Sequential Decoder Stack Reordering for Lower-Complexity MIMO Decoding
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Solution Overview
Problem
Existing wireless communication systems face challenges in managing computational complexity and stack size limitations during sequential decoding, particularly with increasing constellation sizes and numbers of antennas, which hinders real-time and high-throughput applications.
Innovation Solution
A decoder system that includes a symbol estimation unit and a stack reordering activation monitoring unit, which reduces node metrics and reorders the stack to maintain only the nodes with the lowest metrics, allowing for efficient stack management and adaptation to available resources and desired convergence times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the stack size is increased to maintain more nodes during sequential decoding, then the decoding accuracy is improved, but the memory requirements and hardware complexity increase
Solution Approach 1:
The patent dynamically adjusts the stack size parameter based on channel conditions and decoding progress. The stack size is not fixed but adapts to maintain optimal decoding accuracy while minimizing memory usage. This resolves the contradiction by making the stack size a variable parameter rather than a static resource constraint.
Solution Approach 2:
The decoding process transitions from a static stack size approach to a dynamic one where the stack size changes during the decoding process. The system can expand the stack when needed for accuracy and contract it when resources are constrained, allowing the system to adapt to varying conditions and resolve the trade-off between accuracy and memory requirements.
2Productivity
If the constellation size or number of antennas is increased to improve communication capacity, then the throughput is improved, but the computational complexity of the decoder increases
Solution Approach 1:
The patent segments the decoding process into multiple stages or layers, handling different aspects of the decoding task separately. This segmentation allows the system to manage high-dimensional constellations and multiple antennas by breaking down the complex decoding problem into more manageable sub-problems, thereby reducing the overall computational complexity while maintaining high communication capacity.
Solution Approach 2:
The system performs partial decoding actions at different stages rather than attempting to decode all possibilities simultaneously. By performing only the necessary decoding operations at each stage and avoiding excessive computation, the system can handle larger constellation sizes and more antennas without proportionally increasing decoder complexity.
3Reliability
If the exhaustive search is performed to achieve optimal ML decoding, then the decoding performance is optimized, but the computational complexity becomes prohibitively high
Solution Approach 1:
The patent extracts and removes unnecessary search operations from the exhaustive ML decoding process. By identifying and eliminating redundant computations and focusing only on the most promising search paths, the system achieves near-optimal decoding performance without the prohibitively high computational complexity of complete exhaustive search.
Solution Approach 2:
The system performs preliminary actions to prune the search space before conducting the full ML decoding search. By pre-processing the received signal, pre-calculating metric values, or pre-identifying promising search paths, the system reduces the number of operations required during the actual decoding process, thereby achieving optimal performance with reduced computational complexity.
Data Source
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AI summary
There is provided a decoder (310) for sequentially decoding a data signal received through a transmission channel in a communication system, the received data signal carrying transmitted symbols, the decoder comprising a symbol estimation unit (311) configured to determine estimated symbols representative of the transmitted symbols carried by the received signal from information stored in a stack, the stack being filled by iteratively expanding child nodes of a selected node of a decoding tree comprising a plurality of nodes, each node of the decoding tree corresponding to a candidate component of a symbol of the received data signal and each node being associated with a predetermined metric, the stack being filled at each iteration with at least some of the expanded child nodes and being ordered by increasing values of the metrics associated with the nodes, the selected node for each iteration corresponding to the node having the lowest metric in the stack. The decoder further comprises a stack reordering activation monitoring unit (313) configured to monitor at least one stack reordering activation condition and, in response to a stack reordering activation condition being verified, to cause the symbol estimation unit to : - reduce the metric associated with each node stored in the stack by a quantity, - reorder the stack by increasing value of the reduced metric, and - remove a set of nodes from the reordered stack so as to maintain a number N of nodes in the reordered stack, the maintained nodes corresponding to the N nodes having the lowest metrics in the reordered stack.