After the IECE 802.1 1 standardization group established the first ABSTRACT wireless LAN standard two years ago, 5everaI efforts were start^ ed to increilsc data rates and also to use other bands. This article dcicribes the new wire^ less LAN standards develolped by IEEE 802.1 1, ETSI BRAN, and MMAC. l~hc new stdndards are targeting data rates up lo 1 1 Mh/s in the 2.4 GHz band and up to 54 Mbis in the 5 GH2 band. L i k e t h e 1EEE 802.1 1 s l a i i d a r d , tlic El,ropcan TcIeeolnnliIIIieeti~,Ils St:ind;irils Inslitutc (ETSI) IIIPU11-I A N lvnc I siandard 131 soccilics hiith iiicc tlic hcgiiiiiing o l (lie I9Ulls, wirclcss lnciil xrcn & networks (W I A N s) lor ilic 900 Mllz, 2, 4, iiiiil 5 (itlz iiiduslriiil, scicntific, ;ind iiiccliciil (ISM) haiiils liiivc hccn available Ixiscil on a raugc oC proprietary produeis. I n. l u w 10'17, the Institute ol Elcciric:il ;ind I?lccironics Eiigiiiccrs ;ipprovctl iin inlcrnalioo;il intcropcr~i1,ility si;indard (IliliF, 802. I I) [I], 71ic sl;i~ulard specifics hoth mcdium ~C C C S S contnrl iiid thrcc dillcrcni plrysicxl kiycrs (PlHY). Tlicrc arc two I;idi~~-lxisccl FHYs using thc 2.4 GHI hand. T l~c iliirtl I'IIY nscs iiifnircd lighl. i\ll I'llYs support ii daia ralc of 1 Mh/s i i n d opiionally 2 Mbls. The 2.4 CiHz lrcqtmicy Lxind is iiviiiliililc fur liccnsc cxcnipi ~i s c in H u r q x , tlic llnilcd Stalcs, aiid J:ipiii. 'I'iiblc I list& ilic availa1,lc frcqiicncy hiinds i i l d lhc rcstriciirriis to dcviccs wliicli iisc this h;ind lin c(iiniiiiiiiiciitiiiiis. Uscs dcm;ind lor liiglicr hit rates uid intcriiaiioiid availahilitp of the 2.4 GHz band has spiirrcd thc dcvelopniciit of a higher-spccd cxtcnsiiio tu thc 802. I I siandiird. In .

This study demonstrates the feasibility of the proactive received power prediction by leveraging spatiotemporal visual sensing information toward the reliable millimeter-wave (mmWave) networks.Since the received power on a mmWave link can attenuate aperiodically due to a human blockage, the long-term series of the future received power cannot be predicted by analyzing the received signals before the blockage occurs. We propose a novel mechanism that predicts a time series of the received power from the next moment to even several hundred milliseconds ahead. The key idea is to leverage the camera imagery and machine learning (ML). The time-sequential images can involve the spatial geometry and the mobility of obstacles representing the mmWave signal propagation. ML is used to build the prediction model from the dataset of sequential images labeled with the received power in several hundred milliseconds ahead of when each image is obtained. The simulation and experimental evaluations using IEEE 802.11ad devices and a depth camera show that the proposed mechanism employing convolutional LSTM predicted a time series of the received power in up to 500 ms ahead at an inference time of less than 3 ms with a root-mean-square error of 3.5 dB.Parts of this work were presented at the 85th IEEE Vehicular Technology Conference (VTC Spring) and the IEEE Consumer Communications and Networking Conference (CCNC). 2 Index Terms millimeter-wave communications, link quality prediction, proactive prediction, machine learning, supervised learning, depth image Received power (dBm) Grand truth (a) When the camera was at A low . Received power (dBm) Grand truth (b) When the camera was at A high .

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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% relative to those of the FL benchmark while achieving similar or higher classification accuracy.

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The goal of this study is to improve the accuracy of millimeter wave received power prediction by utilizing camera images and radio frequency (RF) signals, while gathering image inputs in a communication-efficient and privacy-preserving manner. To this end, we propose a distributed multimodal machine learning (ML) framework, coined multimodal split learning (MultSL), in which a large neural network (NN) is split into two wirelessly connected segments. The upper segment combines images and received powers for future received power prediction, whereas the lower segment extracts features from camera images and compresses its output to reduce communication costs and privacy leakage. Experimental evaluation corroborates that MultSL achieves higher accuracy than the baselines utilizing either images or RF signals. Remarkably, without compromising accuracy, compressing the lower segment output by 16x yields 16x lower communication latency and 2.8% less privacy leakage compared to the case without compression.Index Terms-Millimeter-wave communications, received power prediction, multi-modal deep learning, split learning. Received powersReccurent neural network mmWave UE mmWave BS Future received power at BS Uplink dataGround-truth

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