Next-Generation Spectrum Intelligences Enables Smarter RF Optimization for Wi-Fi Networks

Aerohive Networks, a cloud networking leader has released next-generation, advanced spectrum analysis capability. The new capability enables customers and partners to easily diagnose and mitigate network performance issues resulting from RF interference.

They develop a series of cloud networking access points for 802.11 applications. Aerohive's Spectrum Analysis feature detects interference from non-Wi-Fi radio devices. No additional hardware or licenses are required to use this feature, which detects radio signals from devices such as Bluetooth, microwave ovens, and cordless phones. Data on performance-reducing interference sources is fed into the Aerohive Channel Selection Protocol (ACSP) to mitigate interference from non-802.11 devices.

In today’s wireless networks, RF interference will inevitably occur. It can be caused by Wi-Fi enabled devices, like company-issued laptops and smartphones, BYOD and IoT, or by non-Wi-Fi devices, like Bluetooth headsets, cordless phones or microwaves. Aerohive’s advanced spectrum analysis allows network administrators to easily determine the extent and source of the interference, and effectively address the resulting performance impact. Unprecedented ease of use is achieved by seamless integration into HiveManager NG’s alerting and troubleshooting capabilities, and intuitive graphical representation.

Spectrum Intelligence as a troubleshooting feature is complemented by the Aerohive Channel Selection Protocol (ACSP), as a channel-optimization capability. ACSP is an integral part of Aerohive’s unique cooperative control architecture. It enables APs to determine and apply the best channel and power settings to minimize channel interference. Unlike most competitive products, where spectrum analysis is used to manually evaluate and then adjust channel settings, ACSP does so automatically.

Aerohive plans to integrate the Spectrum Intelligence feature with other troubleshooting tools and by adding machine learning functionality with the goal of providing integrated diagnostics and optimization capabilities.