Download or read book Improving Next generation Wireless Network Performance and Reliability with Deep Learning written by Faris Bassam Mismar and published by . This book was released on 2019 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt: A rudimentary question whether machine learning in general, or deep learning in particular, could add to the well-established field of wireless communications, which has been evolving for close to a century, is often raised. While the use of deep learning based methods is likely to help build intelligent wireless solutions, this use becomes particularly challenging for the lower layers in the wireless communication stack. The introduction of the fifth generation of wireless communications (5G) has triggered the demand for “network intelligence” to support its promises for very high data rates and extremely low latency. Consequently, 5G wireless operators are faced with the challenges of network complexity, diversification of services, and personalized user experience. Industry standards have created enablers (such as the network data analytics function), but these enablers focus on post-mortem analysis at higher stack layers and have a periodicity in the time scale of seconds (or larger). The goal of this dissertation is to show a solution for these challenges and how a data-driven approach using deep learning could add to the field of wireless communications. In particular, I propose intelligent predictive and prescriptive abilities to boost reliability and eliminate performance bottlenecks in 5G cellular networks and beyond, show contributions that justify the value of deep learning in wireless communications across several different layers, and offer in-depth analysis and comparisons with baselines and industry standards. First, to improve multi-antenna network reliability against wireless impairments with power control and interference coordination for both packetized voice and beamformed data bearers, I propose the use of a joint beamforming, power control, and interference coordination algorithm based on deep reinforcement learning. This algorithm uses a string of bits and logic operations to enable simultaneous actions to be performed by the reinforcement learning agent. Consequently, a joint reward function is also proposed. I compare the performance of my proposed algorithm with the brute force approach and show that similar performance is achievable but with faster run-time as the number of transmit antennas increases. Second, in enhancing the performance of coordinated multipoint, I propose the use of deep learning binary classification to learn a surrogate function to trigger a second transmission stream instead of depending on the popular signal to interference plus noise measurement quantity. This surrogate function improves the users' sum-rate through focusing on pre-logarithmic terms in the sum-rate formula, which have larger impact on this rate. Third, performance of band switching can be improved without the need for a full channel estimation. My proposal of using deep learning to classify the quality of two frequency bands prior to granting the band switching leads to a significant improvement in users' throughput. This is due to the elimination of the industry standard measurement gap requirement—a period of silence where no data is sent to the users so they could measure the frequency bands before switching. In this dissertation, a group of algorithms for wireless network performance and reliability for downlink are proposed. My results show that the introduction of user coordinates enhance the accuracy of the predictions made with deep learning. Also, the choice of signal to interference plus noise ratio as the optimization objective may not always be the best choice to improve user throughput rates. Further, exploiting the spatial correlation of channels in different frequency bands can improve certain network procedures without the need for perfect knowledge of the per-band channel state information. Hence, an understanding of these results help develop novel solutions to enhancing these wireless networks at a much smaller time scale compared to the industry standards today

Download or read book Next Generation Wireless Networks Meet Advanced Machine Learning Applications written by Com?a, Ioan-Sorin and published by IGI Global. This book was released on 2019-01-25 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt: The ever-evolving wireless technology industry is demanding new technologies and standards to ensure a higher quality of experience for global end-users. This developing challenge has enabled researchers to identify the present trend of machine learning as a possible solution, but will it meet business velocity demand? Next-Generation Wireless Networks Meet Advanced Machine Learning Applications is a pivotal reference source that provides emerging trends and insights into various technologies of next-generation wireless networks to enable the dynamic optimization of system configuration and applications within the fields of wireless networks, broadband networks, and wireless communication. Featuring coverage on a broad range of topics such as machine learning, hybrid network environments, wireless communications, and the internet of things; this publication is ideally designed for industry experts, researchers, students, academicians, and practitioners seeking current research on various technologies of next-generation wireless networks.

