Research
MINT Research Overview
i-Dris: Intelligent Rapid Deployment for Reconfigurable Intelligent Surface in Millimeter Wave Communication System (Future Tech Award 2022)
i-Dris integrates 5G mmWave base stationreconfigurable intelligent surface (RIS) for improving signal coverage/throughput. We design multi-time scaled temporal reinforcement learning for RIS deployment equipped on auto-guided vehicle, which determines future actions from both historical and current rates observed from the edge server. Under blockages with non-line-of-sight, i-Dris can achieve 800~900 Mbps rate (1.2~3.6x more compared to non-RIS). i-Dris only requires 10-min of deployment time (1 hr for other methods). Compared to existing prototypes in academia/industry, i-Dris does not need fiber, huge human resources, re-measurement offline training. It learns from historical data guaranteering robustness to changing environments.
Taiwan News (02/24/2023): 電信三雄高層參展MWC 簽6G備忘錄、秀AI節能網路
Taiwan News (03/01/2023): 遠傳挺進MWC 2023秀肌肉 發佈5G毫米波整合部署方案
Taiwan News (03/01/2023): 遠傳MWC秀肌肉 展台灣首發B5G毫米波黑科技
Taiwan News (03/01/2023): 遠傳全台首發 5G毫米波整合部署解決方案
遠傳與網通大廠光寶科技、國際材料大廠與頂尖學府國立陽明交通大學共同研發的「B5G毫米波整合智慧反射板」同樣在MWC首次登場,為全台首發的5G毫米波室內佈建解決方案,可以強化被環境遮蔽區域的5G涵蓋範圍、包含大樓林立的深層室內,並可以根據環境需求,客製化調整無線電波角度,精準解決真實情境中複雜的盲區覆蓋問題。
6G Research Challenges and Opportunities
Whilst the fifth-generation (5G) systems are being rolled out across the globe, researchers have turned their attention to the exploration of radical next-generation solutions. At this early evolutionary stage we survey five main research facets of this field, namely Facet 1: next-generation architectures, spectrum and services, Facet 2: next-generation networking, Facet 3: Internet of Things (IoT), Facet 4: wireless positioning and sensing, as well as Facet 5: applications of deep learning in 6G networks. In this work, we have provided a critical appraisal of the literature of promising techniques ranging from the associated architectures, networking, applications as well as designs. We have portrayed a plethora of heterogeneous architectures relying on cooperative hybrid networks supported by diverse access and transmission mechanisms. The vulnerabilities of these techniques are also addressed and carefully considered for highlighting the most of promising future research directions. Additionally, we have listed a rich suite of learning-driven optimization techniques. We conclude by observing the evolutionary paradigm-shift that has taken place from pure single-component bandwidth-efficiency, power-efficiency or delay-optimization towards multi-component designs, as exemplified by the twin-component ultra-reliable low-latency mode of the 5G system. We advocate a further evolutionary step towards multi-component Pareto optimization, which requires the exploration of the entire Pareto front of all optiomal solutions, where none of the components of the objective function may be improved without degrading at least one of the other components.
Li-Hsiang Shen, Kai-Ten Feng, and Lajos Hanzo, “Five Facets of 6G: Research Challenges and Opportunities," in ACM Computing Surveys, Nov. 2022. [pdf]
Resource Management and Deployment for Reconfigurable Intelligent Surface (RIS)
Substantially increasing wireless traffic and extending serving coverage is required with the advent of sixthgeneration (6G) wireless communication networks. Reconfigurable intelligent surface (RIS) is widely considered as a promising technique which is capable of improving the system sum rate and energy efficiency. Moreover, full-duplex (FD) multiinput-multi-output (MIMO) transmission provides simultaneous transmit and received signals, which theoretically provides twice of spectrum efficiency. However, the self-interference (SI) in FD system is a challenging task requiring high-overhead cancellation, which can be resolved by configuring appropriate phase shifts of RIS. This work has proposed an RIS-empowered full-duplex interference cancellation (RFIC) scheme in order to alleviate the severe interference in an RIS-FD system. We consider the interference minimization of RIS-FD MIMO while guaranteeing quality-of-service (QoS) of whole system. The closed-form solution of RIS phase shifts is theoretically derived with the discussion of different numbers of RIS elements and receiving antennas. Simulation results reveal that the proposed RFIC scheme outperforms existing benchmarks with more than 50% of performance gain of sum rate.
