Dimitrios Koutsonikolas

Bio: Dimitrios Koutsonikolas is an academic researcher from Northeastern University. The author has contributed to research in topics: Wireless network & Throughput. The author has an hindex of 29, co-authored 117 publications receiving 3424 citations. Previous affiliations of Dimitrios Koutsonikolas include University at Buffalo & State University of New York System.

Towards Environment Independent Device Free Human Activity Recognition

Abstract: Driven by a wide range of real-world applications, significant efforts have recently been made to explore device-free human activity recognition techniques that utilize the information collected by various wireless infrastructures to infer human activities without the need for the monitored subject to carry a dedicated device. Existing device free human activity recognition approaches and systems, though yielding reasonably good performance in certain cases, are faced with a major challenge. The wireless signals arriving at the receiving devices usually carry substantial information that is specific to the environment where the activities are recorded and the human subject who conducts the activities. Due to this reason, an activity recognition model that is trained on a specific subject in a specific environment typically does not work well when being applied to predict another subject's activities that are recorded in a different environment. To address this challenge, in this paper, we propose EI, a deep-learning based device free activity recognition framework that can remove the environment and subject specific information contained in the activity data and extract environment/subject-independent features shared by the data collected on different subjects under different environments. We conduct extensive experiments on four different device free activity recognition testbeds: WiFi, ultrasound, 60 GHz mmWave, and visible light. The experimental results demonstrate the superior effectiveness and generalizability of the proposed EI framework.

WiDraw: Enabling Hands-free Drawing in the Air on Commodity WiFi Devices

Abstract: This paper demonstrates that it is possible to leverage WiFi signals from commodity mobile devices to enable hands-free drawing in the air. While prior solutions require the user to hold a wireless transmitter, or require custom wireless hardware, or can only determine a pre-defined set of hand gestures, this paper introduces WiDraw, the first hand motion tracking system using commodity WiFi cards, and without any user wearables. WiDraw harnesses the Angle-of-Arrival values of incoming wireless signals at the mobile device to track the user's hand trajectory. We utilize the intuition that whenever the user's hand occludes a signal coming from a certain direction, the signal strength of the angle representing the same direction will experience a drop. Our software prototype using commodity wireless cards can track the user's hand with a median error lower than 5 cm. We use WiDraw to implement an in-air handwriting application that allows the user to draw letters, words, and sentences, and achieves a mean word recognition accuracy of 91%.

Enabling Indoor Mobile Millimeter-wave Networks Based on Smart Reflect-arrays

Abstract: The millimeter-wave (mmWave) frequency band has been utilized in the IEEE 802.11ad standard to achieve multi-Gbps throughput. Despite the advantages, mmWave links are highly vulnerable to both user and environmental mobility. Since mmWave radios use highly directional antennas, the line-of-sight (LOS) signal can be easily blocked by various obstacles, such as walls, furniture, and humans. In the complicated indoor environment, it is highly possible that the blocked mmWave link cannot be restored no matter how the access point and the mobile user change their antenna directions. To address the problem and enable indoor mobile mmWave networks, in this paper, we introduce the reconfigurable 60 GHz reflect-arrays to establish robust mmWave connections for indoor networks even when the links are blocked by obstructions. First, the reconfigurable 60 GHz reflect-array is designed, implemented, and modeled. Then a three-party beam-searching protocol is designed for reflect-array-assisted 802.11ad networks. Finally, an optimal array deployment strategy is developed to minimize the link outage probability in indoor mobile mmWave networks. The proposed solution is validated and evaluated by both in-lab experiments and computer simulations.

Path Planning of Mobile Landmarks for Localization inWireless Sensor Networks

Abstract: Many applications of wireless sensor networks require the sensor nodes to obtain their locations. The main idea in most localization methods has been that some nodes with known coordinates (e.g., GPS-equipped nodes) transmit beacons with their coordinates in order to help other nodes to localize themselves. A promising method that significantly reduces the deployment cost is to replace the set of statically deployed GPS-enhanced sensors with one mobile landmark equipped with a GPS unit. In this case, a fundamental research issue is the planning of the path that the mobile landmark should travel along in order to minimize the localization error. In this paper we first study the localization error of three different trajectories for the mobile landmark, namely SCAN, DOUBLE SCAN, and HILBERT. We further study the tradeoffs between the trajectory resolution and the localization accuracy in the presence of 2-hop localization, in which sensors that have already obtained an estimate of their positions help to localize other sensors. Our trajectories are practical and can be easily implemented in mobile robot platforms.

