Machine Learning for Time-of-Arrival Estimation With 5G Signals in Indoor Positioning

An extensive experimental study to characterize frequency range 1 (FR1) (i.e., sub-6 GHz) 5th generation (5G) signals from existing infrastructure for navigation is presented. The study uses a state-of-the-art 5G navigation software-defined radio (SDR) to track 5G signals in different environments and under different conditions to analyze the behavior of the received carrier-to-noise-ratio (C/N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> ), which directly affects the precision of the navigation performance. Three different experimental scenarios were conducted for this purpose with real 5G signals and 4th generation (4G) long-term evolution (LTE) signals for comparison purposes: (i) a stationary indoor scenario to study the effect of wall and floor partitions, (ii) a stationary outdoor scenario to study the effect of sampling rate, antenna grade, and clock quality, and (iii) a mobile outdoor experiment to study the C/N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> as a function of the range. All three scenarios confirmed the potential of downlink 4G and 5G signals for navigation.

As smartphones are increasingly popular, location-based services (LBSs) have become one of the crucial applications in daily lives. While outdoor localization is relatively easy leveraging GPS signals, localization in indoor environments is difficult due to the lack of GPS. Thus, due to the pervasive deployment of WiFi access points, there have been numerous studies on WiFi based indoor positioning. However, the multi-path fading of WiFi signals causes time-varying received signal strengths of WiFi signals, which leads to poor accuracy of WiFi localization. Moreover, WiFi scanning period, about 3∼4 seconds in general smartphone, may provide poor quality of services in the context of refreshment interval. Motivated by these limitations, we study the usability of tw o other sources that are currently available in smartphones: magnetic field strength and cellular signal strength, for indoor positioning purposes. Our preliminary measurements show that these two sources satisfy three properties needed for localization: time-in-variance, location representativeness and universality. Because these three sensors have their own characteristics, some problems in single sensory data based localization could be complementarily overcome. We will show how to combine the three smartphone sensory data for indoor positioning based on each module's characteristics, and how much accuracy is achieved by the hybrid localization.

Acquisition performance of B1I abounding with 5G signals

Using smartphones for accurate indoor localization opens a new frontier of mobile services, offering enormous opportunities to enhance users' experiences in indoor environments. Despite significant efforts on indoor localization in both academia and industry in the past two decades, highly accurate and practical smartphone-based indoor localization remains an open problem. To enable indoor location-based services (ILBS), there are several stringent requirements for an indoor localization system: highly accurate that can differentiate massive users' locations (foot-level); no additional hardware components or extensions on users' smartphones; scalable to massive concurrent users. Current GPS, Radio RSS (e.g. WiFi, Bluetooth, ZigBee), or Fingerprinting based solutions can only achieve meter-level or room-level accuracy. In this paper, we propose a practical and accurate solution that fills the long-lasting gap of smartphone-based indoor localization. Specifically, we design and implement an indoor localization ecosystem Guoguo. Guoguo consists of an anchor network with a coordination protocol to transmit modulated localization beacons using high-band acoustic signals, a realtime processing app in a smartphone, and a backend server for indoor contexts and location-based services. We further propose approaches to improve its coverage, accuracy, and location update rate with low-power consumption. Our prototype shows centimeter-level localization accuracy in an office and classroom environment. Such precise indoor localization is expected to have high impact in the future ILBS and our daily activities.

Future communication system requires large bandwidths to achieve high data rates, thus rendering analog-to-digital conversion (ADC) a bottleneck due to its high power consumption. In this paper, we consider monobit receivers for QPSK. The optimal monobit receiver under Nyquist sampling is obtained and its performance is analyzed. Then, a suboptimal but low-complexity receiver is proposed. The effect of imbalances between In-phase (I) and Quadrature (Q) branches is carefully examined. To combat the performance loss due to IQ imbalances, monobit receivers based on double training sequences and eight-sector phase quantization are proposed. Numerical simulations show that the low-complexity suboptimal receiver suffers 3dB signal-to-noise-ratio (SNR) loss in additive white Gaussian noise (AWGN) channels and only 1dB SNR loss in multipath channels compared with matched-filter monobit receiver with perfect channel state information (CSI). It is further demonstrated that the amplitude imbalance has essentially no effect on monobit receivers. In AWGN channels, receivers based on double training sequences can efficiently compensate for the SNR loss without complexity increase, while receivers with eight-sector phase quantization can almost completely eliminate the SNR loss caused by IQ imbalances. In dense multipath channels, the effect of imbalances on monobit receivers is slight.

