Wi-Fi channel state information (CSI)-based sensing faces challenges in adapting to real-world scenarios due to technical constraints that hinder CSI acquisition. Compressed beamforming report (CBR), standard-compliant channel information in IEEE 802.11ac/ax, is being investigated as an alternative, device-agnostic information source for sensing. In this paper, we implement WiPiCap, a real-time CBR decoding system that is lightweight, easy to deploy, and supports arbitrary antenna, frequency, and bandwidth configurations. In the evaluation, we show that the motion of surrounding objects is reflected in a live plot of CBR data in real time.

Speculative execution in cloud gaming pre-renders predicted frames to mitigate network latency, but faces exponential computational costs and static prediction models that cannot adapt to player behavior. This paper proposes an adaptive retraining framework for LSTM-based pattern prediction that dynamically updates models using real-time user input. Experiments across three game genres demonstrate efficient pattern reduction, achieving performance comparable to that of pre-trained models with short training intervals.

Reducing response delay while maintaining high-quality visual and audio output is a critical challenge in cloud gaming services. Speculative transmission has been proposed to reduce the response delay for video frames, whereas the response delay reduction for spatial sound has remained underexplored. In this study, we propose a model-based speculative transmission scheme to deliver high-quality spatial sound with negligible response delay for cloud gaming services. By presharing compressed impulse responses (IRs) between the cloud server and the user’s low-end device using an implicit neural representation (INR) model, our scheme achieves significant traffic reduction while preserving nearly lossless spatial sound quality. In addition, the INR-based model uses a small neural network architecture to reduce the storage cost required for sharing compressed IRs on low-end user devices. Evaluation results using pyroomacoustics show that our scheme achieves a traffic reduction of more than 89.2% while maintaining nearly lossless spatial sound reconstruction.

With the advancement of immersive services such as Extended Reality (XR), there is an increasing demand for high-quality and low-latency transmission methods for 3D point clouds. However, existing point cloud transmission methods face challenges such as long latency in the compression process, as well as degradation in visual quality. This study proposes a novel point cloud transmission method that simultaneously achieves high visual quality and low compression latency. Specifically, the proposed method determines the importance of each point in the point cloud based on the user’s viewpoint, and realizes point-wise adaptive compression by loading and discarding multiple 3D points in parallel using multiple processing cores. Experimental results on the 8iVFBv2 point cloud dataset demonstrate that the proposed method achieves a frame rate that is 17.3× higher than existing low-latency and user-viewpoint-based compression techniques, while maintaining comparable visual quality.

Efficient tactile communication will pave the way for future augmented reality services. Tactile coding solutions for vibrotactile signals have been proposed as one of the enabling technologies. However, due to the all-or-nothing behavior, such coding solutions cause destructive quality degradation, known as cliff and leveling-off effects, over unstable wireless channels. We propose a novel joint source-channel coding scheme for tactile communication. The proposed scheme transforms the tactile signals into frequency domain representations and transmits them to mitigate cliff and leveling-off effects. Moreover, the proposed scheme unequally scales each frequency domain representation based on human tactile perception to simultaneously realize error resilience against channel noise and maximize the quality of vibrotactile signals with respect to human tactile perception. Evaluations show that the proposed scheme mitigates the cliff and leveling-off effects even when the wireless channel quality fluctuates during vibrotactile communication.

This study aims to enhance the transmission efficiency of microwave power transfer, which has garnered significant attention as a method of wireless power supply for IoT devices and drones, by using Intelligent Reflecting Surface (IRS). IRS is a plate-shaped device that can control the reflection characteristics of radio waves, and it is expected to improve the transmission efficiency when placed between a wireless transmitter and a wireless receiver. However, existing methods of reflection phase optimization for IRS are predominantly based on sequential feedback or a small number of discrete reflection phases, both of which have problems, including feedback overhead and beam shape limitations. In this paper, we propose a novel reflection phase optimization method based on curve fitting. This approach is capable of continuous reflection phase control and optimization of the reflection phase of all reflective elements simultaneously. Through numerical simulation and actual experiments using an implemented IRS prototype, it was observed that the proposed method enhanced the received power by up to 4 dB compared to a conventional sequential optimization method. However, it was also observed that the range limitation of the reflection phase in the implemented IRS hindered the effectiveness of the proposed method.

Non-terrestrial networks (NTNs) contribute significantly to offering connectivity services for the upcoming sixth generation (6G). However, many services demand real-time uplink data transmission. Owing to the dynamic topology, instability of NTN channel status, resource constraints, and tremendous mobile users on ground networks; resilient and effective resource management and channel accessibility pose substantial challenges. This work investigates the uplink sum-rate maximization and latency minimization problems with a channel allocation scheme for user equipments (UEs) to high-altitude platforms (HAPs) of a particular NTN when UEs simultaneously transmit data to HAPs and HAPs forward it to low-earth orbit satellite (LEO-sat) via multiple subcarriers. We formulate a fairness resource allocation optimization problem reflecting the system utility and load of the HAP-LEO-sat link for aerial-to-space uplinks. To tackle the high-dimensional non-convex optimization problems and short visibility of LEO-sat in the learning, we advocate a novel deep reward-based multi-agent reinforcement learning (DeepRMARL) with prioritized experience replay (PER) and priority sampling approach (PSA). Furthermore, we present a long-short-term memory (LSTM) model for future environment channel prediction due to time-varying channels between UEs and HAPs. The results explicitly unveil the feasibility of our method in optimizing total uplink rate, achieving a higher fairness index, minimizing latency by approx. 12.2%, and predicting future environment states with approx. 92.8% accuracy.

