88 research outputs found
Deep Learning for Physical-Layer 5G Wireless Techniques: Opportunities, Challenges and Solutions
The new demands for high-reliability and ultra-high capacity wireless
communication have led to extensive research into 5G communications. However,
the current communication systems, which were designed on the basis of
conventional communication theories, signficantly restrict further performance
improvements and lead to severe limitations. Recently, the emerging deep
learning techniques have been recognized as a promising tool for handling the
complicated communication systems, and their potential for optimizing wireless
communications has been demonstrated. In this article, we first review the
development of deep learning solutions for 5G communication, and then propose
efficient schemes for deep learning-based 5G scenarios. Specifically, the key
ideas for several important deep learningbased communication methods are
presented along with the research opportunities and challenges. In particular,
novel communication frameworks of non-orthogonal multiple access (NOMA),
massive multiple-input multiple-output (MIMO), and millimeter wave (mmWave) are
investigated, and their superior performances are demonstrated. We vision that
the appealing deep learning-based wireless physical layer frameworks will bring
a new direction in communication theories and that this work will move us
forward along this road.Comment: Submitted a possible publication to IEEE Wireless Communications
Magazin
Low-Cost Indoor Localisation Based on Inertial Sensors, Wi-Fi and Sound
The average life expectancy has been increasing in the last decades, creating the need for
new technologies to improve the quality of life of the elderly. In the Ambient Assisted
Living scope, indoor location systems emerged as a promising technology capable of sup porting the elderly, providing them a safer environment to live in, and promoting their
autonomy. Current indoor location technologies are divided into two categories, depend ing on their need for additional infrastructure. Infrastructure-based solutions require
expensive deployment and maintenance. On the other hand, most infrastructure-free
systems rely on a single source of information, being highly dependent on its availability.
Such systems will hardly be deployed in real-life scenarios, as they cannot handle the
absence of their source of information. An efficient solution must, thus, guarantee the
continuous indoor positioning of the elderly.
This work proposes a new room-level low-cost indoor location algorithm. It relies
on three information sources: inertial sensors, to reconstruct users’ trajectories; environ mental sound, to exploit the unique characteristics of each home division; and Wi-Fi,
to estimate the distance to the Access Point in the neighbourhood. Two data collection
protocols were designed to resemble a real living scenario, and a data processing stage
was applied to the collected data. Then, each source was used to train individual Ma chine Learning (including Deep Learning) algorithms to identify room-level positions.
As each source provides different information to the classification, the data were merged
to produce a more robust localization. Three data fusion approaches (input-level, early,
and late fusion) were implemented for this goal, providing a final output containing
complementary contributions from all data sources.
Experimental results show that the performance improved when more than one source
was used, attaining a weighted F1-score of 81.8% in the localization between seven home
divisions. In conclusion, the evaluation of the developed algorithm shows that it can
achieve accurate room-level indoor localization, being, thus, suitable to be applied in
Ambient Assisted Living scenarios.O aumento da esperança média de vida nas últimas décadas, criou a necessidade de desenvolvimento de tecnologias que permitam melhorar a qualidade de vida dos idosos.
No âmbito da Assistência à Autonomia no DomicÃlio, sistemas de localização indoor têm
emergido como uma tecnologia promissora capaz de acompanhar os idosos e as suas atividades, proporcionando-lhes um ambiente seguro e promovendo a sua autonomia. As
tecnologias de localização indoor atuais podem ser divididas em duas categorias, aquelas
que necessitam de infrastruturas adicionais e aquelas que não. Sistemas dependentes de
infrastrutura necessitam de implementação e manutenção que são muitas vezes dispendiosas. Por outro lado, a maioria das soluções que não requerem infrastrutura, dependem
de apenas uma fonte de informação, sendo crucial a sua disponibilidade. Um sistema que
não consegue lidar com a falta de informação de um sensor dificilmente será implementado em cenários reais. Uma solução eficiente deverá assim garantir o acompanhamento
contÃnuo dos idosos.
A solução proposta consiste no desenvolvimento de um algoritmo de localização indoor de baixo custo, baseando-se nas seguintes fontes de informação: sensores inerciais,
capazes de reconstruir a trajetória do utilizador; som, explorando as caracterÃsticas dis tintas de cada divisão da casa; e Wi-Fi, responsável pela estimativa da distância entre o
ponto de acesso e o smartphone. Cada fonte sensorial, extraÃda dos sensores incorpora dos no dispositivo, foi, numa primeira abordagem, individualmente otimizada através de
algoritmos de Machine Learning (incluindo Deep Learning). Como os dados das diversas
fontes contêm informação diferente acerca das mesmas caracterÃsticas do sistema, a sua
fusão torna a classificação mais informada e robusta. Com este objetivo, foram implementadas três abordagens de fusão de dados (input data, early and late fusion), fornecendo um
resultado final derivado de contribuições complementares de todas as fontes de dados.
