Adaptive Channel-State-Information Feedback in Integrated Sensing and Communication Systems.

Efficient design of integrated sensing and communication systems can minimize signaling overhead by reducing the size and/or rate of feedback in reporting channel state information (CSI). To minimize the signaling overhead when performing sensing operations at the transmitter, this paper proposes a procedure to reduce the feedback rate. We consider a threshold-based sensing measurement and reporting procedure, such that the CSI is transmitted only if the channel variation exceeds a threshold. However, quantifying the channel variation, determining the threshold, and recovering sensing information with a lower feedback rate are still open problems. In this paper, we first quantify the channel variation by considering several metrics including the Euclidean distance, time-reversal resonating strength, and frequency-reversal resonating strength. We then design an algorithm to adaptively select a threshold, minimizing the feedback rate, while guaranteeing sufficient sensing accuracy by reconstructing high-quality signatures of human movement. To improve sensing accuracy with irregular channel measurements, we further propose two reconstruction schemes, which can be easily employed at the transmitter in case there is no feedback available from the receiver. Finally, the sensing performance of our scheme is extensively evaluated through real and synthetic channel measurements, considering channel estimation and synchronization errors. Our results show that the amount of feedback can be reduced by 50% while maintaining good sensing performance in terms of range and velocity estimations. Moreover, in contrast to other schemes, we show that the Euclidean distance metric is better able to capture various human movements with high channel variation values.

Integrated Sensing and Communication: Enabling Techniques, Applications, Tools and Data Sets, Standardization, and Future Directions.

The design of integrated sensing and communication (ISAC) systems has drawn recent attention for its capacity to solve a number of challenges. Indeed, ISAC can enable numerous benefits, such as the sharing of spectrum resources, hardware, and software, and improving the interoperability of sensing and communication. In this article, we seek to provide a thorough investigation of ISAC. We begin by reviewing the paradigms of sensing-centric design, communication-centric design, and co-design of sensing and communication. We then explore the enabling techniques that are viable for ISAC (i.e., transmit waveform design, environment modeling, sensing source, signal processing, and data processing). We also present some emergent smart-world applications that could benefit from ISAC. Furthermore, we describe some prominent tools used to collect sensing data and publicly available sensing data sets for research and development, as well as some standardization efforts. Finally, we highlight some challenges and new areas of research in ISAC, providing a helpful reference for ISAC researchers and practitioners, as well as the broader research and industry communities.

Towards Deep Q-Network Based Resource Allocation in Industrial Internet of Things.

With the increasing adoption of Industrial Internet of Things (IIoT) devices, infrastructures, and supporting applications, it is critical to design schemes to effectively allocate resources (e.g., networking, computing, and energy) in IIoT systems, generally formalized as optimization problems. Nonetheless, because the system is highly complex, operation environments are time-varying, and required information may not be available, it is difficult to leverage traditional optimization techniques to solve the optimal resource allocation problem. To this end, in this paper we propose a Deep Q-Network (DQN) based scheme to address both bandwidth utilization and energy efficiency in an IIoT system. In detail, we design a DQN model that consists of two deep neural networks (DNN) and a Q-learning model. The DNN network abstracts the features from the highly dimensional inputs and obtains the approximate Q-function for the Q-learning model. Based on the Q-function, the Q-learning model can generate the Q-table and reward function. After the training process, the DQN model can select appropriate actions for the agents (i.e., robots in a smart warehouse in this study) to improve bandwidth utilization and energy efficiency. To evaluate our proposed scheme, we design a simulation environment to investigate a typical IIoT scenario: the actuation of robotics in a smart warehouse. We then implement the DQN model and conduct extensive experiments to validate the efficacy of our scheme. Our experimental results confirm that our scheme can improve both bandwidth utilization and energy efficiency, as compared to other representative schemes.

Real-Time mmWave Channel Sounding Through Switched Beamforming With 3-D Dual-Polarized Phased-Array Antennas.

