The upcoming fifth generation (5G) of wireless networks is expected to lay a foundation of intelligent networks with the provision of some isolated artificial intelligence (AI) operations. However, fully intelligent network orchestration and management for providing innovative services will only be realized in Beyond 5G (B5G) networks. To this end, we envisage that the sixth generation (6G) of wireless networks will be driven by on-demand self-reconfiguration to ensure a many-fold increase in the network performance and service types. The increasingly stringent performance requirements of emerging networks may finally trigger the deployment of some interesting new technologies, such as large intelligent surfaces, electromagnetic–orbital angular momentum, visible light communications, and cell-free communications, to name a few. Our vision for 6G is a massively connected complex network capable of rapidly responding to the users’ service calls through real-time learning of the network state as described by the network edge (e.g., base-station locations and cache contents), air interface (e.g., radio spectrum and propagation channel), and the user-side (e.g., battery-life and locations). The multi-state, multi-dimensional nature of the network state, requiring the real-time knowledge, can be viewed as a quantum uncertainty problem. In this regard, the emerging paradigms of machine learning (ML), quantum computing (QC), and quantum ML (QML) and their synergies with communication networks can be considered as core 6G enablers. Considering these potentials, starting with the 5G target services and enabling technologies, we provide a comprehensive review of the related state of the art in the domains of ML (including deep learning), QC, and QML and identify their potential benefits, issues, and use cases for their applications in the B5G networks. Subsequently, we propose a novel QC-assisted and QML-based framework for 6G communication networks while articulating its challenges and potential enabling technologies at the network infrastructure, network edge, air interface, and user end. Finally, some promising future research directions for the quantum- and QML-assisted B5G networks are identified and discussed.

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Quantum Machine Learning for 6G Communication Networks: State-of-the-Art and Vision for the Future

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Global Positioning System Theory And Practice

Visible Light Positioning (VLP) provides a promising means to achieve indoor localization with sub-meter accuracy. We observe that the Visible Light Communication (VLC) methods in existing VLP systems rely on intensity-based modulation, and thus they require a high pulse rate to prevent flickering. However, the high pulse rate adds an unnecessary and heavy burden to receiving devices. To eliminate this burden, we propose the polarization-based modulation, which is flicker-free, to enable a low pulse rate VLC. In this way, we make VLP light-weight enough even for resourceconstrained wearable devices, e.g. smart glasses. Moreover, the polarization-based VLC can be applied to any illuminating light sources, thereby eliminating the dependency on LED. This paper presents the VLP system PIXEL, which realizes our idea. In PIXEL, we develop three techniques, each of which addresses a design challenge: 1) a novel color-based modulation scheme to handle users’ mobility, 2) an adaptive downsampling algorithm to tackle the uneven sampling problem of wearables’ low-cost camera and 3) a computational optimization method for the positioning algorithm to enable real-time processing. We implement PIXEL’s hardware using commodity components and develop a software program for both smartphone and Google glass. Our experiments based on the prototype show that PIXEL can provide accurate realtime VLP for wearables and smartphones with camera resolution as coarse as 60 pixel 80 pixel and CPU frequency as low as 300MHz.

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Wearables Can Afford: Light-weight Indoor Positioning with Visible Light (Best Paper Candidate, Best Video Presentation Award)

WLAN-based indoor positioning algorithm has the characteristics of simple layout and low price, and it has gradually become a hotspot in both academia and industry. However, due to the poor stability of Wi-Fi signals, received signal strength (RSS) fingerprints of some adjacent reference positions are difficult to evaluate similarity when utilizing traditional distance-based calculation methods. By clustering these RSS fingerprints into one region, the commonly utilized KNN algorithm in the past cannot achieve accurate positioning in the region. For this, we introduce a concept of the insensitive region of the RSS fingerprint and a new algorithm named DBSCAN-KRF. This algorithm can delete noise sample and detect insensitive region, then, different methods are selected to achieve indoor positioning by judging the region of the estimated fingerprint sample, the KNN algorithm is selected when the region is sensitive, and random forest algorithm is selected when the region is insensitive. The experimental results show that the DBSCAN-KRF algorithm is superior while compared with other alternative indoor positioning algorithms.

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Learning to Improve WLAN Indoor Positioning Accuracy Based on DBSCAN-KRF Algorithm From RSS Fingerprint Data

A set of Wi-Fi RSSI (Received Signal Strength Indicator) measurements is one of basic sensory observation available for indoor localization. One major drawback of the RSSI based localization is maintenance of the RSSI fingerprint database, which should be periodically updated against measurement pattern changes caused by relocation, removal and malfunction of Wi-Fi APs (access points). To address this problem, a new change detection method is proposed in this paper. First, by machine learning techniques, the RSSI database is reconstructed to a probabilistic feature database by the implementations of PCA (Principal Component Analysis) and GP (Gaussian Process). Then, KL (Kullback-Leibler) divergence is used as a metric to measure the similarity of the existing database and a newly arrived test sets. The proposed method is evaluated by a real experiment at a multi-storey building. For experimental study, different cases that provoke changes of RSSI patterns are considered, and the positioning accuracy is examined by the k-NN (Nearest Neighbor) method. From the experimental results, it is found that the bigger the RSSI pattern changes, the large the KL divergences become. Also, when a modified change detection algorithm as the benchmark, which does not implement the PCA feature extraction, is compared, the proposed algorithm yields accurate and fast computing performances. In addition, the required number of survey points is empirically found associated with the threshold value to trigger the detection alarm.