Wireless sensing research is underway to generate 2D images and 2D videos corresponding to an object or space using the measured amplitude and phase changes during RF signal propagation. The obtained 2D images and 2D videos can be used for object recognition and distance measurement based on image processing techniques. However, 2D images only contain visual information about the sensing target from a specific viewpoint. This paper proposes Channel State Information to Point Cloud (CSI2PC) to enable the observation of a sensing target from multiple viewpoints. CSI2PC generates a 3D point cloud corresponding to the 3D structure of the sensing target from Channel State Information (CSI), which stores the variation of amplitude and phase. CSI2PC generates a 3D point cloud from the measured CSI using a neural network (NN) architecture based on Generative Adversarial Networks (GAN) and Graph Neural Networks (GNN). To ensure that the generated point clouds accurately represent the sensing target, the proposed scheme designs 1) a two-stage learning of the proposed NN architecture and 2) a loss function considering the 3D point cloud reconstruction. Experimental results using consumer Wi-Fi devices show that the proposed CSI2PC can reconstruct a clean point cloud from the measured CSI and accurately classify the object using the point cloud-based classification model.

Non-terrestrial networks (NTNs) are crucial for 6G communications but face challenges like classical communication bottlenecks. Semantic communication (SemCom) offers a solution, but efficient resource allocation remains unresolved. This paper proposes a joint approach for resource allocation and semantic information selection in low-earth orbit (LEO) satellite-based image transmission. A novel metric, Quality of Semantic (QoSem), is introduced to measure the SemCom performance. An optimization problem is formulated to maximize QoSem by allocating computing, power, and bandwidth resources and selecting optimal semantic information. Furthermore, we propose a novel deep reinforcement learning (DRL) algorithm, namely Mixed-Action Decision Intelligent Semantic-aware Soft-Actor Critic (MADIS-SAC), to handle the problem’s nonconvexity and NP-hard complexity. Results show 82.5% of QoSem improvement compared to baselines under resource constraints.

This paper shares the challenges facing today's augmented reality (AR) smartphone applications, particularly in the realm of localization and tracking failure. Our research identifies limitations in current vision-based landmarks such as QR codes and AprilTags, commonly used to aid in localization, and the drawbacks of LiDAR integration in variable lighting conditions, compromising AR's accuracy and functionality. We also examine the constraints of Inertial Measurement Units (IMU) on movement speed, highlighting its impact on the dynamic performance of AR applications. Based on our extensive 316 experimental cases for 113 hours, including 34 case studies with 17 subjects in 2 sites, this paper presents the field with a nuanced analysis of the failure modes inherent in smartphone-based AR localization. We further explore a prototype solution which fuses ultra-wideband (UWB)-based sensing with the vision-based systems to alleviate these failure modes. Our approach addresses the immediate challenges of AR localization and opens avenues for future research and development in creating more spatially aware and interactive digital worlds. All of our demonstration videos, code, and datasets are available here1.

The accurate location of objects and people is central to providing contextual information for various AR/VR applications. We developed XRLoc [2], a compact localization module, sized less than 1 m, which can be integrated with television screens, soundbars or independently deployed in rooms to provide accurate locations of these assets. In this demo, we showcase the capability of XRLoc to localize and track UWB tags with cm-level accuracy in realistic room-level scenarios. We will additionally compare the location accuracy of XRLoc with visual-based HTC Vive trackers.

Intelligent reflecting surface (IRS) has emerged as a promising communication technology to increase throughput and facilitate communication in non-line-of-sight scenarios. While existing studies primarily focus on the application of IRS in communication tasks, its unique capability to precisely control radio frequency ($\mathbf{R F}$) signal reflections offers untapped potential for non-communication purposes. This paper ventures into an innovative domain and explores the application of IRS properties for image processing tasks conducted over the air. We introduce a novel scheme called “single antenna imaging”, a proof-of-concept scheme for over-the-air image processing. This approach leverages analog modulation and compressed sensing techniques to reconstruct high-quality images from a limited number of transmissions between multiple transmit antennas and a single receive antenna. Evaluations using the Kodak image dataset show that the proposed scheme can retrieve the same quality images with over 40% reduction in traffic compared to the simple minimum norm solution (MNS) and linear minimum mean square error (LMMSE) schemes.

Conventional point cloud delivery schemes employ tree-based or graph-based digital compression techniques to stream three-dimensional (3D) points and their associated attributes over wireless multiple-input multiple-output (MIMO) channels for 3D scene reconstruction. However, these digital-based delivery methods suffer from the cliff and leveling effects. Moreover, in MIMO communication environments, poor quality in some spatial layers, i.e., subchannels, can lead to point cloud quality degradation. We propose a novel point cloud delivery scheme that addresses both effects simultaneously. Our approach leverages a k-dimensional tree (k-d tree)-based graph Fourier transform (GFT) for energy compaction, a subchannel assignment mechanism to fully utilize subchannel diversity in MIMO links, and analog modulation with non-uniform power allocation for error-resilient and fluctuation-resilient transmission. Evaluation results demonstrate that the proposed scheme delivers superior 3D reconstruction quality compared to conventional digital-based point cloud delivery schemes, such as geometry-based point cloud compression (G-PCC), in noisy MIMO channels.

