Trellis-Extended Codebook for MIMO Feedback Overhead Reduction
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Solution Overview
Problem
In 5G communication systems, acquiring and feeding back channel state information (CSI) in large scale MIMO systems is inefficient due to high computational complexity and increased feedback overhead, especially in frequency division duplexing (FDD) systems, where existing methods struggle to maintain beamforming gain with a large number of transmission antennas.
Innovation Solution
The use of a trellis-extended codebook (TEC) for trellis-coded quantization and trellis-extended successive phase adjustment (TE-SPA) scheme to reduce feedback overhead by truncating and phase-adjusting CSI, allowing for a smaller number of bits to be fed back during short times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional CSI quantization methods are used in large scale MIMO systems, then beamforming gain can be obtained, but feedback overhead increases significantly
Solution Approach 1:
The codebook is segmented into multiple subcodebooks, each corresponding to different channel conditions or antenna groups. This allows the receiver to select and feedback only the relevant subcodebook indices, significantly reducing the number of feedback bits while maintaining accurate channel representation for beamforming
Solution Approach 2:
The patent introduces a hierarchical codebook structure with multiple dimensions (e.g., group index, subcodebook index, vector index). By organizing codebooks in this multi-dimensional framework, the system achieves comprehensive channel coverage with fewer feedback bits compared to traditional flat codebook structures
2Manufacturing precision
If detailed channel state information is fed back to maintain quantization precision, then manufacturing precision of beamforming is improved, but loss of time increases due to longer feedback transmission
Solution Approach 1:
The patent extracts only the essential channel information characteristics by designing codebooks that capture dominant channel components. The receiver feeds back compressed channel representations through codebook indices rather than full channel matrices, achieving sufficient quantization precision with dramatically reduced feedback data volume and transmission time
3Reliability
If the number of transmission antennas is increased to support large scale MIMO, then beamforming gain is improved, but device complexity increases
Solution Approach 1:
The codebook is segmented into multiple subcodebooks, each corresponding to different channel conditions or antenna groups. This allows the receiver to select and feedback only the relevant subcodebook indices, significantly reducing the number of feedback bits while maintaining accurate channel representation for beamforming
Solution Approach 2:
The codebook structure is designed to be universal and configurable, supporting different numbers of transmission antennas and channel conditions through a unified framework. This multi-functionality allows the system to adapt to various large scale MIMO configurations without requiring separate complex processing for each scenario
Data Source
AI summary
The present disclosure relates to a pre-5th-Generation (5G) or 5G communication system to be provided for supporting higher data rates Beyond 4th-Generation (4G) communication system such as Long Term Evolution (LTE). Exemplary embodiment of the present invention provide a scheme for reducing feedback overhead when quantizing and feeding back channel state information in a MIMO system. A method for operating a receiver in a MIMO system, according to one embodiment of the present invention, includes the steps of: performing trellis coded quantization for channel information by using a codebook selected from a plurality of codebooks; and transmitting, to a transmitter, feedback information including the quantization result. The step of performing trellis-coded quantization for the channel information includes a step of truncating the channel information and codewords included in the selected codebook into multiple groups of channel vectors and multiple groups of codewords, respectively, and performing trellis-coded quantization for each of the groups of the channel vectors by using each of the groups of the codewords.