There is no doubt that big data are now rapidly expanding in all science and engineering domains. While the potential of these massive data is undoubtedly significant, fully making sense of them ...requires new ways of thinking and novel learning techniques to address the various challenges. In this paper, we present a literature survey of the latest advances in researches on machine learning for big data processing. First, we review the machine learning techniques and highlight some promising learning methods in recent studies, such as representation learning, deep learning, distributed and parallel learning, transfer learning, active learning, and kernel-based learning. Next, we focus on the analysis and discussions about the challenges and possible solutions of machine learning for big data. Following that, we investigate the close connections of machine learning with signal processing techniques for big data processing. Finally, we outline several open issues and research trends.
5G cellular networks are assumed to be the key enabler and infrastructure provider in the ICT industry, by offering a variety of services with diverse requirements. The standardization of 5G cellular ...networks is being expedited, which also implies more of the candidate technologies will be adopted. Therefore, it is worthwhile to provide insight into the candidate techniques as a whole and examine the design philosophy behind them. In this article, we try to highlight one of the most fundamental features among the revolutionary techniques in the 5G era, i.e., there emerges initial intelligence in nearly every important aspect of cellular networks, including radio resource management, mobility management, service provisioning management, and so on. However, faced with ever-increasingly complicated configuration issues and blossoming new service requirements, it is still insufficient for 5G cellular networks if it lacks complete AI functionalities. Hence, we further introduce fundamental concepts in AI and discuss the relationship between AI and the candidate techniques in 5G cellular networks. Specifically, we highlight the opportunities and challenges to exploit AI to achieve intelligent 5G networks, and demonstrate the effectiveness of AI to manage and orchestrate cellular network resources. We envision that AI-empowered 5G cellular networks will make the acclaimed ICT enabler a reality.
UAVs are expected to be an important complementary component for 5G (and beyond) communication systems to achieve the goal of global access to the Internet for all. To fully exploit the benefits of ...the distinct features of various UAVs, this article proposes a novel hierarchical network architecture enabled by software defined networking, which integrates cross-layer high and low altitude platforms into conventional terrestrial cellular networks to inject additional capacity and expand the coverage for underserved areas in a flexible, seamless, and cost-effective manner. Specifically, we first present a comprehensive comparison and review of different types of UAVs for communication services. Then, we propose an integrated airground heterogeneous network architecture and outline its characteristics and potential advantages. Next, several key enabling techniques for the integrated system are discussed in detail. In addition, we identify the potential application scenarios where the system can further enhance the performance of traditional terrestrial networks, followed by a case study to demonstrate the effectiveness of the proposed architecture. Finally, the discussions on challenges and open research issues are given.
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). Over 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 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. Then, 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 analyze the spear-and-shield relation between Byzantine attack and defense from an interactive game-theoretical perspective. Moreover, we highlight the unsolved research challenges and depict the future research directions.
This paper investigates the issue of spatial-temporal opportunity detection for spectrum-heterogeneous cognitive radio networks, where at a given time secondary users (SUs) at different locations may ...experience different spectrum access opportunities. Most prior studies address either spatial or temporal sensing in isolation and explicitly or implicitly assume that all SUs share the same spectrum opportunity. However, this assumption is not realistic and the traditional non-cooperative sensing (NCS) and cooperative sensing (CS) schemes are not very effective in a more realistic setting considering the heterogeneous spectrum availability among SUs. We define new performance metrics to guide the spatial-temporal opportunity detection and propose a two-dimensional sensing (TDS) framework to improve the opportunity detection performance, which exploits correlations in time and space simultaneously by effectively fusing sensing results in a spatial-temporal sensing window. Furthermore, in terms of maximum interference constrained transmission power (MICTP), we classify the spatial opportunities for SUs into three groups: black, grey, and white, and propose a TDS-based distributed power control scheme to further improve the spectrum utilization by exploiting both grey and white spectrum opportunities. The effectiveness of the proposed scheme is demonstrated through in-depth numerical simulations under a variety of scenarios.
Spectrum inference, also known as spectrum prediction in the literature, is a promising technique of inferring the occupied/free state of radio spectrum from already known/measured spectrum occupancy ...statistics by effectively exploiting the inherent correlations among them. In the past few years, spectrum inference has gained increasing attention owing to its wide applications in cognitive radio networks (CRNs), ranging from adaptive spectrum sensing, and predictive spectrum mobility, to dynamic spectrum access and smart topology control, to name just a few. In this paper, we provide a comprehensive survey and tutorial on the recent advances in spectrum inference. Specifically, we first present the preliminaries of spectrum inference, including the sources of spectrum occupancy statistics, the models of spectrum usage, and characterize the predictability of spectrum state evolution. By introducing the taxonomy of spectrum inference from a time-frequency-space perspective, we offer an in-depth tutorial on the existing algorithms. Furthermore, we provide a comparative analysis of various spectrum inference algorithms and discuss the metrics of evaluating the efficiency of spectrum inference. We also portray the various potential applications of spectrum inference in CRNs and beyond, with an outlook to the fifth-generation mobile communications and next generation high frequency communications systems. Last but not least, we highlight the critical research challenges and open issues ahead.
Current research on Internet of Things (IoT) mainly focuses on how to enable general objects to see, hear, and smell the physical world for themselves, and make them connected to share the ...observations. In this paper, we argue that only connected is not enough, beyond that, general objects should have the capability to learn, think, and understand both physical and social worlds by themselves. This practical need impels us to develop a new paradigm, named cognitive Internet of Things (CIoT), to empower the current IoT with a "brain" for high-level intelligence. Specifically, we first present a comprehensive definition for CIoT, primarily inspired by the effectiveness of human cognition. Then, we propose an operational framework of CIoT, which mainly characterizes the interactions among five fundamental cognitive tasks: perception-action cycle, massive data analytics, semantic derivation and knowledge discovery, intelligent decision-making, and on-demand service provisioning. Furthermore, we provide a systematic tutorial on key enabling techniques involved in the cognitive tasks. In addition, we also discuss the design of proper performance metrics on evaluating the enabling techniques. Last but not the least, we present the research challenges and open issues ahead. Building on the present work and potentially fruitful future studies, CIoT has the capability to bridge the physical world (with objects, resources, etc.) and the social world (with human demand, social behavior, etc.), and enhance smart resource allocation, automatic network operation, and intelligent service provisioning.
This letter investigates the transmit power and trajectory optimization problem for unmanned aerial vehicle (UAV)-aided networks. Different from majority of the existing studies with fixed ...communication infrastructure, a dynamic scenario is considered where a flying UAV provides wireless services for multiple ground nodes simultaneously. To fully exploit the controllable channel variations provided by the UAV's mobility, the UAV's transmit power and trajectory are jointly optimized to maximize the minimum average throughput within a given time length. For the formulated non-convex optimization with power budget and trajectory constraints, this letter presents an efficient joint transmit power and trajectory optimization algorithm. Simulation results validate the effectiveness of the proposed algorithm and reveal that the optimized transmit power shows a water-filling characteristic in spatial domain.
Spectrum misuse behaviors, brought either by illegitimate access or by rogue power emission, endanger the legitimate communication and deteriorate the spectrum usage environment. In this paper, our ...aim is to detect whether the spectrum band is occupied, and if it is occupied, recognize whether the misuse behavior exists. One vital challenge is that the legitimate spectrum exploitation and misuse behaviors probabilistically coexist and the illegitimate user may act in an intermittent and fast-changing manner, which brings about much uncertainty for spectrum sensing. To tackle it, we first formulate the spectrum sensing problems under illegitimate access and rogue power emission as a uniform ternary hypothesis test. Then, we develop a novel test criterion, named the generalized multi-hypothesis Neyman-Pearson (GMNP) criterion. Following the criterion, we derive two test rules based on the generalized likelihood ratio test and the Rao test, respectively, whose asymptotic performances are analyzed and an upper bound is also given. Furthermore, a cooperative spectrum sensing scheme is designed based on the global GMNP criterion to further improve the detection performances. In addition, extensive simulations are provided to verify the proposed schemes' performance under various parameter configurations.
In this paper, we investigate the issue of 2-D direction-of-arrival (DOA) estimation of multiple signals in co-prime planar arrays, where phase ambiguity problem arises due to the large distance ...between adjacent elements for each subarray. According to the co-prime characteristic, the ambiguity problem can be eliminated by searching for the common peaks of the spatial spectrum of each subarray, where the spectrum search involves a tremendous computation burden. In this paper, we exploit the property that all the ambiguous peaks for each DOA are uniformly distributed in a new transformed domain. Relying on the linear relations, we propose a partial spectral search (PSS)-based estimation method, where it involves a limited search over only a small sector. Therefore, the proposed PSS method is very computationally efficient. Numerical results are provided to verify the effectiveness of the proposed method over the state-of-the-art methods, in terms of both computational complexity and estimation accuracy.
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