An impediment of the conventional RIS solutions is that the transmitter and the user have to be within the same 180-degree half-plane, rather than roaming across the entire 360-degree full plane. By contrast, the STAR-RIS architecture, or termed as intelligent omni-surface (IOS), allows full-plane coverage by potentially harnessing full reflection, full transmission, as well as simultaneous transmission and reflection. These modes were discussedwith special emphasis on NTT DOCOMO's prototype. There are three different principles governing their operations, namely the so-called energy-splitting, partitioning and time-switching types, which have their different pros and cons. In the energy-splitting mode the signal impinging upon an element is partially reflected and transmitted. By contrast, the partitioning type may be viewed as having a reflection-only and transmission-only segment of reduced sizes. Finally, the time-switching type is capable of switching the reflective elements between the transmit and reflect modes. There is a huge variety of compelling applications scenarios, such as STAR-RIS-NOMA, STAR-RIS-CoMP and multi-STAR-RIS deployment as well as AI-assisted STAR-RIS, which require further exploration by the research community.
Chia-Jou Ku, Li-Hsiang Shen, and Kai-Ten Feng, "Reconfigurable Intelligent Surface Assisted Interference Mitigation for 6G Full-Duplex MIMO Communication Systems," in Proceedings of IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), Virtual Conference, Sept. 2022. [pdf]
Advanced Beam Seaerch and Handover Scheme for LEO Networks
Conventional Handover Scheme for LEO
Advanced Beam Search and Handover Scheme for LEO
Device-free Wireless Sensing for Human Presence Detection
Human presence detection is a promising technology for detecting a human body’s presence in an area of interest. With the combination of presence detection and smart home concept, we can adopt an intelligence appliance to execute numerous interesting events in the room. Channel state information (CSI) extracted from commercial Wi-Fi router can offer high precision environment detection based on its abundant signal characteristics, which can be applied for wireless sensing, including presence, fall, and respiration rate detections. We propose a recurrent-based environmental adaptive device-free presence detection (REAP) for Wi-Fi CSI-based networks. It extracts meaningful information from CSI, including motion and static features, and applies a recurrent-based machine learning method to identify whether a human is present or absent in a certain room. Moreover, we design an adaptive structure of named REAP-T by using the micro and macro transfer learning, increase the model robustness, and reduce the data and time consumed for training additional new environments. Finally, a robust real-time system and demonstration are presented to show the performance of proposed REAP and REAP-T approaches. The proposed system can be real-time implemented on commercial Wi-Fi routers with high accuracy of human presence detection.
Chia-Che Hsieh, An-Hung Hsiao, Chun-Jie Chiu, and Kai-Ten Feng, " CSI Ratio with Coloring-Assisted Learning for NLoS Motionless Human Presence Detection," in Proceedings of IEEE Vehicular Technology Conference (VTC-Spring), Helsinki, Finland, Jun. 2022. [pdf]
Kai-Jui Chen, An-Hung Hsiao, Chun-Jie Chiu, and Kai-Ten Feng, " Self-Attention based Semi-Supervised Learning for Time-varying Wi-Fi CSI-based Adjoining Room Presence Detection," in Proceedings of IEEE Vehicular Technology Conference (VTC-Spring), Helsinki, Finland, Jun. 2022. [pdf]
Yu-Ming Huang, An-Hung Hsiao, Chun-Jie Chiu, Kai-Ten Feng, and Po-Hsuan Tseng, "Device-Free Multiple Presence Detection using CSI Information with Machine Learning Methods," in Proceedings of IEEE Vehicular Technology Conference (VTC-Fall), Honolulu, Hawaii, Sept. 2019. [pdf]
Kuan-I Lu, Chun-Jie Chiu, Kai-Ten Feng, Po-Hsuan Tseng, "Device-free CSI-based Wireless Localization for High Precision Drone Landing Applications," in Proceedings of IEEE Vehicular Technology Conference (VTC-Fall), Honolulu, Hawaii, Sept. 2019. [pdf]
Hsiao-Chien Tsai, Chun-Jie Chiu, Po-Hsuan Tseng, and Kai-Ten Feng, "Refined Autoencoder-based CSI Hidden Feature Extraction for Indoor Spot Localization," in Proceedings of IEEE Vehicular Technology Conference (VTC-Fall), Chicago, IL, Aug. 2018. (Candidate for the Best Paper Award) [pdf]
