High-speed data transmission is enabled by millimeter wave communication. While the short wavelength may cause reliability issues, which is critical in some scenarios such as vehicle-to-vehicle communication. In this work, an adaptive data transmission management method is proposed by incorporating sensing results. With the moving information of the vehicles, the transmission strategy is adaptively adjusted to improve communication efficiency. The performance of the proposed method is evaluated in simulation experiment by extending the network simulator framework NS3. The results demonstrate that it is promising to utilize the sensing results for intelligent network management.

Food ingredient recognition has received numerous attention for its importance for health-related applications. But there are still some challenges due to food dishes complexity such as detecting ingredients of varying sizes and identifying multiple ingredients within a single image. Addressing this challenge, we propose a novel ingredient recognition network named Dual Discovery Integration Network (DDIN) which consists of two modules: the Region Discovery (RD) module using deconvolution to get probability distribution map for find-grained region discover, the Category Discovery (CD) module using an ingredient dictionary to capture multiple ingredient category. Finally, the output from RD and CD modules are fused to obtain the final prediction results. The experimental results demonstrate that our model achieves state-of-the-art results in ingredient recognition on the Chinese Food dataset Vireo Food-172, and it also outperform existing methods for less parameters and lower computational complexity. Further visualization of discovered ingredient regions also shows the superiority of our method.

Currently, with the extensive application of digital cameras in dynamic capturing, implications such as camera jitter, out-of-focus, and target motion induce various types and degrees of image blurring. Deep learning (DL) is a powerful technique that offers data-adaptive recovery without prior characterization of deblurring filter kernels. However, end-to-end networks can still be improved to restore regions with severe localized blurring. Therefore, we propose a multi-scale circular transformer (MSC-Former) employing averaged neighborhood attention (AvgNA) to solve this problem. It computes the local attention of each feature pixel by learning the correlation between the center and the surrounding windowed neighborhood, then produces integrated attention with direct averaging. We employ a multi-scale circular strategy (MSCS) to compute attention at different spatial scales to expand the receptive field while maintaining a low parameter count. It uses concentric circular regions with varying radii to define neighborhoods at different scales, which expands the receptive field during attention computation while capturing spatial continuity across larger neighborhoods. Experimental results demonstrate that the proposed method surpasses the recent state-of-the-art deblurring techniques on the benchmark dataset.

While the rollout of the fifth-generation mobile network (5G) is underway across the globe with the intention to deliver 4K/8K UHD videos, Augmented Reality (AR), and Virtual Reality (VR) content to the mass amounts of users, the coverage and throughput are still one of the most significant issues, especially in the rural areas, where only 5G in the low-frequency band are being deployed. This called for a highperformance adaptive bitrate (ABR) algorithm that can maximize the user quality of experience given 5G network characteristics and data rate of UHD contents.Recently, many of the newly proposed ABR techniques were machine-learning based. Among that, Pensieve is one of the state-of-the-art techniques, which utilized reinforcement-learning to generate an ABR algorithm based on observation of past decision performance. By incorporating the context of the 5G network and UHD content, Pensieve has been optimized into Pensieve 5G. New QoE metrics that more accurately represent the QoE of UHD video streaming on the different types of devices were proposed and used to evaluate Pensieve 5G against other ABR techniques including the original Pensieve. The results from the simulation based on the real 5G Standalone (SA) network throughput shows that Pensieve 5G outperforms both conventional algorithms and Pensieve with the average QoE improvement of 8.8% and 14.2%, respectively. Additionally, Pensieve 5G also performed well on the commercial 5G NR-NR Dual Connectivity (NR-DC) Network, despite the training being done solely using the data from the 5G Standalone (SA) network.