Download or read book Machine Learning Enabled Radio Resource Management for Next Generation Wireless Networks written by Medhat Elsayed and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: A new era of wireless networks is evolving, thanks to the significant advances in communications and networking technologies. In parallel, wireless services are witnessing a tremendous change due to increasingly heterogeneous and stringent demands, whose quality of service requirements are expanding in several dimensions, putting pressure on mobile networks. Examples of those services are augmented and virtual reality, as well as self-driving cars. Furthermore, many physical systems are witnessing a dramatic shift into autonomy by enabling the devices of those systems to communicate and transfer control and data information among themselves. Examples of those systems are microgrids, vehicles, etc. As such, the mobile network indeed requires a revolutionary shift in the way radio resources are assigned to those services, i.e., RRM. In RRM, radio resources such as spectrum and power are assigned to users of the network according to various metrics such as throughput, latency, and reliability. Several methods have been adopted for RRM such as optimization-based methods, heuristics and so on. However, these methods are facing several challenges such as complexity, scalability, optimality, ability to learn dynamic environments. In particular, a common problem in conventional RRM methods is the failure to adapt to the changing situations. For example, optimization-based methods perform well under static network conditions, where an optimal solution is obtained for a snapshot of the network. This leads to higher complexity as the network is required to solve the optimization at every time slot. Machine learning constitutes a promising tool for RRM with the aim to address the conflicting objectives, i.e., KPIs, complexity, scalability, etc. In this thesis, we study the use of reinforcement learning and its derivatives for improving network KPIs. We highlight the advantages of each reinforcement learning method under the studied network scenarios. In addition, we highlight the gains and trade-offs among the proposed learning techniques as well as the baseline methods that rely on either optimization or heuristics. Finally, we present the challenges facing the application of reinforcement learning to wireless networks and propose some future directions and open problems toward an autonomous wireless network. The contributions of this thesis can be summarized as follows. First, reinforcement learning methods, and in particular model-free Q-learning, experience large convergence time due to the large state-action space. As such, deep reinforcement learning was employed to improve generalization and speed up the convergence. Second, the design of the state and reward functions impact the performance of the wireless network. Despite the simplicity of this observation, it turns out to be a key one for designing autonomous wireless systems. In particular, in order to facilitate autonomy, agents need to have the ability to learn/adjust their goals. In this thesis, we propose transfer in reinforcement learning to address this point, where knowledge is transferred between expert and learner agents with simple and complex tasks, respectively. As such, the learner agent aims to learn a more complex task using the knowledge transferred from an expert performing a simpler (partial) task.

Download or read book Federated Learning for Wireless Networks written by Choong Seon Hong and published by Springer Nature. This book was released on 2022-01-01 with total page 257 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recently machine learning schemes have attained significant attention as key enablers for next-generation wireless systems. Currently, wireless systems are mostly using machine learning schemes that are based on centralizing the training and inference processes by migrating the end-devices data to a third party centralized location. However, these schemes lead to end-devices privacy leakage. To address these issues, one can use a distributed machine learning at network edge. In this context, federated learning (FL) is one of most important distributed learning algorithm, allowing devices to train a shared machine learning model while keeping data locally. However, applying FL in wireless networks and optimizing the performance involves a range of research topics. For example, in FL, training machine learning models require communication between wireless devices and edge servers via wireless links. Therefore, wireless impairments such as uncertainties among wireless channel states, interference, and noise significantly affect the performance of FL. On the other hand, federated-reinforcement learning leverages distributed computation power and data to solve complex optimization problems that arise in various use cases, such as interference alignment, resource management, clustering, and network control. Traditionally, FL makes the assumption that edge devices will unconditionally participate in the tasks when invited, which is not practical in reality due to the cost of model training. As such, building incentive mechanisms is indispensable for FL networks. This book provides a comprehensive overview of FL for wireless networks. It is divided into three main parts: The first part briefly discusses the fundamentals of FL for wireless networks, while the second part comprehensively examines the design and analysis of wireless FL, covering resource optimization, incentive mechanism, security and privacy. It also presents several solutions based on optimization theory, graph theory, and game theory to optimize the performance of federated learning in wireless networks. Lastly, the third part describes several applications of FL in wireless networks.