Path planning of mobile landmarks for localization in wireless sensor networks

Abstract: Many applications of wireless sensor networks require the sensor nodes to obtain their locations. The main idea in most localization methods has been that some statically deployed nodes (landmarks) with known coordinates (e.g., GPS-equipped nodes) transmit beacons with their coordinates in order to help other nodes to localize themselves. A promising method that significantly reduces the cost is to replace the set of statically deployed GPS-enhanced sensors with one mobile landmark equipped with a GPS unit that moves to cover the entire network. In this case, a fundamental research issue is the planning of the path that the mobile landmark should travel along in order to minimize the localization error as well as the time required to localize the whole network. These two objectives can potentially conflict with each other. In this paper, we first study three different trajectories for the mobile landmark, namely Scan, Double Scan, and Hilbert. We show that any deterministic trajectory that covers the whole area offers significant benefits compared to a random movement of the landmark. When the mobile landmark traverses the network area at a fine resolution, Scan has the lowest localization error among the three trajectories, followed closely by Hilbert. But when the resolution of the trajectory is larger than the communication range, the Hilbert space-filling curve offers significantly better accuracy than the other two trajectories. We further study the tradeoffs between the trajectory resolution and the localization accuracy in the presence of 2-hop localization, in which sensors that have already obtained an estimate of their positions help to localize other sensors. We show that under moderate sensor mobility, 2-hop localization along with a good trajectory reduces the average localization error over time by about 40%.

Reconfigurable Intelligent Surfaces for Energy Efficiency in Wireless Communication

Abstract: The adoption of a reconfigurable intelligent surface (RIS) for downlink multi-user communication from a multi-antenna base station is investigated in this paper. We develop energy-efficient designs for both the transmit power allocation and the phase shifts of the surface reflecting elements subject to individual link budget guarantees for the mobile users. This leads to non-convex design optimization problems for which to tackle we propose two computationally affordable approaches, capitalizing on alternating maximization, gradient descent search, and sequential fractional programming. Specifically, one algorithm employs gradient descent for obtaining the RIS phase coefficients, and fractional programming for optimal transmit power allocation. Instead, the second algorithm employs sequential fractional programming for the optimization of the RIS phase shifts. In addition, a realistic power consumption model for RIS-based systems is presented, and the performance of the proposed methods is analyzed in a realistic outdoor environment. In particular, our results show that the proposed RIS-based resource allocation methods are able to provide up to 300% higher energy efficiency in comparison with the use of regular multi-antenna amplify-and-forward relaying.

Towards Smart and Reconfigurable Environment: Intelligent Reflecting Surface Aided Wireless Network

Abstract: IRS is a new and revolutionizing technology that is able to significantly improve the performance of wireless communication networks, by smartly reconfiguring the wireless propagation environment with the use of massive low-cost passive reflecting elements integrated on a planar surface. Specifically, different elements of an IRS can independently reflect the incident signal by controlling its amplitude and/or phase and thereby collaboratively achieve fine-grained 3D passive beamforming for directional signal enhancement or nulling. In this article, we first provide an overview of the IRS technology, including its main applications in wireless communication, competitive advantages over existing technologies, hardware architecture as well as the corresponding new signal model. We then address the key challenges in designing and implementing the new IRS-aided hybrid (with both active and passive components) wireless network, as compared to the traditional network comprising active components only. Finally, numerical results are provided to show the great performance enhancement with the use of IRS in typical wireless networks.

