Next-generation wireless networks are expected to leverage multi-modal data sources in order to execute various wireless communication tasks such as beamforming and blockage prediction with situational-awareness. To do so, multi-modal transformers emerged as an effective tool, however, existing transformer-based approaches suffer from high inference latency and large memory footprints when processing multi-modal data. Hence, such existing solutions cannot handle wireless communication tasks that require fast inference to track a dynamically changing environment with moving vehicles and blockages. One major bottleneck is the reliance on attention mechanisms whose complexity grows quadratically with respect to the number of tokens. Hence, in this paper, a novel, fast multi-modal transformer inference framework is designed to practically support wireless communication tasks by processing only important tokens. To this end, an optimization problem is formulated to find the optimal number of tokens under a target floating point operations (FLOPs) for a given wireless communication task while maintaining the task accuracy. To solve this problem, modality-specific tokenizers are first designed to project each modality into the same embedding dimension. Then, a token router is introduced to learn the importance of each token and process only important tokens. Subsequently, a trainable keep ratio is introduced to learn how many tokens should be processed for each layer under the target FLOPs. Simulation results show that, on DeepSense 6G beamforming tasks, the proposed framework can reduce the inference latency, GPU memory, and FLOPs by 86.2% 35%, and 80%, respectively, with negligible accuracy loss compared to a baseline that processes all tokens. To further validate the feasibility of the proposed framework for real-world deployments, a multi-modal handover dataset is developed using a real-world testbed. Emulation results on the developed dataset show that the proposed framework can proactively initiate handover before blockage, while the baseline experiences significant received signal strength degradation due to higher inference latency.

With the explosive growth of modern graph data, ReRAM-based graph processing paradigms are emerging as promising solutions to the “memory wall” bottleneck. However, existing paradigms often lean on overly idealized ReRAM architectures, overlooking the critical influence of various hardware faults due to the analog nature and immature fabrication processes of realistic ReRAMs. These hardware faults can lead to convergence anomalies or unacceptable output deviations (i.e., Severe Errors), undermining the reliability of ReRAM-based graph processing. While some studies enhance the reliability of ReRAM-based computation through fault-aware remapping or robust algorithm design, the unique graph execution characteristics make these efforts challenging to migrate effectively. In this work, we first develop ReGFI, a microarchitecture-level Fault Injection framework for ReRAM-based Graph processing. Unlike traditional fault injection methods that only introduce random algorithm-level faults, ReGFI precisely maps hardware faults into the microarchitectural graph execution flow to effectively characterize their effects on the execution correctness. Based on ReGFI, we propose FReRAM, a Fault-Resilient graph processing paradigm in realistic ReRAMs. Firstly, observing the fault robustness of graph vertices compared to graph edges, we reverse-map the vertex values to the non-ideal crossbar while using the edge values as inputs, for proactive SE avoidance. Then, leveraging the cell idleness in ReRAM crossbars and bit-wise reliability discrepancies of graph data, we recycle idle crossbar cells and squeeze out approximable Least Significant Bits to robust-encode the fault-sensitive bits, for further SE elimination. Experimental results exhibit that FReRAM achieves 88.84% SE reduction while incurring negligible overhead for fault-resilient ReRAM-based graph processing. Furthermore, we evaluate the effectiveness and performance of FReRAM under different hardware configurations, graph datasets, and data formats.

This paper presents a digital twin-based digital signage platform designed to improve the accuracy of advertising effectiveness measurement and introduce new content switching methods. By utilizing real-time pedestrian data captured through Light Detection And Ranging (LiDAR), the platform enables dynamic content switching based on pedestrian distance and movement direction. A prototype system was implemented, demonstrating the effectiveness of these new switching methods and the platform's enhanced measurement accuracy. Future work includes conducting evaluation experiments to further validate the system's practicality.

This paper presents a digital twin-empowered real-time optimal delivery system specifically validated through a proof-of-concept (PoC) demonstration of a real-world autonomous car-sharing service. This study integrates real-time data from roadside units (RSUs) and connected and autonomous vehicles (CAVs) within a digital twin of a campus environment to address the dynamic challenges of urban traffic. The proposed system leverages the Age of Information (AoI) metric to optimize vehicle routing by maintaining data freshness and dynamically adapting to real-time traffic conditions. Experimental results from the PoC demonstrate a 22% improvement in delivery efficiency compared to conventional shortest-path methods that do not consider information freshness. Furthermore, digital twin-based simulation results demonstrate that this proposed system improves overall delivery efficiency by 12% and effectively reduces the peak average AoI by 23% compared to the conventional method, where each vehicle selects the shortest route without considering information freshness. This study confirms the practical feasibility of cooperative driving systems, highlighting their potential to enhance smart mobility solutions through scalable digital twin deployments in complex urban environments.