A long network delay leads to decreased player performance in online games and an unfair game experience between the players. In this study, we propose a novel delay compensation method to reduce the impact of network delay on player performance in online 3D shooting games. The key contributions of the proposed scheme are two-fold. The first contribution utilizes two deep reinforcement learning (DRL) networks, timing and direction DRL, to predict each player's sudden movement under the network delay. Specifically, the timing DRL outputs when the player will change the movement direction while the direction DRL outputs the indenting direction of the player. The second contribution of this study is to send each player's trained model to other players at the beginning of the game match. This research is the first to discuss how to transfer the trained model, as well as the effect of the model transmissions on the delay compensation performance in the band-limited paths. We evaluated the effectiveness of the proposed delay compensation using an online 3D shooting game implemented by Unity 3D. The obtained evaluation results indicate that the proposed delay compensation accurately reproduces the player's position, even under long network delays. In addition, the transmitted model can predict the player's position when the available rate for the model transmission is approximately 60 KB.

Viewport-based 360-degree video delivery is a typical method to reduce video traffic for virtual reality (VR) applications. However, viewport-based solutions cause some key issues in multi-user VR applications, including large video traffic volumes owing to redundant video transmission across multiple headset users and quality degradation owing to view-port transition. In this study, we propose a 360-degree video delivery scheme for multi-user VR applications. To overcome the abovementioned issues, the proposed approach includes JPEG-XS-based video recompression at the edge server to follow the viewport transition and hybrid unicast and multicast tile delivery to prevent redundant transmissions. Moreover, we present the results of evaluations using 360-degree video and corresponding fixation points of multiple users to show that the proposed scheme prevents redundant transmissions across multiple headset users and provides better video quality for each user than existing solutions for the same video traffic.

The evolution of a cell phone network and a wireless local area network (LAN) has enabled various devices to be connected to wireless networks anytime, anywhere. This has led to the emergence of various applications, such as automatic transportation of parts and cargo by automated guided vehicles, remote control of unmanned vehicles, and drones. It will be a challenge to maintain high-quality connections, which satisfy the requirements of such emerging applications, for a large number of intelligent connected devices. To improve the network performance, we must control the wireless network setting and the behavior of the connected vehicles, i.e., robots. From this perspective, this proposes a novel framework, CoRein, that achieves simultaneous optimization of robot behavior and wireless network setting. In contrast to the existing studies, CoRein provides optimal actions for robots and wireless networks using deep reinforcement learning (DRL) architecture. To the best of our knowledge, our study firstly attempts to solve different tasks corresponding to robots and networking with a unique DRL architecture. To evaluate the effectiveness of CoRein, we considered a scenario where two robots were moving back and forth while communicating with one of two access points. CoRein calculates the robot velocities and AP selection. We carried out simulations and indoor experiments with our robot testbeds to evaluate the network performances. Evaluation results confirmed that the two robots adjusted their positions and the access points to increase the wireless network's performance, i.e., throughput and round trip time while changing the speed of their movements.

Sending tactile information over wireless channels will pave the way for the realization of immersive Extended Reality (XR) applications. Existing vibrotactile communication solutions have integrated traditional source and channel coding to provide high-quality vibrotactile information over band-limited and error-prone wireless channels. However, due to the bit-error vulnerability of source and channel coding and the unrecoverable distortion of source coding, the existing solutions suffer from catastrophic quality degradation. This paper proposes a novel tactile communication scheme inspired by semantic communication. The main contribution to the existing solutions is to replace the source and channel coding with the semantic vibrotactile coder and transceiver. Specifically, the semantic encoder compresses the vibrotactile information using a two-dimensional discrete cosine transform (2D-DCT) without binarization, and the transmitter directly maps the DCT coefficients to the transmission symbols with the optimized power allocation to avoid the catastrophic quality degradation due to the traditional source and channel coding and to maximize the vibrotactile quality under the wireless channel condition. In addition, we design an overhead-less semantic receiver to reduce the communication overhead required for the optimized power allocation. We implement a vibrotactile acquisition system for empirical evaluations, and the results showed that the proposed scheme provides better vibrotactile quality even in low-quality and high-quality wireless channels compared to the tactile communication schemes with the traditional source and channel coding.

Collaborative learning has been qualitatively analyzed by learning science researchers to enhance learning performance. A quantitative analysis system supports the existing qualitative analysis of collaborative learning. We propose an Internet of Things (IoT) system comprising business-card-type badges, radio-over-fiber (RoF)-based synchronization, and a collaboration analysis algorithm to support collaborative learning analytics in different venues (e.g., offline, online, and hybrid). We showed that our proposed system quantitatively supports qualitative analysis of collaborative learning in different environments.

Backscatter technology reduces power consumption during data transmission to 1/1000 that of existing wireless transmitters. However, backscatter communication faces two challenges related to medium access control (MAC). First, the carrier wave is not always supplied by an external device when the backscatter tag attempts to transmit data. In addition, if multiple backscatter tags communicate simultaneously, their outgoing frames collide with each other. This study proposes an asynchronous MAC protocol for backscatter tags without a receiving function. The proposed MAC protocol consists of a pseudo-synchronization system and a devised transmission interval between the carrier wave and each backscatter tag. The success rate of data transmission is improved by devising a carrier supply interval and backscatter tag operation. In addition, frame collisions decrease when using a pseudo-random number series to set the transmission interval for each backscatter tag. In a pseudo-synchronization system, the receiver feedbacks time information to the carrier transmitter when the receivers receive or detect a frame. The carrier transmitter adjusts the timing of the carrier supply to match the frame transmission from the backscatter tag, based on the information fed back. This improves the success rate of the communication without time synchronization. The simulation evaluation reveals that the proposed method improves the communication success rate by approximately 1.43 times in the assumed environment.

