This paper presents a comprehensive wideband indoor propagation characterization via real-world experiments at 105.8 GHz with a 4 GHz bandwidth in a lecture hall environment. Utilizing a double-directional channel sounding system with rotating horn antennas, we measure power angular delay profiles (PADPs) at multiple receiver (RX) positions. Our study derives a path loss model, delay characteristics, and angle characteristics, including path loss exponent, root mean square (RMS) delay spread (DS), K-factor, RMS azimuth angle spread of arrival (ASA), and RMS azimuth angle spread of departure (ASD). We compared these results with the 3GPP indoor hotspot (InH) office model (up to 100 GHz), highlighting both similarities and differences in propagation characteristics. Notably, the path loss exponent was found to be 1.49, which is lower than the 1.73 observed in the 3GPP InH-Office LoS model. Moreover, there were positive correlations between the above spread values and transmitter (TX)-RX distance, and negative correlations between K-factor and TX-RX distance, which were not considered in the 3GPP InH-Office LoS model. Furthermore, our analysis revealed a strong negative correlation in RMS ASA vs K-factor and RMS ASD vs K-factor, which are not accounted for in the current 3GPP InH office model. These findings emphasize the need to consider such correlations in accurate propagation models in lecture hall scenarios at the 105 GHz sub-terahertz band.

This study presents the world's fastest three-dimensional double-directional channel sounding system operating at 105 GHz with a null-to-null bandwidth of 4 GHz. The developed channel sounding system allows a full scan of the 105 GHz multipath channel in both azimuth and elevation angles of the transmitter (TX) and receiver (RX) directional antennas. The developed channel sounding system is calibrated with an anechoic measurement in that the path loss of the observed line-of-sight ray coincides with the free-space path loss. Moreover, the anechoic measurement verifies the performance of the channel sounding system, where the peak width at the half-power in delay domain is lower than 0.5 ns, and the peak width at the half-power in angular domain is approximately 10° in the azimuth plane for both TX and RX.

In fifth-generation (5G) cellular vehicle-to-everything (C-V2X) communication, single-hop communication where a one-hop relay is established among vehicles, roadside units (RSUs), and base stations (BSs) has attracted attention for enabling pervasive and reliable packet delivery in urban inter sections environments and expressways. Regarding C-V2X single-hop communication, a signal-to-noise ratio (SNR)-based routing protocol has been proposed, where SNR between vehicles, RSUs, and BSs is used to assess communication paths as a metric. However, owing to blockage events between communication nodes, SNR values between nodes may fluctuate sharply; therefore, the conventional methods that use an instant SNR as a metric cannot ensure stable communication against blockage events. This paper proposes a novel blockage prediction-based 5G C-V2X routing method that enables the selection of links that remain stable over time. Specifically, assuming that future line-of-sight (LOS) conditions between nodes are available from on-vehicle camera images, we develop a routing method that avoids non-LOS paths to prevent communication failures in single-hop transmission. Moreover, we propose a blockage prediction-based scheduling method, ensuring packet transmission only in the LOS conditions for further reliable packet delivery. Our system-level simulation in an intersection environment defined by 3GPP demonstrates that the proposed blockage prediction-based routing protocol outperforms the conventional SNR-based routing protocol in terms of packet reception ratio.

Sidelink communication has evolved as a distributed device-to-device (D2D) communication system within the third-generation partnership project (3GPP) for the advancements in next-generation distributed autonomous systems. However, the current sidelink communication system basically being considered for operation at microwave band is not suitable for distributed wireless networking with a Gbit/s-level data rate. In this context, we have conceptualized a millimeter wave (mmWave) sidelink-based wireless personal area network (WPAN) to enable high-speed distributed wireless networking among indoor proximate devices. Particularly, inspired by the notion of “common-mode signaling (CMS)” in the IEEE 802.15.3c/11ad mmWave WPAN standards, we proposed an innovative physical sidelink broadcast channel (PSBCH) coined CMS- PSBCH to reach a robust inter-node control signaling to maintain a connection under harsh mmWave channel characteristics. As a sequential study, this paper proposes a robust signal detection and timing synchronization scheme tailored for CMS-PSBCH to lead a successful detection and demodulation, thereby providing a complete set of robust synchronization signal block (SSB) for the sidelink-based mmWave WPAN. Our link-level evaluation shows that the proposed scheme effectively detects synchronization signals and symbol timing even under the signal-to-noise ratio (SNR) of −18 dB. Moreover, the proposed scheme leads to successful demodulation of CMS- PSBCH payload for various multipath channels for a wideband indoor short-range communication scenario at the 60 GHz band.