Download or read book Towards a Robust Unified Internet written by Jyotirmoy Banik and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: A paradigm shift in connectivity is being experienced. Traditional demarcations among different types of networks are getting blurred. This organic development inevitably leads to a unified Internet architecture. Such an architecture would connect different types of networks, which also implies an integration of broad range of entities and services, by adopting Internet Protocol (IP). Undoubtedly, this architecture would invoke a proliferation of volume and diversity of network traffic. To address these challenges, it is of paramount importance to innovate proper architecture and algorithms for different segments of such a unified network. A robust next generation Internet should seamlessly accommodate different types of traffic, including Machine-to-Machine (M2M) type. Primary source of M2M traffic is the Internet of Things (IoT). Wireless network is a crucial component of such a system. In addition, it is evident that 5G is the technology of choice to connect a multitude of networks. Therefore, an improved user experience demands improvement in 5G networks and IoT networks. In this dissertation, innovative architectures and algorithms for both 5G networks and IoT networks are proposed. The first chapter describes the problem of scalability in typical cellular networks. A novel software defined unified architecture based on virtualized core network, which integrates both WLAN and LTE traffic, is proposed to reduce the signaling load on the core network. The second chapter focuses on the need of low-latency (or high throughput) in certain IoT networks. A modified scheduling algorithm based on multi-radio nodes and multiple sink nodes is proposed to address this problem. In addition to this, two novel retransmission strategies are proposed in this chapter. In the third chapter, the problem of stability in the IoT wireless networks caused by inefficient frequency hopping is described, which is followed by a channel blacklisting algorithm to improve the network stability.

Download or read book AI Machine Learning and Deep Learning written by Fei Hu and published by CRC Press. This book was released on 2023-06-05 with total page 347 pages. Available in PDF, EPUB and Kindle. Book excerpt: Today, Artificial Intelligence (AI) and Machine Learning/ Deep Learning (ML/DL) have become the hottest areas in information technology. In our society, many intelligent devices rely on AI/ML/DL algorithms/tools for smart operations. Although AI/ML/DL algorithms and tools have been used in many internet applications and electronic devices, they are also vulnerable to various attacks and threats. AI parameters may be distorted by the internal attacker; the DL input samples may be polluted by adversaries; the ML model may be misled by changing the classification boundary, among many other attacks and threats. Such attacks can make AI products dangerous to use. While this discussion focuses on security issues in AI/ML/DL-based systems (i.e., securing the intelligent systems themselves), AI/ML/DL models and algorithms can actually also be used for cyber security (i.e., the use of AI to achieve security). Since AI/ML/DL security is a newly emergent field, many researchers and industry professionals cannot yet obtain a detailed, comprehensive understanding of this area. This book aims to provide a complete picture of the challenges and solutions to related security issues in various applications. It explains how different attacks can occur in advanced AI tools and the challenges of overcoming those attacks. Then, the book describes many sets of promising solutions to achieve AI security and privacy. The features of this book have seven aspects: This is the first book to explain various practical attacks and countermeasures to AI systems Both quantitative math models and practical security implementations are provided It covers both "securing the AI system itself" and "using AI to achieve security" It covers all the advanced AI attacks and threats with detailed attack models It provides multiple solution spaces to the security and privacy issues in AI tools The differences among ML and DL security and privacy issues are explained Many practical security applications are covered

