Linear Combination Codebook Power Allocation for 5G MIMO
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Solution Overview
Problem
Current power allocation methods in 5G networks, such as using all power on the strongest layer or equally allocating power between layers, do not optimally exploit MIMO capacity and fail to adapt power distribution based on channel conditions.
Innovation Solution
A linear combination codebook framework is enhanced to allow unequal scaling between transmission layers, enabling power allocation to be adjusted based on channel quality, with a new codebook configuration using RRC signaling that allows more power to be allocated to layers with better channel conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If equal power allocation is used between layers, then implementation simplicity is maintained, but MIMO capacity is not optimally exploited
Solution Approach 1:
The patent implements dynamic power allocation by introducing power allocation coefficients (a1, a2, ..., aL) that can be adjusted based on channel conditions. The power allocated to each layer becomes dynamic rather than fixed, allowing the system to adapt to varying channel qualities and optimize MIMO capacity while maintaining manageable complexity through structured coefficient design.
Solution Approach 2:
The patent changes the power allocation parameter from equal distribution to unequal distribution using configurable coefficients. By modifying the power allocation parameters (a1, a2, ..., aL) where the sum equals 1, the system can optimize performance for different channel conditions without fundamentally changing the power allocation mechanism, thus balancing capacity improvement with implementation complexity.
2Use of energy by moving object
If all power is allocated to the strongest layer, then power utilization efficiency is improved, but adaptability to varying channel conditions deteriorates
Solution Approach 1:
The patent makes power allocation dynamic by introducing adjustable coefficients (a1, a2, ..., aL) that can be configured based on channel quality indicators (CQI). This allows the system to adapt power distribution to varying channel conditions while maintaining efficient power utilization, resolving the contradiction between fixed efficient allocation and adaptive flexibility.
Solution Approach 2:
The patent applies different power allocation coefficients to different layers based on their individual channel qualities. Each layer receives power according to its specific channel conditions rather than a uniform approach, allowing optimal power utilization for each layer's local characteristics while maintaining overall system adaptability.
3Productivity
If unequal power allocation based on channel conditions is implemented, then MIMO capacity is improved, but feedback overhead increases
Solution Approach 1:
The patent makes the power allocation coefficients (a1, a2, ..., aL) configurable and reusable across different channel conditions and scenarios. By establishing a universal feedback mechanism that can adapt to various CQI reports, the system achieves improved MIMO capacity without proportionally increasing feedback overhead, as the same coefficient structure serves multiple adaptation purposes.
Solution Approach 2:
The patent optimizes the balance between capacity improvement and feedback overhead by carefully designing the parameter set (a1, a2, ..., aL) to provide sufficient adaptability without excessive granularity. The parameters are configured to achieve the necessary power allocation flexibility while maintaining manageable feedback requirements through selective parameter configuration.
Data Source
AI summary
An enhanced linear combination codebook framework can support power allocation between transmission layers. Scaling between the layers of the codebook can be unequal so that power allocated between the layers can depend on the channel. For example, the network can configure the codebook to use radio resource control signaling to send codebook data to the UE.