In this paper, we propose a novel low-complexity Bayesian receiver design to jointly perform channel, CFO, and data estimation from observations subject to different CFO among users in MU-MIMO-OFDM systems. ICI due to CFO significantly reduces channel estimation accuracy under frequency-selective fading environments, making reliable communications difficult. To tackle this difficulty, a JCCE algorithm is designed based on BP. Our method uses a BG distribution as the prior distribution of the channel coefficient to capture its delay-domain sparsity, and a GM distribution as the prior distribution of the phase shift due to CFO to perform parallel search for the allowable range of CFO defined in the 3GPP standard by the number of mixture components. The proposed algorithm can further improve the accuracy of channel, CFO, and data estimation by treating the tentatively detected data symbols as extra pilots. The efficacy of the proposed method is confirmed by numerical studies, which show that the proposed method not only significantly outperforms the SotA methods with much lower computational cost but also approaches the performance of an idealized Genie-aided scheme.

We propose new schemes for joint channel and data estimation (JCDE) and radar parameter estimation (RPE) in doubly-dispersive channels, such that integrated sensing and communications (ISAC) is enabled by user equipment (UE) independently performing JCDE, and base stations (BSs) performing RPE. The contributed JCDE and RPE schemes are designed for waveforms known to perform well in doubly-dispersive channels, under a unified model that captures the features of either legacy orthogonal frequency division multiplexing (OFDM), state-of-the-art (SotA) orthogonal time frequency space (OTFS), and next-generation affine frequency division multiplexing (AFDM) systems. The proposed JCDE algorithm is based on a Bayesian parametric bilinear Gaussian belief propagation (PBiGaBP) framework first proposed for OTFS and here shown to apply to all aforementioned waveforms, while the RPE scheme is based on a new probabilistic data association (PDA) approach incorporating a Bernoulli-Gaussian denoising, optimized via expectation maximization (EM). Simulation results demonstrate that JCDE in AFDM systems utilizing a single pilot per block significantly outperforms the SotA alternative even if the latter is granted a substantial power advantage. Similarly, the AFDM-based RPE scheme is found to outperform the OTFS-based approach, as well as the sparse Bayesian learning (SBL) technique, regardless of the waveform used.

In high-speed mobile communication environments, an orthogonal time frequency space (OTFS) scheme with robustness to doubly-selective fading channels by spreading symbols in the frequency-time (FT) domain has attracted much attention. However, typical pilot-based channel estimation schemes cause system performance degradation due to the increased overhead of channel state information (CSI) acquisition, and large-scale matrix operations based on the size of OTFS equivalent channels are also problematic in terms of the computational cost. To address this issue, in this paper, we focus on the fact that joint channel and data estimation (JCDE) in the delay-Doppler (DD) domain OTFS systems can be formulated as a large-scale parametric bilinear inference problem, and solve it via Gaussian belief propagation (GaBP) to design a novel low-complexity and high-accuracy JCDE algorithm with the use of relatively short pilot sequences. From computer simulations, we confirm that the proposed method significantly outperforms the conventional two-stage channel and data estimation, and asymptotically approaches the idealized scheme given perfect CSI knowledge.

Machine vision systems play a pivotal role in streamlining manufacturing processes, notably in quality control through automatic in-line visual inspections. A common practice for inspecting parts, components, and final products is to use a master part benchmark for quality comparison. However, challenges arise when objects enter inspection points in unintended orientations. This misalignment potentially leads to erroneous decisions by automated systems, resulting in additional checkpoints or wastage affecting the production rate. To tackle this issue, we propose a visual inspection pipeline that leverages recent machine learning-based approaches to compare the inspection target and a master part virtually oriented to the same perspective. Specifically, we suggest combining 3D Gaussian Splatting and DUSt3R as a practical solution. Our approach demonstrates its efficacy in real-world scenarios through testing on three mock parts and a real industrial component.

We propose a novel joint channel and data estimation (JCDE) algorithm via bilinear Gaussian belief propagation (BiGaBP) for massive multi-user MIMO (MU-MIMO) systems with non-orthogonal pilot sequences. The contribution aims to reduce significantly the communication overhead required for channel acquisition by enabling the use of short non-orthogonal pilots, while maintaining multi-user detection (MUD) capability. Bilinear generalized approximate message passing (BiGAMP), which is systematically derived by extending approximate message passing (AMP) to the bilinear inference problem (BIP), provides computationally efficient approximate implementations of large-scale JCDE via sum-product algorithm (SPA); however, as the pilot length decreases, the estimation accuracy is severely degraded. To tackle this issue, the proposed BiGaBP algorithm generalizes BiGAMP by relaxing its dependence on the large-system limit approximation and leveraging the belief propagation (BP) concept. In addition, a novel belief scaling method complying with the data detection accuracy for each iteration step is designed to avoid the divergence behavior of iterative estimation in the early iterations due to the use of non-orthogonal pilots, especially in insufficient large-system conditions. Simulation results show that the proposed method outperforms the state-of-the-art schemes and approaches the performance of idealized (genie-aided) scheme in terms of mean square error (MSE) and bit error rate (BER) performances.

