Training Sequence Code Signaling for MUROS Capacity
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Solution Overview
Problem
Current radio communication networks face challenges in increasing channel capacity, particularly due to asymmetrical data and voice traffic demands, leading to resource wastage and performance issues like call drops, especially during peak usage events.
Innovation Solution
The implementation of Downlink Advanced Receiver Performance (DARP) with Single-Antenna Interference Cancellation (SAIC) and Dual-Antenna Interference Cancellation (DAIC) techniques allows for efficient co-channel and adjacent-channel interference suppression, enabling multiple users to share the same channel by using different training sequences, thereby increasing network capacity without requiring additional hardware.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional channel allocation methods are used to handle asymmetrical data and voice traffic demands, then network resource allocation is simplified, but network capacity is limited and resources are wasted
Solution Approach 1:
The patent changes the parameter of training sequence code set assignment dynamically based on network conditions. By switching between first and second training sequence code sets depending on whether users are in coverage areas with different interference characteristics, the system optimizes network capacity without requiring complex hardware changes. This parameter change enables more flexible resource allocation that adapts to asymmetrical traffic demands.
2Productivity
If more time is allocated for data transmission, then data throughput increases, but time for scanning neighbor cells decreases causing call drops
Solution Approach 1:
The patent implements dynamic adjustment of training sequence code set assignment based on real-time network conditions and user mobility patterns. The system dynamically switches between different training sequence code sets to balance data transmission time and neighbor cell scanning time, thereby maintaining both high data throughput and call stability. This dynamic approach allows the system to adapt to changing traffic demands while ensuring reliable service.
3Productivity
If additional hardware is deployed to increase network capacity, then channel capacity increases, but hardware costs increase
Solution Approach 1:
The patent achieves increased channel capacity by changing operational parameters (training sequence code set assignment) rather than adding hardware. By intelligently assigning different training sequence code sets to different users based on their coverage area and interference characteristics, the system extracts more capacity from existing hardware resources, avoiding the need for additional network infrastructure.
4Productivity
If downlink resources are increased to serve more data users, then data service capacity increases, but uplink resources become wasted due to lack of pairing
Solution Approach 1:
The patent changes the assignment parameters of training sequence code sets to enable more flexible resource pairing. By assigning users to appropriate training sequence code sets based on network conditions, the system optimizes both downlink and uplink resource utilization, reducing waste while increasing data service capacity. This parameter-based allocation allows better matching of paired resources without requiring asymmetric hardware deployment.
Data Source
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AI summary
The present patent application improves DARP by allowing multiple users on one time slot (MUROS). It comprises means and instructions for signaling training sequence set information to a remote station, comprising receiving signaling from a remote station indicating if a new set of training sequences is supported, and using a channel description to signal the training sequence set to be used by the remote station for a communication channel being established.