Li-Hsiang Shen, Kai-Jui Chen, An-Hung Hsiao, Kai-Ten Feng, "Semi-Supervised Bifold Teacher-Student Learning for Indoor Presence Detection Under Time-Varying CSI" https://arxiv.org/abs/2212.10802
Li-Hsiang Shen, Chia-Che Hsieh, An-Hung Hsiao, Kai-Ten Feng, "CRONOS: Colorization and Contrastive Learning Enhanced NLoS Human Presence Detection using Wi-Fi CSI Signals" https://arxiv.org/abs/2211.10354
Scenarios for Human Presence Detection
Multiple People Presence Detection
Device-free Wireless Presence Detection for Different Spots
Device-free Wireless Presence Detection for Different Users
Wireless Sensing for User Leaving
Wireless Sensing for Intruder Detection
Applications of Deep Learning in 6G Networks
Given the rapid development of AI-empowered deep learning, both supervised learning as well as unsupervised learning and reinforcement learning have found favour in solving challenging communications and networking problems. Specific examples are constituted by radio interference management, resource allocation, multiple parameter optimization, network traffic prediction, computing resource assignment, and flexible configuration of network functions. In supervised learning, ground truth labels and fixed-size inputs constitute a deep layered neural network (NN). However, labeling is not required in unsupervised learning, which exploits the correlation between samples of the dataset. In reinforcement learning, an agent will interact with the environment and then updates the model based on the corresponding rewards. Note that deep learning can deal with comparably complex problems in a non-linear and non-convex manner than that utilizing machine learning. Therefore, we can efficiently manage both vertical and horizontal networks with the aid of deep neural networks.
Li-Hsiang Shen, Kai-Ten Feng, and Lajos Hanzo, "Five Facets of 6G: Research Challenges and Opportunities," in ACM Computing Survey, Nov. 2022.
Li-Hsiang Shen, Ting-Wei Chang, Kai-Ten Feng, and Po-Tsang. Huang, “Design and Implementation for Deep Learning Based Adjustable Beamforming Training for Millimeter Wave Communication Systems,” IEEE Transactions on Vehicular Technology, vol. 70, no. 3, pp. 2413-2427, Mar. 2021.
Po-Chun Hsu, Li-Hsiang Shen, Chun-Hung Liu, and Kai-Ten Feng, “Federated Deep Reinforcement Learning for THz-Beam Search with Limited CSI,” in Proceedings of IEEE Vehicular Technology Conference (VTC-Fall), Virtual Conference, Sep. 2022.
Hung-Yi Yen, Zhong-Ting Tsai, Yuan-Ching Chen, Li-Hsiang Shen, Chun-Jie Chiu, and Kai-Ten Feng, “I/Q Density-based Angle of Arrival Estimation for Bluetooth Indoor Positioning Systems,” in Proceedings of IEEE Vehicular Technology Conference (VTC-Spring), Helsinki, Finland, Apr. 2021.
Zhong-Ting Tsai, Li-Hsiang Shen and Kai-Ten Feng, " Beam AoD-based Indoor Positioning for 60 GHz MmWave System," in Proceedings of IEEE Vehicular Technology Conference (VTC-Fall), Victoria, CA, Oct. 2020.
Ting-Wei Chang, Li-Hsiang Shen and Kai Ten Feng, "Learning-based and Enhanced Beam Training Algorithms for IEEE 802.11ad/ay Transmission Scheme," in Proceedings of IEEE Vehicular Technology Conference (VTC-Spring), Kuala Lumpur, Malaysia, Apr. 2019.
Deep Learning-based Beamforming for mmWave Networks
Millimeter wave (mmWave) technology achieving multi-gigabits speed plays a significant role in beyond 5G and the next 6G wireless communication networks thanks to its huge spectrum utilization and beam-based directional transmissions. To tackle temporary ultra-high data demands of hotspot areas, three-dimensional (3D) heterogeneous network (HetNet) is designed with the integration of mmWave unmanned aerial vehicles (UAV) to provide resilient instantaneous control and data transmissions. However, some critical beam-related issues for mmWave implementation of UAVs/drones are addressed including robust initial beam alignment, mobility-aware beam tracking and beam refinement. In this research, we aim at developing robust and efficient beam control mechanisms by implementing a prototype of 3D flying heterogeneous communications. The backhaul connections operate at mmWave frequency between airship and UAV/drone, while fronthaul links adopt lower frequency bands such as Wi-Fi for multiuser data transmissions. We evaluate system performances for our proposed beam control schemes and provide a real-time prototype of 3D on-demand flying mobile communication for mmWave HetNets.