MmWave is currently attracting attention for its significant role in next-generation communication systems such as the fifth generation and sixth generation mobile networks. In this paper, we propose a novel temporal statistical channel model based on our recently conducted indoor short-range propagation measurements at the 60 GHz band. We constructed the channel model compatibly with the New York University channel simulator (NYUSIM) channel model framework which adopts the unique time cluster spatial lobe approach, which lacks both parameter definitions and suitable channel modeling and regeneration framework for indoor short-range communication applications. The channel regeneration simulations demonstrate that our proposed model exhibits good agreement with experimental data in almost all channel parameters, including the number of time clusters, cluster sub-path numbers, inter-cluster delay, and more. Additionally, the proposed model shows better alignment with experimental data in terms of the root mean squared delay spread, compared to existing models.

This paper proposes a comprehensive 3GPPcompatible channel model with statistical enhancement tailored for indoor short-range device-to-device (D2D) communications operating in the frequency range (FR) of $52.6-71.0 \mathrm{GHz}$ termed FR2-2. Regardless of the existence of various channel models at this band for indoor communications, there will be a need for developing a channel model compatible with and understandable from the current 3GPP stochastic channel model (SCM) to facilitate the discussion in the 3GPP for developing such FR2-2 short-range D2D communication framework based on the fifth-generation (5G) new radio (NR). Indeed, such a futuristic vision can be foreseen from the fact that the 3GPP is discussing the evolution of sidelink, referred to as a D2D communication framework; however, there are no 3GPP SCM-compatible channel models applicable to FR2-2 short-range D2D communications. To fill this void, we propose the channel model coined 3GPPCompFR2-InS that allows us to generate channel impulse responses (CIRs) for computer simulations, which is suitable for various indoor short-range D2D communication scenarios while retailing the similarity in terms of the implementation policy of the 3GPP SCM. 3GPPCompFR2-InS is verified based on the real-world measurements at the $\mathbf{6 0 ~ G H z}$ band from the viewpoint of both the validity of the channel model parameters and that of the statistical behavior of the generated CIRs.

This study analyzes the omnidirectional power delay profile (PDP) characteristics at the 105 GHz band in a personal office desktop area by conducting a multipath propagation measurement. In those days, for the 6th generation communication system, there is a demand for a wider bandwidth, and exploring sub-terahertz (sub-THz) is attracting huge interest. However, the omnidirectional multipath propagation characteristics at the 105 GHz band for a personal desktop environment has not been investigated yet regardless of the potential feasibility of the wireless personal area network (WPAN) communication systems operating at this band in a personal desktop area. In this study, we first conduct a measurement of omnidirectional PDPs for the 105 GHz and 60 GHz bands in a personal desktop environment. The comparison of these two bands unveils an affinity in terms of delay-domain multipath characteristics, where the delay spread difference is less than 1 ns. Moreover, this study derives an appropriate guard interval (GI) length to be 20 ns at most for the personal desktop environment, which is compared with those in the internationally standardized 60 GHz WPAN communication systems. These two comparisons with 60 GHz bands provide evidence for the feasibility of 105 GHz wideband WPAN communication systems, which can be designed analogously from 60 GHz WPAN communication systems.

