Overview of Machine Learning Approaches for Wireless Communication (original) (raw)
Related papers
Machine Learning and Deep Learning Techniques on Wireless Networks
International Journal of Engineering Research and Technology, 2019
In this paper, we address the several issues and challenges for applying machine learning and deep learning techniques in wireless networks. Designing the machine learning base routing algorithm in heterogeneous networks is a big challenge. This article firstly introduces the basic concepts of machine learning and deep learning in wireless networks. Due to the dynamic behaviour of network scenarios in several ad-hoc networks (like vehicular ad-hoc networks and wireless sensor networks), it is very difficult task to prepare a data sets and training of that data. This paper also overviews several works that applied machine learning techniques and deep learning techniques on diverse research areas including networking, communications and lossy environment. The main aim of this survey work is to identify the possible issues and challenging tasks for applying the different deep learning and machine learning algorithms and strategies in wireless networks and find out a proper research direction aiming the realization of a system that detects, predicts and recovers from abnormal situations on wireless networks.
Journal of Communications and Networks, 2020
With recent advances, Artificial Intelligence (AI) and Machine Learning (ML) approaches have emerged to show great promise in the field of wireless communications. Although some researchers are skeptical due to issues concerning complexity and reliability, benefits include the near-optimal performance or the improvement over current state-of-the-art techniques. Luckily, the big data technology delivers an excellent advantage for studying the essential characteristics of wireless networks that can be integrated with AI and ML approaches. Moreover, the recent advances in deep learning, convolutional neural networks, and reinforcement learning hold significant promise. Indeed, they offer new design approaches for solving some challenging problems that, until recently, were considered intractable.
Security and Communication Networks
The intelligent wireless system focuses on integrating with the advanced technologies like machine learning and related approaches in order to enhance the performance, productivity, and output. The implementation of machine learning approaches is mainly applied in order to enhance the efficient communication system, enable creation of variable node locations, support collection of data and information, analyze the pattern, and forecast so as to provide better services to the end users. The efficiency of using these technologies tend to lower the cost and support in deploying the resources effectively. The wireless network system tends to enhance the bandwidth, and the application of novel machine learning approaches supports detection of unrelated data and information and enables analysis of latency at each part of the communication channel. The study involves critically analyzing the key determinants of machine learning approaches in supporting enhanced intelligent network communic...
Security and Communication Networks, 2022
The integration of the Internet of Things (IoT) connects a number of intelligent devices with minimum human interference that can interact with one another. IoT is rapidly emerging in the areas of computer science. However, new security problems are posed by the cross-cutting design of the multidisciplinary elements and IoT systems involved in deploying such schemes. Ineffective is the implementation of security protocols, i.e., authentication, encryption, application security, and access network for IoT systems and their essential weaknesses in security. Current security approaches can also be improved to protect the IoT environment effectively. In recent years, deep learning (DL)/machine learning (ML) has progressed significantly in various critical implementations. Therefore, DL/ML methods are essential to turn IoT system protection from simply enabling safe contact between IoT systems to intelligence systems in security. This review aims to include an extensive analysis of ML systems and state-of-the-art developments in DL methods to improve enhanced IoT device protection methods. On the other hand, various new insights in machine and deep learning for IoT securities illustrate how it could help future research. IoT protection risks relating to emerging or essential threats are identified, as well as future IoT device attacks and possible threats associated with each surface. We then carefully analyze DL and ML IoT protection approaches and present each approach’s benefits, possibilities, and weaknesses. This review discusses a number of potential challenges and limitations. The future works, recommendations, and suggestions of DL/ML in IoT security are also included.
Materials Today: Proceedings, 2020
In the modernized world, each and every individual requires smart gadget connectivity and the tremendous increment of such devices leads a growth of the world further. The predictable thing in future is approximately trillion and more smart gadgets will be there to establish the connection to server via Internet medium. The above quoted statements clearly illustrate the need of internet and wireless communications as well as its importance in future. A trending way to establish the internet over smart gadgets is by means of Internet-of-Things (IoT), which establishes a bridge in between gadgets and server through internet medium. With the help of such IoT device we can emulate the connections between client and server easily without any delay. The client end needs support like robustness, security, easy to connect between entities and privacy preservation with fault-free nature. The technologies are really an important medium to people to accomplish their communication needs, but the security threats and fault identifications over communication is the major concern to deal with such type of communications around internet medium worldwide. So, that this proposed approach mainly concentrate on fault identification mechanism and trust enabled communications with the help of Internet-of-Things (IoT) with powerful machine learning technique called Deep-cNN (DcNN), and the integration of both these techniques assist the proposed communication strategy well and it is combinely named as ''DcNNIoT". The proposed system aims to provide robust communication facility with Quality of Service enabled features along with fault free and trust worthy mode. The proposed result shows that the implementation of DcNNIoT provides maximized network lifetime and reduced network delay around entities.
