An unsupervised learning technique to optimize radio maps for indoor localization

A major burden of signal strength-based fingerprinting for indoor positioning is the generation and maintenance of a radio map, also known as a fingerprint database. Model-based radio maps are generated much faster than measurement-based radio maps but are generally not accurate enough. This work proposes a method to automatically construct and optimize a model-based radio map. The method is based on unsupervised learning, where random walks, for which the ground truth locations are unknown, serve as input for the optimization, along with a floor plan and a location tracking algorithm. No measurement campaign or site survey, which are labor-intensive and time-consuming, or inertial sensor measurements, which are often not available and consume additional power, are needed for this approach. Experiments in a large office building, covering over 1100 m(2), resulted in median accuracies of up to 2.07 m, or a relative improvement of 28.6% with only 15 min of unlabeled training data

Automatic Wi-Fi Fingerprint System based on Unsupervised Learning

Recently, smartphones and Wi-Fi appliances have been generalized in daily life, and location-based service(LBS) has gradually been extended to indoor environments. Unlike outdoor positioning, which is typically handled by the global positioning system(GPS), indoor positioning technologies for providing LBSs have been studied with algorithms using various short-range wireless communications such as Wi-Fi, Ultra-wideband, Bluetooth, etc. Fingerprint-based positioning technology, a representative indoor LBS, estimates user locations using the received signal strength indicator(RSSI), indicating the relative transmission power of the access point(AP). Therefore, a fingerprint-based algorithm has the advantage of being robust to distorted wireless environments, such as radio wave reflections and refractions, compared to the time-of-arrival(TOA) method for non-line-of-sight(NLOS), where many obstacles exist. Fingerprint is divided into a training phase in which a radio map is generated by measuring the RSSIs of all indoor APs and positioning phase in which the positions of users are estimated by comparing the RSSIs of the generated radio map in real-time. In the training phase, the user collects the RSSIs of all APs measured at reference points set at regular intervals of 2 to 3m, creating a radio map. In the positioning phase, the reference point, which is most similar to the RSSI, compares the generated radio map from the training phase to the RSSI measured from user movements. This estimates the real-time indoor position. Fingerprint algorithms based on supervised and semi-supervised learning such as support vector machines and principal component analysis are essential for measuring the RSSIs in all indoor areas to produce a radio map. As the building size and the complexity of structures increases, the amount of work and time required also increase. The radio map generation algorithm that uses channel modeling does not require direct measurement, but it requires considerable effort because of building material, three-dimensional reflection coefficient, and numerical modeling of all obstacles. To overcome these problems, this thesis proposes an automatic Wi-Fi fingerprint system that combines an unsupervised dual radio mapping(UDRM) algorithm that reduces the time taken to acquire Wi-Fi signals and leverages an indoor environment with a minimum description length principle(MDLP)-based radio map feedback(RMF) algorithm to simultaneously optimize and update the radio map. The proposed UDRM algorithm in the training phase generates a radio map of the entire building based on the measured radio map of one reference floor by selectively applying the autoencoder and the generative adversarial network(GAN) according to the spatial structures. The proposed learning-based UDRM algorithm does not require labeled data, which is essential for supervised and semi-supervised learning algorithms. It has a relatively low dependency on RSSI datasets. Additionally, it has a high accuracy of radio map prediction than existing models because it learns the indoor environment simultaneously via a indoor