1 / 14

Fundamental Limits of Channel State Information: Analyzing Measurement Error and Precision Bounds in Wireless Localizati

This study explores the fundamental limits of channel state information in wireless localization by evaluating measurement errors using Fisher Information Theory and a Time Delay Error Bound in a simplified model. The evaluation and conclusion provide insights into improving submeter-level accuracy through an analysis of SNR. The research aims to address the deviation caused by noise and multipath effects on RSSI and phase measurements, impacting localization precision. Future work includes examining the relationship between time delay and error bound, as well as experimental validation of results.

ramirezrichard
Download Presentation

Fundamental Limits of Channel State Information: Analyzing Measurement Error and Precision Bounds in Wireless Localizati

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Fundamentallimitsofchannelstateinformation SijieXiongandSujieZhu

  2. Outline • Introductiontopreviouswork • FisherInformationTheory • TimeDelayErrorBound • SimplifiedModel • EvaluationandConclusion 2

  3. previouswork • RSSIandLDPL(Log-distance path loss)model RSSI->distance&triangulation location multipatheffect-> rough!!!!! • Fingerprintlocalization locationisrelatedtothecharacteristicofrssi takethemultipathintoaccount • CSI(ChannelStateInformation) amplitudeandphasetogetsubmeter-levelaccuracy 3

  4. DinaKatabiwork Phase of the signal𝜋 4

  5. Motivation • Inreality,themeasurementofRSSIandphasearebothaffectedbythenoiseaswellasthemultipatheffect. • Thiscausesthemeasuredresulthasdeviationfromthetheoreticalvalue. • Thedeviationaffectsthelocalizationprecision. • Canweevaluatethemeasurementerror? • Canwegiveanerrorboundofthemeasurementresult?

  6. FisherInformationMatrix When there are N parameters, so that then the Fisher information takes the form of an N × N matrix,the Fisher Information Matrix (FIM), with typical element: Wheref(X; θ)istheprobability densityfunction for Xundertheparametersθ 6

  7. FIMfor Multivariate normal distribution The FIM for a N-variate multivariate normal distribution, X ~ N (μ(θ),Σ(θ)), has a special form. Let the K-dimensional vector of parameters be θ = [θ1 , . . . , θk ]T , and the vector of random normal variables be X =[ X1 , . . . , XN]T, with mean values μ(θ) = [μ1 (θ),...,μN (θ)]T, and let Σ(θ) be the covariance matrix. 7

  8. The construction of parameter vector The received signal: The parameter vector: 8

  9. PDF of received signal Discretization process: …… Covariance matrix: where E is the identity matrix

  10. The fisher information matrix The elements of the information matrix：

  11. Equivalent Fisher Information Matrix Given The FIM where EFIM

  12. A simplified model The parameter vector: The FIM： The time delay error bound：

  13. Analyze SNR ↑ error bound ↓ Supposing The time delay error bound：

  14. Future work The relationship between T/N and bound AP deployment Experiments to confirm results

More Related