Performance Bounds of Near-Field Sensing with Circular Arrays: Analyzing the Joint Impact of Bandwidth and Array Size

Alapfogalmak

The performance bounds of near-field sensing with circular arrays are analyzed, revealing the joint impact of bandwidth and array size on angle and distance estimation accuracy.

Kivonat

The paper studies the performance bounds of near-field sensing using circular arrays, focusing on the joint impact of bandwidth and array size. The key insights are:

Closed-form Cramér-Rao bounds (CRBs) for angle and distance estimation are derived, showing the scaling laws with bandwidth and array size.

Contrary to expectations, enlarging the array size does not always enhance sensing performance. The derived expressions include the existing results as special cases.

Angle estimation performance is more robust to the array size than distance estimation. Increasing the array aperture is more advantageous for angle estimation than increasing the number of antennas.

Bandwidth has a more significant impact on distance estimation than angle estimation. A larger bandwidth diminishes the near-field effect, making the performance approach the far-field bound more rapidly.

When the target distance is moderate, expanding the array aperture is more advantageous than increasing the bandwidth for distance estimation, without incurring additional hardware costs or using more spectrum resources.

The performance gain from adding more antennas and subcarriers stems solely from the increase in the total number of observations, rather than changing the near-field and wideband effects.

Statisztikák

The closed-form CRB for angle estimation is:
CRBθ = 3σ2 / (ρLNMR^2 (12f_c^2 + B^2 - Δf^2))
The closed-form CRB for distance estimation is:
CRBr = 3σ2 / (2ρLNM [12f_c^2 (1 - R^2 / (2r^2)) - K^2(r/R) + (B^2 - Δf^2)(1 - R^2 / (2r^2))])
where ρ is the channel gain and beamforming gain, R is the radius of the circular array, B is the signal bandwidth, and Δf is the subcarrier spacing.

Idézetek

"Contrary to expectations, enlarging array size does not always enhance sensing performance."
"Bandwidth has a more significant impact on distance estimation than angle estimation."
"When the target distance is moderate, expanding the array aperture is more advantageous than increasing the bandwidth for distance estimation."

Főbb Kivonatok

Performance Bounds of Near-Field Sensing with Circular Arrays

by Zhaolin Wang... : arxiv.org 04-29-2024

https://arxiv.org/pdf/2404.17318.pdf

Mélyebb kérdések

How can the performance bounds be further improved by optimizing the array geometry or the beamforming strategy

To further enhance the performance bounds of near-field sensing with circular arrays, optimizing the array geometry and beamforming strategy can play a crucial role.
Array Geometry Optimization:

Non-Uniform Array Configurations: Instead of a uniform circular array, utilizing non-uniform geometries can provide spatial diversity, improving angle estimation accuracy.
Nested Arrays: Implementing nested arrays within the circular array structure can offer additional degrees of freedom for signal processing, enhancing resolution and reducing estimation errors.
Sparse Arrays: Employing sparse arrays can help in reducing mutual coupling effects and improving the spatial resolution of the system.

Beamforming Strategy Optimization:

Adaptive Beamforming: Implementing adaptive beamforming techniques can dynamically adjust the array response to optimize the signal-to-noise ratio and mitigate interference, leading to more accurate estimation.
Hybrid Beamforming: Combining digital and analog beamforming can provide the benefits of both approaches, enabling efficient utilization of array elements and enhancing the overall system performance.
Multi-Beamforming: Utilizing multiple beams simultaneously can improve coverage, reduce blind spots, and enhance the robustness of the system in complex environments.

By strategically optimizing the array geometry and beamforming strategy, the system can achieve higher resolution, improved interference mitigation, and enhanced overall performance in near-field sensing applications.

What are the practical limitations and tradeoffs in implementing the near-field sensing system with circular arrays in real-world scenarios

Implementing near-field sensing systems with circular arrays in real-world scenarios involves practical limitations and tradeoffs that need to be considered:
Practical Limitations:

Hardware Complexity: Increasing the number of antennas in the circular array can lead to higher hardware complexity, including power consumption, calibration requirements, and cost considerations.
Signal Processing Complexity: Processing a large number of antenna elements in real-time can pose challenges in terms of computational complexity and latency, requiring efficient algorithms and hardware implementations.
Environmental Factors: Near-field sensing performance can be affected by environmental factors such as multipath propagation, interference, and non-line-of-sight conditions, which may degrade system accuracy.

Tradeoffs:

Resolution vs. Complexity: There is a tradeoff between achieving higher resolution in angle and distance estimation and the complexity of the system. Increasing resolution often requires more complex hardware and signal processing algorithms.
Range vs. Accuracy: Balancing the range of the sensing system with the accuracy of measurements is essential. Extending the range may compromise the accuracy of estimations, especially in near-field scenarios.
Cost vs. Performance: Optimizing the array geometry and beamforming strategy for improved performance must be weighed against the cost implications of deploying and maintaining such systems in practical applications.

Navigating these limitations and tradeoffs is crucial in designing near-field sensing systems with circular arrays that meet the requirements of specific real-world applications while ensuring a balance between performance, cost, and complexity.

What are the potential applications and use cases of the near-field sensing technology beyond wireless communications, and how can the insights from this work be extended to those domains

The insights gained from near-field sensing technology with circular arrays extend beyond wireless communications to various other domains, opening up diverse applications and use cases:
Potential Applications:

Automotive Industry: Near-field sensing can enhance object detection and localization in autonomous vehicles, improving safety and navigation in complex environments.
Healthcare: In medical imaging, near-field sensing can be utilized for precise localization of medical devices or monitoring vital signs with high accuracy in real-time.
Industrial IoT: Near-field sensing can optimize asset tracking, inventory management, and predictive maintenance in industrial settings, enhancing operational efficiency and productivity.
Environmental Monitoring: Near-field sensing technology can be applied for precise localization of environmental phenomena, such as pollution sources or wildlife tracking, aiding in conservation efforts.
Smart Agriculture: Implementing near-field sensing in agriculture can enable targeted irrigation, pest control, and crop monitoring, leading to sustainable farming practices and increased yields.

By leveraging the insights from this work, such as optimizing array geometry, beamforming strategies, and performance bounds, these applications can benefit from enhanced accuracy, efficiency, and reliability in near-field sensing systems. The adaptability of these insights across diverse domains showcases the versatility and potential impact of near-field sensing technology beyond wireless communications.