Millimeter wave (mmWave) in vehicle-to-everything (V2X) communications offers incredibly high data rates and low latency while facing significant reliability challenges due to signal blockages and limited range. This paper introduces a blockage-aware system for managing dynamic multi-hop mmWave V2X communications, with a focus on predictive blockage detection and avoidance. The proposed system integrates a mobility digital twin (DT), allowing the multi-hop routing control plane to anticipate blockages before they occur and proactively adjust routing decisions. The embedded control plane algorithm combines high-precision environmental data with trajectory prediction to predict imminent line-of-sight (LoS) disruptions and dynamically reconfigure V2X paths before communication quality degrades. We establish the mobility DT of the Nishi-Shinjuku area for simulations. The results indicate a higher connectivity ratio by using our proposed system, and this system can robustly handle both fully connected and mixed-traffic environments.

The commercialization of 5G has been initiated for a while. Yet, we haven't seen wide penetrations of the disruptive applications as promised, like the extended reality (XR), internet of vehicles (IoV), telesurgery, etc. Digital twins (DTs) are one of these highly anticipated applications. However, DTs demand all 5G capabilities, including enhanced mobile broadband (eMBB), ultra-reliable and low latency communications (URLLC), and massive machine type communication (mMTC) simultaneously, which makes their implementation more challenging. Owing to the advanced sensing, communication, and computing networks for beyond 5G (B5G) and 6G at Tokyo Institute of Technology, we are able to build a real-time DT on campus and demonstrate two smart mobility applications for safety and traffic efficiency, respectively, by utilizing DT-derived knowledge. Our demonstrations will convince society of the values and potentials of DTs and encourage current 5G progress towards B5G and 6G.

The burgeoning proliferation of graph data has rendered graph processing within conventional von Neumann computing architectures increasingly inefficient. Resistive Random Access Memory (ReRAM) emerges as a promising solution to circumvent the "memory wall" bottleneck encountered in graph processing. Nevertheless, the inherent non-idealities of ReRAM devices raise reliability concerns, the ramifications of which for graph processing necessitate a comprehensive exploration. In this work, we propose CraftRGP, a Comprehensive Reliability Analysis Framework Towards ReRAM-Based Graph Processing. CraftRGP simulates the operation of graph processing on ReRAM devices at the underlying hardware level, and employs software fault injection to simulate three primary fault models on ReRAM devices: permanent faults, transient faults, and sensing faults. Based on CraftRGP, we systematically evaluate the fault resilience of ReRAM-based graph processing along the dimensions of graph applications, fault models, and hardware architectures. Finally, utilizing the observed reliability characteristics with CraftRGP, we exemplify two use cases to demonstrate that CraftRGP can guide system designers to facilitate better reliability designs.

Recently, vehicular over-the-air (OTA) updates have been raised as an intelligent solution for the autonomous vehicle revolution. It is critical to download important vehicular safety and stability updates onboard. The forthcoming six-generation (6G) systems might consider OTA a cach service delivered by base stations or roadside units (RSUs). This paper introduces the practical implementation of OTA updates using roadside units and dual-band WiGig/Wi-Fi communications. The system's performance is investigated against different blocking vehicles (small, medium, and large), simulating realistic scenarios. Experimental results ensure the 60 GHz WiGig performance efficiency regarding download speed and response time.

Digital twin (DT) technology can replicate physical entities in cyberspace. A mobility DT digitalizes connected and autonomous vehicles (CAVs) and their surrounding traffic environment, allowing to monitor the maneuvering and distribution of CAVs in real-time, which is crucial for managing vehicle-to-everything (V2X) connectivity, especially when millimeter wave (mmWave) is adopted. MmWave V2X relies on dynamic multi-hop communications to ensure high reliability. Therefore, in this paper, the challenges of mmWave V2X are presented to motivate the utilization of DT, and then we introduce the system model for DT-based multi-hop routing management, incorporating two different routing algorithms: with and without future trajectory prediction. For proof of concept, we implement the proposed DT system using Unity-based AWSIM and evaluate the proposed algorithms via simulations. The results show that, compared to the conventional routing algorithm in vehicular ad hoc networks (VANETs), the DT-based algorithms significantly improve the reliability of mmWave V2X, and such improvements can be seen in both fully connected and mixed traffic scenarios.

