Self-Organizing/ Self-Optimizing (SON) Small Cell Technology

SON Auto-Configuration

SON Auto-Configuration

When you have a Self-Organizing/Self-Optimizing “SON” system like the E-RAN implements, it does most of the “heavy lifting” when commissioning the system. Self-organizing and self-optimizing features (SON) optimize the user experience as well as system management. A network can be set up in a matter of days by installers with Wi-Fi experience rather than macro cellular RF experts. SpiderCloud’s Self Organizing Network (SON) capability configures and optimizes the SpiderCloud small cell network to provide a high-performance mobile broadband coverage with very little user intervention. SON is a core product feature that dramatically reduces installation time, fine-tunes the network for high performance, and periodically optimizes the environment to maintain effective network operation. Without this feature, an installer would have to setup the network manually, requiring many weeks (depending on the network complexity) to create an optimal working configuration.

Besides reducing time-to-install, the feature ensures optimal RF coverage and handoff within the SpiderCloud network and with macro and inter-RAT networks. During network operation, this feature continually monitors the RF environment, makes adjustments to the radio transmit power to adapt to any changes in the RF conditions, and maintains optimal network access.

SON Architecture and External Interfaces

The E-RAN’s SON capabilities include discovering the macro cells in the area, discovering the internal small cell topology, assigning UMTS primary scrambling codes and LTE physical cell identifier, setting maximum transmit power levels, and automatically configuring cell neighbor lists to make the system operational.

Visualization Map SpiderCloud SON

The SON algorithms are centrally anchored on the local controller, the services node. The services node orchestrates the SON process, controls the operation of different radio nodes during neighbor discovery, gathers information from different radio nodes and creates optimized neighbor lists based on information received from the neighbor scans. During macro-network topology discovery, the services node ensures that the small cell network is quiet to ensure optimal macro topology discovery. During the small cell network topology discovery, the services node ensures that the small cell can detect each other accurately by coordinating scans between the small cells. The neighbor lists are constructed to optimize the system for soft handover as well as ensure smooth mobility in and out of the network.

Periodic Optimization and Self-Maintenance

While the system is in operational mode, a power optimization feature is used to periodically adjust the transmit power levels in order to achieve uniform coverage across the small cell deployment. The algorithm takes into account several factors:  

  • The interference level from macro networks as measured by the radio nodes
  • The relative signal strength at which each radio node measures neighboring radio nodes
  • Periodic signal quality measurements made by user devices across the network and reported back to the services node

The service node uses measurements collected over time to fine-tune the network. For example, it might reduce the power level of a congested cell to decrease the number of users on that cell, while powering up lightly loaded cells. The system can also be configured to periodically monitor for changes in topology (added or deleted external and internal cells) and changes in the physical RF environment of the deployment area. For example, the system can be configured to go into scan mode during weekends, when no traffic is expected on the network.


Cellular systems benefit from dedicated and exclusive spectrum that shouldn’t be used by anyone else. This allows planners and system designers to pre-assign frequencies and RF power settings to optimize performance. While the presence and demand of individual cell phone users at any specific time can’t be predicted, the available capacity and system performance is well known.

Wi-Fi systems are intended to be more ad-hoc and quickly adapt to changes. While professional engineered and designed systems in offices and public venues can achieve high performance, they are still at the mercy of any Wi-Fi smartphone hotspot that may pop-up and any other nearby uncoordinated Wi-Fi equipment. This makes it considerably more difficult to accommodate and plan for sustained reliable performance in busier environments.

Larger enterprise Wi-Fi deployments have their own SON functions that centrally orchestrate the band plan/channel allocations used by each Wi-Fi access point. Nonetheless, each access point must actively “listen-before-talk” (LBT), sensing any other transmission in a channel before starting to use it. This requires some local intelligence, typically built into the hardware itself.

E-RAN has incorporated similar techniques into the LTE-LAA radio nodes. A dedicated Wi-Fi chip doesn’t just listen to other transmissions but actively decodes Wi-Fi beacons, identifying when and which other devices are about to transmit. It also uses the LBT protocol to co-exist with existing Wi-Fi deployments.  This works well at the physical layer, interworking with other Wi-Fi and LTE-LAA equipment using the same well-known protocols and methods in use today.

Additionally, a centralized SON software component aggregates known use of each 5GHz channel and calculates an optimal band plan for LAA use. Each Radio Node has a different unlicensed channel ensuring optimum performance for LAA network. The centralized SON algorithm also eliminates the need for any careful planning with existing Wi-Fi networks. The channel assignment per Radio Node are periodically refreshed to accommodate changes in traffic loading and usage within the E-RAN system as well on the Wi-Fi networks

Simulations indicate that an extra 20 to 30% system wide performance improvement is achieved by use of centralised SON compared with independent standalone ad-hoc operation.

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