To meet increasing demands for higher-rate communication, exploring the sub-terahertz (THz) band is attracting huge attention. The first standardization of a sub-THz communication was performed at the IEEE 802.15.3d task group, which designed an ultra-wideband short-range communication operating in a 300 GHz band. However, to alleviate hardware challenges, the usage of a much lower-sub-THz band around 100 GHz should also be considered. Moreover, in terms of the advantage of using shared signal processing circuits with the 5G new radio (NR), the frame format should be designed in a compatible manner with the commercially pervasive 5G NR, which cannot be reached by IEEE 802.15.3d. Motivated by these backgrounds, this paper proposes a 5G NR-based ultra-wideband short-range communication operating in a low sub-THz band that lies in 90–110 GHz. This can be achieved by using 5G NR sidelink communication with the bandwidth expansion to several GHz, and no changes are made for the frame format to retain compatibility. This study conducts a performance evaluation of such a 5G NR sidelink system with a 4 GHz bandwidth by using a channel model at the 93–97 GHz band recently developed for device-to-device short-range communications. The evaluation reveals that the proposed communication system using the 5G NR sidelink can achieve the required block error rate (BLER) equal of 0.1 for both control and user-data transmissions even when a 4 GHz bandwidth is utilized. Moreover, it is shown that transmission at a meter-level distance is feasible even when an omnidirectional antenna in an azimuth plane is used.

This study conducts a wideband multi-path propagation measurement at the 95 GHz sub-terahertz band for short-range communication in a conference room desktop scenario. Regardless of the fact that the current 3rd generation partnership (3GPP) stochastic channel model (SCM) targets the frequency up to 100 GHz for various scenarios, neither detailed measurements at the 95 GHz band nor a compatible channel modeling/generation framework for indoor short-range communication scenarios have been conducted. To fill these voids, based on a real-world measurement at 95 GHz with a bandwidth of 4 GHz, this study analyzes the multi-path propagation characteristics and yields the following insights for developing a 3GPP SCM-compatible channel generation framework at this band. First, the exponential power decay with delay time and quasi-uniform azimuth angles of arrival (AAoAs) are observed, which should be revisited to develop a channel generation framework. Secondly, distribution models for root mean squared (RMS) delay/AAoA spreads and omnidirectional path loss model are derived, which serves as a foundation for developing a channel generation framework at this band. Moreover, these established models are compared with the recently conducted measurement results at the 60 GHz band in the same scenario, shedding light on the hypothesis that the models for these parameters at the 60 GHz can be generalized for the 95 GHz band.

With advancements in distributed autonomous systems (e.g., vehicles, sensors, and robots) in the 5G/6G era, sidelink communication technology has evolved as a distributed communication system in the third-generation partnership project (3GPP). However, the current sidelink communication design focusing on information dissemination or point-to-point communication with a low rate is not suitable for rapid development of such autonomous systems. Instead, based on sidelink, developing distributed wireless personal area networks (WPANs) with a drastically higher rate for transmitting user data is essential. The overarching goal of this study is to explore the possibility of sidelink communication evolution to 1) form a distributed and autonomous WPAN and 2) support millimeter wave (mmWave) bands. Our core idea is to merge several design concepts of the precedented mmWave WPAN standards, i.e., IEEE 802.15.3c/11ad, into the sidelink communications, thereby bridging the gap between the two separated systems. This paper presents the anatomy of the IEEE 802.15.3c/11ad system with a focus on the formation of mmWave WPANs among distributed nodes and their operation. In addition, the current status of sidelink communication system design is highlighted, along with the missing building blocks, which are required to develop 3GPP sidelink-based mmWave WPAN systems. Simulation results shed light on merging IEEE 802.15.3c/11ad concepts into 3GPP sidelink communication regarding a control data transmission scheme, which should be designed to enhance robustness and is a crucial step for subsequent high-rate user data transmission.

Recomposing channel state information (CSI) from the beamforming feedback matrix (BFM), which is a compressed version of CSI and can be captured because of its lack of encryption, is an alternative way of implementing firmware-agnostic WiFi sensing. In this study, we propose the use of camera images toward the accuracy enhancement of CSI recomposition from BFM. The key motivation for this vision-aided CSI recomposition is to draw a first-hand insight that the BFM does not fully involve spatial information to recompose CSI and that this could be compensated by camera images. To leverage the camera images, we use multimodal deep learning. We conducted experiments using IEEE 802.11ac devices and revealed that the recomposition accuracy of the proposed multimodal framework is improved compared to the single-modal framework only using images or BFMs.

