Combined Wireless Ranging and Frequency Transfer for Internode Coordination in Open-Loop Coherent Distributed Antenna Arrays

Ellison, Sean M.; Mghabghab, Serge R.; Doroshewitz, John; Nanzer, Jeffrey A.

doi:10.1109/tmtt.2019.2943292

Cited by 26 publications

(7 citation statements)

References 28 publications

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“…Evolution of wireless systems from large single-platform architectures to a composite of multiple small spatially distributed systems that make local decisions to achieve a shared global objective has resulted in significant applications in distributed coordinated beamforming [1], [2], network densification in 5G [3], and coherent distributed array (CDA) [4]- [6]. In particular, CDA operate as a network of small, low-cost, and spatially distributed nodes where each node has its own separate transceiver, and the nodes align their transceivers within the fraction of the wavelength of wireless operation in such a way that their signals add up coherently at the destination.…”

Section: Introductionmentioning

confidence: 99%

Frequency and Phase Synchronization in Distributed Antenna Arrays Based on Consensus Averaging and Kalman Filtering

Rashid¹,

Nanzer²

2022

Preprint

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A decentralized approach for joint frequency and phase synchronization in distributed antenna arrays is presented. The nodes in the array locally broadcast signals to share their frequencies and phases with their neighboring nodes, and use consensus averaging to align these parameters across the array. The architecture is fully distributed, requiring no centralization. Each node has a local oscillator and we consider a signal model where intrinsic frequency and phase errors of the local oscillators on each node caused by the frequency drift and phase jitter as well as the frequency and phase estimation errors at the nodes are included and modeled using practical statistics. A decentralized frequency and phase consensus (DFPC) algorithm is proposed which uses an average consensus method in which each node in the array iteratively updates its frequency and phase by computing an average of the frequencies and phases of their neighboring nodes. Simulation results show that upon convergence the DFPC algorithm can align the frequencies and phases of all the nodes up to a residual phase error that is governed by the oscillators and the estimation errors. To reduce this residual phase error and thus improve the synchronization between the nodes, a Kalman filter based decentralized frequency and phase consensus (KF-DFPC) algorithm is presented. The total residual phase error at the convergence of the KF-DFPC and DFPC algorithms is derived theoretically. The synchronization performances of these algorithms are compared to each other in light of this theoretical residual phase error by varying the duration of the signals, connectivity of the nodes, the number of nodes in the array, and signal to noise ratio of the transmitted signals. Simulation results demonstrate that the proposed KF-DFPC algorithm Manuscript received 2022.

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Section: Introductionmentioning

confidence: 99%

Frequency and Phase Synchronization in Distributed Antenna Arrays Based on Consensus Averaging and Kalman Filtering

Rashid¹,

Nanzer²

2022

Preprint

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“…In this work we consider the coherent gain to be greater than 90% of that of the ideal array (X = 0.9). We previously showed that as the array size increases, obtaining P (G C ≥ X) requires ranging errors of λ/15 or less for arbitrary steering angles [12], [26]. For the more stringent end-fire case, this requirement is closer to λ/20, as shown in Fig.…”

Section: Ranging Requirements For Open-loop Distributed Beamformingmentioning

confidence: 84%

“…To achieve phase alignment and implement a phase-based beamsteering operation, the relative positions of the individual nodes must be known to within a fraction of the wavelength of the beamforming frequency. Previous works have shown that these internode ranging measurements must have accuracies of less than λ/15 to have no more than 0.5 dB reduction in coherent gain with a probability of 90% [12], [26]. To operate in the microwave range this calls for accuracies on a sub-centimeter level.…”

Section: Introductionmentioning

confidence: 99%

Multi-Node Open-Loop Distributed Beamforming Based on Scalable, High-Accuracy Ranging

2022

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We present a distributed antenna array supporting open-loop distributed beamforming at 1.5 GHz. Based on a scalable, high-accuracy internode ranging technique, we demonstrate open-loop beamforming experiments using three transmitting nodes. To support distributed beamforming without feedback from the destination, the relative positions of the nodes in the distributed array must be known with accuracies below λ/15 of the beamforming carrier frequency to ensure that the array maintains at least 90% coherent beamforming gain at the receive location. For operations in the microwave range, this leads to range estimation accuracies of centimeters or lower. We present a scalable, high-accuracy waveform and new approaches to refine range measurements to significantly improve the estimation accuracy. Using this waveform with a three-node array, we demonstrate high-accuracy ranging simultaneously between multiple nodes, from which phase corrections on two secondary nodes are implemented to maintain beamforming with the primary node, thereby supporting open-loop distributed beamforming. Upon movement of the nodes, the range estimation is used to dynamically update the phase correction, maintaining beamforming as the nodes move. We show the first openloop distributed beamforming at 1.5 GHz with two-node and three-node arrays, demonstrating the ability to implement and maintain phase-based beamforming without feedback from the destination.

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“…The localization capability of a waveform can be determined by considering the waveform time delay estimation accuracy, which is given by the Cramer-Rao Lower Bound (CRLB) [29], [30] var…”

Section: Theoretical Localization Accuracy Of the Legacy Preamblmentioning

confidence: 99%

“…The peak of each matched filter output was upsampled by 1000 points using spline interpolation. At each attenuation level the variance of 100 estimated values was calculated, and the SNR was estimated using an eigenvalue decomposition process [30], [31]. The resulting measured variances, simulations of the performance of the 802.11ac waveform along with the corresponding SFW, and the theoretical bounds can be seen in Fig.…”

Section: Experimental Validationmentioning

confidence: 99%

High-Accuracy Localization Using IEEE 802.11 WiFi Legacy Preamble

Ellison

Nanzer

2020

IEEE Access

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We explore the use of synchronization bits from a standard 802.11 preamble for highaccuracy localization. The multi-tone short training field present in 802.11 standards that use a Legacy preamble is investigated in comparison to a common remote sensing waveform known as the stepped frequency waveform that shares similar waveform characteristics to the preamble. We present the 802.11 standard and the connection to the stepped frequency waveform, derive the theoretical lower bound on the short training field, present a comparison of simulated results, and present experimental wireless measurements demonstrating ranging accuracies on the order of 1.9 mm.

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Combined Wireless Ranging and Frequency Transfer for Internode Coordination in Open-Loop Coherent Distributed Antenna Arrays

Cited by 26 publications

References 28 publications

Frequency and Phase Synchronization in Distributed Antenna Arrays Based on Consensus Averaging and Kalman Filtering

Frequency and Phase Synchronization in Distributed Antenna Arrays Based on Consensus Averaging and Kalman Filtering

Multi-Node Open-Loop Distributed Beamforming Based on Scalable, High-Accuracy Ranging

High-Accuracy Localization Using IEEE 802.11 WiFi Legacy Preamble

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