimeter wave (mmWave) frequencies. Design of mmWave systems, however, is challenging due to strict power, cost and hardware constraints at higher bandwidths. To achieve a good SNR for communication, mmWave systems use large antenna arrays. Beamforming with highly directional beams is one way to use the antennas. As the channel changes over time, the beams that maximize the SNR have to be estimated quickly to reduce the training overhead. Prior work has exploited the observation that mmWave channels are sparse to perform compressed sensing (CS) based beam alignment with few channel measurements. Most of the existing CS-based algorithms, however, assume perfect synchronization and fail in the presence of carrier frequency offset (CFO). This paper presents Swift-Link, a fast beam alignment algorithm that is robust against the offset. Swift-Link includes a novel randomized beam training sequence that minimizes the beam alignment errors due to CFO and a low-complexity algorithm that corrects these errors. Even with strict hardware constraints, our algorithm uses fewer channel measurements than comparable CS algorithms and has analytical guarantees. Swift-Link requires a small output dynamic range at the analog-to-digital converter compared to beam-scanning techniques.Index Terms-Compressive beam training, carrier frequency offset, robust beam alignment, low-complexity sparse selfcalibration, robust compressive sensing, mm-wave.

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Section: B Swift-link's Type II Trajectory and Shift Correctionmentioning

confidence: 99%

Swift-Link: A Compressive Beam Alignment Algorithm for Practical mmWave Radios

Myers

Mezghani

Heath

2019

IEEE Trans. Signal Process.

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“…There are also recent works that consider some practical aspects of IA. For example, frequency offset robust algorithms in narrowband mmW beam training are reported in [1], [24], [25]. There are several hardware prototypes that consider a practical approach of using received signal strength (RSS) in CS-based beam training.…”

Section: A Related Workmentioning

confidence: 99%

“…It is worth noting that the proposed approach can be extended to support multi-path training which has been covered by a variety of works in CS-based approaches [16], [18], [20]- [22], [24], [25]. However, the main motivation of this work is to showcase and analyze pseudorandom sounding beams in the initial access and initial beam training.…”

Section: Joint Aoa and Aod Estimation Robust To Cfomentioning

confidence: 99%

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Compressive Initial Access and Beamforming Training for Millimeter-Wave Cellular Systems

Han

Čabrić

2019

IEEE J. Sel. Top. Signal Process.

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Initial access (IA) is a fundamental physical layer procedure in cellular systems where user equipment (UE) detects nearby base station (BS) as well as acquire synchronization. Due to the necessity of using antenna array in millimeter-wave (mmW) IA, the channel spatial information can also be inferred. The state-of-the-art directional IA (DIA) uses sector sounding beams with limited angular resolution, and thus requires additional dedicated radio resources, access latency and overhead for refined beam training. To remedy the problem of access latency and overhead in DIA, this work proposes to use a quasi-omni pseudorandom sounding beam for IA, and develops a novel algorithm for joint initial access and fine resolution initial beam training without requiring extra radio resources. We provide the analysis of the proposed algorithm miss detection rate under synchronization error, and further derive Cramér-Rao lower bound of angular estimation under frequency offset. Using QuaDRiGa simulator with mmMAGIC model at 28 GHz, the numerical results show that the proposed approach is advantageous to DIA with hierarchical beam training. The proposed algorithm offers up to two order of magnitude access latency saving compared to DIA, when the same discovery, post training SNR, and overhead performance are targeted. This conclusion holds true in various propagation environments and 3D locations of a mmW pico-cell with up to 140m radius.

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“…Most of prior work on channel estimation at mmWave, however, assumes perfect synchronization at the receiver side [2], [3], [4], [5], [6]. Prior work on channel estimation under synchronization impairments for mmWave MIMO focuses on a narrowband channel model [7], [8]. An analog-only architecture is assumed in [7], while [8] considers a hybrid MIMO system but does not address the problems of frame synchronization and phase noise compensation, which are crucial to establish synchronization.…”

Section: Introductionmentioning

confidence: 99%

Joint Synchronization and Compressive Estimation for Frequency-Selective mmWave MIMO Systems

Rodríguez-Fernández

González–Prelcic

2018

2018 52nd Asilomar Conference on Signals, Systems, and Computers

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The large beamforming gain used to operate at millimeter wave (mmWave) frequencies requires obtaining channel information to configure hybrid antenna arrays. Previously proposed wideband channel estimation strategies, however, assume perfect time-frequency synchronization and neglect phase noise, making these approaches impractical. Consequently, achieving time-frequency synchronization between transmitter and receiver and correcting for phase noise (PN) as the channel is estimated, is the greatest challenge yet to be solved in order to configure hybrid precoders and combiners in practical settings. In this paper, building upon our prior work, we find the Maximum A Posteriori (MAP) solution to the joint problem of timing offset (TO), carrier frequency offset (CFO), PN and compressive channel estimation for broadband mmWave MIMO systems with hybrid architectures. Simulation results show that, using significantly less training symbols than in the beam training protocol in the 5G New Radio communications standard, joint synchronization and channel estimation at the low SNR regime can be achieved, and near-optimum data rates can be attained.

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A compressive channel estimation technique robust to synchronization impairments

Cited by 17 publications

References 16 publications

Swift-Link: A Compressive Beam Alignment Algorithm for Practical mmWave Radios

Swift-Link: A Compressive Beam Alignment Algorithm for Practical mmWave Radios

Compressive Initial Access and Beamforming Training for Millimeter-Wave Cellular Systems

Joint Synchronization and Compressive Estimation for Frequency-Selective mmWave MIMO Systems

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