Download or read book Wireless Networks written by Hamid Jahankhani and published by Springer Nature. This book was released on 2023-09-24 with total page 352 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years, wireless networks communication has become the fundamental basis of our work, leisure, and communication life from the early GSM mobile phones to the Internet of Things and Internet of Everything communications. All wireless communications technologies such as Bluetooth, NFC, wireless sensors, wireless LANs, ZigBee, GSM, and others have their own challenges and security threats. This book addresses some of these challenges focusing on the implication, impact, and mitigations of the stated issues. The book provides a comprehensive coverage of not only the technical and ethical issues presented by the use of wireless networks but also the adversarial application of wireless networks and its associated implications. The authors recommend a number of novel approaches to assist in better detecting, thwarting, and addressing wireless challenges and threats. The book also looks ahead and forecasts what attacks can be carried out in the future through the malicious use of the wireless networks if sufficient defenses are not implemented. The research contained in the book fits well into the larger body of work on various aspects of wireless networks and cyber-security. The book provides a valuable reference for cyber-security experts, practitioners, and network security professionals, particularly those interested in the security of the various wireless networks. It is also aimed at researchers seeking to obtain a more profound knowledge in various types of wireless networks in the context of cyber-security, wireless networks, and cybercrime. Furthermore, the book is an exceptional advanced text for Ph.D. and master’s degree programs in cyber-security, network security, cyber-terrorism, and computer science who are investigating or evaluating a security of a specific wireless network. Each chapter is written by an internationally-renowned expert who has extensive experience in law enforcement, industry, or academia. Furthermore, this book blends advanced research findings with practice-based methods to provide the reader with advanced understanding and relevant skills.

Download or read book Driving 5G Mobile Communications with Artificial Intelligence towards 6G written by Dragorad A. Milovanovic and published by CRC Press. This book was released on 2023-04-06 with total page 503 pages. Available in PDF, EPUB and Kindle. Book excerpt: - provides some fundamental concepts related to 5G networks and the 5G NR signal processing. A review of AI and state of the art machine learning techniques is also given. - deals with the 5G/6G and AI enabled applications such as AR/VR, autonomous vehicles, mobile multimedia services, context aware communications, Industrial IoT and security. -elaborates on how AI techniques can enhance network and traffic management in 5G/6G networks. These include AI based mobility management, routing, scheduling, network performance optimization and even energy efficiency. -discusses the application of AI to 5G/6G NR signal processing and also the air interface. AI and deep learning techniques for channel coding, automatic modulation detection, channel estimation and equalization as well as spectrum management are presented with a view to highlight the benefits of using AI as compared to traditional techniques.

Download or read book On Efficiency and Intelligence of Next generation Wireless Networks written by Pedram Kheirkhah Sangdeh and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The ever-increasing demand for data-hungry wireless services and rapid proliferation of wireless devices in sub-6 GHz band have pushed the current wireless technologies to a breaking point, necessitating efficient and intelligent strategies to utilize scarce communication resources. This thesis aims at leveraging novel communication frameworks, artificial intelligence techniques, and synergies between them in bringing efficiency and intelligence to the next generation of wireless networks.In the first chapter of this thesis, we propose a novel spectrum sharing scheme to address spectrum shortage, a fundamental issue in current and future wireless networks. Our proposed scheme enables transparent spectrum utilization for a small cognitive radio network by leveraging two interference management techniques that are not reliant on inter-network coordination, fine-grained synchronization, and knowledge about other occupants of the spectrum. We further extend this idea in the second chapter of this thesis and enable concurrent device-to-device and cellular communications in cellular networks where the base station and wireless devices exploit interference management techniques to avoid causing interference to each other, making concurrent spectrum utilization possible for both cellular and device-to-device communications. In the third chapter, to enhance spectral efficiency, connectivity, and throughput of Wireless Local Area Networks (WLAN), we propose a non-orthogonal multiplexing scheme (NOMA). In our proposed scheme, the access point (AP) is equipped with a novel precoder design and user grouping which are tailored based on the requirements of power-domain NOMA. Also, a novel successive interference cancellation technique is designed for users which does not require channel estimation to decode the signals and is more resilient to interference compared to the existing techniques. The second part of this thesis focuses on taking advantage of artificial intelligence for solving communication and networking challenges and also taking advantage of novel communication frameworks to let future wireless networks indulge intelligence-oriented networking and resource management. In the fourth chapter, we propose a new solution to solve a long-standing issue ahead of multi-user multiple-input multiple-output (MU-MIMO) communications in WLANs, which is the large sounding overhead for acquiring the channel state information (CSI). Our learning-based solution includes an automated mechanism that enables access points to collect, clear, and balance dataset, and also deep neural networks to compress CSI and reduce the airtime overhead for channel acquisition. However, with provisioning concurrent MU-MIMO and orthogonal frequency division multiple access (OFDMA) in the new generation of WLANs, not only the sounding overhead problem becomes more acute, but it also marries with a complex resource allocation problem which makes designing a practical enabler of MU-MIMO-OFDMA transmissions necessary for WLANs. In the fifth chapter of this thesis, we propose DeepMux, which comprises a deep-learning-based channel sounding and a deep-learning-based resource allocation both of which reside in access points and impose no computational/communication burden on users, enabling efficient downlink MU-MIMO-OFDMA transmissions in WLANs. We finally design a communication framework for accelerating federated learning in future intelligent transportation systems, where heterogeneous capabilities and mobility of users along with limited available bandwidth for communications are huge obstacles toward making the network intelligent in a distributed manner. With the aid of a deadline-driven scheduler and asynchronous uplink multi-user MIMO, our proposed solution reduces data loss at vehicles in a dynamic vehicular environment, making a concrete step toward the practical adoption of federated learning in future transportation systems.