two-dimensional map(2-D map). The produced radio map is used to estimate the real-time positioning of users in the positioning phase. Simultaneously, the proposed MDLP-based RMF algorithm analyzes the distribution characteristics of the RSSIs of newly measured APs and feeds the analyzed results back to the radio map. The MDLP, which is applied to the proposed algorithm, improves the performance of the positioning and optimizes the size of the radio map by preventing the indefinite update of the RSSI and by updating the newly added APs to the radio map. The proposed algorithm is compared with a real measurement-based radio map, confirming the high stability and accuracy of the proposed fingerprint system. Additionally, by generating a radio map of indoor areas with different structures, the proposed system is shown to be robust against the change in indoor environment, thus reducing the time cost. Finally, via a euclidean distance-based experiment, it is confirmed that the accuracy of the proposed fingerprint system is almost the same as that of the RSSI-based fingerprint system.|최근 스마트폰과 Wi-Fi가 실생활에 보편화되면서 위치기반 서비스에 대한 개발 분야가 실내 환경으로 점차 확대되고 있다. GPS로 대표되는 실외 위치 인식과 달리 위치기반 서비스를 제공하기 위한 실내 위치 인식 기술은 Wi-Fi, UWB, 블루투스 등과 같은 다양한 근거리 무선 통신 기반의 알고리즘들이 연구되고 있다. 대표적인 실내 위치인식 알고리즘 중 하나인 Fingerprint는 사용자가 수신한 AP 신호의 상대적인 크기를 나타내는 RSSI를 사용하여 위치를 추정한다. 따라서 Fingerprint기반의 알고리즘은 장애물이 많이 존재하는 비가시 거리에서 TOA 방식에 비해 전파의 반사 및 굴절과 같이 왜곡된 무선 환경에 강인하다는 장점이 있다. Fingerprint는 실내의 모든 AP의 RSSI들을 측정하여 Radio map을 제작하는 과정인 학습 단계와 생성된 Radio map의 RSSI들을 실시간으로 측정된 RSSI와 비교하여 사용자의 위치를 추정하는 위치인식 단계로 나누어진다. 학습 단계에서는 위치를 구분하기 위하여 사용자가 2~3m의 일정한 간격으로 설정된 참조 위치들마다 측정되는 모든 AP들의 RSSI를 수집하고 Radio map으로 제작한다. 위치인식 단계에서는 학습 단계에서 제작된 Radio map과 사용자의 이동에 의해 측정되는 RSSI의 비교를 통해 가장 유사한 RSSI 패턴을 가지는 참조 위치가 실시간 실내 위치로 추정된다. 서포트 벡터 머신(SVM), 주성분 분석(PCA) 등과 같이 지도 및 준지도 학습기반의 Fingerprint 알고리즘은 Radio map을 제작하기 위해 모든 실내 공간에서 RSSI의 측정이 필수적이다. 이러한 알고리즘들은 건물이 대형화되고 구조가 복잡해질수록 측정 공간이 늘어나면서 작업과 시간 소모가 또한 급격히 증가한다. 채널모델링을 통한 Radio map 생성 알고리즘은 직접적인 측정 과정이 불필요한 반면에 건물의 재질, 3차원적인 구조에 따른 반사 계수 및 모든 장애물에 대한 수치적인 모델링이 필수적이기 때문에 상당히 많은 작업량이 요구된다. 따라서 본 논문에서는 이러한 문제점들을 해결하고자 학습 단계에서 Wi-Fi 신호의 수집시간을 최소화하면서 실내 환경이 고려된 Unsupervised Dual Radio Mapping(UDRM) 알고리즘과 위치인식 단계에서 Radio map의 최적화가 동시에 가능한 Minimum description length principle(MDLP)기반의 Radio map Feedback(RMF) 알고리즘이 결합된 비지도학습기반의 자동 Wi-Fi Fingerprint를 제안한다. 학습 단계에서 제안하는 UDRM 알고리즘은 뉴럴 네트워크 기반의 비지도 학습 알고리즘인 Autoencoder와 Generative Adversarial Network (GAN)를 공간구조에 따라 선택적으로 적용하여 하나의 참조 층에서 측정된 Radio map을 기반으로 건물전체의 Radio map을 생성한다. 제안하는 비지도 학습 기반 UDRM 알고리즘은 지도 및 준지도 학습에서 필수적인 Labeled data가 필요하지 않으며 RSSI 데이터 세트의 의존성이 상대적으로 낮다. 또한 2차원 실내 지도를 통해 실내 환경을 동시에 학습하기 때문에 기존의 예측 모델에 비해 Radio map의 예측 정확도가 높다. 제안한 알고리즘에 의해 제작된 Radio map은 위치인식 단계에서 사용자의 실시간 위치인식에 적용된다. 동시에 제안하는 MDLP 기반의 자동 Wi-Fi 업데이트 알고리즘은 새롭게 측정되는 AP들의 RSSI의 분포특성을 분석하고 그 결과를 Radio map에 피드백한다. 제안한 알고리즘에 적용된 MDLP는 무분별한 RSSI의 업데이팅을 방지하고 추가되는 AP를 Radio map에 업데이트함으로서 위치인식의 성능을 향상시키고 Radio map의 크기의 최적화가 가능하다. 제안한 알고리즘은 실제 측정기반의 Radio map과 서로 비교를 통해 제안한 Fingerprint 시스템의 높은 안정성과 정확도를 확인하였다. 또한 구조가 다른 실내공간의 Radio map 생성 결과를 통해 실내 환경 변화에 강인함과 학습 시간 측정을 통한 시간 비용이 감소함을 확인하였다. 마지막으로 Euclidean distance 기반 실험을 통하여 실제 측정한 RSSI기반의 Fingerprint 시스템과 제안한 시스템의 위치인식 정확도가 거의 일치함을 확인하였다.Contents Contents ⅰ Lists of Figures and Tables ⅲ Abstract ⅵ Chapter 1 Introduction 01 1.1 Background and Necessity for Research 01 1.2 Objectives and Contents for Research 04 Chapter 2 Wi-Fi Positioning and Unsupervised Learning 07 2.1 Wi-Fi Positioning 07 2.1.1 Wi-Fi Signal and Fingerprint 07 2.1.2 Fingerprint Techniques 15 2.2 Unsupervised Learning 23 2.2.1 Neural Network 23 2.2.2 Autoencoder 28 2.2.3 Generative Adversarial Network 31 Chapter 3 Proposed Fingerprint System 36 3.1 Unsupervised Dual Radio Mapping Algorithm 36 3.2 MDLP-based Radio Map Feedback Algorithm 47 Chapter 4 Experiment and Result 51 4.1 Experimental Environment and Configuration 51 4.2 Results of Unsupervised Dual Radio Mapping Algorithm 56 4.2 Results of MDLP-based Radio Map Feedback Algorithm 69 Chapter 5 Conclusion 79 Reference 81Docto

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

A survey of machine learning techniques applied to self organizing cellular networks

In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial