75 research outputs found

    Towards addressing training data scarcity challenge in emerging radio access networks: a survey and framework

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    The future of cellular networks is contingent on artificial intelligence (AI) based automation, particularly for radio access network (RAN) operation, optimization, and troubleshooting. To achieve such zero-touch automation, a myriad of AI-based solutions are being proposed in literature to leverage AI for modeling and optimizing network behavior to achieve the zero-touch automation goal. However, to work reliably, AI based automation, requires a deluge of training data. Consequently, the success of the proposed AI solutions is limited by a fundamental challenge faced by cellular network research community: scarcity of the training data. In this paper, we present an extensive review of classic and emerging techniques to address this challenge. We first identify the common data types in RAN and their known use-cases. We then present a taxonomized survey of techniques used in literature to address training data scarcity for various data types. This is followed by a framework to address the training data scarcity. The proposed framework builds on available information and combination of techniques including interpolation, domain-knowledge based, generative adversarial neural networks, transfer learning, autoencoders, fewshot learning, simulators and testbeds. Potential new techniques to enrich scarce data in cellular networks are also proposed, such as by matrix completion theory, and domain knowledge-based techniques leveraging different types of network geometries and network parameters. In addition, an overview of state-of-the art simulators and testbeds is also presented to make readers aware of current and emerging platforms to access real data in order to overcome the data scarcity challenge. The extensive survey of training data scarcity addressing techniques combined with proposed framework to select a suitable technique for given type of data, can assist researchers and network operators in choosing the appropriate methods to overcome the data scarcity challenge in leveraging AI to radio access network automation

    Beam Training and Tracking with Limited Sampling Sets: Exploiting Environment Priors

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    Beam training and tracking (BTT) are key technologies for millimeter wave communications. However, since the effectiveness of BTT methods heavily depends on wireless environments, complexity and randomness of practical environments severely limit the application scope of many BTT algorithms and even invalidate them. To tackle this issue, from the perspective of stochastic process (SP), in this paper we propose to model beam directions as a SP and address the problem of BTT via process inference. The benefit of the SP design methodology is that environment priors and uncertainties can be naturally taken into account (e.g., to encode them into SP distribution) to improve prediction efficiencies (e.g., accuracy and robustness). We take the Gaussian process (GP) as an example to elaborate on the design methodology and propose novel learning methods to optimize the prediction models. In particular, beam training subset is optimized based on derived posterior distribution. The GP-based SP methodology enjoys two advantages. First, good performance can be achieved even for small data, which is very appealing in dynamic communication scenarios. Second, in contrast to most BTT algorithms that only predict a single beam, our algorithms output an optimizable beam subset, which enables a flexible tradeoff between training overhead and desired performance. Simulation results show the superiority of our approach

    A Tutorial on Environment-Aware Communications via Channel Knowledge Map for 6G

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    Sixth-generation (6G) mobile communication networks are expected to have dense infrastructures, large-dimensional channels, cost-effective hardware, diversified positioning methods, and enhanced intelligence. Such trends bring both new challenges and opportunities for the practical design of 6G. On one hand, acquiring channel state information (CSI) in real time for all wireless links becomes quite challenging in 6G. On the other hand, there would be numerous data sources in 6G containing high-quality location-tagged channel data, making it possible to better learn the local wireless environment. By exploiting such new opportunities and for tackling the CSI acquisition challenge, there is a promising paradigm shift from the conventional environment-unaware communications to the new environment-aware communications based on the novel approach of channel knowledge map (CKM). This article aims to provide a comprehensive tutorial overview on environment-aware communications enabled by CKM to fully harness its benefits for 6G. First, the basic concept of CKM is presented, and a comparison of CKM with various existing channel inference techniques is discussed. Next, the main techniques for CKM construction are discussed, including both the model-free and model-assisted approaches. Furthermore, a general framework is presented for the utilization of CKM to achieve environment-aware communications, followed by some typical CKM-aided communication scenarios. Finally, important open problems in CKM research are highlighted and potential solutions are discussed to inspire future work

    Edge Learning for 6G-enabled Internet of Things: A Comprehensive Survey of Vulnerabilities, Datasets, and Defenses

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    The ongoing deployment of the fifth generation (5G) wireless networks constantly reveals limitations concerning its original concept as a key driver of Internet of Everything (IoE) applications. These 5G challenges are behind worldwide efforts to enable future networks, such as sixth generation (6G) networks, to efficiently support sophisticated applications ranging from autonomous driving capabilities to the Metaverse. Edge learning is a new and powerful approach to training models across distributed clients while protecting the privacy of their data. This approach is expected to be embedded within future network infrastructures, including 6G, to solve challenging problems such as resource management and behavior prediction. This survey article provides a holistic review of the most recent research focused on edge learning vulnerabilities and defenses for 6G-enabled IoT. We summarize the existing surveys on machine learning for 6G IoT security and machine learning-associated threats in three different learning modes: centralized, federated, and distributed. Then, we provide an overview of enabling emerging technologies for 6G IoT intelligence. Moreover, we provide a holistic survey of existing research on attacks against machine learning and classify threat models into eight categories, including backdoor attacks, adversarial examples, combined attacks, poisoning attacks, Sybil attacks, byzantine attacks, inference attacks, and dropping attacks. In addition, we provide a comprehensive and detailed taxonomy and a side-by-side comparison of the state-of-the-art defense methods against edge learning vulnerabilities. Finally, as new attacks and defense technologies are realized, new research and future overall prospects for 6G-enabled IoT are discussed

    An Overview on IEEE 802.11bf: WLAN Sensing

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    With recent advancements, the wireless local area network (WLAN) or wireless fidelity (Wi-Fi) technology has been successfully utilized to realize sensing functionalities such as detection, localization, and recognition. However, the WLANs standards are developed mainly for the purpose of communication, and thus may not be able to meet the stringent requirements for emerging sensing applications. To resolve this issue, a new Task Group (TG), namely IEEE 802.11bf, has been established by the IEEE 802.11 working group, with the objective of creating a new amendment to the WLAN standard to meet advanced sensing requirements while minimizing the effect on communications. This paper provides a comprehensive overview on the up-to-date efforts in the IEEE 802.11bf TG. First, we introduce the definition of the 802.11bf amendment and its formation and standardization timeline. Next, we discuss the WLAN sensing use cases with the corresponding key performance indicator (KPI) requirements. After reviewing previous WLAN sensing research based on communication-oriented WLAN standards, we identify their limitations and underscore the practical need for the new sensing-oriented amendment in 802.11bf. Furthermore, we discuss the WLAN sensing framework and procedure used for measurement acquisition, by considering both sensing at sub-7GHz and directional multi-gigabit (DMG) sensing at 60 GHz, respectively, and address their shared features, similarities, and differences. In addition, we present various candidate technical features for IEEE 802.11bf, including waveform/sequence design, feedback types, as well as quantization and compression techniques. We also describe the methodologies and the channel modeling used by the IEEE 802.11bf TG for evaluation. Finally, we discuss the challenges and future research directions to motivate more research endeavors towards this field in details.Comment: 31 pages, 25 figures, this is a significant updated version of arXiv:2207.0485

    Towards Context Information-based High-Performing Connectivity in Internet of Vehicle Communications

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    Internet-of-vehicles (IoV) is one of the most important use cases in the fifth generation (5G) of wireless networks and beyond. Here, IoV communications refer to two types of scenarios: serving the in-vehicle users with moving relays (MRs); and supporting vehicle-to-everything (V2X) communications for, e.g., connected vehicle functionalities. Both of them can be achieved by transceivers on top of vehicles with growing demand for quality of service (QoS), such as spectrum efficiency, peak data rate, and coverage probability. However, the performance of MRs and V2X is limited by challenges such as the inaccurate prediction/estimation of the channel state information (CSI), beamforming mismatch, and blockages. Knowing the environment and utilizing such context information to assist communication could alleviate these issues. This thesis investigates various context information-based performance enhancement schemes for IoV networks, with main contributions listed as follows.In order to mitigate the channel aging issue, i.e., the CSI becomes inaccurate soon at high speeds, the first part of the thesis focuses on one way to increase the prediction horizon of CSI in MRs: predictor antennas (PAs). A PA system is designed as a system with two sets of antennas on the roof of a vehicle, where the PAs positioned at the front of the vehicle are used to predict the CSI observed by the receive antennas (RAs) that are aligned behind the PAs. In PA systems, however, the benefit is affected by a variety of factors. For example, 1) spatial mismatch between the point where the PA estimates the channel and the point where the RA reaches several time slots later, 2) antenna utilization efficiency of the PA, 3) temporal evolution, and 4) estimation error of the PA-base station (BS) channel. First, in Paper A, we study the PA system in the presence of the spatial mismatch problem, and propose an analytical channel model which is used for rate adaptation. In paper B, we propose different approximation schemes for the analytical investigation of PA systems, and study the effect of different parameters on the network performance. Then, involving PAs into data transmission, Paper C and Paper D analyze the outage- and the delay-limited performance of PA systems using hybrid automatic repeat request (HARQ), respectively. As we show in the analytical and the simulation results in Papers C-D, the combination of PA and HARQ protocols makes it possible to improve spectral efficiency and adapt the transmission parameters to mitigate the effect of spatial mismatch. Finally, a review of PA studies in the literature, the challenges and potentials of PA as well as some to-be-solved issues are presented in Paper E.The second part of the thesis focuses on using advanced technologies to further improve the MR/IoV performance. In Paper F, a cooperative PA scheme in IoV networks is proposed to mitigate both the channel aging effect and blockage sensitivity in millimeter-wave channels by collaborative vehicles and BS handover. Then, in Paper G, we study the potentials and challenges of dynamic blockage pre-avoidance in IoV networks

    Indoor Positioning and Navigation

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    In recent years, rapid development in robotics, mobile, and communication technologies has encouraged many studies in the field of localization and navigation in indoor environments. An accurate localization system that can operate in an indoor environment has considerable practical value, because it can be built into autonomous mobile systems or a personal navigation system on a smartphone for guiding people through airports, shopping malls, museums and other public institutions, etc. Such a system would be particularly useful for blind people. Modern smartphones are equipped with numerous sensors (such as inertial sensors, cameras, and barometers) and communication modules (such as WiFi, Bluetooth, NFC, LTE/5G, and UWB capabilities), which enable the implementation of various localization algorithms, namely, visual localization, inertial navigation system, and radio localization. For the mapping of indoor environments and localization of autonomous mobile sysems, LIDAR sensors are also frequently used in addition to smartphone sensors. Visual localization and inertial navigation systems are sensitive to external disturbances; therefore, sensor fusion approaches can be used for the implementation of robust localization algorithms. These have to be optimized in order to be computationally efficient, which is essential for real-time processing and low energy consumption on a smartphone or robot

    Cell-Free Massive MIMO: Challenges and Promising Solutions

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    Along with its primary mission in fulfilling the communication needs of humans as well as intelligent machines, fifth generation (5G) and beyond networks will be a virtual fundamental component for all parts of life, society, and industries. These networks will pave the way towards realizing the individuals’ technological aspirations including holographic telepresence, e-Health, pervasive connectivity in smart environments, massive robotics, three-dimensional unmanned mobility, augmented reality, virtual reality, and internet of everything. This new era of applications brings unprecedented challenging demands to wireless network, such as high spectral efficiency, low-latency, high-reliable communication, and high energy efficiency. One of the major technological breakthroughs that has recently drawn the attention of researchers from academia and industry to cope with these unprecedented demands of wireless networks is the cell-free (CF) massive multiple-input multiple-output (mMIMO) systems. In CF mMIMO, a large number of spatially distributed access points are connected to a central processing unit (CPU). The CPU operates all APs as a single mMIMO network with no cell boundaries to serve a smaller number of users coherently on the same time-frequency resources. The system has shown substantial gains in improving the network performance from different perspectives, especially for cell-edge users, compared it other candidate technologies for 5G networks, \ie co-located mMIMO and small-cell (SC) systems. Nevertheless, the full picture of a practical scalable deployment of the system is not clear yet. In this thesis, we provide more in-depth investigations on the CF mMIMO performance under various practical system considerations. Also, we provide promising solutions to fully realize the potential of CF mMIMO in practical scenarios. In this regard, we focus on three vital practical challenges, namely hardware and channel impairments, malicious attacks, and limited-capacity fronthaul network. Regarding the hardware and channel impairments, we analyze the CF mMIMO performance under such practical considerations and compare its performance with SC systems. In doing so, we consider that both APs and user equipment (UE)s are equipped with non-ideal hardware components. Also, we consider the Doppler shift effect as a source of channel impairments in dynamic environments with moving users. Then, we derive novel closed-form expressions for the downlink (DL) spectral efficiency of both systems under hardware distortions and Doppler shift effect. We reveal that the effect of non-ideal UEs is more prominent than the non-ideal APs. Also, while increasing the number of deployed non-ideal APs can limit the hardware distortion effect in CF mMIMO systems, this leads to an extra performance loss in SC systems. Besides, we show that the Doppler shift effect is more harsh in SC systems. In addition, the SC system operation is more suitable for low-velocity users, however, it is more beneficial to adopt CF mMIMO system for network operation under high-mobility conditions. Capitalizing on the latter, we propose a hybrid CF mMIMO/SC system that can significantly outperforms both CF mMIMO and SC systems by providing different mobility conditions with high data rates simultaneously. Towards a further improvement in the CF mMIMO performance under high mobility scenarios, we propose a novel framework to limit the performance degradation due to the Doppler shift effect. To this end, we derive novel