arXiv subjects

Kota Nakashima

Publications and source records attributed to Kota Nakashima.

6 recordsLinked to original sources

arXiv2022

Dual optical frequency combs with ultra-low relative phase jitters from 550 nm to 1020 nm for precision spectroscopy

Here, ultra-low relative phase jitters over a wide optical spectrum were achieved for dual Ti:Sapphire optical frequency combs. The two optical frequency combs were independently phase-locked to a Sr optical lattice clock laser delivered through a commercial optical fiber network. We confirmed that the relative phase jitters between the two combs integrated from 8.3 mHz to 200 kHz were below 1 rad, corresponding to a relative linewidth of below 8.3 mHz, over the entire wavelength of the optical frequency combs ranging from 550 nm to 1020 nm. Our work paves the way for ultrahigh-precision dual-comb spectroscopy covering a wide optical spectral range with a simple setup, and provides an absolute optical frequency reference with great stability over a wide range of wavelengths.

physics.optics↗

arXiv2020

Handover Management for mmWave Networks with Proactive Performance Prediction Using Camera Images and Deep Reinforcement Learning

For millimeter-wave networks, this paper presents a paradigm shift for leveraging time-consecutive camera images in handover decision problems. While making handover decisions, it is important to predict future long-term performance---e.g., the cumulative sum of time-varying data rates---proactively to avoid making myopic decisions. However, this study experimentally notices that a time-variation in the received powers is not necessarily informative for proactively predicting the rapid degradation of data rates caused by moving obstacles. To overcome this challenge, this study proposes a proactive framework wherein handover timings are optimized while obstacle-caused data rate degradations are predicted before the degradations occur. The key idea is to expand a state space to involve time consecutive camera images, which comprises informative features for predicting such data rate degradations. To overcome the difficulty in handling the large dimensionality of the expanded state space, we use a deep reinforcement learning for deciding the handover timings. The evaluations performed based on the experimentally obtained camera images and received powers demonstrate that the expanded state space facilitates (i) the prediction of obstacle-caused data rate degradations from 500 ms before the degradations occur and (ii) superior performance to a handover framework without the state space expansion

cs.NI↗

arXiv2019

Deep Reinforcement Learning-Based Channel Allocation for Wireless LANs with Graph Convolutional Networks

Last year, IEEE 802.11 Extremely High Throughput Study Group (EHT Study Group) was established to initiate discussions on new IEEE 802.11 features. Coordinated control methods of the access points (APs) in the wireless local area networks (WLANs) are discussed in EHT Study Group. The present study proposes a deep reinforcement learning-based channel allocation scheme using graph convolutional networks (GCNs). As a deep reinforcement learning method, we use a well-known method double deep Q-network. In densely deployed WLANs, the number of the available topologies of APs is extremely high, and thus we extract the features of the topological structures based on GCNs. We apply GCNs to a contention graph where APs within their carrier sensing ranges are connected to extract the features of carrier sensing relationships. Additionally, to improve the learning speed especially in an early stage of learning, we employ a game theory-based method to collect the training data independently of the neural network model. The simulation results indicate that the proposed method can appropriately control the channels when compared to extant methods.

eess.SP↗

arXiv2019

Direct Phase Locking of a Ti:Sapphire Optical Frequency Comb to a Remote Optical Frequency Standard

We report on an ultralow noise optical frequency transfer from a remotely located Sr optical lattice clock laser to a Ti:Sapphire optical frequency comb through telecom-wavelength optical fiber networks. The inherent narrow linewidth of the Ti:Sapphire optical frequency comb eliminates the need for a local reference high-finesse cavity. The relative fractional frequency instability of the optical frequency comb with respect to the remote optical reference was $6.7(1) \times 10^{-18}$ at 1 s and $1.05(3) \times 10^{-19}$ at 1,000 s including a 2.9 km-long fiber network. This ensured the optical frequency comb had the same precision as the optical standard. Our result paves the way for ultrahigh-precision spectroscopy and conversion of the highly precise optical frequency to radio frequencies in a simpler setup.

physics.ins-det↗

arXiv2018

Proactive Received Power Prediction Using Machine Learning and Depth Images for mmWave Networks

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.

cs.NI↗

arXiv2017

Generation of highly pure Schrödinger's cat states and real-time quadrature measurements via optical filtering

Until now, Schrödinger's cat states are generated by subtracting single photons from the whole bandwidth of squeezed vacua. However, it was pointed out recently that the achievable purities are limited in such method (J. Yoshikawa, W. Asavanant, and A. Furusawa, arXiv:1707.08146 [quant-ph] (2017)). In this paper, we used our new photon subtraction method with a narrowband filtering cavity and generated a highly pure Schrödinger's cat state with the value of $-0.184$ at the origin of the Wigner function. To our knowledge, this is the highest value ever reported without any loss corrections. The temporal mode also becomes exponentially rising in our method, which allows us to make a real-time quadrature measurement on Schrödinger's cat states, and we obtained the value of $-0.162$ at the origin of the Wigner function.

quant-ph↗