Owing to the plentiful information released by the commodity devices, Wi-Fi signals have been widely studied for various wireless sensing applications. In many works, both received signal strength indicator (RSSI) and the channel state information (CSI) are utilized as the key factors for precise sensing. However, the calculation and relationship between RSSI and CSI is not explained in detail. Furthermore, there are few works focusing on the measurement variation of the Wi-Fi signal which impacts the sensing results. In this article, the relationship between RSSI and CSI is studied in detail and the measurement variation of amplitude and phase information is investigated by extensive experiments. In the experiments, the transmitter and receiver are directly connected by power divider and RF cables and the signal transmission is quantitatively controlled by RF attenuators. By changing the intensity of attenuation, the measurement of RSSI and CSI is carried out under different conditions. From the results, it is found that in order to get a reliable measurement of the signal amplitude and phase by commodity Wi-Fi, the attenuation of the channels should not exceed 60 dB. Meanwhile, the difference between two channels should be lower than 10 dB. An active control mechanism is suggested to ensure the measurement stability. The findings and criteria of this work is promising to facilitate more precise sensing technologies with Wi-Fi signal.

5G Standalone (SA) is the goal of the 5G evolution, which aims to provide higher throughput and lower latency than the existing LTE network. One of the main applications of 5G is the real-time distribution of Ultra High-Definition (UHD) content with a resolution of 4K or 8K. In Q2/2021, Advanced Info Service (AIS), the biggest operator in Thailand, launched 5G SA, providing both 5G SA/NSA service nationwide in addition to the existing LTE network. While many parts of the world are still in process of rolling out the first phase of 5G in Non-Standalone (NSA) mode, 5G SA in Thailand already covers more than 76% of the population. In this paper, UHD video will be a real-time live streaming via MPEG-DASH over different mobile network technologies with minimal buffer size to provide the lowest latency. Then, performance such as the number of dropped segments, MAC throughput, and latency are evaluated in various situations such as stationary, moving in the urban area, moving at high speed, and also an ideal condition with maximum SINR. It has been found that 5G SA can deliver more than 95% of the UHD video segment successfully within the required time window in all situations, while 5G NSA produced mixed results depending on the condition of the LTE network. The result also reveals that the LTE network failed to deliver more than 20 % of the video segment within the deadline, which shows that 5G SA is absolutely necessary for low-latency UHD video streaming and 5G NSA may not be good enough for such task as it relies on the legacy control signal.

For massive multiple-input multiple-output (MIMO) systems, pilot contamination reduces the data transmission capacity owing to the inter-cell interference of non-orthogonal pilots reusage. To develop efficient mobile communication, it is necessary to mitigate pilot contamination. To address this problem, we propose an annealing-based pilot allocation method using Digital Annealer to provide solution for Ising machine. The proposed method is a max k-cut-based approach, where the graph represents the potential strength of pilot contamination among users in other cells. By using this proposed method, users who have strong relationship with pilot contamination will be assigned different pilots. Experiment results show that the proposed method can realize optimal pilot allocation and mitigate pilot contamination. Compared with conventional methods, the proposal achieves the best performance which can increase the minimum achievable rate and show higher SINR, especially when the numbers of users and cells are large.

Dynamic adaptive streaming over HTTP (DASH) has been widely adopted in modern video streaming services. In DASH, the core technique is adaptive bitrate (ABR) control which can adjust the requested video bitrate level according to the network conditions to tradeoff between video quality and rebuffering risk. It is a challenge for the ABR methods in the scenarios when multiple DASH streaming users compete over the network bottleneck. This paper proposes a client-side ABR control method, flexible relaxation assisted by buffer (FRAB), to achieve fair, stable and efficient video streaming among different users. The idea of FRAB is to "relax" the change of the video quality based on current buffer level, which can enhance the stability of video streaming. Meanwhile, by flexibly adjusting the relaxation, the efficiency and fairness among all users are improved. FRAB is evaluated in real experiments under three different network conditions and compared with conventional multi-user ABR algorithms. Results indicate FRAB has the best performance in fairness, which reduces the unfairness by a maximum of 69.5% under real-world measured network condition. It also improves the efficiency by 71.3% comparing with PANDA, and enhances the stability by 73.3% comparing with TFDASH. The experiment results demonstrated that the proposed method has superior performances in multi-user DASH video streaming.

