Multi-Stage Decoder Reconfiguration for BER and Power Tradeoffs
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Solution Overview
Problem
Communication systems face high computational complexity and power consumption in decoding processes, particularly in base stations, which is hardware-intensive and costly, especially as data rates increase from Mbps to gigabits, necessitating more efficient decoding techniques.
Innovation Solution
The method involves dynamically changing decoding parameters in an iterative multi-stage decoder by adjusting the number of computational units based on detected signal-to-noise ratios to maintain a specified bit error rate, using programmable circuits to reconfigure the decoder for varying signal conditions, and configuring decoders in base stations to achieve different minimum bit-error-rates for different service plans.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the number of decoding iterations is increased to maintain bit error rate, then decoding reliability is improved, but power consumption and computational complexity increase
Solution Approach 1:
The patent implements dynamic adaptation of decoding parameters by monitoring signal-to-noise ratio and adjusting the number of iterations, computational units, and processing stages in real-time. This allows the system to use more iterations only when signal quality degrades, rather than constantly operating at maximum complexity, thus resolving the contradiction between reliability and power consumption.
Solution Approach 2:
The system changes operational parameters (number of iterations, computational units per stage, processing stages) based on detected signal conditions. When signal-to-noise ratio is high, fewer iterations are used; when it degrades, more iterations are allocated, dynamically optimizing the balance between bit error rate performance and power consumption.
2Productivity
If more computational units are allocated to handle higher data rates, then productivity is improved, but device complexity increases
Solution Approach 1:
The decoder is divided into multiple processing stages with a configurable number of computational units in each stage. This segmentation allows the system to allocate computational resources across stages rather than using a monolithic complex structure, enabling scalable complexity adaptation to different data rate requirements.
Solution Approach 2:
The number of computational units in each processing stage is dynamically configurable based on signal-to-noise ratio and data rate requirements. This allows the system to scale complexity up or down as needed, rather than being fixed at maximum complexity, thus improving productivity without permanently increasing device complexity.
3Reliability
If the number of processing stages is increased to improve decoding accuracy, then reliability is improved, but use of energy increases
Solution Approach 1:
The system dynamically adjusts the number of processing stages activated based on signal-to-noise ratio detection. When signal quality is good, fewer stages are processed; when quality degrades, additional stages are engaged to maintain accuracy. This dynamic staging resolves the contradiction by making energy consumption proportional to actual decoding needs rather than always maximum.
4Ease of operation
If fixed decoding parameters are used to simplify system operation, then ease of operation is improved, but adaptability to varying signal conditions deteriorates
Solution Approach 1:
The decoder performs self-adjustment by automatically detecting signal-to-noise ratio and selecting appropriate decoding parameters (iterations, computational units, stages) without external intervention. This self-service capability maintains ease of operation while achieving high adaptability to varying signal conditions, as the system autonomously optimizes itself based on real-time measurements.
Solution Approach 2:
The system implements feedback loops where decoding performance and signal quality are continuously monitored, and parameter adjustments are made based on this feedback. This closed-loop control maintains simple operation for the user while achieving sophisticated adaptability through automatic parameter optimization based on detected conditions.
Data Source
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AI summary
A communication system includes an iterative multi-stage decoder (170) that may be dynamically configured to achieve a particular bit-error-rate. In one embodiment, a circuit comprises a first decoder block (158) and a second decoder block (159) to decode data received over a communication channel. A control circuit (171 ) may change a number of iterations performed by the decoder blocks (158, 159) to decode received data based on a specified bit error rate and a detected signal-to-noise ratio of the received data. The number of computational units (160, 161 ) used in the decoder blocks may be changed dynamically to achieve desired system performance. In one embodiment, resources are allocated based on a system initiating the connection. Programmable circuits are used in some embodiments to reconfigure the multi- stage decoder.