Understanding the location of ultra-wideband (UWB) tag-attached objects and people in the real world is vital to enabling a smooth cyber-physical transition. However, most UWB localization systems today require multiple anchors in the environment, which can be very cumbersome to set up. In this work, we develop XRLoc, providing an accuracy of a few centimeters in many real-world scenarios. This paper will delineate the key ideas that allow us to overcome the fundamental restrictions that plague a single anchor point from localization of a device to within an error of a few centimeters. We deploy a VR chess game using everyday objects as a demo and find that our system achieves 2.4 cm median accuracy and 5.3 cm 90th percentile accuracy in dynamic scenarios, performing at least 8× better than state-of-art localization systems. Additionally, we implement a MAC protocol to furnish these locations for over 10 tags at update rates of 100 Hz, with a localization latency of ~1 ms. We have additionally open-sourced our system's codebase at https://github.com/ucsdwcsng/xrloc.git

This study proposes mainly 2 different backscatter medium access control (MAC) protocols. Both proposed protocols work for half-duplex and full-duplex transmission modes in a bistatic backscatter system. The first one, namely express backscatter MAC (XBM), allows higher transmission rates in a bistatic backscatter network, aiming to increase network capacity. It takes advantage of carrier signals for faster transmissions whenever they are generated. The second protocol, reliable backscatter MAC (RBM), provides more reliability to the backscatter transmission, especially when hidden nodes exist. It brings more reliability by introducing a two-step CTS_to_Self mechanism to separate backscatter communication from Wi-Fi communication and avoid hidden node problem. In addition, our MAC protocols enable simultaneous backscatter transmissions at the MAC level by letting several tags carry their data on the same carrier signal. We evaluated and compared our proposals through numerical analyses that highlighted agreeable performance levels.

In backscatter communications, data is transmitted with up to 1/1000 of the power consumption, instead of the typical several tens of milliwatts. However, if there are multiple nodes for backscatter communication in the same space, harmonics and sidebands limit the number of multiplexing nodes. Both are suppressed by switching the impedance in multiple steps or generating the signal with opposite phases; however, these methods increase the hardware cost of the backscatter tag. This paper proposes harmonics-controlled frequency division multiple access (HC-FDMA), which allows multiple access interference in backscatter communications without hardware modification. HC-FDMA consists of two modules: a modulation scheme and a channel selection algorithm. The modulation scheme controls the harmonics to an arbitrary channel using ∆Σ modulation. The channel selection algorithm optimizes the frequencies of the signal component, harmonics, and sidebands. Targeting IEEE 802.15.4-compatible backscatter, backscatter communication using HC-FDMA achieves 1.45 times multiplexing compared to the case without HC-FDMA. In addition, the analysis results characterize the capability of HC-FDMA.

Multicast-based multimedia streaming is expected to be a key application in the next generation of wireless and mobile networks. For such applications, we utilize hierarchical non-terrestrial networks (NTNs) consisting of low earth orbit (LEO) satellites, high-altitude platforms (HAPs), and user equipment (UE). However, existing multicast protocols for NTN cause a long delay and jitter owing to packet loss, thus being the cause of low-quality streaming services. We propose a reliable multi-cast protocol, namely, overhearing and non-orthogonal multiple access (NOMA) repairing reliable multicast (ONRM), for delay reduction. In ONRM, the HAP overhears packets from the LEO to UE and retransmits the lost packets using NOMA on behalf of the LEO to prevent delay increments. We carried out evaluations based on the release of Third Generation Partnership Project (3GPP) in terms of delay, throughput, and jitter. We prepared existing multicast protocols, pragmatic general multi-cast (PGM), reliable and efficient multicast protocol (REMP), and extended bit-indexed explicit replication (extended BIER) for comparison. Results show that ONRM outperforms other multicast protocols. Especially, the proposed ONRM can realize a low jitter and enables high-quality multimedia streaming over NTN.

In this paper, we present an experimental evaluation of the several phase optimization algorithms for distributed cooperative microwave power transfer. The proposed evaluation uses multiple Tx antennas distributed in a space. When the Tx antennas are in the far field from each other, appropriate phase control can generate constructive interference at the target location. The phase optimization algorithm optimizes the phase of each Tx antenna based on the received-signal-strength indication (RSSI) feedback from the target. The experimental results indicate that the RSSI measurement resolution and the controllable phase resolution at each Tx antenna affect the performance of the phase optimization.

Wireless communications for wireless devices require a significant power consumption for signal amplification. Such a significant power consumption may induce short lifetime of battery-operated wireless devices, especially, sensor devices and Internet of Things (IoT) devices. Backscatter communications have been proposed in recent years for ultra-low-power wireless communications, e.g., Wi-Fi. In this study, we propose and implement a novel backscatter communication scheme for sensor devices, i.e., IEEE 802.15.4-compatible backscatter. The proposed backscatter scheme consists of three devices: RF signal generator, backscatter transmitter, and standard IEEE 802.15.4 receiver. Specifically, the backscatter transmitter receives the RF signals with/without frequency hopping emitted from the RF signal generator and modulates the received signals into IEEE 802.15.4 packets by switching the impedance of the transmission antenna. Since the backscatter transmitter does not need signal amplification, instead, only needs to switch on/off state of the transmission antenna for packet transmissions, the power consumption of the proposed scheme is significantly lower than the conventional IEEE 802.15.4-based schemes. From the experimental evaluations, we demonstrated that the proposed backscatter scheme can realize IEEE 802.15.4 packet transmissions with low power at a certain angle and communication distance. In addition, we also clarified the frequency hopping at the RF signal generator can increase the number of the received packets since it may reduce the dead spots due to multi-path fading.