We propose a 28.5-GHz channel sounder that switches through all antennas of multiple dual-polarized 8 × 8 phased arrays at the transmitter and receiver and performs beamforming in postprocessing through digital weights to synthesize a sweepable beam. To our knowledge, we are the first to implement-what we refer to as-switched beamforming with phased arrays for millimeter-wave channel sounding, realized through highly stable Rubidium clocks and local oscillators coupled with precision over-the-air calibration techniques developed in house. By circumventing the time-consuming programming of analog weights that is associated with analog beamforming-what phased arrays are designed for-we can sweep a 3-D double-omnidirectional dual-polarized channel in just 1.3 ms, for real-time sounding. By in turn circumventing the coarse precision of analog weights, we can synthesize ideal beam patterns thanks to the effectively infinite precision of digital weights, enabling fine weight calibration for the nonidealities of the system hardware and fine weight tapering for sidelobe suppression. This translates to average estimation errors of 0.47° in 3-D double-directional angle, 0.48 dB in co-polarized path gain, and 0.18 ns in delay, as substantiated by field measurements.

Reinforcement Learning-Based Control and Networking Co-design for Industrial Internet of Things.

Industrial Internet-of-Things (IIoT), also known as Industry 4.0, is the integration of Internet of Things (IoT) technology into the industrial manufacturing system so that the connectivity, efficiency, and intelligence of factories and plants can be improved. From a cyber physical system (CPS) perspective, multiple systems (e.g., control, networking and computing systems) are synthesized into IIoT systems interactively to achieve the operator's design goals. The interactions among different systems is a non-negligible factor that affects the IIoT design and requirements, such as automation, especially under dynamic industrial operations. In this paper, we leverage reinforcement learning techniques to automatically configure the control and networking systems under a dynamic industrial environment. We design three new policies based on the characteristics of industrial systems so that the reinforcement learning can converge rapidly. We implement and integrate the reinforcement learning-based co-design approach on a realistic wireless cyber-physical simulator to conduct extensive experiments. Our experimental results demonstrate that our approach can effectively and quickly reconfigure the control and networking systems automatically in a dynamic industrial environment.

Towards Edge-Based Deep Learning in Industrial Internet of Things.

As a typical application of the Internet of Things (IoT), the Industrial Internet of Things (IIoT) connects all the related IoT sensing and actuating devices ubiquitously so that the monitoring and control of numerous industrial systems can be realized. Deep learning, as one viable way to carry out big data-driven modeling and analysis, could be integrated in IIoT systems to aid the automation and intelligence of IIoT systems. As deep learning requires large computation power, it is commonly deployed in cloud servers. Thus, the data collected by IoT devices must be transmitted to the cloud for training process, contributing to network congestion and affecting the IoT network performance as well as the supported applications. To address this issue, in this paper we leverage fog/edge computing paradigm and propose an edge computing-based deep learning model, which utilizes edge computing to migrate the deep learning process from cloud servers to edge nodes, reducing data transmission demands in the IIoT network and mitigating network congestion. Since edge nodes have limited computation ability compared to servers, we design a mechanism to optimize the deep learning model so that its requirements for computational power can be reduced. To evaluate our proposed solution, we design a testbed implemented in the Google cloud and deploy the proposed Convolutional Neural Network (CNN) model, utilizing a real-world IIoT dataset to evaluate our approach. Our experimental results confirm the effectiveness of our approach, which can not only reduce the network traffic overhead for IIoT, but also maintain the classification accuracy in comparison with several baseline schemes.

Efficient MU-MIMO Beamforming Protocol for IEEE 802.11ay WLANs.

IEEE 802.11ay supports multi-user multiple-input-multiple-output (MU-MIMO). However, the MU-MIMO beam-forming training (BFT) is a time-consuming process for finding appropriate directional antenna patterns, and inefficient BFT results in a long training time. Thus, in this letter, we propose an algorithm that configures the transmit antenna with the aim of reducing the number of redundant transmissions during MU-MIMO BFT. Both analytic and simulation results show that our proposed algorithm can significantly reduce the training time.

A user application-based access point selection algorithm for dense WLANs.

