List Decoding List Size Adaptation for Wireless Receivers
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Solution Overview
Problem
Conventional methods for determining the list size for list decoding operations in wireless communications are resource intensive and inefficient, as they often require selecting a maximal list size that increases computational complexity, resource usage, and power consumption without necessarily improving communication reliability.
Innovation Solution
The method determines a minimal list size for list decoding operations based on the payload size and channel capacity metric of a received codeword, allowing for efficient resource allocation and reduced power consumption while maintaining communication reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a maximal list size is used for list decoding operations, then communication reliability is improved, but resource consumption and power usage increase
Solution Approach 1:
The patent dynamically adjusts the list size parameter based on channel conditions and payload characteristics. Instead of using a fixed maximal list size, the system calculates an optimal list size by considering channel capacity metrics and payload size, thereby reducing power consumption while maintaining adequate communication reliability through adaptive parameter optimization
Solution Approach 2:
The system transitions from a static maximal list size approach to a dynamic adaptation mechanism. The list size is continuously adjusted based on real-time channel conditions, payload size, and decoded block error rate feedback, allowing the system to optimize the balance between reliability and power consumption under varying operational conditions
2Reliability
If a maximal list size is used for list decoding operations, then communication reliability is improved, but computational complexity increases
Solution Approach 1:
The patent optimizes the list size parameter by calculating it based on channel capacity metrics and payload size rather than using a fixed maximal value. This parameter optimization reduces the number of decoding paths that need to be processed, thereby lowering computational complexity while maintaining sufficient communication reliability through mathematically determined optimal values
Solution Approach 2:
The system uses just enough list size to achieve acceptable block error rates rather than exhaustively exploring all possible decoding paths. By determining a minimal sufficient list size based on channel conditions and payload characteristics, the system avoids unnecessary computational overhead while maintaining adequate decoding performance
3Reliability
If a maximal list size is used for list decoding operations, then communication reliability is improved, but resource usage increases
Solution Approach 1:
The patent dynamically optimizes the list size parameter based on channel capacity metrics and payload size, reducing the quantity of computational resources required for decoding operations. By calculating an optimal list size that is sufficient for the given channel conditions rather than using a maximal fixed size, the system decreases memory usage, processing load, and overall resource consumption while maintaining communication reliability
4Use of energy by moving object
If a minimal list size is used for list decoding operations, then resource consumption is reduced, but block error rates increase
Solution Approach 1:
The system employs feedback mechanisms to monitor decoded block error rates and adjust the list size accordingly. By observing the actual performance under current channel conditions and payload characteristics, the system can refine its list size selection to achieve an optimal balance between power consumption and block error rate performance through iterative optimization
Solution Approach 2:
The patent performs preliminary calculations of channel capacity metrics and payload size analysis before executing the list decoding operation. This preliminary assessment allows the system to pre-determine an optimal list size that is sufficient for the specific decoding task, avoiding both excessive power consumption from overly large list sizes and excessive error rates from overly small list sizes
Data Source
AI summary
Certain aspects of the present disclosure provide techniques for Aspects of the present disclosure relate to wireless communications, and more particularly, to techniques for determining a minimal list size to use in list decoding operations for reducing resource consumption (e.g., compute, memory, and power) at a decoder. A method includes receiving a codeword comprising a plurality of channel bits encoded with an error-correcting code, the plurality of channel bits comprising, at least, a plurality of information bits, determining a payload size of the codeword, determining a channel capacity metric for the plurality of channel bits, determining a minimal list size for a list decoding operation based on at least the payload size and the channel capacity metric; and performing the list decoding operation on the codeword based on the minimal list size to obtain the plurality of information bits.