Li-Hsiang Shen, Ting-Wei Chang, Kai-Ten Feng, and Po-Tsang Huang, “Design and Implementation for Deep Learning-based Adjustable Beamforming Training for Millimeter Wave Communication Systems," accepted and to be appeared in IEEE Transactions on Vehicular Technology, 2021. [pdf]
Li-Hsiang Shen, Kai-Ten Feng, and Lajos Hanzo, “Coordinated Multiple Access Point Multiuser Beamforming Training Protocol for Millimeter Wave WLANs," in IEEE Transactions on Vehicular Technology, vol. 69, no. 11, pp. 13875 – 13889, Nov. 2020. [pdf]
Li-Hsiang Shen and Kai-Ten Feng, “Mobility-Aware Subband and Beam Resource Allocation Schemes for Millimeter Wave Wireless Networks," in IEEE Transactions on Vehicular Technology, vol. 69, no. 10, pp. 11893 – 11908, Oct. 2020. [pdf]
Li-Hsiang Shen, Yi-Ching Chen, and Kai-Ten Feng, “Design and Analysis of Multi-User Association and Beam Training Schemes for Millimeter Wave based WLANs," in IEEE Transactions on Vehicular Technology, vol. 69, no. 7, pp. 7458 – 7472, Jul. 2020. [pdf]
Kai-Ten Feng, Li-Hsiang Shen and, Chi-Yu Li, Po-Tsang Huang, Sau-Hsuan Wu, Li-Chun Wang, Yi-Bing Lin, and Mau-Chung Frank Chang, "3D On-Demand Flying Mobile Communication for Millimeter Wave Heterogeneous Networks," in IEEE Network Magazine, Early Access, pp. 1 – 7, Mar. 2020. [pdf]
Li-Hsiang Shen and Kai-Ten Feng, "Millimeter Wave Multiuser Beam Clustering and Iterative Power Allocation Schemes," in Proceedings of IEEE Vehicular Technology Conference (VTC-Fall), Honolulu, Hawaii, Sept. 2019. [pdf]
Ting-Wei Chang, Li-Hsiang Shen, and Kai-Ten Feng, "Learning-based Beam Training Algorithms for IEEE 802.11ad/ay Networks," in Proceedings of IEEE Vehicular Technology Conference (VTC-Spring), Kuala Lumpur, Malaysia, Apr. 2019. [pdf]
Li-Hsiang Shen, Yi-Ching Chen, and Kai-Ten Feng, “Mobility-Aware Fast Beam Training Scheme for IEEE 802.11ad/ay Wireless Systems," in Proceedings of IEEE Wireless Communication and Networking Conference (WCNC 2018), Barcelona, Spain, Apr. 2018. [pdf]
Yi-Ching Chen, Li-Hsiang Shen, and Kai-Ten Feng, “Enhanced Multi-User Beamforming Protocol for Millimeter Wave Wireless Local Area Networks," in Proceedings of IEEE Wireless Communication and Networking Conference (WCNC 2018), Barcelona, Spain, Apr. 2018. [pdf]
Li-Hsiang Shen and Kai-Ten Feng, “Joint Beam and Subband Resource Allocation with QoS Requirement for Millimeter Wave MIMO Systems," in Proceedings of IEEE Wireless Communication and Networking Conference (WCNC 2017), San Francisco, CA, Mar. 2017. [pdf]
NS-3 Implementation of Fast Beam Training (FBT) Algorithm for mmWave Networks
High-Definition Video Streaming using Drone-based mmWave Communications
Multiuser Transmission for mmWave Networks
Fast Beam Training for mmWave UAV Communications
Resource Allocation for Multi-Numerology and Full-duplex Wireless Networks
We study joint interference cancellation and resource allocation for full-duplex (FD) cloud radio access networks (C-RANs) with antenna correlation. The target is to maximize the capacity of downlink (DL) user equipments (UEs) with guaranteed quality of service (QoS) of uplink (UL) UEs under the constraints of DL and UL transmit power. Intractability of the considered problem involves non-convexity and coupling between postcoding and remote radio head (RRH) selection. To deal with the high coupling between system variables, an estimation-free self-interference (SI) cancellation (EFSC) scheme with merit of significantly reducing signaling overhead for channel estimation is proposed. The reduced signaling overhead is also derived in closed-form expression. Furthermore, we propose a generalized Bender's decomposition-based resource allocation (GRA) algorithm, which separates the continuous and discrete variables to solve the optimization problem. With the design of a flexible utility function, the tradeoff between DL capacity and co-channel interference (CCI) can be achieved. Moreover, we identify the scenario in which antenna correlation will be beneficial for both the UL and DL communications in FD C-RAN with the implementation of EFSC scheme. The effectiveness of the proposed methods and theorems are verified via simulation results.