Smart radio environments empowered by reconfigurable AI meta-surfaces: an idea whose time has come

Abstract: Future wireless networks are expected to constitute a distributed intelligent wireless communications, sensing, and computing platform, which will have the challenging requirement of interconnecting the physical and digital worlds in a seamless and sustainable manner. Currently, two main factors prevent wireless network operators from building such networks: (1) the lack of control of the wireless environment, whose impact on the radio waves cannot be customized, and (2) the current operation of wireless radios, which consume a lot of power because new signals are generated whenever data has to be transmitted. In this paper, we challenge the usual “more data needs more power and emission of radio waves” status quo, and motivate that future wireless networks necessitate a smart radio environment: a transformative wireless concept, where the environmental objects are coated with artificial thin films of electromagnetic and reconfigurable material (that are referred to as reconfigurable intelligent meta-surfaces), which are capable of sensing the environment and of applying customized transformations to the radio waves. Smart radio environments have the potential to provide future wireless networks with uninterrupted wireless connectivity, and with the capability of transmitting data without generating new signals but recycling existing radio waves. We will discuss, in particular, two major types of reconfigurable intelligent meta-surfaces applied to wireless networks. The first type of meta-surfaces will be embedded into, e.g., walls, and will be directly controlled by the wireless network operators via a software controller in order to shape the radio waves for, e.g., improving the network coverage. The second type of meta-surfaces will be embedded into objects, e.g., smart t-shirts with sensors for health monitoring, and will backscatter the radio waves generated by cellular base stations in order to report their sensed data to mobile phones. These functionalities will enable wireless network operators to offer new services without the emission of additional radio waves, but by recycling those already existing for other purposes. This paper overviews the current research efforts on smart radio environments, the enabling technologies to realize them in practice, the need of new communication-theoretic models for their analysis and design, and the long-term and open research issues to be solved towards their massive deployment. In a nutshell, this paper is focused on discussing how the availability of reconfigurable intelligent meta-surfaces will allow wireless network operators to redesign common and well-known network communication paradigms.

Intelligent Reflecting Surface-Aided Wireless Communications: A Tutorial

Abstract: Intelligent reflecting surface (IRS) is an enabling technology to engineer the radio signal propagation in wireless networks. By smartly tuning the signal reflection via a large number of low-cost passive reflecting elements, IRS is capable of dynamically altering wireless channels to enhance the communication performance. It is thus expected that the new IRS-aided hybrid wireless network comprising both active and passive components will be highly promising to achieve a sustainable capacity growth cost-effectively in the future. Despite its great potential, IRS faces new challenges to be efficiently integrated into wireless networks, such as reflection optimization, channel estimation, and deployment from communication design perspectives. In this paper, we provide a tutorial overview of IRS-aided wireless communications to address the above issues, and elaborate its reflection and channel models, hardware architecture and practical constraints, as well as various appealing applications in wireless networks. Moreover, we highlight important directions worthy of further investigation in future work.

Towards Smart and Reconfigurable Environment: Intelligent Reflecting Surface Aided Wireless Network

Abstract: Although the fifth-generation (5G) technologies will significantly improve the spectrum and energy efficiency of today's wireless communication networks, their high complexity and hardware cost as well as increasingly more energy consumption are still crucial issues to be solved. Furthermore, despite that such technologies are generally capable of adapting to the space and time varying wireless environment, the signal propagation over it is essentially random and largely uncontrollable. Recently, intelligent reflecting surface (IRS) has been proposed as a revolutionizing solution to address this open issue, by smartly reconfiguring the wireless propagation environment with the use of massive low-cost, passive, reflective elements integrated on a planar surface. Specifically, different elements of an IRS can independently reflect the incident signal by controlling its amplitude and/or phase and thereby collaboratively achieve fine-grained three-dimensional (3D) passive beamforming for signal enhancement or cancellation. In this article, we provide an overview of the IRS technology, including its main applications in wireless communication, competitive advantages over existing technologies, hardware architecture as well as the corresponding new signal model. We focus on the key challenges in designing and implementing the new IRS-aided hybrid (with both active and passive components) wireless network, as compared to the traditional network comprising active components only. Furthermore, numerical results are provided to show the potential for significant performance enhancement with the use of IRS in typical wireless network scenarios.