To supply energy for Internet of Things (IoT) devices, this paper proposes phase-controlled wireless power transfer by generating constructive standing waves, on multiple IoT devices, using distributed multiple antennas. The proposed approach realizes equitable power supply distribution among all the IoT devices by switching the phase sets for TX antennas in a time-division manner, and each phase set is optimized for individual IoT devices. In particular, our approach uses received signal strength indicator (RSSI) feedback packets to optimize the phase set of each TX antenna and applies a heuristic algorithm to reduce the number of RSSI feedback packets. We evaluated our approach using computer simulations and experimental micro benchmarks. The computer simulation assumes 64 antennas installed at 2 m intervals on the ceiling of a 15 m by 15 m by 2.5 m room and, each antenna emits 100 mW continuous nonmodulated sine waves. The simulation results show that each of the 10 IoT devices placed on the floor is able to receive an average of approximately 53.6 uW, which is approximately 5.1 times the existing method. The experimental micro benchmarks show the feasibility of the proposed method.

Wireless 360-degree video delivery provides virtual reality (VR) immersive experience where each user can freely switch his/her viewing orientation. The existing schemes of the wireless 360-degree video streaming use digital-based compression and transmission. However, they have many disadvantages in terms of video traffic and quality: cliff effect due to unstable wireless channels, large video traffic due to the extremely high resolution of the 360-degree video, and the perceptual redundancy within the transmitted video. To solve the above problems, this paper proposes a novel wireless 360-degree video transmission scheme called 360Cast. 360Cast adopts the analog-based transmission, including power allocation and analog modulation, to achieve graceful video quality improvement even in time-varying wireless channels. Here, the power allocation considers the distortion of human perception and sphere-to-plane projection to maximize the human perceptual quality at the 360-degree video playback. In addition, 360Cast predicts and transmits the user's viewport based on the recent HMD user's orientation by using dynamic linear regression (DLR) and only transmits the viewport for traffic reduction. Evaluation results show that the proposed 360Cast provides better human perceptual quality via HMD compared with the existing analog transmission schemes, i.e., SoftCast and FoveaCast, by using the integration of viewport prediction, power allocation, and analog modulation.

Backscatter communication is attracting attention for its ultra-low-power consumption ability. The said capability will enhance IoT which aims to enable a large number of novel applications for object-to-object communication. In such a network, where numerous tags may transmit simultaneously, it is important to make sure that the number of simultaneous transmissions allows reliable decoding operations on a large scale. Here we introduce QuadScatter, a series of algorithms that make profit of the maximum number of simultaneous backscatter transmissions to enable a network of a significant number of transmitters, tags, and readers. The maximum number of simultaneous transmissions is verified by simulations and the overall capacity of the network allows successful communications above 3dB of signal-to-noise ratio (SNR). QuadScatter shows agreeable results compared to the exhaustive search algorithm. The simulation results highlight computational time and simultaneous transmission improvements of at least 250x and 2x. Furthermore, while the exhaustive search is limited to a few nodes (<0), our proposal uses a larger number of nodes.

In this paper, a traffic reduction scheme for videobased call and chat applications that uses deep neural network (DNN) based super resolution is proposed. Specifically, a sender transmits low-quality, low-resolution video frames containing face information in order to reduce the amount of video traffic. The receiver uses DNN-based super resolution to reconstruct highquality, high-resolution video frames from the low-quality video frames. The proposed scheme makes two contributions. First, face features are adopted for parameter optimization of DNNbased super resolution for high-quality image reconstruction, and second, the scheme includes a newly designed loss function that considers face features that allow high-quality face-containing video frames to be reconstructed at the receiver. According to our evaluation results using real video frames of video calls, the proposed scheme reduces the amount of video traffic by more than 90% as compared with conventional schemes that implement the standard video encoder. In this case, the proposed scheme achieves a reconstructed image quality up to 0.85 in terms of structural similarity (SSIM).

Understanding humans and its environment is a key enabler of smart, intelligent applications and services for a future smart society. To deploy such services in our ambient environment, it is expected to fully utilize battery-less and maintenance-free IoT devices and technologies for more ambient, distributed computing. In recent years, Wi-Fi-based communications are becoming more energy-efficient, and channel state information (CSI) has the potential to sense more detailed information about the things in the real world. Besides, ambient backscatter has appeared as a promising technology for zero-energy sensing and communications. Leveraging those state-of-the-art technologies, energy harvested IoT devices for context recognition of humans and objects will be in reality. A significant challenge is how to make use of inferior, less-powerful zero-energy IoT devices to achieve processing of interest, i.e., accurate recognition of humans and objects, while a single device does not work. Therefore, we consider orchestrating distributed tiny IoT devices for both sensing and communications. Particularly, distributed machine learning in the local environment will achieve highly promising sensing in our ambient environment. In this paper, we survey the state-of-the-art technologies for zero-energy sensing and communications in the context of humans and objects sensing and recognition. Then, we address the challenges to be tackled in terms of such distributed, intelligent sensing using zero-energy devices. Finally, we introduce the concept of utilizing distributed IoT devices, followed by the statement about our ongoing work toward future zero-energy sensing and processing.