Chun-Hao Fang and Kai-Ten Feng, “QoE-Maximized Beam Assignment and Rate Control for Dynamic mm-Wave based Full-Duplex Small Cell Networks," in IEEE Open Journal of the Communications Society, Jan. 2023. [pdf]
Chun-Hao Fang, Kai-Ten Feng, Lie-Liang Yang, “Resource Allocation for URLLC Service in In-Band Full-Duplex Based V2I Networks," in IEEE Transactions on Communications, vol. 70, no. 5, pp. 3266 – 3281, May 2022. [pdf]
Chun-Hao Fang, Li-Hsiang Shen, Tun-Ping Huang, and Kai-Ten Feng, “Delay-Aware Admission Control and Beam Allocation for 5G Functional Split Enhanced Millimeter Wave Wireless Fronthaul Networks," in IEEE Transactions on Wireless Communications, vol. 21, no. 4, pp. 2430 – 2444, Apr. 2022. [pdf]
Li-Hsiang Shen, Pei-Yin Wu, and Kai-Ten Feng, “Energy Efficient Resource Allocation for Multi-Numerology Enabled Hybrid Services in B5G Wireless Mobile Networks," in IEEE Transactions on Wireless Communications, Sept. 2022. [pdf]
Li-Hsiang Shen, Chia-Yu Su, and Kai-Ten Feng, “CoMP Enhanced Subcarrier and Power Allocation for Multi-Numerology based 5G-NR Networks," in IEEE Transactions on Vehicular Technology, vol. 71, no. 5, 5460 – 5476, May 2022. [pdf]
Chun-Hao Fang and Kai-Ten Feng, "Queue-Aware Beam Assignment and Rate Control for Time-Varying mm-Wave based Full-Duplex Small Cell Networks," in IEEE Communications Letters, vol. 24, no. 1, pp. 222 – 226, Jan. 2020. [pdf]
Chun-Hao Fang, Pei-Rong Li, and Kai-Ten Feng, "Joint Interference Cancellation and Resource Allocation for Full-duplex Cloud Radio Access Networks," in IEEE Transactions on Wireless Communications, vol. 18, no. 6, pp. 3019 – 3033, Jun. 2019. [pdf]
Chia-Yu Su, Chun-Hao Fang, and Kai-Ten Feng, "Effective Capacity Maximization for Multi-Numerology based 5G NR Networks," in Proceedings of IEEE Vehicular Technology Conference (VTC-Fall), Victoria, CA, Oct. 2020. [pdf]
Chun-Hao Fang, Pei-Rong Li, and Kai-Ten Feng, "Oblique Projection-Based Interference Cancellation in Full-Duplex MIMO Systems," in Proceedings of IEEE International Conference on Communications (ICC 2016), Kuala Lumpur, Malaysia, May 2016. [pdf]
IEEE VTC Fall 2020 Presentation
Skeleton-based Location Estimation and Tracking for Wireless Networks
Map information can assist indoor localization to avoid improbable cases and achieve accurate location estimation. We proposed an automatic method to extract useful information from indoor map as spatial skeleton database (SSD). Based on conventional probabilistic fingerprinting technique and particle filter tracking algorithm, we also proposed spatial skeleton-based dynamic probabilistic fingerprinting database (S-DFD) to filter out reference points (RPs) in fingerprinting database according to the previous target location and the walking distance between RPs. Finally, we proposed a spatial skeleton-based particle filter tracking (S-PT) which use SSD to construct realistic transition model. According to the experiment result, the whole system consists of SSD, S-DFD and S-PT called spatial skeleton-enhanced location tracking for indoor localization (SELT) can achieve accurate location estimation.