The current commercial access point (AP) selection schemes are mostly based on received signal strength, but perform poorly in many situations. To address this problem, a number of alternative schemes collect and analyze the actual load of every candidate AP. However, these schemes may incur significant latency and signaling overhead in dense wireless local area networks (WLANs). To mitigate such overhead, we propose a user application-based AP selection scheme that considers historical information about AP performance. Without inducing any signaling activity, our scheme monitors the amount of network traffic used by applications and estimates the achievable throughput of APs. Our scheme employs the characteristics of application traffic with the intent of accurately predicting AP performance. Using a measurement study in dense WLAN environments, we show that our scheme achieves higher throughput and lower association latency than those of existing schemes in places highly accessible to the user.

A Survey on Industrial Internet of Things: A Cyber-Physical Systems Perspective.

The vision of Industry 4.0, otherwise known as the fourth industrial revolution, is the integration of massively deployed smart computing and network technologies in industrial production and manufacturing settings for the purposes of automation, reliability, and control, implicating the development of an Industrial Internet of Things (I-IoT). Specifically, I-IoT is devoted to adopting the Internet of Things (IoT) to enable the interconnection of anything, anywhere, and at anytime in the manufacturing system context to improve the productivity, efficiency, safety and intelligence. As an emerging technology, I-IoT has distinct properties and requirements that distinguish it from consumer IoT, including the unique types of smart devices incorporated, network technologies and quality of service requirements, and strict needs of command and control. To more clearly understand the complexities of I-IoT and its distinct needs, and to present a unified assessment of the technology from a systems perspective, in this paper we comprehensively survey the body of existing research on I-IoT. Particularly, we first present the I-IoT architecture, I-IoT applications (i.e., factory automation (FA) and process automation (PA)) and their characteristics. We then consider existing research efforts from the three key systems aspects of control, networking and computing. Regarding control, we first categorize industrial control systems and then present recent and relevant research efforts. Next, considering networking, we propose a three-dimensional framework to explore the existing research space, and investigate the adoption of some representative networking technologies, including 5G, machine-to-machine (M2M) communication, and software defined networking (SDN). Similarly, concerning computing, we again propose a second three-dimensional framework that explores the problem space of computing in I-IoT, and investigate the cloud, edge, and hybrid cloud and edge computing platforms. Finally, we outline particular challenges and future research needs in control, networking, and computing systems, as well as for the adoption of machine learning, in an I-IoT context.

Towards Integrating Distributed Energy Resources and Storage Devices in Smart Grid.

Internet of Things (IoT) provides a generic infrastructure for different applications to integrate information communication techniques with physical components to achieve automatic data collection, transmission, exchange, and computation. The smart grid, as one of typical applications supported by IoT, denoted as a re-engineering and a modernization of the traditional power grid, aims to provide reliable, secure, and efficient energy transmission and distribution to consumers. How to effectively integrate distributed (renewable) energy resources and storage devices to satisfy the energy service requirements of users, while minimizing the power generation and transmission cost, remains a highly pressing challenge in the smart grid. To address this challenge and assess the effectiveness of integrating distributed energy resources and storage devices, in this paper we develop a theoretical framework to model and analyze three types of power grid systems: the power grid with only bulk energy generators, the power grid with distributed energy resources, and the power grid with both distributed energy resources and storage devices. Based on the metrics of the power cumulative cost and the service reliability to users, we formally model and analyze the impact of integrating distributed energy resources and storage devices in the power grid. We also use the concept of network calculus, which has been traditionally used for carrying out traffic engineering in computer networks, to derive the bounds of both power supply and user demand to achieve a high service reliability to users. Through an extensive performance evaluation, our data shows that integrating distributed energy resources conjointly with energy storage devices can reduce generation costs, smooth the curve of bulk power generation over time, reduce bulk power generation and power distribution losses, and provide a sustainable service reliability to users in the power grid.

A Throughput Study for Channel Bonding in IEEE 802.11ac Networks.

Several analytical models for the channel bonding feature of IEEE 802.11ac have previously been presented for performance estimation, but their accuracy has been limited by the assumptions that there are no collisions or all nodes are in saturated state. Therefore, in this letter, we develop an analytical model for the throughput performance of channel bonding in IEEE 802.11ac, considering the presence of collisions under both saturated and non-saturated traffic loads, and our numerical results were validated by a simulation study.