Download or read book Proceedings of the 12th International Conference on Computer Engineering and Networks written by Qi Liu and published by Springer Nature. This book was released on 2022-10-19 with total page 1506 pages. Available in PDF, EPUB and Kindle. Book excerpt: This conference proceeding is a collection of the papers accepted by the CENet2022 – the 12th International Conference on Computer Engineering and Networks held on November 4-7, 2022 in Haikou, China. The topics focus but are not limited to Internet of Things and Smart Systems, Artificial Intelligence and Applications, Communication System Detection, Analysis and Application, and Medical Engineering and Information Systems. Each part can be used as an excellent reference by industry practitioners, university faculties, research fellows and undergraduates as well as graduate students who need to build a knowledge base of the most current advances and state-of-practice in the topics covered by this conference proceedings. This will enable them to produce, maintain, and manage systems with high levels of trustworthiness and complexity.

Download or read book Machine Learning and Deep Learning Techniques in Wireless and Mobile Networking Systems written by K. Suganthi and published by CRC Press. This book was released on 2021-09-14 with total page 270 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers the latest advances and results in the fields of Machine Learning and Deep Learning for Wireless Communication and provides positive and critical discussions on the challenges and prospects. It provides a broad spectrum in understanding the improvements in Machine Learning and Deep Learning that are motivating by the specific constraints posed by wireless networking systems. The book offers an extensive overview on intelligent Wireless Communication systems and its underlying technologies, research challenges, solutions, and case studies. It provides information on intelligent wireless communication systems and its models, algorithms and applications. The book is written as a reference that offers the latest technologies and research results to various industry problems.

Download or read book Communication Efficient Federated Learning for Wireless Networks written by Mingzhe Chen and published by Springer Nature. This book was released on with total page 189 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Towards Machine Learning Enabled Future generation Wireless Network Optimization written by Peizhi Yan and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: We anticipate that there will be an enormous amount of wireless devices connected to the Internet through the future-generation wireless networks. Those wireless devices vary from self-driving vehicles to smart wearable devices and intelligent house- hold electrical appliances. Under such circumstances, the network resource optimization faces the challenge of the requirement of both flexibility and performance. Current wireless communication still relies on one-size-fits-all optimization algorithms, which require meticulous design and elaborate maintenance, thus not flexible and cannot meet the growing requirements well. The future-generation wireless networks should be "smarter", which means that the artificial intelligence-driven software-level design will play a more significant role in network optimization. In this thesis, we present three different ways of leveraging artificial intelligence (AI) and machine learning (ML) to design network optimization algorithms for three wireless Internet of things network optimization problems. Our ML-based approaches cover the use of multi-layer feed-forward artificial neural network and the graph convolutional network as the core of our AI decision-makers. The learning methods are supervised learning (for static decision-making) and reinforcement learning (for dynamic decision-making). We demonstrate the viability of applying ML in future- generation wireless network optimizations through extensive simulations. We summarize our discovery on the advantage of using ML in wireless network optimizations as the following three aspects: 1. Enabling the distributed decision-making to achieve the performance that near a centralized solution, without the requirement of multi-hop information; 2. Tackling with dynamic optimization through distributed self-learning decision- making agents, instead of designing a sophisticated optimization algorithm; 3. Reducing the time used in optimizing the solution of a combinatorial optimization problem. We envision that in the foreseeable future, AI and ML could help network service designers and operators to improve the network quality of experience swiftly and less expensively.

