More and more telecom operators around the world began to build and to deploy fifth-generation (5G) mobile networks. With the advent of 5G service scenarios, it brings new challenges and increases the complexity of management. To deliver good quality of experience of these 5G applications for customers, service assurance is important. In this paper, we propose an end-to-end service assurance solution for 5G crosshaul networks. The 5G crosshaul network architecture consists of fronthaul, midhaul, and backhaul networks. Our solution deployed in 5G trial network in Chunghwa Telecom in Taiwan. The solution focus on slicing service assurance and synchronization monitoring. Besides, our solution adjusts most of network management in the operation support system (OSS) to apply to 5G crosshaul network. Our goal is to provide stable 5G transport network and to assure quality of experience for customers.

IEEE 802.11 Wireless Local Area Networks (WLANs) has become one of the most popular networking environments. With high density WLANs, it causes the interference among access points (APs) and degrades the performance of APs, especially in public area. To solve this problem, we design and implement a Cluster-based Channel Assignment (CCA), a combination of RSS-Aware Channel Selection Module (RCSM) and Interference-Aware Clustering Module (ICM), which is applied to a network management system (NMS) as a central coordinator. Based on the Received Signal Strength (RSS) information collected by NMS, RCSM can assign channels with less interference and ICM can reasonably partition interference domains of WLANs. The CCA leads to more efficient channel assignment in high density WLANs. We applied our scheme in a high density real Public WLANs, called Huashan. The results show it effectively increases the AP's throughput.

The tremendous bandwidth demand for content-rich services on service providers initiates new access networks deployment and leads the networks increasingly complex and functional. In general, the commercial access networks are composed of a large number heterogeneous, multi-domain, multi-technology and multi-vendor network devices. In such a network environment, the network migration is a challenging task for any service providers. The process of network migration traditionally involved a series of manual and repetitive steps. The manual process of network migration requires highly skilled staffs and is prone to human error. In this paper, we present the integrated and automated provisioning operational supporting systems (OSSs) for in-service network migration. The developed OSSs provide two kinds of in-service network migration mechanisms and support a diversity of network migration scenarios in heterogeneous networks. Finally, the results show that the proposed efficient provisioning mechanism significantly speeds up the network migration and reduces service providers' operational expenditures (OPEX).

Tag anticollision has long been an important issue in RFID systems. To accelerate tag identification, some researchers have recently adopted bit tracking technology that allows the reader to detect the locations of collided bits in a collision slot. However, these methods still encounter the problem of too many collisions occurring at the beginning of identification. This paper proposes an optimal query tracking tree protocol (OQTT) that tries to separate all of the tags into smaller sets to reduce collisions at the beginning of identification. Using bit tracking technology, OQTT mainly adopts three proposed approaches, bit estimation, optimal partition, and query tracking tree. Bit estimation first estimates the number of tags based on the locations of collided bits. Optimal partition then determines the optimal number of the initial sets based on this estimation. Query tracking tree splits a set of collided tags into two subsets using the first collided bit in the tag IDs. This paper analyzes the efficiency of OQTT, which represents how many tags can be identified in a slot. Results show that its efficiency is close to 0.614, the highest efficiency published to date. The simulation results further show that OQTT outperforms other existing algorithms.