Using a highly innovative approach that uses existing wireless equipment and signal processing techniques to convert multipaths into virtual antennas, combined with the physical principle of time reversal and machine learning, it covers fundamental theory, extensive experimental results, and real-world practical use cases developed for products and applications. Beibei Wang is the Chief Wireless Network Scientist at Origin Wireless, Inc. and is also affiliated with the University of Maryland.
Preface
- Introduction
- Multipaths as Virtual Antennas
- Time-Reversal Principle
- Principle of Effective Bandwidth
Then, in Chapter 19, we consider how the spatial focusing effect can be exploited for network design, and finally we introduce the tunneling effect of the time-reversal principle for the cloud radio access network. In physics, TR spatio-temporal resonance can be seen as the result of the resonance of the electromagnetic (EM) field in response to the environment [26].
Liu, "Time-reversal massiv multipath effect: A single-antenne massiv MIMO-løsning,"IEEE Transactions on Communications, vol. Liu, "Time-reversal division multiple access over multi-path channels,"IEEE Transactions on Communications, vol.
Indoor Locationing and Tracking
Introduction
Furthermore, due to the dense deployment of the antennas, the implementation cost is very high. However, most of the existing IPS algorithms cannot achieve a desired centimeter-level localization accuracy value, especially for a single AP operating in the NLOS state.
Time Reversal Indoor Positioning System
- Offline Training Phase
- Online Positioning Phase
In the online positioning phase, we first estimate the CIR information based on the signal received at the AP. To match the logical locations in the database, we first extract the feature using the resonant strength TR for each location.
Experiments
- Channel Reciprocity
- Channel Stationarity
- Spatial Focusing
Channel reciprocity is investigated by examining the CIR for forward and reverse links between TD and AP. Note that the center tap is the resonant power TR between the forward and reverse CIRs.
Introduction
However, CFR-based IPSs can hardly achieve an accuracy in the centimeter level due to the limited bandwidth in Wi-Fi systems. Is it possible to achieve the centimeter level accuracy on a Wi-Fi platform using the TR technique.
Related Work
- Lateration
- Angulation
- Lateration Combined with Angulation
Then SpotFi identifies the LOS path by performing clustering on the aggregated estimates from multiple packets. Like ArrayTrack and Phaser, SpotFi assumes that at least one AP can establish a LOS connection to the device.
Preliminaries
Nu, Nu is the number of useful subcarriers, Hnt,nr[uk] is the frequency domain representation of hnt,nr[]. 0,1,..,Lnt ,nr−1 on the kth carrier, Xi,nt[uk] is the representation of the frequency domain xnt[n] within the area of the ith LTF on the kth carrier and Wi,nt[uk] is the frequency domain noise of the ith LTF on the kth subcarrier.
Algorithm Design
- Training Phase
- Positioning Phase
- Configuration of Threshold
2 The reflectors in the environment also introduce linear phase shifts in the frequency domain CFRs. With a constraint imposed on the detection rate asPD,0 and the false alarm rate as PF A,0, the IPS automatically learns from CFRs inDtrain in the training phase.
Experiment Results
- Devices
- Details of Experiments
One participant is asked to walk randomly near the STA on the measurement structure with d=5 cm as the distance unit. Exp.3 analyzes the localization performance when we introduce environmental dynamics by moving the furniture in the office. Agrawala, "The Horus WLAN Location Finding System," in Proceedings of the 3rd ACM International Conference on Mobile Systems, Applications, and Services, 2005, pp.
Introduction
CFRs are then post-processed to mitigate synchronization errors as well as interference from other Wi-Fi networks. However, interference from other Wi-Fi networks can damage the fingerprint, which is neglected in [21]. We develop an IPS that achieves centimeter accuracy in an NLOS environment with a pair of single-antenna Wi-Fi devices.
Preliminaries
The proposed IPS eliminates the impact of interference from other Wi-Fi networks through the process of CFR screening. In Section 4.2, we introduce the TR technique and the channel estimation in Wi-Fi systems. Before transmitting the data payloads, the Wi-Fi transmitter sends preambles composed of short training headers (STPs), long training headers (LTPs) and cyclic prefixes.
Algorithm Design
- Offline Phase
CFR Sanitization
CFR Sifting
Bandwidth Concatenation
- Online Phase
- Frequency Hopping Mechanism
- Experiment Results
- Environment
- Configurations
- Details of Measurement
- Cumulative Density Functions of Diagonal and Off-Diagonal Entries under Different W e
- Discussion
- Introduction
- Related Works
- TR Focusing Ball Method for Distance Estimation
- Moving Direction Estimation and Error Correction
- Performance Evaluation
Thus, the computational complexity of the presented IPS scales linearly with the number of location fingerprints stored in the database. Agrawala, "The Horus WLAN Locating System," in Proceedings of the 3rd ACM International Conference on Mobile Systems, Applications, and Services, pp. WiBall estimates the paths the RX travels i.e. the location of the RXxat timeti is estimated as .
The procedures of the proposed TR-based distance estimator are summarized in the flowchart shown in Figure 5.7. In the following, the influence of the window length on the performance of the presented TR-based distance estimator is studied.
Wireless Sensing and Analytics
Introduction
In [14] the histograms of the CSI amplitudes were used to distinguish between different human activities. TR based indoor localization system was an active localization system in the sense that the object had to be located to carry one of the transmitting or receiving devices so the difference in the TR resonances between different locations of devices is large. Based on a similar principle to TRIPS, we use the TR technique to capture the variations in the multipath CSI due to different indoor events, and present TRIEDS for indoor event detection.
