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.

1. 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]

2. 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]

3. 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 in Proceedings of IEEE Vehicular Technology Conference (VTC-Fall), Chicago, IL, Aug. 2018. (Candidate for the Best Paper Award) [pdf]

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.

1. 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]

2. 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]

3. 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]

4. 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]

5. 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]

6. 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]

7. 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]

8. 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]

9. 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]

10. 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]

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.

1. 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.

2. 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.

3. 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.

4. 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.

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.

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.

With the advent of the sixth-generation (6G) wireless, it requires substantially increasing wireless traffics and extending serving coverage. Reconfigurable intelligent surface (RIS) is considered as a promising technique which can potentially increase the system rata rates, energy efficiency and coverage extension along with the benefit of low power consumption. RIS comprises massive ultra-thin meta material-based elements, which can naturally reflect received signals on the surface and out without further signal processing. Moreover, benefited by cost-effective RIS, it can be deployed for wireless communication readily and arbitrarily We can reconfigure the RIS to by turning the phase shifts of all element in order to change the wireless path of the signal. Moreover, RIS can perform interference management which potentially suppresses the strong interference and enhances desired signal by adjusting them in different transmission directions of phase shifter of RIS. Full-duplex (FD) transmission is capable of simultaneously transmitting and receiving signals, which has theoretically twice spectrum efficiency. However, the self-interference (SI) in FD is a challenging task requiring complex and high-overhead cancellation methods. Therefore, with adjustment of phase shifter of RIS elements, we can compromise the severe SI of FD transmission improving system performance. The proposed scheme of RIS-empowered FD SI cancellation (RFSC) can be theoretically proved in a closed-form solution substantially alleviating the SI effects.