JR
J.P.A. Romme
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4 records found
1
Conference paper
(2022)
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Minyoung Song, Yu Huang, More Authors..., Yiyu Shen, Chengyao Shi, Arjan Breeschoten, Mario Konijnenburg, Huib Visser, Jac Romme, Barundeb Dutta, Morteza S. Alavi
Intra-cortical extracellular neural sensing is being rapidly and widely applied in several clinical research and brain-computer interfaces (BCIs), as the number of sensing channels continues to double every 6 years. By distributing multiple high-density extracellular micro-electrode arrays (MEAs) in vivo across the brain, each with 1000's of sensing channels, neuroscientists have begun to map the correlation of neuronal activity across different brain regions, with single-neuron precision [1]. Since each neural sensing channel typically samples at 20 to 50kS/s with a > 10b ADC, multiple MEAs demand a data transfer rate up to Gb/s [2]. However, these BCIs are severely hindered in many clinical uses due to the lack of a high-data-rate and miniature-wireless-telemetry solution that can be implanted below the scalp, i.e., transcutaneously (Fig. 24.2.1). The area of the wireless telemetry module should be miniaturized to ~3cm2 due to neurosurgical implantation constraints. A transmission range up to 10cm is highly desirable, in order to improve the reliability of the wireless link against e.g., antenna misalignment, etc. Finally, the power consumption of the wireless telemetry should be limited to ~10mW to minimize thermal flux from the module's surface area, avoiding excessive tissue heating. Most of the conventional transcutaneous wireless telemetry systems adopt inductive coupling, but the data-rate is limited to a few Mb/s. A near-infrared (NIR) optical transcutaneous TX using a vertical-cavity-surface-emitting laser (VCSEL) [2] demonstrated a data-rate up to 300Mb/s but suffers from a limited transmission range (4mm) and requires a sub-mm precise alignment between the implant TX and a wearable RX. Impulse-radio UWB (IR-UWB) is promising for the targeted requirements [3]–[5].
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Intra-cortical extracellular neural sensing is being rapidly and widely applied in several clinical research and brain-computer interfaces (BCIs), as the number of sensing channels continues to double every 6 years. By distributing multiple high-density extracellular micro-electrode arrays (MEAs) in vivo across the brain, each with 1000's of sensing channels, neuroscientists have begun to map the correlation of neuronal activity across different brain regions, with single-neuron precision [1]. Since each neural sensing channel typically samples at 20 to 50kS/s with a > 10b ADC, multiple MEAs demand a data transfer rate up to Gb/s [2]. However, these BCIs are severely hindered in many clinical uses due to the lack of a high-data-rate and miniature-wireless-telemetry solution that can be implanted below the scalp, i.e., transcutaneously (Fig. 24.2.1). The area of the wireless telemetry module should be miniaturized to ~3cm2 due to neurosurgical implantation constraints. A transmission range up to 10cm is highly desirable, in order to improve the reliability of the wireless link against e.g., antenna misalignment, etc. Finally, the power consumption of the wireless telemetry should be limited to ~10mW to minimize thermal flux from the module's surface area, avoiding excessive tissue heating. Most of the conventional transcutaneous wireless telemetry systems adopt inductive coupling, but the data-rate is limited to a few Mb/s. A near-infrared (NIR) optical transcutaneous TX using a vertical-cavity-surface-emitting laser (VCSEL) [2] demonstrated a data-rate up to 300Mb/s but suffers from a limited transmission range (4mm) and requires a sub-mm precise alignment between the implant TX and a wearable RX. Impulse-radio UWB (IR-UWB) is promising for the targeted requirements [3]–[5].
In wireless networks, an essential step for precise range-based localization is the high-resolution estimation of multipath channel delays. The resolution of traditional delay estimation algorithms is inversely proportional to the bandwidth of the training signals used for channel probing. Considering that typical training signals have limited bandwidth, delay estimation using these algorithms often leads to poor localization performance. To mitigate these constraints, we exploit the multiband and carrier frequency switching capabilities of wireless transceivers and propose to acquire channel state information (CSI) in multiple bands spread over a large frequency aperture. The data model of the acquired measurements has a multiple shift-invariance structure, and we use this property to develop a high-resolution delay estimation algorithm. We derive the Cramér-Rao Bound (CRB) for the data model and perform numerical simulations of the algorithm using system parameters of the emerging IEEE 802.11be standard. Simulations show that the algorithm is asymptotically efficient and converges to the CRB. To validate modeling assumptions, we test the algorithm using channel measurements acquired in real indoor scenarios. From these results, it is seen that delays (ranges) estimated from multiband CSI with a total bandwidth of 320 MHz show an average RMSE of less than 0.3 ns (10 cm) in 90% of the cases.
