Multi-TRP Positioning Models for Non-Orthogonal Reference Signals
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Solution Overview
Problem
Existing wireless communication systems face challenges in accurately interpreting reference signals from multiple transmission reception points (TRPs) for precise positioning, particularly in environments where subsets of these signals are non-orthogonal, leading to inefficiencies in determining the location of wireless devices.
Innovation Solution
A positioning model utilizing artificial intelligence machine learning (AIML) is trained to interpret reference signals from multiple TRPs, enabling accurate estimation of positioning information such as time of arrival, reference signal time difference, and line-of-sight identification by leveraging simultaneous measurements from non-orthogonal subsets of signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If reference signals from multiple TRPs are transmitted using non-orthogonal subsets, then signal resource efficiency is improved, but measurement precision deteriorates
Solution Approach 1:
The system performs preliminary configuration of measurement resources and signal patterns before actual positioning measurements. The network configures multiple TRPs to transmit reference signals with predetermined non-orthogonal patterns, and the UE is pre-configured with measurement parameters, enabling efficient simultaneous measurement without real-time resource allocation overhead.
Solution Approach 2:
The system dynamically adapts the measurement process by allowing the UE to simultaneously measure multiple non-orthogonal reference signal subsets from different TRPs. The measurement configuration can be dynamically adjusted based on channel conditions and positioning requirements, optimizing the balance between resource efficiency and measurement precision.
2Speed
If multiple TRPs transmit reference signals simultaneously, then positioning speed is improved, but signal interference increases
Solution Approach 1:
The reference signal resources are segmented into multiple non-orthogonal subsets, each assigned to different TRPs. This segmentation allows simultaneous transmission from multiple TRPs while maintaining distinguishable signal characteristics through code division multiplexing and pattern differentiation, reducing interference effects.
Solution Approach 2:
The system changes signal parameters such as cyclic shifts, orthogonal cover codes, and time-frequency patterns to differentiate reference signals from multiple TRPs. By adjusting these parameters, the system enables simultaneous transmissions while managing interference through parameter orthogonality and diversity.
3Device complexity
If non-orthogonal reference signal subsets are used, then device complexity is reduced, but positioning accuracy deteriorates
Solution Approach 1:
The system uses copied and transformed versions of base reference signal patterns for different TRPs. Instead of designing completely independent signal structures, the same base pattern is copied and modified with different cyclic shifts and orthogonal cover codes, reducing the complexity of signal design and processing while maintaining measurability.
Solution Approach 2:
The reference signal design achieves multi-functionality where the same signal structure serves multiple purposes: channel estimation, positioning measurement, and TRP identification. This universal design reduces processing complexity by using a unified signal framework that handles multiple functions simultaneously.
Data Source
AI summary
A user equipment (UE) may receive a set of reference signals (RSs) from each of a plurality of network nodes. The set of RSs may include a first subset of RSs from a first network node of the plurality of network nodes and a second subset of RSs from a second network node of the plurality of network nodes. The first subset of RSs and the second subset of RSs may be non-orthogonal relative to one another and may be received during a same time period. The UE may measure the set of RSs from each of the plurality of network nodes received during the same time period. The UE may transmit, for a network entity, positioning information associated with measuring the set of RSs from each of the plurality of network nodes.