Multi-Polarized MIMO Codebook via Block Diagonal Precoding
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Solution Overview
Problem
Current MIMO systems with multi-polarized antennas lack effective codebooks, leading to increased correlation between wireless channels and reduced data transmission rates, as existing codebooks designed for single-polarized systems are not optimized for multi-polarized scenarios.
Innovation Solution
A method and apparatus for generating a codebook in multi-polarized MIMO systems using single-polarized precoding matrices, organized in a block diagonal structure, which can reconstruct precoding matrices according to transmission rank and adapt to changes in polarization direction, thereby reducing channel correlation and enhancing performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multi-polarized antennas are used to reduce antenna spacing and increase channel capacity, then channel capacity increases and correlation between wireless channels decreases, but effective codebooks for multi-polarized MIMO systems are lacking
Solution Approach 1:
The codebook is segmented into multiple sub-codebooks, each corresponding to different polarization configurations. This allows the system to handle multi-polarized MIMO by dividing the complex codebook design into manageable parts, where each sub-codebook can be optimized for specific polarization scenarios while collectively covering all multi-polarized cases.
Solution Approach 2:
The codebook design achieves universality by creating a unified structure that works across single-polarized, dual-polarized, and general multi-polarized MIMO scenarios. The codebook is designed to be adaptable to different polarization configurations through parameterization, allowing the same codebook framework to serve multiple polarization purposes without requiring separate dedicated codebooks for each case.
2Ease of manufacture
If existing single-polarized codebooks are used in multi-polarized MIMO systems, then implementation is simple, but the codebooks cannot be optimized for multi-polarized scenarios leading to reduced performance
Solution Approach 1:
The codebook design uses parameter changes to adapt from single-polarized to multi-polarized scenarios. By introducing polarization-related parameters and configuring the codebook structure according to different polarization states, the system maintains implementation simplicity while achieving optimization for multi-polarized scenarios. The codebook can be configured with different parameters to match various polarization configurations without requiring completely different codebook designs.
3Area of stationary object
If antenna spacing is reduced to save physical space, then area for installing antennas decreases, but correlation between wireless channels increases reducing reliability
Solution Approach 1:
The system transitions from spatial separation (one dimension) to polarization separation (another dimension) to reduce channel correlation. By utilizing multiple polarization directions, the system can achieve effective channel independence without requiring large physical spacing between antennas, thus solving the space vs. correlation trade-off by operating in the polarization dimension rather than solely relying on spatial dimension.
Data Source
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AI summary
A method of generating a codebook for a multiple-input multiple-output (MIMO) system is provided. The codebook generation method includes: assigning a single-polarized precoding matrix to diagonal blocks among a plurality of blocks arranged in a block diagonal format in which a number of diagonal blocks corresponds to a number of polarization directions of transmitting antennas; and assigning a zero matrix to remaining blocks excluding the diagonal blocks.