This paper proposes a novel angle-of-arrival (AoA) estimation-aided Bayesian joint channel and data estimation (JCDE) algorithm for uplink signal detection in millimeter-wave (mmWave) massive multi-user MIMO (MU-MIMO) systems with short non-orthogonal pilots. In the proposed method, the prior distribution of mmWave channels in the angular domain after digital beamforming is approximated by a Bernoulli-Gaussian (BG) distribution, and the mismatch with the actual distribution is corrected based on AoA estimation by low-complexity angle rotation (AR) method. The resultant beam-domain JCDE algorithm has an inherent mechanism to update path gains of the channels estimated based on AoA for every iteration. This receiver design allows us to take the advantage of both stochastic (i.e., Bayesian) and deterministic (i.e., AR) approaches. Efficacy of the proposed method over the state-of-the-art is confirmed via computer simulations in terms of bit error rate (BER) performance compared to the state-of-the-art alternatives.

This paper deals with uplink multi-user detection (MUD) via generalized approximate message passing (GAMP) in highly correlated large multi-user multi-input multi-output (MUMIMO) systems. The most vital mechanism of GAMP is Onsager correction for decoupling the self-noise feedback of beliefs across iterations; this makes it possible to exchange extrinsic values. First, we show that by introducing the belief scaling method proposed in the context of Gaussian belief propagation (GaBP) for adjusting the convergence speed into GAMP, the Onsager correction works properly even under spatial fading correlation, which significantly improves detection capability. Surprisingly, the performance is much better compared to that of the GaBP with belief scaling, and even asymptotically approaches that of computationally expensive expectation propagation (EP) detectors. Based on the results, we clarify why such a dramatic performance improvement is possible only for GAMP in terms of the suppression mechanism of self-noise feedback.

We propose a novel joint channel and data estimation (JCDE) scheme for highly-correlated large multi-user multi-input multi-output (MIMO) systems with short non-orthogonal pilots. Bayesian JCDE via scalar-wise tensor products is a solid strategy for achieving multi-user detection (MUD) with extremely low computational cost, but the convergence property is significantly degraded in practical MIMO systems assuming spatially correlated fading channels. When using ultra-short pilots aiming at significant overhead reduction, the reliable MUD via JCDE becomes increasingly challenging. To address this issue, the proposed method estimates channel coefficients via a maximum a-posteriori (MAP)-like approach with the aid of long-term channel statistics. Furthermore, while the data detection is performed via probabilistic data association (PDA) to suppress the observation correlation, the channel estimation is performed via Gaussian belief propagation (GaBP) by leveraging the pseudo-orthogonality of the pilot-plus-data sequences, which makes it possible to realize low-complexity and high-accuracy Bayesian JCDE for highly-correlated MUD. Computer simulation demonstrates the validity of our proposed method in terms of bit error rate (BER) performance and computational cost.

This paper proposes bilinear Gaussian belief propagation (BiGaBP) for joint channel and data estimation (JCDE) in large multi-user multi-input multi-output (MU-MIMO) systems. JCDE is a well-known strategy for realizing a high-precision MU detection (MUD) with short pilots by utilizing the orthogonality of data sequences. For massive MIMO scenarios, the JCDE via bilinear generalized approximate message-passing (BiGAMP), which is systematically derived by extending AMP to the bilinear inference problem (BIP), achieves extremely low computational cost. However, the use of short non-orthogonal pilots to reduce the channel acquisition overhead significantly degrades the convergence property of BiGAMP. To resolve the lack of an appropriate JCDE scheme based on insufficient pilots, we design a novel MP rule based on GaBP, which is given by relaxing the large-system approximation from AMP. Furthermore, the belief scaling complying with the detection state in each iteration step is introduced to suppress the negative impact of non-orthogonal pilots even in the insufficient large-system conditions. Numerical results show the validity of our proposed method in terms of bit error rate (BER) and mean square error (MSE) performances.