Towards flexible channel measurements of millimeter wave (mmWave) communications, this paper proposes a time-alignment algorithm of multiple power delay profiles (PDPs) separately measured with different antenna rotation angles. To characterize mmWave channels, capturing both delay time and angular property of multi-path components is necessary, which requires multiple PDP measurements with antenna rotations and alignment of the delay time with respect to a standard time, e.g., time of departure or the arrival time of a line-of-sight ray. While this alignment conventionally requires hardware synchronization between the transmitter and the receiver, this study turns this synchronization into the softwarelized post-processing to eliminate any nuisances incurred by the hardware synchronizations during measurements. To this end, we propose a time of arrival (ToA) estimation method of a maximum power peak in PDPs unsynchronized among different antenna rotation angles, which can be performed without any hardware synchronizations. The core idea is to align the time of the PDPs so that the powers between adjacent antenna rotation angles are highly correlated to each other because the ground-truth time-aligned PDP should possess such higher correlations. This paper formulates this problem as maximum-likelihood estimations; thereby deriving the algorithm to reach this objective. The experimental results based on real measurements demonstrate the feasibility of the time-alignment of the unsynchronized PDPs by comparing to the results by raytracing simulation.

This paper first summarizes the specifications of standardized wireless personal area networks (WPANs) and wireless local-area networks (WLANs) using the 60 GHz band, for example IEEE 802.15.3c and 802.11ad standards, and the significance of the standardization, together with the standardization transition of third-generation partnership project (3GPP)-based public wireless communication systems and other radio systems in the 2010s. The concept of a virtual community network (VCN) using a millimeter-wave wireless communication system in the 6G era is then proposed based on the 3GPP-based orthogonal frequency-division multiple access (OFDMA) system. Moreover, by using a latest propagation characteristics model of the 60-GHz propagation characteristics in 3GPP, we evaluated the block error rate (BLER) performance of the proposed VCN by changing the parameters of the physical layer such as the subcarrier spacing (SCS) through computer simulation. The results showed that a transmission distance of 5 m per terminal could be achieved even with a low-gain antenna, demonstrating the feasibility of a VCN based on the high-density installation of many terminals in a room.

This paper reports a first wideband indoor channel measurement at the 105 GHz sub-terahertz (sub-THz) band and analyzes the multipath characteristics in terms of the omnidirectional path-loss and angular characteristics. The measurement campaigns with the 4 GHz bandwidth are performed focusing on indoor short-range communication scenarios in a conference room, corridor, and office room, which have been considered in the primary 60 GHz communication systems standardized by the IEEE 802.15.3c/11ad. Moreover, to draw full understanding to scale the 60 GHz indoor channel models and 60 GHz system designs for the 105 GHz band, we also conduct 60 GHz channel measurements in the same environment with few modifications in the channel-sounding system and performed the comparison between these two bands. Based on these measurements, we demonstrate the affinity that exists between 105 GHz and 60 GHz bands in terms of path loss exponent and angular characteristics of multipath components, shedding light on the hypothesis that several system designs of the 60 GHz communication systems (e.g., beam switching for non-line-of sight conditions) can be applied to the 105 GHz sub-THz communication systems.

This study develops a federated learning (FL) framework overcoming largely incremental communication costs due to model sizes in typical frameworks without compromising model performance. To this end, based on the idea of leveraging an unlabeled open dataset, we propose a distillation-based semi-supervised FL (DS-FL) algorithm that exchanges the outputs of local models among mobile devices, instead of model parameter exchange employed by the typical frameworks. In DS-FL, the communication cost depends only on the output dimensions of the models and does not scale up according to the model size. The exchanged model outputs are used to label each sample of the open dataset, which creates an additionally labeled dataset. Based on the new dataset, local models are further trained, and model performance is enhanced owing to the data augmentation effect. We further highlight that in DS-FL, the heterogeneity of the devices’ dataset leads to ambiguous of each data sample and lowing of the training convergence. To prevent this, we propose entropy reduction averaging, where the aggregated model outputs are intentionally sharpened. Moreover, extensive experiments show that DS-FL reduces communication costs up to 99 percent relative to those of the FL benchmark while achieving similar or higher classification accuracy.