Deep Learning in IoT systems: A Review
The expansion of the internet, along with its interconnection of devices has made it possible to increase the world's interconnectedness in these days, with the growth in internet connectivity capabilities and quality, a lot of items are interconnected, which means they communicate with each other using new and powerful techniques. Innovative sensor systems are spreading their consumers are strongly connected to the internet. The growth of linked sensors and systems has an incremental impact on the quantity of data. Regardless of its purpose, it is accumulating whole data. The Internet of Things (IoT) has a practical use for industries such as obtaining field data, tracking it and keeping them, all connected. To imitate the human intelligence level, the machine or software is made smarter by using advanced deep learning. In the paper, several diverse types of IoT technologies will be referenced, including intelligent cities, smart health care, mobility networks, and educational systems, among others. In addition, a range of novel deep learning algorithms that were implemented to simplify the intelligent usage of the machines without involving human control has been reviewed and good results of each algorithm in different categories are demonstrated as a table of comparison. This paper gives an overview of the applications that need to combine deep learning to serve IoT applications in an efficient and automated manner. IJSB Literature review
Deep Learning in Mobile and Wireless Networking: A Survey
—The rapid uptake of mobile devices and the rising popularity of mobile applications and services pose unprecedented demands on mobile and wireless networking infrastructure. Upcoming 5G systems are evolving to support exploding mobile traffic volumes, agile management of network resource to maximize user experience, and extraction of fine-grained real-time analytics. Fulfilling these tasks is challenging, as mobile environments are increasingly complex, heterogeneous, and evolving. One potential solution is to resort to advanced machine learning techniques to help managing the rise in data volumes and algorithm-driven applications. The recent success of deep learning underpins new and powerful tools that tackle problems in this space. In this paper we bridge the gap between deep learning and mobile and wireless networking research, by presenting a comprehensive survey of the crossovers between the two areas. We first briefly introduce essential background and state-of-the-art in deep learning techniques with potential applications to networking. We then discuss several techniques and platforms that facilitate the efficient deployment of deep learning onto mobile systems. Subsequently, we provide an encyclopedic review of mobile and wireless networking research based on deep learning, which we categorize by different domains. Drawing from our experience, we discuss how to tailor deep learning to mobile environments. We complete this survey by pinpointing current challenges and open future directions for research.
Machine Learning in IoT Security: Current Solutions and Future Challenges
IEEE Communications Surveys & Tutorials, 2020
The future Internet of Things (IoT) will have a deep economical, commercial and social impact on our lives. The participating nodes in IoT networks are usually resourceconstrained, which makes them luring targets for cyber attacks. In this regard, extensive efforts have been made to address the security and privacy issues in IoT networks primarily through traditional cryptographic approaches. However, the unique characteristics of IoT nodes render the existing solutions insufficient to encompass the entire security spectrum of the IoT networks. This is, at least in part, because of the resource constraints, heterogeneity, massive real-time data generated by the IoT devices, and the extensively dynamic behavior of the networks. Therefore, Machine Learning (ML) and Deep Learning (DL) techniques, which are able to provide embedded intelligence in the IoT devices and networks, are leveraged to cope with different security problems. In this paper, we systematically review the security requirements, attack vectors, and the current security solutions for the IoT networks. We then shed light on the gaps in these security solutions that call for ML and DL approaches. We also discuss in detail the existing ML and DL solutions for addressing different security problems in IoT networks. At last, based on the detailed investigation of the existing solutions in the literature, we discuss the future research directions for ML-and DL-based IoT security.
Thirty Years of Machine Learning: The Road to Pareto-Optimal Next-Generation Wireless Networks
arXiv (Cornell University), 2019
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 (Het-Nets), 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.