With the live video streaming becomes more and more common in daily life such as live meeting and live video call, it is an urgent task to ensure high-quality and low-delay live video streaming service. High user quality of experience (QoE) should be ensured to satisfy the requirement of user, for which latency is one of the important factors. In this paper, a high-QoE live streaming method is proposed with reinforcement learning. Experiments are conducted to evaluate the proposed method. Results demonstrate that the proposal shows the best performance with highest QoE compared with conventional methods in three network conditions. In Ferry case, the QoE is almost twice of the QoE of other methods.

With the dramatically increasing video streaming in the total network traffic, it is critical to develop effective algorithms to ensure the quality of content delivery service. Adaptive bitrate (ABR) control is the most essential technique which determines the proper bitrate to be chosen based on network conditions, thus realize high-quality video streaming. In this paper, a novel ABR strategy is proposed based on Ising machine by using the quadratic unconstrained binary optimization (QUBO) method and Digital Annealer (DA) for the first time. The proposed method is evaluated by simulation with the real-world measured throughput and compared with other state-of-the-art methods. Experiment results show that the proposed QUBO-based method can outperform the existing methods, which demonstrating the superior of the proposed QUBO-based method.

With the increasing video demand in daily network traffic, it is an urgent task to develop effective algorithms to facilitate high-quality content delivery service. Recently, numerous adaptive streaming algorithms have been proposed to improve the user perceived experience. However, these algorithms were mainly developed from the perspective of single user. There is not yet systematical evaluation and comparison of the bitrate adaptation methods for multi-user video streaming. Besides, the Quality of Experience (QoE) metrics were not unified.In this work, we propose a new mininet-based testbed framework which is able to conduct real-time video streaming emulation in various multi-user scenarios. Seven state-of-the-art adaptation methods are incorporated into the testbed. Meanwhile, ITU-T P.1203 model, the world's first standard for measuring QoE of HTTP adaptive streaming, is implemented to calculate the mean opinion scores of different methods. Using the developed testbed, the performance of current adaptation methods in multi-user network is analyzed and compared. A variety of experiments are carried out by changing the user number and network conditions, in which the QoE of different users are investigated. It is found that current algorithms perform inconsistently in various network scenarios. In the excessive user and limited bandwidth cases, machine learning and scheduling techniques show superiority in providing high and equal QoE for all users. While in the high-delay case, the buffer-based approaches show robust performance. Overall, the findings of this work give an insight for designing and choosing adaptive streaming strategies in different multi-user network conditions.

Recently, a fifth-generation cellular system (5G) is widely expected to provide plenty of wireless network resources (i.e., broadband capacity). In this paper, to validate 5G system performances, such as physical-layer and TCP-layer throughputs, we carry out a field trial at an actual indoor train station, named Haneda International Airport Terminal Station. In the field trial, we deploy the prototype 5G system (Central Unit, Distribution Unit, Radio Unit and 5G UE (tablet)) on the train station platform and evaluate mobile 5G downlink throughputs. Through the actual measurements, the results confirm that the prototype 5G system can achieve mobile broadband capacity (more than 1 Gbps) even when the UE is located anywhere at the indoor train station platform.

WiFi signal has become accessible everywhere, providing high-speed data transmission experience. Besides the communication service, channel state information (CSI) of the WiFi signals is widely employed for numerous Internet-of-Things (IoT) applications. Recently, most of these applications are based on the analysis of the microwave reflections caused by the physical movement of the objective. In this article, a novel contactless wireless sensing technique named WiEps is developed to measure the dielectric properties of the material, exploiting the transmission characteristics of the WiFi signals. In WiEps, the material under test is placed between the transmitter antenna and receiver antenna. A theoretical model is proposed to quantitatively describe the relationship between CSI data and dielectric properties of the material. During the experiment, the phase and amplitude of the transmitted WiFi signals are extracted from the measured CSI data. The parameters of the theoretical model are calculated using measured data from the known materials. Then, WiEps is utilized to estimate the dielectric properties of unknown materials. The proposed technique is first applied to the ethanol/water mixtures. Then, additional liquids are measured for further verification. The estimated permittivities and conductivities show good agreement with the actual values, with the average error of 4.0% and 8.9%, respectively, indicating the efficacy of WiEps. By measuring the dielectric property, this technique is promising to be applied to new IoT applications using ubiquitous WiFi signals, such as food engineering, material manufacturing process monitoring, and security check.

