203 research outputs found

    Surface MIMO: Using Conductive Surfaces For MIMO Between Small Devices

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    As connected devices continue to decrease in size, we explore the idea of leveraging everyday surfaces such as tabletops and walls to augment the wireless capabilities of devices. Specifically, we introduce Surface MIMO, a technique that enables MIMO communication between small devices via surfaces coated with conductive paint or covered with conductive cloth. These surfaces act as an additional spatial path that enables MIMO capabilities without increasing the physical size of the devices themselves. We provide an extensive characterization of these surfaces that reveal their effect on the propagation of EM waves. Our evaluation shows that we can enable additional spatial streams using the conductive surface and achieve average throughput gains of 2.6-3x for small devices. Finally, we also leverage the wideband characteristics of these conductive surfaces to demonstrate the first Gbps surface communication system that can directly transfer bits through the surface at up to 1.3 Gbps.Comment: MobiCom '1

    Landing AI on Networks: An equipment vendor viewpoint on Autonomous Driving Networks

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    The tremendous achievements of Artificial Intelligence (AI) in computer vision, natural language processing, games and robotics, has extended the reach of the AI hype to other fields: in telecommunication networks, the long term vision is to let AI fully manage, and autonomously drive, all aspects of network operation. In this industry vision paper, we discuss challenges and opportunities of Autonomous Driving Network (ADN) driven by AI technologies. To understand how AI can be successfully landed in current and future networks, we start by outlining challenges that are specific to the networking domain, putting them in perspective with advances that AI has achieved in other fields. We then present a system view, clarifying how AI can be fitted in the network architecture. We finally discuss current achievements as well as future promises of AI in networks, mentioning a roadmap to avoid bumps in the road that leads to true large-scale deployment of AI technologies in networks

    Graphene-based Wireless Agile Interconnects for Massive Heterogeneous Multi-chip Processors

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    The main design principles in computer architecture have recently shifted from a monolithic scaling-driven approach to the development of heterogeneous architectures that tightly co-integrate multiple specialized processor and memory chiplets. In such data-hungry multi-chip architectures, current Networks-in-Package (NiPs) may not be enough to cater to their heterogeneous and fast-changing communication demands. This position paper makes the case for wireless in-package nanonetworking as the enabler of efficient and versatile wired-wireless interconnect fabrics for massive heterogeneous processors. To that end, the use of graphene-based antennas and transceivers with unique frequency-beam reconfigurability in the terahertz band is proposed. The feasibility of such a nanonetworking vision and the main research challenges towards its realization are analyzed from the technological, communications, and computer architecture perspectives.Comment: 8 pages, 4 figures, 1 table - Accepted at IEEE Wireless Communications Magazin

    SD-MCAN: A Software-Defined Solution for IP Mobility in Campus Area Networks

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    Campus Area Networks (CANs) are a subset of enterprise networks, comprised of a network core connecting multiple Local Area Networks (LANs) across a college campus. Traditionally, hosts connect to the CAN via a single point of attachment; however, the past decade has seen the employment of mobile computing rise dramatically. Mobile devices must obtain new Internet Protocol (IP) addresses at each LAN as they migrate, wasting address space and disrupting host services. To prevent these issues, modern CANs should support IP mobility: allowing devices to keep a single IP address as they migrate between LANs with low-latency handoffs. Traditional approaches to mobility may be difficult to deploy and often lead to inefficient routing, but Software-Defined Networking (SDN) provides an intriguing alternative. This thesis identifies necessary requirements for a software-defined IP mobility system and then proposes one such system, the Software-Defined Mobile Campus Area Network (SD-MCAN) architecture. SD-MCAN employs an OpenFlow-based hybrid, label-switched routing scheme to efficiently route traffic flows between mobile hosts on the CAN. The proposed architecture is then implemented as an application on the existing POX controller and evaluated on virtual and hardware testbeds. Experimental results show that SD-MCAN can process handoffs with less than 90 ms latency, suggesting that the system can support data-intensive services on mobile host devices. Finally, the POX prototype is open-sourced to aid in future research

    Measurement-based feasibility exploration on detecting and localizing multiple humans using MIMO radio channel properties