Chun-Jie Chiu, Hsiao-Chien Tsai, Kai-Ten Feng, and Po-Hsuan Tseng "Indoor Positioning Based Consecutive Pattern Mining for Pedestrian Flow Analysis," in Proceedings of IEEE Vehicular Technology Conference (VTC-Spring), Apr. 2021. [pdf]
Chun-Jie Chiu, Po-Hsuan Tseng, and Kai-Ten Feng, “Spatial Skeleton-enhanced Location Tracking for Indoor Localization," in Proceedings of IEEE Wireless Communication and Networking Conference (WCNC 2017), San Francisco, CA, Mar. 2017. [pdf]
Pei-Hsuan Chiu, Po-Hsuan Tseng, and Kai-Ten Feng, "Interactive Mobile Augmented Reality System for Image and Hand Motion Tracking," in IEEE Transactions on Vehicular Technology, vol. 67, no. 10, pp. 9995 – 10009, Oct. 2018. [pdf]
Yun-Ting Hung, Kai-Ten Feng, and Po-Hsuan Tseng, “Automatic Hybrid Access Point Deployment for Wireless Localization Systems," in Proceedings of IEEE Wireless Communication and Networking Conference (WCNC 2017), San Francisco, CA, Mar. 2017. [pdf]
Skeleton-based Location Tracking
You can observe both the Spatial Skeleton and Geospatial Skeleton from the following two demo sites by inserting a map image.
Device-free Human Breath Rate Detection
We are developing device-free breath detection system using commercial Wi-Fi routers. Our target is to detect user's breath rate with hassle-free and user-centric focuses for potential Apnea in healthcare applications. Sleep apnea syndrome is a common sleep disorder that can lead to a variety of diseases. The traditional diagnostic method, polysomnography (PSG), is time-consuming, expensive, and inconvenient for patients. In this research, we proposed the transfer learning based apnea detection (TLAD) system as a non-contact based method utilizing the channel state information (CSI) from commercial Wi-Fi devices. In order to reduce the overhead of collecting CSI data and improving efficiency during training process, the transfer learning technique is applied to establish pre-trained model by utilizing open source contact-based thoracic movement data. Moreover, existing research works detect apnea based on breathing pauses and shallow breathing periods, which are not effective to identify complex apnea characteristics. This potential drawback is overcome in proposed TLAD system since both CSI amplitude and frequency features are extracted for apnea classification. Our experimental results showed that the TLAD system achieves an F1-score of 90.1, which is superior to other existing methods.
Chia-Yu Chen, An-Hung Hsiao, Chun-Jie Chiu, and Kai-Ten Feng, "Contactless Transfer Learning Based Apnea Detection System for Wi-Fi CSI Networks," in Proceedings of IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), Virtual Conference, Sept. 2022. [pdf]
Device-free Respiration Rate Detection
Indoor Device-free Respiration Rate Detection
Advanced Angle of Arrival (AoA) Estimation Techniques
In recent years, indoor positioning system employing Bluetooth has attracted tremendous attention. However, it is investigated that its received channel information is significantly affected by hardware configuration and wireless environments, especially the received dataset of angle of arrival (AOA), which leads to inaccurate channel estimation and positioning. Furthermore, AOA estimation at larger angle direction is severely influenced by the variant wireless environment and signal distortion, which has not been resolve in existing literature. We propose an advanced I/Q density-based AOA estimation (IQDAE) to deal with the above-mentioned problem, which consists of two sub-schemes. We firstly employ the designed phase difference (PD) filter to convert I/Q signals to phase information and then select the candidate sets by eliminating outliers. Afterward, we conceive a PD density-based classification algorithm to estimate AOA. The experimental results show that the mean absolute error of proposed IQDAE algorithm is comparably smaller than that from the other schemes, including commercial solutions, especially at larger angles. The results indicate that we can effectively increase the service range for the Bluetooth positioning system by adopting the proposed algorithm.