Adaptive bitrate control (ABR) is an important technique for video streaming. This technique selects the video quality adaptively according to various network conditions, to ensure the quality of experience (QoE) for users. In the previous works, the ABR methods are mainly tested in single user environment. In this paper, an emulation testbed is constructed for QoE performance evaluation in multi-user networks. The state-of-the-art ABR methods are incorporated into the proposed environment. Emulation experiments are carried out to evaluate the performance of the methods. Preliminary results show that in FESTIVE, which has the least QoE variation in the six-user experiments, the user QoE of the worst case is only 27.5% of that of best case, demonstrating that the state-of-the-art ABR methods are not effective enough to optimize the QoE for all users under multi-user condition. Future design of the ABR method should take factors such as the fairness and resource allocation into consideration.

To realize highly reliable video surveillance and provide ultrahigh-definition/immersive video streaming, it is planned to adopt the 5G cellular system using millimeter-wave (mmWave) as the wireless-network infrastructure. However, mmWave communication has a challenging issue: mmWave communication is extremely sensitive to obstacles, such as walls, pillars, and even human bodies, and this issue easily increases the packet loss rates and round trip time (RTT) (or disconnection from the base station) due to a no line of sight (NLOS) environment. Therefore, in this work, 5G throughput performances were evaluates in an indoor train station by considering the effect of an NLOS environment caused by blockage by human bodies. In addition, to improve the robustness of TCP transmission in a high-RTT and high-packet-loss environment (e.g., an NLOS environment), a state-of-the-art TCP, TCP-FSO, was used. In the evaluations, the MATLAB 5G library was used to simulate the 5G environment, and a Linux software-based network emulator, Traffic Control, was used to emulate the 5G network. From the evaluations, it the 5G mobile throughput characteristics were confirmed in three different crowded patterns (low, middle, and high density), and the TCP-FSO advantage against CUBIC-TCP was validated.

Throughput prediction is essential for ensuring high quality of service for video streaming transmissions. However, current methods are incapable of accurately predicting throughput in mobile networks, especially for moving user scenarios. Therefore, we propose a TCP throughput prediction method TRUST using machine learning for mobile networks. TRUST has two stages: user movement pattern identification and throughput prediction. In the prediction stage, the long short-term memory (LSTM) model is employed for TCP throughput prediction. TRUST takes all the communication quality factors, sensor data and scenario information into consideration. Field experiments are conducted to evaluate TRUST in various scenarios. The results indicate that TRUST can predict future throughput with higher accuracy than the conventional methods, which decreases the throughput prediction error by maximum 44% under the moving bus scenario.

In order to recognize the transportation modes without any additional sensor devices, we propose a recognition method by using communication quality factors. In the proposed method, instead of Global Positioning System (GPS) and accelerometer sensors, we collect mobile TCP throughputs, Received Signal Strength Indicators (RSSIs), and cellular base station IDs (Cell IDs) through in-line network measurement when the user enjoys mobile services, such as video streaming service. In accuracy evaluations, we conduct two different field experiments to collect the data in five typical transportation modes (static, walking, riding a bicycle, a bus and a train,) and then construct the classifiers by applying Support Vector Machine (SVM), k-Nearest Neighbor (k-NN) and Random Forest (RF). Results conclude that these transportation modes can be recognized by using communication quality factors with high accuracy as well as the use of accelerometer sensors.

Throughput prediction is one of good solutions to improve quality of mobile applications (e.g., YouTube or Netflix) for video streaming delivery services in mobile networks. This is because such applications require monitoring the network performances to control content quality, thus guarantee quality of service (QoS) and quality of experience (QoE). In this paper, we propose a history-based TCP throughput prediction method incorporating communication quality features using SVR (Support Vector Regression). By taking history of communication quality features such as historical throughput and Received Signal Strength Indication (RSSI) into consideration, the throughput prediction error can be decreased. We conduct experiments with the proposed method and compare the prediction accuracy with a variety of methods in different scenarios of various moving modes of users. Results show that the proposed model could predict throughput effectively in various scenarios and decrease throughput prediction errors by a maximum of 26.47% compared with other methods.