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In wireless networks, an essential step for precise range-based localization is the high-resolution estimation of multipath channel delays. The resolution of traditional delay estimation algorithms is inversely proportional to the bandwidth of the training signals used for channel probing. Considering that typical training signals have limited bandwidth, delay estimation using these algorithms often leads to poor localization performance. To mitigate these constraints, we exploit the multiband and carrier frequency switching capabilities of wireless transceivers and propose to acquire channel state information (CSI) in multiple bands spread over a large frequency aperture. The data model of the acquired measurements has a multiple shift-invariance structure, and we use this property to develop a high-resolution delay estimation algorithm. We derive the Cramér-Rao Bound (CRB) for the data model and perform numerical simulations of the algorithm using system parameters of the emerging IEEE 802.11be standard. Simulations show that the algorithm is asymptotically efficient and converges to the CRB. To validate modeling assumptions, we test the algorithm using channel measurements acquired in real indoor scenarios. From these results, it is seen that delays (ranges) estimated from multiband CSI with a total bandwidth of 320 MHz show an average RMSE of less than 0.3 ns (10 cm) in 90% of the cases.
The presence of rich scattering in indoor and urban radio propagation scenarios may cause a high arrival density of multipath components (MPCs). Often the MPCs arrive in clusters at the receiver, where MPCs within one cluster have similar angles and delays. The MPCs arriving within a single cluster are typically unresolvable in the delay domain. In this paper, we analyze the effects of unresolved MPCs on the bias of the delay estimation with a multiband subspace fitting algorithm. We treat the unresolved MPCs as a model error that results in perturbed subspace estimation. Starting from the first-order approximation of the perturbations, we derive the bias of the delay estimate of the line-of-sight (LOS) component. We show that it depends on the power and relative delay of the unresolved MPCs in the first cluster compared to the LOS component. Numerical experiments are included to show that the derived expression for the bias well describes the effects of unresolved MPCs on the delay estimation.
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The presence of rich scattering in indoor and urban radio propagation scenarios may cause a high arrival density of multipath components (MPCs). Often the MPCs arrive in clusters at the receiver, where MPCs within one cluster have similar angles and delays. The MPCs arriving within a single cluster are typically unresolvable in the delay domain. In this paper, we analyze the effects of unresolved MPCs on the bias of the delay estimation with a multiband subspace fitting algorithm. We treat the unresolved MPCs as a model error that results in perturbed subspace estimation. Starting from the first-order approximation of the perturbations, we derive the bias of the delay estimate of the line-of-sight (LOS) component. We show that it depends on the power and relative delay of the unresolved MPCs in the first cluster compared to the LOS component. Numerical experiments are included to show that the derived expression for the bias well describes the effects of unresolved MPCs on the delay estimation.
Sensor calibration is one of the fundamental challenges in large-scale Internet of Things networks. In this article, we address the challenge of reference-free calibration of a densely deployed sensor network. Conventionally, to calibrate an in-place sensor network (or sensor array), a reference is arbitrarily chosen with or without prior information on sensor performance. However, an arbitrary selection of a reference could prove fatal, if an erroneous sensor is inadvertently chosen. To avert single point of dependence, and to improve estimator performance, we propose unbiased reference-free algorithms. Although our focus is on reference-free solutions, the proposed framework allows the incorporation of additional references, if available. We show, with the help of simulations, that the proposed solutions achieve the derived statistical lower bounds asymptotically. In addition, the proposed algorithms show improvements on real-life datasets, as compared to prevalent algorithms.
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Sensor calibration is one of the fundamental challenges in large-scale Internet of Things networks. In this article, we address the challenge of reference-free calibration of a densely deployed sensor network. Conventionally, to calibrate an in-place sensor network (or sensor array), a reference is arbitrarily chosen with or without prior information on sensor performance. However, an arbitrary selection of a reference could prove fatal, if an erroneous sensor is inadvertently chosen. To avert single point of dependence, and to improve estimator performance, we propose unbiased reference-free algorithms. Although our focus is on reference-free solutions, the proposed framework allows the incorporation of additional references, if available. We show, with the help of simulations, that the proposed solutions achieve the derived statistical lower bounds asymptotically. In addition, the proposed algorithms show improvements on real-life datasets, as compared to prevalent algorithms.