Network Node Candidate Set Determination for NB-IoT UL CoMP
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Solution Overview
Problem
Current methods for determining a candidate set of base stations in communication networks, particularly for Narrowband Internet of Things (NB-IOT), face challenges due to differences between LTE and NB-IOT operations, such as limited battery capacity and bandwidth constraints, which hinder the determination of neighbor cells and candidate sets for uplink CoMP, necessitating a method that can function without UE measurements and ensure early decision-making for short data transmissions.
Innovation Solution
A method involving a network node that forms a set of base stations by receiving neighbor cell information, determining selection criteria like transport bandwidth and latency, and selecting base stations based on RACH information, including RACH detection and timing advance estimation, to determine a candidate set for UL CoMP, suitable for both eNB-based and virtualized NB-IOT deployments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If UE measurements are used to determine candidate set in LTE, then measurement precision is improved, but device complexity and energy consumption increase for NB-IOT devices
Solution Approach 1:
The network node performs self-service by autonomously determining the candidate set of base stations using RACH information received from base stations, without requiring measurement capabilities on the UE side. This shifts the measurement and evaluation burden from the UE to the network, resolving the contradiction between accurate candidate set determination and UE complexity constraints.
Solution Approach 2:
RACH information acts as an intermediary that carries timing advance and signal strength data from base stations to the network node. This intermediary mechanism enables the network to indirectly obtain measurement data without requiring direct measurement capabilities on the UE, thus resolving the contradiction between measurement precision and device complexity.
2Reliability
If comprehensive neighbor cell information is collected, then candidate set quality is improved, but loss of time increases due to extensive information gathering
Solution Approach 1:
The network node performs preliminary actions by proactively requesting and collecting RACH information from potential neighbor base stations before actual data transmission occurs. This advance preparation ensures that when short data transmissions need to occur, the candidate set is already determined, resolving the contradiction between comprehensive information gathering and time loss.
Solution Approach 2:
The information gathering process is segmented into discrete RACH opportunities at different base stations. Instead of attempting to gather all information simultaneously, the network node collects RACH information from multiple base stations across different time intervals, making the process manageable and time-efficient while still achieving comprehensive coverage.
3Manufacturing precision
If RACH information from multiple base stations is collected, then manufacturing precision of candidate set selection is improved, but device complexity of network node increases
Solution Approach 1:
The network node changes evaluation parameters by using RACH-based metrics (timing advance, signal strength) instead of traditional UE measurement parameters. This parameter transformation allows for accurate candidate set selection using simpler, more directly observable quantities that reduce the complexity of processing while maintaining or improving selection accuracy.
4Productivity
If early decision-making is implemented for short data transmissions, then productivity is improved, but measurement precision may be compromised
Solution Approach 1:
The network node performs preliminary candidate set determination using RACH information before actual data transmission begins. This preliminary action enables immediate data transmission without waiting for additional measurements, thus improving productivity while maintaining accuracy through the use of pre-collected RACH data that contains timing and signal strength information.
Data Source
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AI summary
The method includes forming a first set of base stations, receiving random access channel (RACH) information from one or more of the first set of base stations for a first transmission time interval, determining the candidate set of base stations for the first transmission time interval based on the RACH information, the candidate set of base stations being in the first set of base stations, and controlling an operation of the communication network based on the candidate set of base stations. A network node is configured to perform the method.