With regard to the implementation of WiFi sensing agnostic according to the availability of channel state information (CSI), we investigate the possibility of estimating a CSI matrix based on its compressed version, which is known as beamforming feedback matrix (BFM). Being different from the CSI matrix that is processed and discarded in physical layer components, the BFM can be captured using a medium-access-layer frame-capturing technique because this is exchanged among an access point (AP) and stations (STAs) over the air. This indicates that WiFi sensing that leverages the BFM matrix is more practical to implement using the pre-installed APs. However, the ability of BFM-based sensing has been evaluated in a few tasks, and more general insights into its performance should be provided. To fill this gap, we propose a CSI estimation method based on BFM, approximating the estimation function with a machine learning model. In addition, to improve the estimation accuracy, we leverage the inter-subcarrier dependency using the BFMs at multiple subcarriers in orthogonal frequency division multiplexing transmissions. Our simulation evaluation reveals that the estimated CSI matches the ground-truth amplitude. Moreover, compared to CSI estimation at each individual subcarrier, the effect of the BFMs at multiple subcarriers on the CSI estimation accuracy is validated.

In IEEE 802.11bc, the broadcast mode on wireless local area networks (WLANs), data rate control that is based on acknowledgement (ACK) mechanism similar to the one in the current IEEE 802.11 WLANs is not applicable because the ACK mechanism is not implemented. This paper addresses this challenge by proposing ACK-less data rate adaptation methods by capturing non-broadcast uplink frames of STAs. In IEEE 802.11bc, a use case is assumed, where a part of STAs in the broadcast recipients is also associated with non-broadcast APs, and such STAs periodically transmit uplink frames including ACK frames. The proposed method is based on the idea that by overhearing such uplink frames, the broadcast AP surveys channel conditions at partial STAs, thereby setting appropriate data rates for the STAs. Furthermore, to avoid reception failures in a large portion of STAs, this paper proposes deep reinforcement learning (DRL)-based data rate adaptation framework that uses a sim-to-real approach. Therein, information of reception success/failure at broadcast recipient STAs, that could not be notified to the broadcast AP in real deployments, is made available by simulations beforehand, thereby forming data rate adaptation strategies. Numerical results show that utilizing overheard uplink frames of recipients makes it feasible to manage data rates in ACK-less broadcast WLANs, and using the sim-to-real DRL framework can decrease reception failures.

Wireless channels can be inherently privacy preserving by distorting the received signals due to channel noise, and superpositioning multiple signals over-the-air. By harnessing these natural distortions and superpositions by wireless channels, we propose a novel privacy-preserving machine learning (ML) framework at the network edge, coined over-the-air mixup ML (AirMixML). In AirMixML, multiple workers transmit analog-modulated signals of their private data samples to an edge server who trains an ML model using the received noisy-and-superpositioned samples. AirMixML coincides with model training using mixup data augmentation achieving comparable accuracy to that with raw data samples. From a privacy perspective, AirMixML is a differentially private (DP) mechanism limiting the disclosure of each worker's private sample information at the server, while the worker's transmit power determines the privacy disclosure level. To this end, we develop a fractional channel-inversion power control (PC) method, a-Dirichlet mixup PC (DirMix(a)-PC), wherein for a given global power scaling factor after channel inversion, each worker's local power contribution to the superpositioned signal is controlled by the Dirichlet dispersion ratio a. Mathematically, we derive a closed-form expression clarifying the relationship between the local and global PC factors to guarantee a target DP level. By simulations, we provide DirMix(α)-PC design guidelines to improve accuracy, privacy, and energy-efficiency. Finally, AirMixML with DirMix(a)-PC is shown to achieve reasonable accuracy compared to a privacy-violating baseline with neither superposition nor PC.

For reliable millimeter-wave (mmWave) networks, this paper proposes cooperative sensing with multi-camera operation in an image-to-decision proactive handover framework that directly maps images to a handover decision. In the framework, camera images are utilized to allow for the prediction of blockage effects in a mmWave link, whereby a network controller triggers a handover in a proactive fashion. Furthermore, direct mapping allows for the scalability of the number of pedestrians. This paper experimentally investigates the feasibility of adopting cooperative sensing with multiple cameras that can compensate for one another's blind spots. The optimal mapping is learned via deep reinforcement learning to resolve the high dimensionality of images from multiple cameras. An evaluation based on experimentally obtained images and received powers verifies that a mapping that enhances channel capacity can be learned in a multi-camera operation. The results indicate that our proposed framework with multi-camera operation outperforms a conventional framework with single-camera operation in terms of the average capacity.