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Change Detection of RSSI Fingerprint Pattern for Indoor Positioning System

Smartphones equipped with Wi-Fi technology are widely used nowadays. Due to the need for inexpensive indoor positioning systems (IPSs), many researchers have focused on Wi-Fi-based IPSs, which use wireless local area network received signal strength (RSS) data that are collected at distinct locations in indoor environments called reference points. In this study, a new framework based on symmetric Bregman divergence, which incorporates k-nearest neighbor (kNN) classification in signal space, was proposed. The coordinates of the target were determined as a weighted combination of the nearest fingerprints using Jensen-Bregman divergences, which unify the squared Euclidean and Mahalanobis distances with information-theoretic Jensen-Shannon divergence measures. To validate our work, the performance of the proposed algorithm was compared with the probabilistic neural network and multivariate Kullback-Leibler divergence. The distance error for the developed algorithm was less than 1 m.

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Machine Learning Algorithm for Wireless Indoor Localization

Due to relatively high location accuracy, fingerprint-based localization is one of the best approaches in indoor localization that depends on received signal strength (RSS) from different Wi-Fi based indoor environments. Nowadays, Indoor positioning systems (IPS) are used in a wide range of daily life applications that need direction and navigation services. For example, IPS can play an essential role in the lives of people with vision impairment, who may become lost or disoriented in unfamiliar buildings, or need emergency healthcare services. In this paper, we propose the K-Means-Jensen-Shannon divergence, which is the original k-means algorithm extended into a meta-algorithm. The Bregman divergence is a versatile family of distance measurements that unifies the statistical entropic measures with the quadratic Euclidean distance. Nevertheless, the Bregman divergence is asymmetric; we took the right-sided and the left-sided data to symmetrize the centroid as the minimizer of the average Bregman divergence. To validate our proposed algorithm, the results were compared with the traditional k-mean and the affinity propagation algorithm. Our results indicate that our integrated system outperforms other results with around 1m accuracy in an academic building.

K-means-Jensen-Shannon divergence for a WLAN indoor positioning system

For decades, humans have been keen on creating smart spaces where advanced technology is utilized to provide enhanced services. Receiving directions and/or being recognized within indoor spaces is one feature of smart spaces that is currently heavily researched. Indoor positioning systems (IPS) can be used to provide a wide range of user navigation and directions services, particularly in abnormal conditions such as needing emergency healthcare services and being in unfamiliar complex buildings where some may become disoriented or lost. IPS also can be a friendly tool for people with vision impairment to allow for better livable communities for them. Other applications for IPS fall under tracking applications which may include activity recognition for security purposes and observation for the elderly or infirmed individuals. An indoor positioning system can be a hybrid system that uses multiple technologies such as wireless LAN, vision via cameras, motion sensors, or lasers to name few. In this paper we propose a technique for IPS using WiFi. The technique is based on a probabilistic neural network (PNN) scheme in which we incorporate the Jensen-Shannon divergence method. To validate our proposed method, we compare our results with the nearest neighbor method. Results indicate that our integrated system outperforms this method in terms of nearest neighbor estimation. Our results show that this method has the ability to achieve less than 1m accuracy in an academic building.

A probability neural network-Jensen-Shannon divergence for a fingerprint based localization

Modern indoor positioning system services are important technologies that play vital roles in modern life, providing many services such as recruiting emergency healthcare providers and for security purposes Several large companies, such as Microsoft, Apple, Nokia, and Google, have researched location-based services Wireless indoor localization is key for pervasive computing applications and network optimization Different approaches have been developed for this technique using WiFi signals WiFi fingerprinting-based indoor localization has been widely used due to its simplicity, and algorithms that fingerprint WiFi signals at separate locations can achieve accuracy within a few meters However, a major drawback of WiFi fingerprinting is the variance in received signal strength (RSS), as it fluctuates with time and changing environment As the signal changes, so does the fingerprint database, which can change the distribution of the RSS (multimodal distribution) Thus, in this paper, we propose that symmetrical Holder divergence, which is a statistical model of entropy that encapsulates both the skew Bhattacharyya divergence and Cauchy-Schwarz divergence that are closed-form formulas that can be used to measure the statistical dissimilarities between the same exponential family for the signals that have multivariate distributions The Holder divergence is asymmetric, so we used both left-sided and right-sided data so the centroid can be symmetrized to obtain the minimizer of the proposed algorithm The experimental results showed that the symmetrized Holder divergence consistently outperformed the traditional k nearest neighbor and probability neural network In addition, with the proposed algorithm, the position error accuracy was about 1 m in buildings

https://www.mdpi.com/1099-4300/20/9/639/pdf

Convex Optimization via Symmetrical Hölder Divergence for a WLAN Indoor Positioning System

Various techniques have been developed for Indoor Positioning Systems (IPS), a method that fingerprints the Received Signal Strength (RSS) of WiFi at specific places that can achieve high accuracy of about one meter at the exact location. A large range of indoor navigation needs and user services can be provided by using IPS, especially in unusual conditions such as being in large complex buildings or emergency healthcare needs, etc. In this paper, a framework that incorporates the probabilistic neural network (PNN) and Jensen-Bregman Divergence (JBD) is proposed. To validate our algorithm, the results were compared with PNN and kNN nearest neighbor. Where implemented inside an academic building, the algorithm results have high accuracy with an error of less than 1m distance.

Osamah Ali Abdullah

Papers

Machine Learning Algorithm for Wireless Indoor Localization

K-means-Jensen-Shannon divergence for a WLAN indoor positioning system

A probability neural network-Jensen-Shannon divergence for a fingerprint based localization

Convex Optimization via Symmetrical Hölder Divergence for a WLAN Indoor Positioning System

A PNN- Jensen-Bregman Divergence symmetrization for a WLAN Indoor Positioning System