In this paper, we consider an impulse-based ultra- wideband (UWB) receiver and assess the performance of different multiple access and modulation schemes, including time-hopping pulse position modulation (TH-PPM), time-hopping binary phase shift keying (TH-BPSK), and BPSK-BPSK, under both the additive white Gaussian noise (AWGN) and indoor multipath (CM3) channel assumptions. The assumed receiver combines the received pulses to increase the signal to noise ratio (SNR) and correlates the summed signal with the locally generated template. The performance of the different techniques are estimated using Monte-Carlo simulations and validated by comparing them with the theoretical results obtained for the AWGN channel with the characteristic function (CF) method presented in [1], Amongst other results, we show that the AWGN channel assumption provides rather optimistic results as compared to the more realistic CM3 indoor channel assumption.

The next step in location services will be repeating the success of location services in outdoor use for navigation and local search in the indoor environment. In order to make indoor positioning and navigation reality in large scale not only compelling business opportunities must be present, but also the technology must be low-cost, low-power, have low maintenance expenses and require minimal amount of new infrastructure. This paper reviews the challenges and solution models for indoor positioning starting from the service level aspects, reflecting them to the architectural considerations and finally discusses selected technical implementation issues.

In this paper, a novel coded modulation scheme named Trellis-Coded Differential Chaotic Modulation (TC-DCM) is proposed, which combines trellis codes with M-ary Differential Chaotic Shift Keying (M-DCSK). The new scheme not only reduces the multipath interference efficiently due to its inherent spreading property, but also avoids the use of Channel State Information (CSI), exact synchronization, despreading and the Rake receiver, making the corresponding receiver design simpler and more suitable for band-limited communication systems under severe multipath propagation conditions. Analytical Bit-Error-Rate (BER) bounds are derived and verified over Additive White Gaussian Noise (AWGN) channels. Both analytical performances and simulation results show that the proposed scheme with different states and different coding rates can obtain significant coding gains, compared to the corresponding uncoded schemes with the same bandwidth over AWGN channels. In addition, it is found from simulations that over multipath Rayleigh fading channels without CSI, the proposed TC-DCM system outperforms the Trellis-Coded Modulation Direct-Sequence Spread-Spectrum (TCM DS/SS) system with the same bandwidth efficiency.

We analyze the effect of a nonlinearity on the bit error rate performance of multicarrier spread spectrum (MCSS) systems in the presence of multipath fading. A 5th order polynomial model is used to represent the amplifier amplitude nonlinearities (AM/AM) and a flat Rayleigh fading channel is assumed for each modulated carrier. It is found that for the additive white Gaussian noise and multipath fading channel, the bit error degradation from a nonlinear amplification is not as significant as in the additive white Gaussian noise channel alone.

Nowadays, automatic modulation classification (AMC) plays an important role in both cooperative and non-cooperative communication applications. Very often, multipath fading channels result in the severe AMC performance degradation or induce large classification errors. The negative impacts of multipath fading channels on AMC have been discussed in the existing literature but no solution has ever been proposed so far to the best of our knowledge. In this paper, we propose a new robust AMC algorithm, which applies higher-order statistics (HOS) in a generic framework for blind channel estimation and pattern recognition. We also derive the Cramer-Rao lower bound for the fourth-order cumulant estimator when the AMC candidates are BPSK and QPSK over the additive white Gaussian noise channel, and it is a nearly minimum-variance estimator leading to robust AMC features in a wide variety of signal-to-noise ratios. The advantage of our new algorithm is that, by carefully designing the essential features needed for AMC, we do not really have to acquire the complete channel information and therefore it can be feasible without any a priori information in practice. The Monte Carlo simulation results show that our new AMC algorithm can achieve the much better classification accuracy than the existing AMC techniques.

With the rapid development of mobile communication technology, ranging and angling methods based on LTE and 5G signals have been widely used in location-based services (LBSs). However, mobile communication signals also undergo different degrees of occlusion and refraction during propagation because of the influence of buildings and human activities. The conventional ranging and angling methods are unable to extract high-precision navigation observations from these non-line-of-sight (NLOS) signals. Regarding the NLOS signals that seriously affect the performance of wireless positioning systems, this paper proposes a deep learning method based on the self-attention mechanism by distinguishing the difference characteristics in the autocorrelation functions of line-of-sight (LOS) and NLOS signals. It provides a basis for developing a stable LBS by accurately determining the state of received signals in complex indoor and outdoor environments. The simulation and field tests were carried out separately to evaluate the performance of the method developed in this paper under different scenarios, which achieved a discrimination accuracy better than 99.9% for NLOS signals.