There is a demand for device-free user location estimation with high accuracy in order to realize various indoor applications. This paper proposes an IEEE 802.11ac explicit feedback-based channel state information (CSI) learning system which can be used for device-free user location estimation. The proposed CSI learning system captures CSI feedback from off-the-shelf Wi-Fi devices and extracts 624 features from a CSI feedback frame defined in IEEE 802.11ac. We evaluated the proposed system using location estimation with six patterns: different combinations of device-free user movement and access point antenna orientation. The evaluation results show that the machine learning based localization achieves approximately 96% accuracy for seven positions of the user, and the divergence of CSI improves localization performance.

Inter-cell interference coordination (ICIC) has been widely studied for mitigating the effects of severe inter-cell interference (ICI) in cell- edge users. However, based on the scarcity of frequency resources in orthogonal multiple access systems, the ICIC methods proposed in the previous papers have difficulty in maintaining the overall performance and fairness of a system. Non-orthogonal multiple access (NOMA) is a promising radio access technology that can serve multiple users simultaneously with the same frequency resources. However, most previous work has not considered the ICI problem in NOMA systems. We propose a centralized adaptive ICIC framework for downlink NOMA systems, including a distributed clustering algorithm, a distributed power allocation algorithm, and a centralized frequency allocation algorithm. Simulation results demonstrate that the proposed framework outperforms all benchmark frameworks and can improve both the overall performance of a system and fairness among users.

This paper proposes a system that estimates crowdedness, such as the numbers of people and wireless communication devices, from the received signal strength indication (RSSI), which is measured using sensor nodes already-deployed for a different purpose. For example, the system estimates crowdedness using sensor nodes of structural health monitoring. Our system consists of two parts: an RSSI synchronous measurement and a crowdedness estimation algorithm. The RSSI synchronous measurement enables us to measure and collect two kinds of RSSI by synchronized RSSI sampling: the inter-node RSSI and surrounding RSSI. The crowdedness estimation algorithm estimates the number of people from the collected inter-node RSSI and the number of wireless communication devices from the collected surrounding RSSI in a location. We evaluated the proposed system in the laboratory at Osaka University. The evaluation results demonstrate that the system estimates the presence or absence of people in the laboratory with approximately 92% accuracy and the number of people with approximately 79% accuracy, with errors of up to two people.

In 360-degree video streaming, a user may watch a part of video frames, namely, viewport, through an interactive display, e.g., head-mounted display, for the immersive experience. One of major issues for high-quality 360-degree video delivery is how to reduce perceptual redundancy in video frames according to user's viewing viewport. To this end, an existing scheme divides video frames into multiple tiles and adaptively encodes each tile based on user's viewing viewport using digital-based video compression. However, the video compression causes an issue in wireless channel called cliff effect, which causes sudden quality degradation in user's viewport and poor immersion in immersive applications. To reduce perceptual redundancy without cliff effect in wireless 360-degree video delivery, we propose a novel transmission scheme. Our scheme skips quantization and entropy coding, instead, directly transmits linear-transformed signals based on three-dimensional discrete cosine transform (3D-DeT) or the combination of one-dimensional DeT (1D-DeT) and spherical wavelet transform (SWT). By skipping both operations, the quality of viewport can be simply enhanced by its power allocation since it is directly applied at the pixel level instead of the bit level. It is demonstrated with 360-degree video sequences that the proposed scheme offers a great advantage over the conventional digital-based schemes.

Cognitive radio (CR) has emerged as a promising technology to solve the tremendous spectrum scarcity problem. Although CR has shown very interesting results, it is still dealing with some drawbacks such as blocking or forced termination events in opportunistic mode and capacity limitation in underlay mode. In this paper carrier aggregation is proposed for wireless mesh networks to enhance the network throughput in underlay CR condition. In the proposed scenario, the primary user uses traditional channels whereas the secondary user aggregates carriers in PU's channels for its transmission. An analytical performance study is carried out which shows the effectiveness of our proposal. With relatively few aggregated carriers per channel, carrier aggregation outperforms single channel and channel aggregation due to its higher detection probability. These results can be used to extend the already well known properties of wireless mesh networks and their applications.

Existing studies on multiview video streaming are proposed for terrestrial environments. They can be classified into request-reply and all camera transmission models, while both models suffer from a long switching delay and low video quality in an underwater acoustic path. The implementation of multiview video transmission in an underwater acoustic path requires reduction of the switching delay and the maintenance of high video quality. To this end, we propose a video transmission scheme, namely, Dolphin. Dolphin comprises time-shifted slot assignment, stochastic transmission, correlation-based compensation, and greedy transmission. Dolphin assigns asymmetric and time-shifted transmission slots to an encoder node and a user node according to a propagation delay. Stochastic transmission sends a potential camera's video that will be the most likely to be played back by the user before playback. If the stochastic transmission fails, the video frames of the user's desired camera are encoded with the mispredicted video frames to prevent switching delay from increasing. Moreover, greedy transmission sends all potential video frames, which are potentially displayed by a user, and determines rate allocation of the video frames based on the display probability. Evaluations using Mitsubishi Electric Research Laboratories' benchmark test sequences demonstrated that Dolphin achieved a short switching delay with a slight degradation in video quality irrespective of communication distance and acoustic path quality. For example, Dolphin decreases the switching delay by 92.7% compared with an existing request-reply model with 0.4-dB quality degradation at a communication distance of 300 m when a user tends to gaze at one camera.