expressions for tight lower bound of the average DL and uplink (UL) data rates. Capitalizing on the derived analytical results, we provide an analytical framework that optimizes the frame length to minimize the Doppler shift effect on DL and UL data rates according to some criterion. Our results reveal that the optimal frame lengths for maximizing the DL and UL data rates are different and depend mainly on the users' velocities. Besides, adapting the frame length according to the velocity conditions significantly limits the Doppler shift effect, compared to applying a fixed frame length. To empower the CF mMIMO systems with secure transmission against malicious attacks, we propose two different approaches that significantly increases the achievable secrecy rates. In the first approach, we introduce a novel secure DL transmission technique that efficiently limits the eavesdropper (Eve) capability in decoding the transmitted signals to legitimate users. Differently, in the second approach, we adopt the distinctive features of Reconfigurable intelligent surfaces (RIS)s to limit the information leakage towards the Eve. Regarding the impact of limited capacity of wired-based fronthaul links, we drive the achievable DL data rates assuming two different CF mMIMO system operations, namely, distributed and centralized system operations. APs and CPU are the responsible entities for carrying out the signal processing functionalities in the distributed and centralized system operations, respectively. We show that the impact of limited capacity fronthaul links is more prominent on the centralized system operation. In addition, while the distributed system operation is more preferable under low capacities of fronthaul links, the centralized counterpart attains superior performance at high capacities of fronthaul links. Furthermore, considering the distributed and centralized system operations, and towards a practical and scalable operation of CF mMIMO systems, we propose a wireless-based fronthaul network for CF mMIMO systems under three different operations, namely, microwave-based, mmWave-based, and hybrid mmWave/microwave. Our results show that the integration between the centralized operation and the hybrid-based fronthaul network provides the highest DL data rates when APs are empowered with signal decoding capabilities. However, integrating the distributed operation with the microwave-based fronthaul network achieves ultimate performance when APs are not supported with decoding capabilities

    Transmission Design for Reconfigurable Intelligent Surface-Aided Wireless Systems

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    The performance benefits promised by Reconfigurable Intelligent Surface (RIS) are strongly dependent on the availability of highly accurate and up-to-date Channel State Information (CSI), which, however, is challenging to obtain. This thesis proposes efficient transceiver designs for a variety of CSI challenges such as worst channel condition in multicast systems, channel uncertainties caused by the presence of random blockages in millimeter wave systems, by the channel estimation error in downlink systems and by the presence of eavesdropper in security systems. First, a low-complexity transceiver design scheme in the multicast systems is proposed. In order to ensure the quality of service of the user with the worst channel condition, this thesis deploys an RIS to enhance signal coverage, and proposes two novel and efficient algorithms to jointly design the Base Station (BS) and RIS beamformings. The low-complexity algorithm with closed-form solutions is proved to have the same performance as the general second-order cone programming based algorithm. Second, novel fairness-oriented robust transceiver design schemes are proposed in RIS-aided millimeter wave systems. The channel uncertainty caused by the random blockages is analyzed, and the metric of maximum outage probability minimization is proposed. To address this problem, stochastic optimization techniques are adopted and closed-form solutions of the BS and RIS beamformings are then obtained. The proposed stochastic optimization algorithms are proved to converge to the set of stationary points. Third, a framework of robust transceiver design scheme is proposed to address the channel uncertainty caused by the cascaded BS-RIS-user channel estimation error. Two cascaded channel error models are analyzed, and the correspondingly two robust beamforming design problems are proposed. The optimization theory is used to address the complex non-convex optimization problems. The numerical results show that the proposed robust scheme can effectively resist channel uncertainty. Finally, robust transceiver design schemes are proposed in RIS-aided physical layer security systems. The schemes analyze the channel uncertainties caused by the eavesdropper who launches an active attack, and by the eavesdropper conducting passive eavesdropping. Numerical results show that the negative effect of the eavesdropper’s channel error is larger than that of the legitimate user
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