Functional split technique enables conventional cellular base stations to partition their full functions into separate central unit (CU) and distributed unit (DU) along with transmission radio units (RUs). The radio accessing layers are partitioned based on service requirements, i.e., CU possesses application layer to higher-medium access control (MAC) layers, whilst DU takes care of physical (PHY) and lower-MAC layers. Moreover, coordinated multi-point transmission/reception (CoMP) can improve the system throughput performance by employing joint transmission (JT) techniques. Based on functional split enablers, multiple DUs with RUs can utilize JT to jointly serve edge users, which are controlled by a single CU. The developed WiSE platform emulates 5G new radio (NR) scenarios with network topology and channel modeling. Under the designed resource blocked scheduling along with functional split-based CoMP transmissions, we can improve SINR of 8 dB in average compared to traditional confined centralized functional base stations.

1. Yung-Ting Huang, Chun-Hao Fang, Li-Hsiang Shen, and Kai-Ten Feng, "Optimal Functional Split for Processing Sharing based CoMP for Mixed eMBB and uRLLC Traffic," in Proceedings of IEEE Global Telecommunications Conference (Globecom), Taipei, Taiwan, Dec. 2020. [pdf]

2. Tun-Ping Huang, Chun-Hao Fang, Pei-Rong Li, and Kai-Ten Feng, "QoE- Oriented Admission Control and Resource Allocation for Functional Split Millimeter-Wave Fronthaul Networks," in Proceedings of IEEE Vehicular Technology Conference (VTC-Spring), Kuala Lumpur, Malaysia, Apr. 2019.

Internet-of-Thing (IoT) is a promising technology attracting huge attentions in recent years, allowing an excessive number of connections between sensors and devices. Different from conventional human-oriented applications, Long Range (LoRa) developed in IoT facilitates massive simultaneous sensor data transmissions for the low-power wide-area-network (LPWAN), where massive LoRa devices perform packet contention and backoff mechanisms to access opportunity for uplink data transfer. Therefore, it is compellingly imperative to take into account the fluctuation of different channel qualities and various traffic-buffer types for the optimum contention policy, which are not considered in open literatures. In this paper, we propose a traffic-aware channel and backoff window size allocation (TCBA) scheme to improve network capacity and latency. Moreover, a statistical latency-aware network model is designed to derive the closed-forms of the optimum packet transmission probability and maximum number of LoRa devices supported. The performance results validates that the theoretical analysis approaches the simulated one. Moreover, in both simulated and experimental results, our proposed TCBA scheme is capable of supporting massive LoRa connections achieving the highest throughput and the lowest end-to-end latency compared to other schemes in existing literatures.