Towards ultra-reliable and low-latency communications, this paper proposes a packet mapping algorithm in an enhanced distributed channel access (EDCA) scheme using policy gradient reinforcement learning (RL). The EDCA scheme provides higher priority packets with more transmission opportunities by mapping packets to a predefined access category (AC); thereby, the EDCA scheme supports a higher quality of service in wireless local area networks. In this paper, it is noted that by mapping high priority packets to lower priority ACs, the one-packet delay of a high priority packet can be reduced. In contrast, the mapping algorithm cannot minimize the multiple-packets delay because the mapping algorithm is based on the current status. This is because, from a long-term perspective, mapping high priority packets is required as a countermeasure for collisions, to minimize the multiple-packets delay. As a solution, this paper proposes a new mapping algorithm using RL because RL is suitable for maximizing the reward from a long-term perspective. The key idea is to design the state such that the state involves the number of packets having arrived at each AP in the past, which is an indicator expressing past status. In the designed RL task, the reward, i.e., the multiple-packets delay depends on an overall sequence of states and actions; hence, the recursive value function-based RL algorithms are not compatible. To solve this problem, this paper utilizes policy gradient RL, which learns the packet mapping policy from an overall state-action sequence and a consequent multiple-packets delay. The simulation result reveals that the transmission delay of the proposed mapping algorithm is 13.8% shorter than that of the conventional EDCA mapping algorithm.

Over-the-air computation (AirComp)-based federated learning (FL) enables low-latency uploads and the aggregation of machine learning models by exploiting simultaneous co-channel transmission and the resultant waveform superposition. This study aims at realizing secure AirComp-based FL against various privacy attacks where malicious central servers infer clients' private data from aggregated global models. To this end, a differentially private AirComp-based FL is designed in this study, where the key idea is to harness receiver noise perturbation injected to aggregated global models inherently, thereby preventing the inference of clients' private data. However, the variance of the inherent receiver noise is often uncontrollable, which renders the process of injecting an appropriate noise perturbation to achieve a desired privacy level quite challenging. Hence, this study designs transmit power control across clients, wherein the received signal level is adjusted intentionally to control the noise perturbation levels effectively, thereby achieving the desired privacy level. It is observed that a higher privacy level requires lower transmit power, which indicates the tradeoff between the privacy level and signal-to-noise ratio (SNR). To understand this tradeoff more fully, the closed-form expressions of SNR (with respect to the privacy level) are derived, and the tradeoff is analytically demonstrated. The analytical results also demonstrate that among the configurable parameters, the number of participating clients is a key parameter that enhances the received SNR under the aforementioned tradeoff. The analytical results are validated through numerical evaluations.

This paper proposes a robust adversarial reinforcement learning (RARL)-based multi-access point (AP) coordination method that is robust even against unexpected decentralized operations of uncoordinated APs. Multi-AP coordination is a promising technique towards IEEE 802.11be, and there are studies that use RL for multi-AP coordination. Indeed, a simple RL-based multi-AP coordination method diminishes the collision probability among the APs; therefore, the method is a promising approach to improve time-resource efficiency. However, this method is vulnerable to frame transmissions of uncoordinated APs that are less aware of frame transmissions of other coordinated APs. To help the central agent experience even such unexpected frame transmissions, in addition to the central agent, the proposed method also competitively trains an adversarial AP that disturbs coordinated APs by causing frame collisions intensively. Besides, we propose to exploit a history of frame losses of a coordinated AP to promote reasonable competition between the central agent and adversarial AP. The simulation results indicate that the proposed method can avoid uncoordinated interference and thereby improve the minimum sum of the throughputs in the system compared to not considering the uncoordinated AP.