Os resultados experimentais mostram que o desempenho do algoritmo desenvolvido
melhorou com a inclusão de informação multi-sensor, alcançando um valor para F1-
score de 81.8% na distinção entre sete divisões domésticas. Concluindo, o algoritmo de
localização indoor, combinando informações de três fontes diferentes através de métodos
de fusão de dados, alcançou uma localização room-level e está apto para ser aplicado num
cenário de Assistência à Autonomia no DomicÃlio
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
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
The vast proliferation of sensor devices and Internet of Things enables the
applications of sensor-based activity recognition. However, there exist
substantial challenges that could influence the performance of the recognition
system in practical scenarios. Recently, as deep learning has demonstrated its
effectiveness in many areas, plenty of deep methods have been investigated to
address the challenges in activity recognition. In this study, we present a
survey of the state-of-the-art deep learning methods for sensor-based human
activity recognition. We first introduce the multi-modality of the sensory data
and provide information for public datasets that can be used for evaluation in
different challenge tasks. We then propose a new taxonomy to structure the deep
methods by challenges. Challenges and challenge-related deep methods are
summarized and analyzed to form an overview of the current research progress.
At the end of this work, we discuss the open issues and provide some insights
for future directions
Enabling AI in Future Wireless Networks: A Data Life Cycle Perspective
Recent years have seen rapid deployment of mobile computing and Internet of
Things (IoT) networks, which can be mostly attributed to the increasing
communication and sensing capabilities of wireless systems. Big data analysis,
pervasive computing, and eventually artificial intelligence (AI) are envisaged
to be deployed on top of the IoT and create a new world featured by data-driven
AI. In this context, a novel paradigm of merging AI and wireless
communications, called Wireless AI that pushes AI frontiers to the network
edge, is widely regarded as a key enabler for future intelligent network
evolution. To this end, we present a comprehensive survey of the latest studies
in wireless AI from the data-driven perspective. Specifically, we first propose
a novel Wireless AI architecture that covers five key data-driven AI themes in
wireless networks, including Sensing AI, Network Device AI, Access AI, User
Device AI and Data-provenance AI. Then, for each data-driven AI theme, we
present an overview on the use of AI approaches to solve the emerging
data-related problems and show how AI can empower wireless network
functionalities. Particularly, compared to the other related survey papers, we
provide an in-depth discussion on the Wireless AI applications in various
data-driven domains wherein AI proves extremely useful for wireless network
design and optimization. Finally, research challenges and future visions are
also discussed to spur further research in this promising area.Comment: Accepted at the IEEE Communications Surveys & Tutorials, 42 page
Improving Wifi Sensing And Networking With Channel State Information
In recent years, WiFi has a very rapid growth due to its high throughput, high efficiency, and low costs. Multiple-Input Multiple-Output (MIMO) and Orthogonal Frequency-Division Multiplexing (OFDM) are two key technologies for providing high throughput and efficiency for WiFi systems. MIMO-OFDM provides Channel State Information (CSI) which represents the amplitude attenuation and phase shift of each transmit-receiver antenna pair of each carrier frequency. CSI helps WiFi achieve high throughput to meet the growing demands of wireless data traffic. CSI captures how wireless signals travel through the surrounding environment, so it can also be used for wireless sensing purposes. This dissertation presents how to improve WiFi sensing and networking with CSI. More specifically, this dissertation proposes deep learning models to improve the performance and capability of WiFi sensing and presents network protocols to reduce CSI feedback overhead for high efficiency WiFi networking. For WiFi sensing, there are many wireless sensing applications using CSI as the input in recent years. To get a better understanding of existing WiFi sensing technologies and future WiFi sensing trends, this dissertation presents a survey of signal processing techniques, algorithms, applications, performance results, challenges, and future trends of CSI-based WiFi sensing. CSI is widely used for gesture recognition and sign language recognition. Existing methods for WiFi-based sign language recognition have low accuracy and high costs when there are more than 200 sign gestures. The dissertation presents SignFi for sign language recognition using CSI and Convolutional Neural Networks (CNNs). SignFi provides high accuracy and low costs for run-time testing for 276 sign gestures in the lab and home environments. For WiFi networking, although CSI provides high throughput for WiFi networks, it also introduces high overhead. WiFi transmitters need CSI feedback for transmit beamforming and rate adaptation. The size of CSI packets is very large and it grows very fast with respect to the number of antennas and channel width. CSI feedback introduces high overhead which reduces the performance and efficiency of WiFi systems, especially mobile and hand-held WiFi devices. This dissertation presents RoFi to reduce CSI feedback overhead based on the mobility status of WiFi receivers. CSI feedback compression reduces overhead, but WiFi receivers still need to send CSI feedback to the WiFi transmitter. The dissertation presents EliMO for eliminating CSI feedback without sacrificing beamforming gains
Single-Frequency Network Terrestrial Broadcasting with 5GNR Numerology
L'abstract è presente nell'allegato / the abstract is in the attachmen
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