Byzantine Attack and Defense in Cognitive Radio Networks: A Survey

The Byzantine attack in cooperative spectrum sensing (CSS), also known as the spectrum sensing data falsification (SSDF) attack in the literature, is one of the key adversaries to the success of cognitive radio networks (CRNs). In the past couple of years, the research on the Byzantine attack and defense strategies has gained worldwide increasing attention. In this paper, we provide a comprehensive survey and tutorial on the recent advances in the Byzantine attack and defense for CSS in CRNs. Specifically, we first briefly present the preliminaries of CSS for general readers, including signal detection techniques, hypothesis testing, and data fusion. Second, we analyze the spear and shield relation between Byzantine attack and defense from three aspects: the vulnerability of CSS to attack, the obstacles in CSS to defense, and the games between attack and defense. Then, we propose a taxonomy of the existing Byzantine attack behaviors and elaborate on the corresponding attack parameters, which determine where, who, how, and when to launch attacks. Next, from the perspectives of homogeneous or heterogeneous scenarios, we classify the existing defense algorithms, and provide an in-depth tutorial on the state-of-the-art Byzantine defense schemes, commonly known as robust or secure CSS in the literature. Furthermore, we highlight the unsolved research challenges and depict the future research directions.Comment: Accepted by IEEE Communications Surveys and Tutoiral

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

D11.2 Consolidated results on the performance limits of wireless communications

Deliverable D11.2 del projecte europeu NEWCOM#The report presents the Intermediate Results of N# JRAs on Performance Limits of Wireless Communications and highlights the fundamental issues that have been investigated by the WP1.1. The report illustrates the Joint Research Activities (JRAs) already identified during the first year of the project which are currently ongoing. For each activity there is a description, an illustration of the adherence and relevance with the identified fundamental open issues, a short presentation of the preliminary results, and a roadmap for the joint research work in the next year. Appendices for each JRA give technical details on the scientific activity in each JRA.Peer ReviewedPreprin

On the Intersection of Communication and Machine Learning

The intersection of communication and machine learning is attracting increasing interest from both communities. On the one hand, the development of modern communication system brings large amount of data and high performance requirement, which challenges the classic analytical-derivation based study philosophy and encourages the researchers to explore the data driven method, such as machine learning, to solve the problems with high complexity and large scale. On the other hand, the usage of distributed machine learning introduces the communication cost as one of the basic considerations for the design of machine learning algorithm and system.In this thesis, we first explore the application of machine learning on one of the classic problems in wireless network, resource allocation, for heterogeneous millimeter wave networks when the environment is with high dynamics. We address the practical concerns by providing the efficient online and distributed framework. In the second part, some sampling based communication-efficient distributed learning algorithm is proposed. We utilize the trade-off between the local computation and the total communication cost and propose the algorithm with good theoretical bound. In more detail, this thesis makes the following contributionsWe introduced an reinforcement learning framework to solve the resource allocation problems in heterogeneous millimeter wave network. The large state/action space is decomposed according to the topology of the network and solved by an efficient distribtued message passing algorithm. We further speed up the inference process by an online updating process.We proposed the distributed coreset based boosting framework. An efficient coreset construction algorithm is proposed based on the prior knowledge provided by clustering. Then the coreset is integrated with boosting with improved convergence rate. We extend the proposed boosting framework to the distributed setting, where the communication cost is reduced by the good approximation of coreset.We propose an selective sampling framework to construct a subset of sample that could effectively represent the model space. Based on the prior distribution of the model space or the large amount of samples from model space, we derive a computational efficient method to construct such subset by minimizing the error of classifying a classifier