Preferred RAT Acquisition Through Crowdsourced Cell Lists
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Solution Overview
Problem
User equipment (UE) may not be able to connect to a preferred radio access technology (RAT) due to incomplete neighboring cell lists received from cellular networks, leading to potential data throughput reduction and increased transmission latency.
Innovation Solution
A crowdsourced cell selection method where UE receives a crowdsourced cell list from a database generated from multiple neighboring cell lists, allowing it to identify and camp on cells associated with the highest preferred RAT, even if omitted from initial lists.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If UE uses neighboring cell lists received from cellular networks to select a cell to camp on, then the UE can perform cell selection based on available network information, but the UE may not be able to connect to preferred RATs due to incomplete cell lists
Solution Approach 1:
A crowdsourcing database is introduced as an intermediary system between cellular networks and UE. The database collects and stores comprehensive cell information from multiple networks, allowing UE to access complete cell lists without requiring direct support from individual networks for all RATs.
Solution Approach 2:
The crowdsourcing database pre-collects and stores cell information from multiple cellular networks in advance, so that when UE needs cell selection, the comprehensive data is already available without requiring real-time queries to all networks.
2Productivity
If UE selects a cell based on incomplete neighboring cell lists, then cell selection can be performed with available data, but data throughput is reduced and transmission latency increases
Solution Approach 1:
The crowdsourcing database acts as an intermediary that provides complete and accurate cell information, enabling UE to select optimal cells for preferred RATs, thereby improving data throughput and reducing transmission latency.
Solution Approach 2:
The patent replaces the traditional mechanical approach of relying on network-provided cell lists with a database-driven system that aggregates cell information from multiple sources, enabling more informed cell selection decisions.
3Adaptability or versatility
If network vendors do not upgrade RATs or networks do not support handoffs, then existing network infrastructure is maintained, but cells are omitted from neighboring cell lists and preferred RAT connections are lost
Solution Approach 1:
The crowdsourcing database serves as an intermediary that aggregates cell information from multiple networks with different RAT capabilities. This allows UE to access comprehensive cell lists including cells from networks that may not actively support handoffs or have outdated RATs, enabling UE to select optimal cells independently.
Solution Approach 2:
The system creates a copy of comprehensive cell information from multiple networks in the crowdsourcing database, allowing UE to access this replicated data without requiring real-time coordination with all individual networks.
Data Source
AI summary
A user equipment (UE) sends location information to a crowdsourcing server that is configured to generate crowdsourced cell lists from neighboring cell lists received from one or more cellular networks. After receiving the location information from the UE, the crowdsourcing server selects a crowdsourced cell list for transmission based on the location information and transmits the selected crowdsourced cell list to the UE. In response to receiving the crowdsourced cell list, the UE compares the crowdsourced cell list to one or more stored neighboring cell lists to determine if the crowdsourced cell list identifies any cells not identified in the neighboring cell lists. From the cells identified in the crowdsourced cell list but not in the neighboring cell lists, the UE selects a cell to camp on based on an RAT priority list and camps on the selected cell.