Abstract
Network roll-out is a process composed of several actions, which, if not completed correctly, may degrade the final system performance. A common error during the roll-out process is the case in which feeders from baseband units to radio units or feeders from radio units to antennas are interchanged. Consequently, in sectorized sites, the service areas of two or more co-sited cells become swapped, affecting the initial network design. This paper proposes to utilize the location of the user during mobility procedures to enhance the state-of-the-art methods to detect swapped sectors automatically. There are multiple solutions to obtain the location of subscribers in mobile communications networks. However, the most accurate ones are either costly or not possible in current networks. Therefore, this paper addresses the problem of applying time-of-arrival techniques based on multilateration in present-day mobile networks by proposing an alternative using standardized signaling. One of the main benefits of the proposed method is its ease of being implemented in real networks without adding additional costs to the operators. Finally, the results are presented to demonstrate that the proposed method improves the performance of state-of-the-art methods.
Highlights
During the design phase of a cellular network, operators try to find the best balance between performance and cost
This paper proposes a method to improve state-of-the-art techniques for the detection of swapped sectors based on mobility statistics
As part of the proposed method for detecting swapped sectors, an estimator of the User Equipment (UE) location based on Time of Arrival (ToA) techniques has been presented
Summary
During the design phase of a cellular network, operators try to find the best balance between performance and cost In this phase, network elements and their configuration are planned in detail since any change in this configuration will affect the performance of the later deployed network. Despite the efforts that operators make to plan and design the roll-out strategy, the human factor is always present and, the process is prone to mistakes. To overcome this and many more problems related to network deployment and operation, self-healing algorithms arose as part of Self-Organizing Networks (SON) [1]. These algorithms make use of network live data in order to find problems, classify these and apply corrections to fix them
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