Location-based service is one of the most popular buzzwords in the field of U-city. Positioning a user is an essential ingredient of a location-based system on a U-city. For the outdoor positioning, GPS based practical solutions have been introduced. However, GPS measurement error is too big to be used for U-campus services because the size of a campus is relatively smaller than a city. We propose Relative-Interpolation Method in order to improve the correctness of outdoor positioning. Besides, indoor positioning is necessary for U-campus while GPS signal is not available inside buildings. For the indoor positioning, Cricket, Active Badge, and so on have been introduced. These methods require special equipments dedicated for positioning. Our method does not require such equipments because it determines the user’s position based on the receiver signal strength indicators (RSSI) from access points (AP) which are already installed for WLAN. The algorithm we are using for indoor positioning is a kind of finger prints method. However our algorithm builds a decision tree instead of a look-up table in the off-line phase. Therefore, our method is faster than existing indoor positioning methods in the real-time phase. We have integrated our indoor and outdoor positioning methods and implemented a prototype of our indoor-outdoor positioning method on a laptop. Our experimental results are discussed in this paper.

Modern smartphones and location-based services and apps are poised to transform our daily life. However, current smartphone-based localization solutions are limited mainly to outdoor, mostly missing practical, robust and accurate indoor location solutions. Despite significant efforts on indoor localization in both academia and industry in the past two decades, highly accurate and practical smartphone-based indoor localization remains an open problem. To enable indoor location-based services (ILBS), e.g., step-by-step navigation for the Blind and visually impaired, there are several stringent requirements: highly accurate (foot-level); no additional hardware components or extensions on users’ smartphones; scalable to massive concurrent users. Current GPS, Radio RSS (e.g. Wi-Fi, Bluetooth, ZigBee), or Fingerprinting based solutions can only achieve meter-level or room-level accuracy. In this paper, we propose a practical and accurate solution that fills the long-lasting gap of smartphone-based fine-grained indoor localization. Specifically, we design and implement an indoor localization ecosystem Guoguo. Guoguo consists of an anchor network with a coordination protocol to transmit modulated localization beacons using high-band acoustic signals, a realtime processing app in a smartphone, and a backend server for indoor contexts and location-based services. We further propose approaches to improve its coverage, accuracy, and location update rate with low-power consumption. Our prototype shows centimeter-level localization accuracy in several typical indoor environments. Such precise indoor localization is expected to have high impact in the future ILBS and our daily activities.

Indoor navigation will become an important application on a smartphone for Location-Based Service (LBS).An indoor navigation system should work under normal circumstances and during emergencies, such as fires, during a building power shut down, alarm, etc.The LBS should be able to help users find the best exit route to the outside of the building under all circumstances and with high reliability.In this research, we develop an indoor ontology model for indoor navigation.This ontology model defines the indoor environment attributes such as location nodes, and connection points.The location nodes with the location information allow navigation in the indoor environment.Connection points are able to separate the map zones and the building floors into a "Map sheet."This ontology approach allows the LBS works in both normal circumstances and emergencies.This model provides a reliable indoor navigation system for LBS.

Indoor localization is a research field that has drawn the attention of many researchers over the last years. In some application scenarios, for example when assisting visually impaired persons with finding their way around within indoor environments, the aspect of dynamic obstacle detection plays an important role. In this work, an optimized localization algorithm based on field strength measurements is presented. Furthermore, methods for dynamic obstacle detection are developed and evaluated. For this purpose, a low-cost infrared array sensor is used. Since most of the encountered dynamic obstacles in indoor environments are generally persons, the major advantage of the proposed concept is that the anonymity of the detected persons is preserved and there are no conflicts with data protection regulations. It will be shown that reliable and accurate indoor localization based on field strength measurements within a wireless mesh network can be carried out. Also the detection of moving persons is possible and even the movement patterns of two persons simultaneously crossing the field of view of the sensor can be displayed. The achieved results are verified by extensive measurements and descriptive statistics.