In Cooperative Intelligent Transportation System (C-ITS), cooperative perception emerges as a core application empowered by Vehicle-to-Everything (V2X) communications to improve road safety. Connected vehicles are able to share and receive sensor information from any other V2X-capable facilities. While most research interests lie in the utilization and integration of V2X data inside the connected vehicle systems, we stress the importance of understanding the unique role of roadside units (RSUs) for cooperative perception, as well as appropriate strategies for them to deliver this application. To that end, we designed an SDVN-based cooperative perception framework running on RSUs. By leveraging Dedicated Short-Range Communications (DSRC) and Millimeter Wave (mmWave) V2X, this framework system can adaptively support message-based cooperative perception and raw sensor data-based cooperative perception. In addition, we propose a DSRC-triggered dynamic sensor ON/OFF strategy to improve energy efficiency and RSU scheduling flexibility. The field trial results agree well with our design expectations, i.e., small delay for activation (< 30 ms), significant power reduction (~60 %), and good scalability.

During the past decade, smart mobility and intelligent vehicles have attracted increasing attention, because they promise to create a highly efficient and safe transportation system in the future. Meanwhile, digital twin, as an emerging technology, will play an important role in automated driving and intelligent transportation systems. This technology is applied in this paper to design a platform for smart mobility, providing large-scale route planning services. Utilizing sensing technologies and cloud/edge computing, we build a digital twin system model that reflects the static and dynamic objects from the real world in real time. With the smart mobility platform, we realize traffic monitoring and route planning through cooperative environment perception to help automated vehicles circumvent jams. A proof-of-concept test with a real vehicle in real traffic is conducted to validate the functions and the delay performance of the proposed platform.

This paper proposes a framework of blind-spot visualization to see through obstacle via augmented reality (AR) glasses for safe driving enabled by millimeter-wave vehicle-to-everything (V2X) communications. Cooperative perception is known as promising technology to realize safe (automated-) driving where the driver can make immediate and accurate decisions by widely collecting information on the surrounding traffic environment. It is realized by extended sensors such as high-definition cameras and light detection and ranging (LiDAR) to be installed in road-side units (RSUs) all over the place. Camera and LiDAR sensor data of the blind-spot are transferred from RSU to the vehicle, and these sensor fusion data are created as the colored three-dimensional point cloud. It is then projected on the AR glasses. The key feature of our proposal is to provide blind-spot information as a realistic view so that drivers can recognize it without having to look at other display devices. Our initial implementation presents its feasibility through indoor and outdoor trials.

Millimeter wave (mmWave) has great potential to be used for Vehicle-to-Vehicle (V2V) communications due to the characteristics of ultra-high throughput and ultra-low latency. Besides, one mmWave channel can be reused in all V2V links since it is easily blocked by surrounding obstacles, which highly improves the spectrum efficiency. However, links with reflected interference still exist that degrade the throughput of mmWave V2V communications, even though the direct interference link can not penetrate vehicle bodies. To mitigate this negative effect, in this paper, we firstly analyze the channel model of mmWave V2V under interference from ground and surrounding reflections. In order to maintain a higher practical throughput than the required data rate (1 Gbps), a new method of ZigZag antenna configuration is proposed. Secondly, via simulation, the antenna height is optimized under ZigZag antenna configuration. At the optimal antenna height, throughputs of mmWave V2V communications with/without ZigZag antenna configuration are fully compared with non-equal inter-vehicle distance at 60 GHz, where the ZigZag antenna configuration significantly suppresses the destructive interference. Finally, the effectiveness of ZigZag antenna configuration is proved by outdoor experiments.

Conventional wisdom to enhance driving safety of automated vehicles focuses on the massive deployments of intelligent sensors, which generate enormous amount of data. As the solution of 5G era, creating a highly connected vehicular network is considered more reliable to guarantee the driving safety because single vehicle is prone to estimation error with blocked vision. Millimeter- wave, which is one of the key enablers of 5G, is promising to provide multi-gigabit ultra-high data rate and millisecond-level ultra-low latency data transmission. At the same time, software defined networking (SDN), which has proved its ability of dynamic resource management in wired networks, attracts us to extend its advantages into the vehicular environment. In this paper, we enumerate the benefits of mmWave and SDN on the vehicular network. In order to highlight their benefit on driving safety, we propose an architecture of SDN- based mmWave V2X network for HD dynamic map reception and distribution, i.e., cooperative perception. In addition, a proof-of-concept (PoC) is practically conducted in an outdoor environment to validate the performances of the proposed system.