NB-IoT NTN RACH Backoff Using RAR Sniffing and RAPID Thresholds
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Solution Overview
Problem
Existing RACH procedures in non-terrestrial networks (NTNs) for narrowband Internet of Things (NB-IoT) deployments suffer from high failure rates and inefficiencies, leading to increased delays and power consumption due to network congestion and deep fading.
Innovation Solution
Implementing optimized RACH procedures that include RAR sniffing for network load detection, applying backoff durations based on RAPID thresholds, monitoring user-specific search spaces for uplink grants, and adjusting transmission strategies to minimize collisions and failures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional RACH procedures are used in NB-IoT NTN GSO, then network coverage is provided, but high failure rates occur due to network congestion and deep fading
Solution Approach 1:
The UE performs RAR sniffing to detect the number of RAPIDs in advance before initiating RACH procedures. This preliminary detection allows the UE to assess network load conditions and make informed decisions about whether to proceed with RACH attempts, thereby avoiding congestion-related failures
Solution Approach 2:
The system implements a feedback mechanism where the UE monitors RAR messages to count RAPIDs, uses this information to determine network load, and adjusts its RACH transmission behavior accordingly. This closed-loop feedback enables dynamic adaptation to changing network conditions, improving success rates
2Reliability
If multiple RACH attempts are made to overcome failures, then connection establishment probability increases, but transmission delays increase
Solution Approach 1:
The UE performs RAR sniffing and RAPID counting before initiating RACH procedures to predict network load conditions. This preliminary assessment prevents unnecessary RACH attempts in congested conditions, reducing delays while maintaining connection establishment probability by only attempting when conditions are favorable
Solution Approach 2:
The system dynamically adjusts RACH transmission behavior based on real-time network load assessment. The UE can switch between attempting RACH procedures and deferring transmissions based on RAPID counts, creating a dynamic adaptation mechanism that optimizes both success probability and delay performance
3Reliability
If RACH procedures are repeated to handle deep fading, then connection reliability improves, but power consumption increases
Solution Approach 1:
The UE performs RAR sniffing to detect network load and RAPID counts before initiating power-intensive RACH transmissions. This preliminary detection prevents wasteful power consumption by avoiding RACH attempts when network conditions indicate high likelihood of failure due to congestion
Solution Approach 2:
The system enables the UE to self-assess network conditions through RAR sniffing and autonomously decide whether to proceed with RACH procedures. This self-service mechanism allows the UE to avoid unnecessary power consumption by making informed decisions based on detected RAPID counts and network load conditions
Data Source
AI summary
This disclosure provides methods, components, devices and systems for optimized RACH procedures in NTNs including NB-IoT. A UE performs in an NTN one or more of a first or second procedure in an idle mode, or a third procedure in a connected mode. In the first procedure, the UE considers a number of RAPIDs in msg2, and if the number meets a threshold, the UE applies a backoff to delay sending msg1. In the second procedure, if the UE fails to decode msg2 or msg4 after transmitting msg1, the UE applies a backoff before performing subsequent RACH. In the third procedure, following a failure to receive an uplink grant when monitoring a USS and after transmitting msg1 for a SR, the UE continues monitoring the USS for the grant until the start of a RAR window. If the uplink grant is received during this time, the UE cancels msg2 monitoring.