To achieve reliable small cell millimeter-wave wireless backhauls, this study installs small cell base stations (SBSs) on overhead messenger wires to gain flexibility in physical deployments of SBSs ensuring in the line-of-sight connections between SBSs and gateway BSs. These installations pose challenges in aligning directional beams, whereby complicated wind-forced dynamics in on-wire SBSs require frequent beam training, and consequently, a large signaling overhead. To address this, this study aims at demonstrating the feasibility of learning-based beam tracking where a beam tracking policy is learned a priori to fix beam misalignment caused by the wind-forced dynamics. Because wind-forced dynamics in SBSs can be three-dimensional (3D), the proposed beam tracking newly exploits the 3D position/velocity of the SBS as state information. As a solution to fix beam misalignment, the beam tracking policy is learned via deep reinforcement learning wherein the 3D information and beam direction are regarded as a state and an action, respectively, and the received signal power at a gateway BS is maximized. The simulation results depict the feasibility of learning an appropriate beam tracking policy to prevent beam misalignment induced by wind-forced 3D dynamics in on-wire SBSs.

This study demonstrates the feasibility of proactive received power prediction by leveraging spatiotemporal visual sensing information towards reliable millimeter-wave (mmWave) networks. As the received power on a mmWave link can attenuate aperiodically owing to human blockages, a long-term series of the future received power cannot be predicted by analyzing the received signals prior to the blockage occurring. We propose a novel mechanism that predicts the time series of received power from the next moment to as many as several hundred milliseconds ahead. The key idea is to leverage camera imagery and machine learning (ML). Time-sequential images may involve the spatial geometry and mobility of obstacles representing mmWave signal propagation. ML is used to construct a prediction model from a dataset of sequential images labeled with received power in several hundred milliseconds ahead of the time at which each image is obtained. The simulation and experimental evaluations conducted using IEEE 802.11ad devices and a depth camera demonstrated that the proposed mechanism employing convolutional long short-term memory predicted a time series of received power up to 500 ms ahead, with an inference time of less than 3 ms and a root-mean-square error of 3.4 dB.

This paper experimentally finds the optimum number of input images of a machine learning-based mmWave received signal strength (RSS) value prediction scheme from depth images. By modeling the relationships between time-sequential depth images and RSS values based on machine learning, it is possible to predict the future RSS values, and thereby, a predictive handover makes a moment of degradation of the RSS value avoidable. As prediction models of RSS value, three machine learning models are compared: the convolutional neural networks (CNN), the combination of CNN and convolutional long short-term memory (CNN+ConvLSTM), and random forest. As the number of input images increases, the prediction accuracy generally improves, however, too numerous input images may make the prediction accuracy worse because of over-fitting. Experimental results reveal that the number of input images that are input in order to predict the RSS value the most accurately is 16.

This paper discusses a measurement method of time‐variant attenuation of IEEE 802.11ad wireless LAN signals in the 60 GHz band induced by human blockage. The IEEE 802.11ad access point (AP) transmits frames intermittently, not continuously. Thus, to obtain the time‐varying signal attenuation, it is required to estimate the duration in which the AP transmitted signals. To estimate whether the AP transmitted signals or not at each sampling point, this paper applies a simple two‐state hidden Markov model. In addition, the validity of the model is tested based on Bayesian information criterion in order to prevent model overfitting and consequent invalid results. The measurement method is validated in that the distribution of the time duration in which the signal attenuates by 5 dB is consistent with the existing statistical model and the range of the measured time duration in which the signal attenuation decreases from 5 dB to 0 dB is similar to that in the previous report.

This paper presents a measurement of time-varying attenuation of IEEE802.11ad wireless LAN (WLAN) signals in 60GHz band induced by human blockage. The present measurement is a novel approach to obtain the attenuation, where a commercially available IEEE802.11ad access point (AP) and station (STA) are employed and the measurement is conducted under intermittent packet transmission. This paper also presents a hidden Markov model (HMM)-based signal power estimation scheme so that the attenuation is estimated from data obtained with a microwave spectrum analyzer which cannot detect signals of IEEE 802.11ad WLAN in itself. In this scheme, whether 11ad WLAN signals exist or not at each sampling instant is estimated based on HMM. Before the application of HMM, the scheme detects the number of HMM states via Bayesian information criterion and, thereby, prevents model over-fitting and consequent invalid power estimation. Our experiment revealed that the IEEE802.11ad WLAN signal attenuates by 5dB in a duration of 51.5ms when a human moves across the path between the AP and the STA at a velocity of 0.5m/s. This result is consistent with a previous report about an IEEE 802.11ad WLAN channel model.