Download or read book Security and Privacy for Next Generation Wireless Networks written by Sheng Zhong and published by Springer. This book was released on 2018-11-22 with total page 183 pages. Available in PDF, EPUB and Kindle. Book excerpt: This timely book provides broad coverage of security and privacy issues in the macro and micro perspective. In macroperspective, the system and algorithm fundamentals of next-generation wireless networks are discussed. In micro-perspective, this book focuses on the key secure and privacy techniques in different emerging networks from the interconnection view of human and cyber-physical world. This book includes 7 chapters from prominent international researchers working in this subject area. This book serves as a useful reference for researchers, graduate students, and practitioners seeking solutions to wireless security and privacy related issues Recent advances in wireless communication technologies have enabled the large-scale deployment of next-generation wireless networks, and many other wireless applications are emerging. The next generation of mobile networks continues to transform the way people communicate and access information. As a matter of fact, next-generation emerging networks are exploiting their numerous applications in both military and civil fields. For most applications, it is important to guarantee high security of the deployed network in order to defend against attacks from adversaries, as well as the privacy intrusion. The key target in the development of next-generation wireless networks is to promote the integration of the human, cyber, and physical worlds. Previous work in Cyber Physical Systems (CPS) considered the connection between the cyber world and the physical world. In the recent studies, human involvement brings new channels and initiatives in this interconnection. In this integration process, security and privacy are critical issues to many wireless network applications, and it is a paramount concern for the growth of next-generation wireless networks. This is due to the open nature of wireless communication and the involvement of humans. New opportunities for tackling these security and privacy issues in next-generation wireless networks will be achieved by leveraging the properties of interaction among human, computers and things.

Download or read book Proceedings of the 2022 6th International Seminar on Education Management and Social Sciences ISEMSS 2022 written by Ghaffar Ali and published by Springer Nature. This book was released on 2023-09-16 with total page 3792 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is an open access book. The aim of 2022 6th International Seminar on Education, Management and Social Sciences (ISEMSS 2022) is to bring together innovative academics and industrial experts in the field of Education, Management and Social Sciences to a common forum. The primary goal of the conference is to promote research and developmental activities in Education, Management and Social Sciences and another goal is to promote scientific information interchange between researchers, developers, students, and practitioners working all around the world. The conference will be held every year to make it an ideal platform for people to share views and experiences in Education, Management and Social Sciences and related areas.

Download or read book Dynamic Spectrum Management written by Ying-Chang Liang and published by Springer Nature. This book was released on 2019-11-02 with total page 166 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book, authored by a world-leading researcher in this field, describes fundamentals of dynamic spectrum management, provides a systematic overview on the enabling technologies covering cognitive radio, blockchain, and artificial intelligence, and offers valuable guidance for designing advanced wireless communications systems. This book is intended for a broad range of readers, including students and professionals in this field, as well as radio spectrum policy makers.

Download or read book Deep Learning Powered Technologies written by Khaled Salah Mohamed and published by Springer Nature. This book was released on 2023-06-23 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers various, leading-edge deep learning technologies. The author discusses new applications of deep learning and gives insight into the integration of deep learning with various application domains, such as autonomous driving, augmented reality, AIOT, 5G and beyond.