TRIEDS Overview
The principle of the TR technique can be referred to Chapter 1. As first investigated in phase compensation through telephone line [25], the TR technique was then extended to acoustics [26]. The definition of the TR resonant strength (TRRS) is given in (6.3), where h1andh2 represents the multipath profiles in the logical CSI space and g2 is the TR signature in the TR space. The higher the TRRS, the more similar the two points are in the TR space.
System Model
In TRIPS, each of the indoor physical locations was mapped to a logical location in TR space and can be easily separated and identified using TRRSs. Then the TR spatial-temporal resonance strength is a metric that quantifies the similarity between these two multipath profiles in the mapped TR space. The higher the TRRS is, the more two multipath profiles appear in TR space.
Experimental Evaluation
- LOC A: NLOS Case
- LOC B: LOS and NLOS Case
- LOC C: LOS Case
- Evaluations on LOC B
- Evaluations on LOC C
Meanwhile, the door D1 to be detected falls outside the LOS link between the transmitter and the receiver. Furthermore, the detection accuracy of TRIEDS improves as the distance between the transmitter and receiver increases. Moreover, as the distance between the receiver and transmitter increases, the accuracy of both.
Discussion
Harras, “WiGest: A Ubiquitous WiFi-Based Gesture Recognition System,” in Proceedings of the IEEE Conference on Computer Communications (INFOCOM), p. Ni, “WiFall: Device-less fall detection with wireless networks,” in Proceedings of the IEEE International Conference on Computer Communications (INFOCOM), p. Counting crowd using WiFi,” in Proceedings of the IEEE International Conference on Computer Communications (INFOCOM), p.
Introduction
To begin with, traditional passive wireless sensor systems are mainly based on received signal strength (RSS) [1-5]. Later, the amplitude and phase information of CSI was used in [7] to detect the dynamics of an indoor environment. Although the system achieved over 96.9% accuracy in multiple event detection using complex-valued CSI information, the system required a transmission below 125 MHz bandwidth, which cannot be implemented with commodity Wi-Fi.
Preliminaries
Moreover, the CSI received by Wi-Fi devices is in the frequency domain, i.e., the CSI is in the form of channel frequency response (CFR). As illustrated by the example in Figures 7.2(a) and 7.2(b), the log-normal distribution fits better over CSI samples captured from real channels, compared to the normal distribution. Furthermore, the derived log-normal distribution model is further investigated on the simulated CSI samples by studying the root mean square errors of the parameter estimates versus the signal-to-noise ratio (SNR), a.k.a., σ−1 in dB.
Design of TRIMS
- Offline Training Phase
- Online Monitoring Phase
- Phase I. Offline Training
- Phase II. Online Monitoring
In this section, the details of a statistics-based event detector are presented, and the diagram illustrating how the event detector works is shown in Figure 7.3. Details are discussed below. N, (7.14) where N is the size of S, i.e., the number of events of interest, and Mis the number of connections between transmitter and receiver. After that, for each indoor training event, the TRRS between the CSI proxy and the test measurement is calculated.
Experimental Results
- Study on Location of TX-RX
- Operational Test in House #1
- Long-Term Test in House #1
- Operational Test in House #2
- Long-Term Test in House #2
Therefore, it is critical to study how the locations of the TX and the RX affect the performance of the presented event detector. In addition, between opening the door in both figures, the decision of the presented event detector may become "Unknown". Furthermore, we perform a long-term monitoring test for the presented event detector in TRIMS in House #1 for 6 days.
Discussions
Ni, "WiFall: Device-free fall detection by wireless networks," i Proceedings of the International Conference on Computer Communications, s. Liu, "E-eyes: Device-free location-oriented activity identification using fine-grained WiFi signatures," i Proceedings of the 20th Annual ACM International Conference on Mobile Computing and Networking, s. Lu, "Forståelse og modellering af WiFi-signalbaseret menneskelig aktivitetsgenkendelse," i Proceedings of the 21st Annual ACM International Conference on Mobile Computing and Networking, s.
Introduction
In this chapter, we present a TR human identification system to identify individuals through the walls (i.e., in the absence of any LOS path), based on the human radiobiometrics in Wi-Fi signals. The system consists of two main algorithmic parts: refinement of human radiobiometrics and the TR-based identification. This chapter demonstrates the potential of using commercial Wi-Fi signals to capture human radiobiometrics for individual identifications.
TR Human Identification
Using the TR technique, human radiobiometrics in the form of complex-valued matrices are related to the corresponding individual through a real-valued scalar, the TRRS. Refinement of human radiobiometrics: This module extracts the human biometric information from the raw CSI measure, a complex-valued matrix of 9 × 114. Using the TR technique, human radiobiometrics are mapped into TR space , and the TRRS quantifies the differences between different radiobiometrics.
System Model
Specifically, we develop a background subtraction algorithm so that the common information in CSI can be removed and the characteristic human radiobiometrics preserved. After adjustment, based on the assumption that the human radiobiometrics contribute only small changes to the multipath, the background can be obtained by averaging multiple CSI measurements. Here, δh(m)i is the raw human radiobiometric information of the individual embedding in the CSI of this link.
Radio Biometrics Refinement Algorithm
After all the linear phase differences of the CSI measurements based on the reference have been compensated, the next step is to cancel the initial CSI phase for each link, including the reference. To simplify the notation, we will use instead of align to denote aligned CSI in the rest of the chapter. The human radio biometrics for each individual can then be derived by subtracting a scaled version of the background in (8.13) from the original CSI.
Performance Evaluation
For the system presented, if there are K subjects to be identified, the computational complexities for building the training database and testing are both O(M×(K+1)× Nlog2N), where Miss is the number of either the training CSI samples or testing CSI examples for each topic.