In V2X communication on the actual road, both the length of communication distance and the total size of data transmission must be maximized at the same time when vehicle are running on the road. The conventional single wireless communication such as Wi-Fi, IEEE802.11p, LPWA, cannot satisfy those conditions at the same time. In order to resolve such problems, N-wavelength wireless communication method is newly introduced in our research. Multiple standard wireless networks with different wavelengths are integrated to organize a cognitive wireless communication. The best link of the cognitive wireless is determined by considering their RSSI values. In order to verify the effects of our proposed method, a prototype system is constructed at the actual road and tested the performance, such as communication distance and total transmission data. Through the performance evaluation, the effects of our suggest method could be verified over the single network.

In this paper, we introduce an experimentation of V2X communication in real environment for a road alert information sharing system. Traffic accidents and traffic hazard are serious social problems. Understanding road condition in advance is important and necessary to prevent traffic accidents and traffic hazards. In addition, getting information without Internet connection is necessary to provide road information for drivers in real-time because there is an environment that getting Internet connection is difficult. We construct a road alert information sharing system with multiple vehicles using V2X communication. Our goal is to share road alert information without Internet connection. To realize our goal, we experiment V2X communication in various environments and evaluate experimental results.

In this paper, we introduce an estimation of communication range using Wi-Fi for V2X communication environment. Traffic accidents and traffic hazard are serious social problems. Understanding road condition in advance is important and necessary to prevent traffic accidents and traffic hazards. In addition, getting information without Internet connection is necessary to provide road information for drivers in real-time because there is an environment that getting Internet connection is difficult. We construct a road condition sharing system with multiple vehicles using V2X communication. Our goal is to share road condition without Internet connection. To realize our goal, we experiment V2X communication, evaluate experimental results and estimate communication range.

Recently, the number of automotive sensors has increased with the spread of electronic control systems such as driver assistance functions, navigation systems and hybrid systems. Time-Spacial automotive sensor data from various environmental and internal information of a vehicle is used for many vehicle control functions and services for passengers. To handle such time-spacial sensor data, we propose and develop an automotive sensor information platform named CoMoSE, which provides the utilized methods of automotive sensor information and serves the various services. In this paper, we consider a road surface freezing information providing system and define requests for communication by the system, and implement the road-to-vehicle and vehicle-to-vehicle communication using CoMoSE platform.

In this paper, we introduce a road condition sharing system using Vehicle-to-Vehicle communication in various communication environment. Japan is prone to natural disasters. In addition, traffic hazards and traffic accidents occur due to snow. A wide range and quick condition understanding and monitoring are needed. In addition, it is important to understand the condition of the destination and the road condition to the destination. But, especially after a disaster has occurred, areas that mass media can provide disaster condition information are limited. Recently, various higher technologies have been developed and noticed. By using those technologies, we develop our system. Using our system, we can realize road condition understanding, monitoring and recording as an alert information using multiple sensor data, information sharing between each vehicles and information is provided as web application. As a quantitative evaluation, we measure vehicle to-vehicle communication quality.

Recently, many large-scale natural disasters, such as earthquake and tsunami occur all over the world. One of the major problems after a disaster is the damage caused to the communication and power infrastructure, such as damaged base station and power grid. As a result, disaster victims are unable to communicate with outside area for an extended period of time. Therefore, it is essential to deploy a communication network, which can operate even without power supply or infrastructure. In this paper, we focus on Wireless Mesh Networks (WMNs), which consists of Solar Powered Base Station (SPBS) equipped with air interfaces. These WMNs can be promptly setup. However, because the power generated from solar panel is easily affected by weather condition, it is insufficient and unstable. Additionally, because the power consumption is affected by the distance between the SPBSs and the number of wireless links in each SPBS, it is difficult to maintain network connectivity in the WMNs that are consisted of SPBSs. Therefore, to address the network connectivity problem in the assumed network, we aim to reduce the power consumption of the wireless links by controlling the on-off cycle of air interfaces. We first analyze the network connectivity issue in disaster area and formulate this problem, and propose the on-off scheme of controlling the wireless links of air interfaces based on graph theory. Simulation results of our proposal show that our proposed scheme can ensure the network connectivity.

Recently, reduction cost and size of the on-board sensor, the development of ITS and the spread of smartphones bring significant progress to the automotive information environment. In this paper, we introduce the COMOSE (Co-Operative MObile Sensor Environment) platform that collects sensor information using the wireless plug-and-play technology, to realize the effective utilization of the sensor information on a distributed network.