H3C WA6628E-T Wi-Fi 6 Rail Transit Access Point
An all-metal Wi-Fi 6 AP for trains and vehicles, with M12 connectors and resistance to strong electromagnetic interference.
Price on Request
The analytics engine of H3C AD-Campus, using telemetry and AI to monitor campus network health, find faults fast and predict problems.
Island-wide delivery to all 25 districts
| Health analysis | Health overview / · Support graphical display of the physical topology of the entire network, allowing real-time viewing of the health status and basic information of devices and links · Support hierarchical and area-based display of physical network topology · Support floor topology display, visually display AP physical location and AP health status information · Support regional analysis, display network health and user health by region · Support wireless quality analysis, analyzing the wireless quality of the entire network from multiple indicators such as onboarding success rate, time for |
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| Network analysis | · Support quantitative calculation of device health, integrating information such as device system plane, data plane, control plane, and air interface performance to calculate the overall health of the device · Support displaying network health and trend charts by region, allowing real-time viewing of the overall network status over a designated time span, as well as the online status of wired and wireless users · Support multi-dimensional data statistics for AP, such as onboarding failure statistics, wireless terminal count statistics, and channel utilization rates · Support displaying the li |
| User analysis | · Support quantitative calculation of wired users health based on various indexes, such as authentication success rate, packet loss rate, packet error rate and etc. · Support quantitative calculation of wireless user health based on various indexes, such as signal strength, uplink and downlink speed, latency, packet loss rate, and access issues and etc. · Support the summary display of the health status of wired and wireless users, and real-time display of user health trend charts · Support multiple users-related data statistics, such as time for access, signal strength, onboarding failure, et |
| Application analysis | · Support analysis of audio and video applications based on SIP and H323 protocols, displaying MOS quality distribution, session information, traffic flow information, and application quality analysis · Support DPI based wireless application analysis, display session information, traffic flow information, and application quality analysis · Support iNQA based wired application analysis, display session information, traffic flow information, and application quality analysis |
| Fault diagnosis | Issue center / · Support problem classification and display, real-time display of network fault trends · Support identifying common faults in the five major categories within minutes, including networks, devices, protocols, overlay, service categories, pinpoint the root cause, and provide recommended action Common fault examples · Device:ACL/ARP/MAC/Route/ND table entry resource exceeds threshold, table entry resource depletion, port protocol status down, device fan failure, card failure, power module failure, MMU blocked, etc. · Network:layer 2 loop, IP address conflict, link down, suspected |
| Data plane verification | Support DPV data plane verification technology to comprehensively verify the campus network. Provide verification and diagnosis of network reachability, routing loops, and link consistency |
| Wireless diagnosis | · Support one click diagnosis, conduct comprehensive examination of wireless AC, covering 19 items such as configuration, network status, and operating status, etc. · Support Doctor AP feature, leverage online AP to simulate as a wireless user accessing the wireless network, and diagnose the health and potential issues of WLAN network · Support wireless security detection (WIPS), by monitoring and analyzing channels, detect wireless behaviors or devices that threaten network security, interfere with network services, and affect network performance |
| Predict analysis | Intelligent prediction / Using statistical learning and machine learning algorithms to analyze the temporal data of networks, users, and application modules, and achieve trend prediction and anomaly detection of various KPI indicators for switches, routers, and AP devices |
| Assurance & optimization | Intelligent problem cure / Support automatic identification of problems in wireless networks and timely resolution of issues without the need for manual intervention |
| Wireless optimization | Support one click optimization to automatically optimize and adjust the channel, power, and bandwidth of APs with poor wireless performance, in order to improve user experience and simplify maintenance |
| Intelligent power saving | Support the analysis of AP's history and real-time data, automatically deploy energy-saving configurations during AP's idle time, achieve intelligent energy-saving while maintaining wireless network continuity, and supports power consumption analysis, power consumption comparison, and energy-saving details. |
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