In current medical information networks, there is a problem because the cost of core switches is increasing owing to the need to meet demands in terms of throughput and robustness. To address this problem, we propose MediFlow as a medical information network based on OpenFlow. MediFlow replaces expensive, high-performance core switches with a MediFlow core network comprising multiple inexpensive and fully connected OpenFlow switches. The MediFlow core network adaptively allocates the communication capacity to time-varying traffic in a medical information network. If a link fails, MediFlow dynamically assigns a path to ensure that the communication system remains robust. We implemented MediFlow using commercial OpenFlow switches. The experimental evaluation showed that MediFlow could improve the throughput by allocating individual paths to each flow about 7 s after the occurrence of congestion by converging multiple flows. In addition, even if a link failure occurred in the path used by the flow, MediFlow re-established a path with a disconnection time of about 0.7 s.

With the increasing popularity of smartphones and Internet of Things (IoT) devices, there has been an increase in the amount of mobile traffic on networks. In this paper, we discuss an access strategy for downlink/uplink decoupling (DUDe) with multiple frequency channels to enable the efficient use of frequencies in wireless networks. DUDe is a wireless communications scheme that effectively utilizes frequencies when communicating by connecting the uplink and downlink to different base stations. We propose two base station selection methods, i.e., signal-to-interference-plus-noise ratio (SINR)-based decoupling with re-association (SBD-RA) and SINR-based decoupling with first-come first-association (SBD-FCFA). Based on the evaluation results obtained by event driven simulations, we show that both SBD-FCFA and SBD-RA provide higher throughput by utilizing frequencies more efficiently than DUDe when using the same channel, or without using DUDe. We also show that SBD-RA achieves a high throughput although SBD-RA has a larger computational complexity than SBD-FCFA.

Backscatter wireless communication (BWC) is a promising research area for the low power Internet of Things (IoT) devices. A substantial number of research works have been conducted in this area to increase the range of BWC. Along with physical layer achievements in BWC, an efficient medium access control (MAC) protocol is mandatory to attain the optimal benefit. In this paper, a novel MAC protocol is proposed to support BWC in a Wi-Fi network, where the AP and the Wi-Fi clients are capable of in-band full-duplex (IBFD) transmission, but the wireless tags (WTags) are half-duplex capable. In this MAC protocol, the AP suppresses the uplink transmission of the corresponding Wi-Fi client while transmitting the downlink data to support the BWC. Some new control frames have been introduced to facilitate this task. A mathematical analysis of this proposed MAC is presented here together with an evaluation of its performance. The mean overhead time is observed as 121 μs with the downlink utilization factor of 80%. The average throughput of the network decreases as the number of WTag increases. This average throughput is observed as 63.5 Mbps and 59 Mbps (with 30 WTags and 30 Wi-Fi clients) for the data delivery frequency of two times/WTag and eight times/WTag respectively.

Current one-to-one wireless communications is reaching the Shannon limit. Previous works have studied space multiplexing schemes, e.g., superposition coding and successive interference cancellation, to overcome the limitation. In this paper, we focus on a network multiple-input multiple- output (MIMO) architecture, which is one such space multiplexing scheme, where multiple access points in the network MIMO system cooperate with each other to improve wireless communications capacity. However, channel sounding overhead for estimating the channel state information of every path between multiple access points and multiple clients is a significant problem. Likewise, the computational overhead for deciding which paths to use for the network MIMO transmission is also high. We propose Throughput-Aware Random Clustering (TARC) of access points to reduce the network MIMO overhead. TARC takes a cross-layer approach for choosing access points to participate in network MIMO transmission on the physical layer based on throughput in the data link layer. From our validation using simulations, we show that the proposed method is able to achieve approximately 2.5 times higher throughput than using all access points in the network and 1.4 times better throughput than adopting static cluster sizes.

Full-duplex wireless information and power transfer allows the implementation of wireless sensor networks without batteries. In a full-duplex wireless network, an access point and sensor nodes transmit information and power signals simultaneously. However, this simultaneous transmission leads to a collision problem. To solve this collision problem, we propose a media access control (MAC) protocol, namely the Full-Duplex Simultaneous Wireless Information and Power Transfer Medium Access Control (FD-SWIPT MAC) protocol, for full-duplex wireless information and power transfer. The evaluation results show that the proposed FD-SWIPT MAC protocol outperforms other MAC protocols.

This paper presents our results of the acquisition of actual data of collapsing buildings, as part of our work on the Battleship Island monitoring project. Our Battleship Island monitoring system performs its task by acquiring data of images, sounds, and acceleration of collapsing buildings. The system is powered by solar energy. To enable the efficient use of solar-powered energy, this paper proposes a data-centric task scheduling system which consists of the DC-LQ (Data Centric LQ-Tracker) and BLB scheduling (battery-level based scheduling). An evaluation using computer simulation shows that data-centric task scheduling outperforms conventional task scheduling.

Combining wireless transmission of data and power signals enables us to use wireless devices without charging batteries. To improve the utilization of wireless resources, a sender could simultaneously transmit data and power signals in the same frequency channel. A disadvantage of simultaneous transmission is that it induces interference between data and power signals. To minimize the effect of interference, we propose a new frequency-sharing system. The proposed system makes two contributions. The first contribution is interference cancellation of the power signal to receive data from the collided signal using a combination of digital and analog interference cancellation techniques. The second contribution is a media access control protocol to receive the transmitted power effectively by varying the sleep time. To evaluate the performance of the proposed system, we built an experimental apparatus using software-defined radio. Evaluations show that it is feasible to transmit data and power simultaneously using the proposed system.