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    This paper explores the feasibility of using the multiple-input multiple-output (MIMO) radio channel properties to passively detect and localize multiple humans in indoor environments. We propose to utilize the unique reverberation characteristics of indoor channels for the purpose of detecting, and the power angular delay profile (PADP) for localizing humans. On the one hand, the reverberation time corresponds with the decay rate of multipath in a closed or partially closed cavity, and varies with the change of the number of humans or the moving of humans relative to the antennas at link ends. On the other hand, the PADP is proposed to be calculated by the Multiple Signal Classification (MUSIC) super resolution algorithm with frequency smoothing preprocessing. The proposed approach is evaluated based on real-world MIMO radio channel measurements obtained from a meeting room. Measurements with and without the presence of humans have been conducted, where the maximum number of humans considered is four. Humans facing different directions, either in parallel or orthogonal to the direct line between the transmit and the receive antennas have been taken into account. In term of the detection feasibility, it is found that the change of the number of humans as well as the change of their facing/moving directions inside the partial reverberant region can be reflected on the change of the reverberation time estimated from the power delay profile of channel. In term of the localization feasibility, it is found that single human location can be well associated to the peak of the variation of the PADP during his/her movement, while multiple humans' movements result in obvious power variation in the very vicinity of some of them, and also in the vicinity of some background objects that is far from target humans

    Adapting Deep Learning for Underwater Acoustic Communication Channel Modeling

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    The recent emerging applications of novel underwater systems lead to increasing demand for underwater acoustic (UWA) communication and networking techniques. However, due to the challenging UWA channel characteristics, conventional wireless techniques are rarely applicable to UWA communication and networking. The cognitive and software-defined communication and networking are considered promising architecture of a novel UWA system design. As an essential component of a cognitive communication system, the modeling and prediction of the UWA channel impulse response (CIR) with deep generative models are studied in this work. Firstly, an underwater acoustic communication and networking testbed is developed for conducting various simulations and field experiments. The proposed test-bed also demonstrated the capabilities of developing and testing SDN protocols for a UWA network in both simulation and field experiments. Secondly, due to the lack of appropriate UWA CIR data sets for deep learning, a series of field UWA channel experiments have been conducted across a shallow freshwater river. Abundant UWA CIR data under various weather conditions have been collected and studied. The environmental factors that significantly affect the UWA channel state, including the solar radiation rate, the air temperature, the ice cover, the precipitation rate, etc., are analyzed in the case studies. The obtained UWA CIR data set with significant correlations to weather conditions can benefit future deep-learning research on UWA channels. Thirdly, a Wasserstein conditional generative adversarial network (WCGAN) is proposed to model the observed UWA CIR distribution. A power-weighted Jensen–Shannon divergence (JSD) is proposed to measure the similarity between the generated distribution and the experimental observations. The CIR samples generated by the WCGAN model show a lower power-weighted JSD than conventional estimated stochastic distributions. Finally, a modified conditional generative adversarial network (CGAN) model is proposed for predicting the UWA CIR distribution in the 15-minute range near future. This prediction model takes a sequence of historical and forecast weather information with a recent CIR observation as the conditional input. The generated CIR sample predictions also show a lower power-weighted JSD than conventional estimated stochastic distributions

    How Physicality Enables Trust: A New Era of Trust-Centered Cyberphysical Systems

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    Multi-agent cyberphysical systems enable new capabilities in efficiency, resilience, and security. The unique characteristics of these systems prompt a reevaluation of their security concepts, including their vulnerabilities, and mechanisms to mitigate these vulnerabilities. This survey paper examines how advancement in wireless networking, coupled with the sensing and computing in cyberphysical systems, can foster novel security capabilities. This study delves into three main themes related to securing multi-agent cyberphysical systems. First, we discuss the threats that are particularly relevant to multi-agent cyberphysical systems given the potential lack of trust between agents. Second, we present prospects for sensing, contextual awareness, and authentication, enabling the inference and measurement of ``inter-agent trust" for these systems. Third, we elaborate on the application of quantifiable trust notions to enable ``resilient coordination," where ``resilient" signifies sustained functionality amid attacks on multiagent cyberphysical systems. We refer to the capability of cyberphysical systems to self-organize, and coordinate to achieve a task as autonomy. This survey unveils the cyberphysical character of future interconnected systems as a pivotal catalyst for realizing robust, trust-centered autonomy in tomorrow's world
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