Hung-Yi Yen, Zhong-Ting Tsai, Yuan-Ching Chen, Li-Hsiang Shen, Chun-Jie Chiu, and Kai-Ten Feng, "I/Q Density-based Angle of Arrival Estimation for Bluetooth Indoor Positioning Systems," in Proceedings of IEEE Vehicular Technology Conference (VTC-Spring), Apr. 2021. [pdf]
Zhong-Ting Tsai, Li-Hsiang Shen and Kai-Ten Feng, "Beam AoD-based Indoor Positioning for 60 GHz MmWave System," in Proceedings of IEEE Vehicular Technology Conference (VTC-Fall), Victoria, CA, Oct. 2020. [pdf]
Federated Edge Learning for Next Generation Hybrid mmWave/THz Wireless Communications
In the next sixth generation (6G) wireless communications, there emerge exponentially high data demands for diverse services and applications. Therefore, terahertz (THz) is considered as a promising technology using wider hundreds-of-GHz bandwidth compared to millimeter wave (mmWave) transmissions, which is capable of supporting Tbps high speed data traffic. However, THz transmission possesses more severe path loss due to higher operating frequencies at 0.1-10 THz. Accordingly, new hybrid beamforming techniques should be enhanced by array-of-subarray (AoSA) equipped with tens-of-thousands of antenna elements. Due to short and medium distance-based wireless transmissions, a large number of THz-enabled base stations (BSs) should be deployed to support seamless coverage and high throughput performance. When using ultra-thin THz beams, beam training becomes compellingly imperative due to enormous number of beam training steps, which potentially induces high training latency overhead. In addition to beams, there are power, time, and subbands resources providing abundant but complex wireless environments, which the optimal solutions cannot be readily obtained by using conventional optimization methods. Empowered by state-of-the-art machine learning and deep learning in artificial intelligence (AI) fields, the system is capable of supporting resilient, automatic and intelligent THz/mmWave wireless networks. In a multi-BS and multi-network scenarios, federated edge learning (FEL) can perform resource assignment collaboratively among local BSs and devices. Aided by FEL-empowered central server, hybrid TH/mmWave beamforming and AI-enhanced radio resource management (RRM) have the capability to provide flawless services by guaranteeing globally-optimal data rate, latency, utilization, fairness, security, and energy/spectrum efficiencies.
Po-Chun Hsu, Li-Hsiang Shen, Chun-Hung Liu, and Kai-Ten Feng, "Federated Deep Reinforcement Learning for THz-Beam Search with Limited CSI," in Proceedings of IEEE Vehicular Technology Conference (VTC-Fall), Sept. 2022. [pdf]
IEEE VTC Fall 2022 Presentation
Reinforcement Learning for Channel Allocations in WiFi Networks
IEEE 802.11ax system has been adopted to provide enhanced throughput performance for next-generation wireless local area networks. Its orthogonal frequency division multiple access (OFDMA) allows massive users to concurrently utilize different subbands for data transmission from their corresponding access points (APs). However, severe adjacent channel interference (ACI) incurs overlapping channels under the scenarios of dense users with multiple APs, which should be properly alleviated to provide adequate system throughput. In this work, we propose a long-/short-term reinforcement learning channel allocation (LSRCA) scheme to effectively mitigate ACI for multi- AP scenarios in IEEE 802.11ax systems. With the considerations of signal features from both long and short time durations, the LSRCA algorithm can maximize effective sum rate through online adaptation and learning via the updates of two Q-tables for weighting adjustments and action execution. Experimental results in realistic fields have demonstrated the effectiveness of LSRCA scheme by providing higher system throughput compared to existing benchmark methods.
Sheng-Han Chung, Li-Hsiang Shen, and Kai-Ten Feng, “Long-/Short-Term Reinforcement Learning for Multi-APs Channel Allocation in IEEE 802.11ax WLANs," in Proceedings of IEEE Wireless Communication and Networking Conference (WCNC 2023), Glasgow, Scotland, UK, Mar. 2023. [pdf]
Li-Hsiang Shen, Kuan-Hsun Liao, and Kai-Ten Feng, “Queue-Aware Arbitration-based Contention and Resource Assignment for Multi-AP Deployment in IEEE 802.11ax WLANs," in Proceedings of IEEE Wireless Communication and Networking Conference (WCNC 2022), Austin TX, Apr. 2022. [pdf]