Neighbour Relation Table

Description

The Neighbour Relation Table (NRT) is a fundamental data structure stored within the management plane of a 3GPP base station—an eNodeB in LTE or a gNB in 5G NR. It is a local database that catalogs the known neighboring cells for each cell served by that base station. Each entry in the NRT represents a neighbor relation (NR) and contains a comprehensive set of attributes necessary for mobility and radio resource management. Key data fields typically include the target cell's global identity (E-UTRAN Cell Global Identifier (ECGI) or NR Cell Global Identifier (NCGI)), physical cell identity (PCI), carrier frequency, tracking area code, and various radio configuration parameters relevant for handover execution.

Architecturally, the NRT resides within the base station's SON and RRM functional blocks. It is both a source of configuration for handover algorithms and a target for updates from the ANR function. The operation of the NRT is central to the ANR process, which has three main phases: Neighbor Discovery, Neighbor Removal, and Neighbor Relation Management. In the discovery phase, the base station instructs UEs in connected mode to perform measurements and report detected cell identities (PCIs) that are not in the NRT. The base station then requests the UE to read the global cell identity (ECGI/NCGI) and tracking area code of the newly detected cell. This new information is used to create a new entry in the NRT.

How it works in practice involves continuous interaction between the UE, the serving base station, and the Operation, Administration, and Maintenance (OAM) system. Once an entry is added to the NRT, the base station can configure handover parameters towards that cell. The NRT also stores state information for each neighbor relation, such as whether it is a "No Remove" relation (manually configured and protected from automatic deletion) or a "No HO" relation (a blacklisted cell where handover is not permitted). The OAM system can provision initial NRT entries and set policies, but the ANR function automates most of the lifecycle management. This automation drastically reduces the manual planning and configuration effort required for neighbor lists, especially in dense and dynamically changing network deployments, and is a cornerstone of LTE and 5G SON capabilities.

Purpose & Motivation

The NRT and the associated ANR functionality were created to solve the massive operational burden and error-proneness of manual neighbor list configuration in cellular networks. In 2G and 3G networks, engineers had to manually define and provision the list of neighboring cells for every base station sector—a tedious, time-consuming process prone to human error. Missing neighbor relations led to dropped calls during mobility (handover failures), while incorrect relations could cause interference or ping-pong handovers. As networks grew denser with the advent of LTE and heterogeneous deployments (macro, micro, pico cells), this manual approach became utterly unsustainable.

The introduction of the NRT as part of the SON framework in 3GPP Release 8/9 was a paradigm shift motivated by the need for operational efficiency and network robustness. It addressed the limitations of static planning by enabling the network to self-discover its topology. The NRT provides the structured data store that makes this automation possible. From a historical perspective, this was a critical step towards autonomous networks, reducing operational expenditure (OPEX) and improving service quality by ensuring handover neighbor lists are always accurate and up-to-date as new cells are deployed or removed.

Furthermore, the NRT solves the problem of network optimization in real-time. Changing radio conditions, temporary cell outages, or the deployment of new small cells can be dynamically reflected in the NRT through the ANR process. This ensures optimal mobility performance without manual intervention, which is essential for maintaining high reliability and user experience in modern, complex radio access networks. It forms the data backbone for advanced SON use cases like Mobility Robustness Optimization (MRO) and Mobility Load Balancing (MLB), which rely on accurate neighbor knowledge.