The use of millimeter wave (mmWave) bands is a promising approach to increase the capacity of mobile networks. However, the mm Wave link quality (LQ) is strongly affected by the condition of the line of sight (LOS) path. In order to stably utilize mm Wave bands, an effective solution is to predict future LQ and adaptively control wireless communication. LQ prediction based on Deep Neural Network (DNN) using physical space information, such as the position information of user equipment (UE), is effective to enable the proactive control. On the other hand, huge training data is required to provide the prediction model. In a supervised learning approach, the prediction models must be generated for all the base stations with which the UE communicates. Thus, it is a key to generate the prediction models for different base stations using no or less amount of the training data. In this paper, we propose a method for preprocessing physical space information to reduce the amount of training data when the UE accesses to the different base station. The proposal normalizes the position and direction information of UE by using the position of the base station. To evaluate the effectiveness of the proposed method, we conducted experiments using an automated humanoid robot in a commercial 5G network. Experiment results show that the proposed method reduces the root-mean-square-error (RMSE) of the differences between the predicted and actual throughputs by 35.1 and 51.0% at the different base station when using no samples and 100 samples as training data, respectively, compared to without physical space information preprocessing.

Recent advances in wireless communication technology such as fifth-generation (5G) have enabled the creation of various novel applications. As a result, a large number of devices are now being connected to mobile networks, and mobile traffic is increasing year by year. Although the use of the millimeter-wave (mmWave) bands is a promising approach to increasing the capacity of mobile networks, there are many challenges to use mmWave bands. The link quality (LQ) of mmWave wireless links is impacted by the surrounding objects. Therefore, in order to stably utilize mmWave bands, we believe that it is necessary to predict future LQ predictions and adaptively control wireless communications. In this paper, we evaluated the throughput prediction methods using physical space information of the target UE and surroundings in a commercial 5G network. The evaluation entails measuring the throughput in an actual indoor environment where both the target UE and surrounding objects are moving. To create the huge dataset necessary to allow the moving terminal holder and surrounding pedestrian (objects) to be modelled, we develop two autonomous humanoid robots and make one move so as to block the LOS of the other robot, which is the UE holder. The experiments shows that our proposed method using physical space information yields a 57.5 % improvement in prediction accuracy at the 50th percentile absolute error value over a naive prediction model that uses past throughput information.

This paper proposes a feature extraction method from three-dimensional (3D) point cloud employing spatial frequency analysis for point cloud-based link quality prediction. The proposed method aims to address the human blockage problem in millimeter-wave (mmWave) communications, where proactive communication control through machine learning-based future link quality prediction has been shown to be effective in preventing link quality degradation. However, the use of 3D point clouds presents challenges such as increased transmission cost and computational complexity due to the large data size. To address these challenges, we propose a preprocessing method that can efficiently extract features from the point cloud and reduce data size without compromising the accuracy of mmWave link quality prediction. Our approach uses spatial frequency-based filtering to isolate signals related to moving obstacles, such as pedestrians, in the frequency domain. It also removes large, static background objects like walls and furniture. The proposed method can extract relevant objects in the point cloud based on their spatial frequency without requiring object detection or segmentation. The experimental results demonstrate that our proposed method significantly reduces feature data size by approximately 99% compared to conventional methods, while still maintaining high link quality prediction accuracy.

Given the recent advances in wireless communication technology, all things are being connected to networks, and it is expected that various new and novel applications will be created. The diversification in applications will yield various requirements for wireless communication. We believe that autonomous robot services will be popular after COVID, and the management, monitoring, and operation of the mobility robots requires highly reliable wireless links. In this paper, we propose a wireless link quality prediction system that uses camera images. The proposal generates a prediction model for each camera and combines the output of the models using weights calculated by the outputs of reliability models. By providing separate prediction models for each camera, the prediction system easily handles new additional cameras and drops the cameras found to have low reliability. Indoor experiments show that the proposed prediction scheme outperforms the prediction method that uses all camera images as input features without regard to their reliability.

The link quality prediction facilitates high quality video streaming over mobile networks. However, the existing link quality prediction algorithms focus on minimizing the gap between the ground truth and the prediction result, while it remains a challenge to exploit such information to achieve high quality video streaming with minimum Quality of Experience (QoE) degradation. The accurate link quality prediction is one of keys to enable beyond 5G/6G world. In this paper, we produce artificial intelligence (AI) based link quality prediction which consists two steps: 1. We explore and exploit the temporal correlation in time series to adaptively learn and predict its short-term behavior based on Gaussian Process (GP). 2. The GP-based prediction is tailored to maximize QoE by finding a proper piece-wise convex envelope of the predicted link quality in an online manner. By using the measured uplink throughputs, the video streaming QoE of the proposed framework were evaluated.

Modern networks have complex configurations to provide advanced functions. Network softwarization, a promising new movement in the networking community, could make networks more complexly configured due to the nature of software. Since these complexities make the networks error-prone, network verification is attracting attention as a key technology to detect inconsistencies between a configuration and an operational policy. Existing verifiers are, unfortunately, either inefficient or incomplete (operational policies are not rigorously checked). This paper presents a novel framework of data-plane verification. So as to efficiently manage the large search space defined by packet headers, our framework formalizes the consistency check by applying simple set operations defined in a small quotient space of packet header. This paper also reveals that the two spaces can be connected via the windowing query in computational geometry. Two windowing algorithms are proposed and backed by solid theoretical analyses. Experiments on real network datasets show that our framework with the windowing algorithms is surprisingly fast; when verifying policy compliance in a real network with thousands of switches, our framework reduces the verification time of all-pairs reachability from ten hours to ten minutes.

Network verification is attracting attention as a key technology to detect configuration errors before deploying the network. In verification, a set of packets to be inspected is usually specified by a window -- a multi-dimensional rectangle defined by packet header fields (e.g., address prefixes and port ranges). Network operators have to know the forwarding behaviors of packets inside the window, this can be regarded as the windowing query problem in computation geometry. This paper proposes a novel windowing algorithm for network verification. Unlike existing windowing algorithms, our algorithm runs on a compressed data structure, because the search space has to be represented in a compressed form due to the space complexity.

Modern networks have complex configurations to provide advanced functions, but the complexity also makes them error-prone. Network verification is attracting attention as a key technology to detect inconsistencies between a configuration and a policy before deployment. Existing verifiers, however, either generally verify various properties over the policy at the cost of efficiency, or efficiently perform configuration analysis without paying much attention to the policy. This paper presents a novel framework of data-plane verification, which flexibly checks the inconsistency with great efficiency. For the purpose of generality, our framework formalizes a verification process with three abstract steps: each step is related to 1) packet behaviors defined by a configuration, 2) operator intentions described in a policy, and 3) the inspection of their relation. These steps work efficiently with each other on the simple quotient set of packet headers. This paper also reveals how the second step can be regarded as the windowing query problem in computational geometry. Two novel windowing algorithms are proposed with solid theoretical analyses. Experiments on real network datasets show that our framework with the windowing algorithms is surprisingly fast even when verifying the policy compliance; e.g., in a medium-scale network with thousands of switches, our framework reduces the verification time of all-pairs reachability from ten hours to ten minutes.