Multi-view video system includes three sections: acquisition, transmission, and display. This paper focuses on the acquisition of multi-view video. Existing multi-view video acquisition studies exploit multi-camera arrays mutually connected by wires. However, this imposes the limitations of places and objects. To overcome the limitations, we exploit multiple mobile cameras and wireless networks for multi-view video acquisition. The acquisition of the multi-view video needs to achieve a reduction in video traffic while maintaining high video quality for communication between mobile cameras and an access point. This paper proposes Multi-view Video Streaming with Mobile Cameras (MVS/MC) to satisfy these requirements. MVS/MC has two features: packet overhearing and transmission order control. First, each mobile camera overhears other cameras' video packets, and encodes its own video frames using the overheard video packets. Second, the access point controls the transmission order of the mobile cameras, thus realizing bidirectional interview prediction. Bidirectional inter-view prediction exploits the inter-camera domain correlation among the mobile cameras to further remove the redundant information. Evaluations using multi-view video sequences show that, compared with existing methods, MVS/MC reduces the volume of traffic with only a slight degradation in video quality. For example, MVS/MC reduces traffic by 52 % compared to existing methods when PSNR is 36 dB.

This paper describes a prototype system of remote music therapy for elderly people with dementia using the latest communication technology in Japan. The system uses the latest high-speed optical network, called NGN (Next Generation Network) and the latest terminal especially for audio data communication called Hikari DUETTO NY1, which was released at the end of the last year. We did an experiment of the system using the real communication environment in this January between Tokyo and Osaka. The communication time of the system between Tokyo and Osaka was about 25.5 msec. Thus, we confirmed that the system was able to provide almost real-time audio data communication. Based on the result, the feasibility of music therapy activities, such as singing and exercises with music, using the system was examined. We confirmed that the singing activity which is the most important one in the music therapy was feasible.

Wireless full-duplexing enables a transmission and a reception on the same frequency channel at the same time, and has the potential to improve the end-to-end throughput of wireless multi-hop networks. In the present paper, we propose a media access control (MAC) protocol for wireless full- duplex and multi-hop networks called Relay Full- Duplex MAC (RFD-MAC). The RFD-MAC is an asynchronous full-duplex MAC protocol, which consists of a primary transmission and a secondary transmission. The RFD-MAC increases the full-duplex links by overhearing frames, which include 1-bit information concerning the existence of a successive frame, and selecting a secondary transmission node using the gathered information. The gathered information is also used to avoid a collision between the primary and secondary transmission. Simulation results reveal that the proposed RFD-MAC improves up to 68%, 49% and 56% of end-to-end throughput compared to CSMA/CA, FD-MAC and MFD-MAC, respectively.

Data-intensive wireless sensor network applications, such as structural health monitoring and earthquake monitoring, require high throughput bulk data collection. Based on the fact that each node stores the same amount of sensor data, we propose a Maximum-Subtree-First Collection Protocol (MSFCP), which adopts Maximum-Subtree-First scheduling on top of multi-channel block transfer to maximize overall throughput. We present the theoretical analysis that MSFCP can achieve optimal throughput in the ideal propagation environment, and we achieve overall throughput of 135 kbps on the IRIS Mote platform.

In distributed spectrum sensing, we deploy ten thousands of sensor nodes, and a low-cost sensor node is necessary. In this work, we design the sensor node specialized in power level measurement for distributed spectrum sensing, and we discuss 3 components of the node, which are frequency range, RBW and a detector.

Radio spectrum has turned into a precious natural resource from free goods due to the rapid development of wireless communication technology. In order to efficiently utilize radio spectrum, a spectrum policy should be enough appropriate to create new industries and new technologies. From this point of view, we have developed a distributed spectrum sensing system which collects dense information of spectrum usage over large area. In this work, we discuss the requirements and the design of the distributed spectrum sensing system.

Brainstorming has been greatly used as a method to generate a large number of ideas by variety of each participant's knowledge. However, brainstorming does not always work well because of spatial and communication limitations. Moreover, brainstorming techniques present limited scalability. Meanwhile, genetics algorithms have been mostly regarded as an engineering or technological tool. However, the innovation intuition suggests that genetic algorithms may be also regarded as models of human innovation and creativity. This paper focuses on online creativity sessions. Modeling those creative efforts using selecto-recombinative mechanism can provide three times more novel ideas, increase the posting frequency by a 2.6 factor, and help overcome superficiality on online communications by favoring synthetic thinking.

No abstract available.

In this paper we present a high-density earthquake monitoring system using wireless sensor networks. For high-precision monitoring, we developed Pavenet OS, which is a hard-realtime operating system for sensor nodes, and acceleration sensor board. Sensor nodes of the system sample acceleration with less than 0.3 us jitter with Pavenet OS. The system provides earthquake engineering researchers the ability to measure vibrations of structures during earthquakes at less cost and higher node density than previous systems.

Wireless sensor network technologies are expected to be a key technology to support various innovative applications in the future ubiquitous computing environment. So far, the mainstream of sensor network research has been focused on simulation-based development of battery-aware communication protocols. However, when we apply sensor network technologies to practical applications such as environmental monitoring or factory automation, we find a lot of practical problems that are not considered in simulation-based approach. We believe that finding and solving such practical problems are quite important for deploying sensor network technologies beyond laboratory use. From this point of view, we have been trying to find such problems through various implementations of sensor network system for several years. This paper describes our opinions of designing sensor network system and introduces our approaches for developing practical systems.

Ubiquitous computing as the integration of sensors, middleware, and networking technologies to form a "smart space" environment relies on the development of both software and hardware solutions. For over 3 years, our group has been developing a smart-space environment, involving the exploration of core technologies and attractive applications. We introduce the challenges faced and lessons learned in designing and developing a smart-space environment, including device control services, locating systems, wireless sensor nodes, and middleware services.