NB-IoT Predictive Reception for Battery Lifetime
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Solution Overview
Problem
NarrowBand Internet-of-Things (NB-IoT) devices experience permanent service outages in signal-challenging locations due to conservative redundancy allocation and lack of channel state feedback, leading to high power consumption and reduced battery life.
Innovation Solution
A method for predictive reception of physical layer downlink repetitions in NB-IoT devices, using a redundancy estimation function and repetition reception control function to estimate and adjust the number of sub-frame repetitions based on channel conditions and feedback, allowing for incremental redundancy and power-efficient decoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conservative redundancy allocation is used in NB-IoT downlink transmissions, then reliability is improved, but power consumption increases and battery life decreases
Solution Approach 1:
The patent applies dynamics by transitioning from static conservative redundancy allocation to dynamic adaptive redundancy allocation. The base station estimates channel quality indicators (CQI) and block error rates (BLER) in real-time, then dynamically adjusts the number of downlink repetitions and redundancy levels based on current channel conditions. This allows the system to use minimal redundancy when channel conditions are good (reducing power consumption) while maintaining high reliability when channel conditions deteriorate.
Solution Approach 2:
The patent implements feedback mechanisms where the UE measures downlink channel quality and provides CQI feedback to the base station. The base station uses this feedback to continuously adapt the redundancy allocation and repetition timing. This closed-loop feedback system enables optimal balance between reliability and power consumption by adjusting redundancy levels based on actual channel conditions rather than using fixed conservative values.
2Reliability
If the number of downlink repetitions is increased to ensure successful reception, then reliability is improved, but the time required for successful data reception increases
Solution Approach 1:
The patent applies preliminary action by having the base station predict the required number of repetitions and schedule them in advance based on estimated channel conditions and BLER targets. Instead of transmitting a fixed large number of repetitions regardless of actual channel quality, the system pre-calculates and schedules only the necessary number of repetitions, thereby reducing reception time while maintaining the required reliability level.
Solution Approach 2:
The patent changes the parameter of repetition count from a fixed conservative value to a dynamically adjusted value based on channel conditions. By modifying this parameter adaptively, the system achieves high successful reception probability when needed while minimizing reception time when channel conditions permit, thus resolving the contradiction between reliability and time loss.
3Device complexity
If fixed redundancy allocation is used without channel state feedback, then device complexity is reduced, but adaptability to different channel conditions deteriorates
Solution Approach 1:
The patent introduces an intermediary function at the base station that performs channel quality estimation, BLER calculation, and repetition scheduling based on CQI feedback from the UE. This intermediary processing at the network side enables adaptive redundancy allocation without requiring complex processing at the UE side, thus maintaining low device complexity while achieving high adaptability to different channel conditions.
Data Source
AI summary
The disclosure discloses a method for predictive reception of physical layer downlink repetitions in NB-IoT UE. The object of the disclosure to find a method that prolongs the battery lifetime of IoT devices will be achieved by a method for predictive reception of physical layer downlink repetitions in NB-IoT devices, the method comprising the following steps: estimating an expected number of repeated sub-frames required for a successful reception of a current encoded and in sub-frames rate-matched downlink transmission from a base station to an IoT device by applying a redundancy estimation function, using the estimated expected number of repeated sub-frames as input of a repetition reception control function, where a feedback-loop between the redundancy estimation function and the repetition reception control function is used for refining and adapting the predictive reception of physical layer downlink repetitions in NB-IoT.