MIMO-NOMA Precoding and Power Allocation for MSE Minimization
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Solution Overview
Problem
Current MIMO-NOMA systems do not effectively utilize spatial multiplexing and diversity gains, leading to suboptimal performance in power allocation and decoding.
Innovation Solution
A joint power allocation, precoding, and decoding method that decomposes MIMO-NOMA channels using block diagonalization and considers mean-squared error (MSE) performance to optimize power allocation factors and precoders, iteratively refining solutions to improve MSE performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If MIMO-NOMA system directly decomposes transmission channel into parallel SISO NOMA channels, then power domain multiplexing is achieved, but spatial multiplexing and diversity gains are not utilized
Solution Approach 1:
The patent segments the MIMO-NOMA system into K independent clusters, each containing two users (strong user and weak user). Within each cluster, the channel is decomposed into parallel SISO NOMA channels, enabling power domain multiplexing while maintaining spatial multiplexing through the multi-cluster structure. This segmentation allows independent optimization of power allocation and precoding for each cluster.
Solution Approach 2:
The patent transitions from a single SISO channel model to a multi-dimensional MIMO channel model by introducing spatial multiplexing across K clusters. Each cluster operates in a separate spatial dimension, and the base station employs precoding matrices to manage interference between dimensions. This dimensional expansion enables simultaneous utilization of power domain (NOMA) and spatial domain (MIMO) multiplexing gains.
2Reliability
If more transmission power is allocated to user signals with poor channel conditions, then successful decoding is achieved, but system overall performance is compromised
Solution Approach 1:
The patent applies local quality by differentiating power allocation based on user channel conditions within each cluster. The base station calculates channel gains for strong and weak users and allocates power accordingly: more power to weak users who require it for successful decoding, and less power to strong users. This localized power allocation strategy ensures decoding success for all users while optimizing overall system throughput.
Solution Approach 2:
The patent dynamically adjusts the power allocation factor αk for each cluster based on channel conditions. The base station optimizes αk to balance the trade-off between ensuring successful decoding for weak users and maintaining high throughput. By changing the power allocation parameter adaptively, the system achieves both reliable decoding and efficient resource utilization.
3Reliability
If block diagonalization precoding is used to cancel inter-cluster interference, then spatial multiplexing gain is achieved, but inter-cluster interference cancellation is incomplete
Solution Approach 1:
The patent employs preliminary action by using block diagonalization precoding to pre-cancel inter-cluster interference before the signals reach the users. The base station designs precoding matrices that nullify interference from other clusters in advance, creating cleaner signal channels for the users. This preliminary interference cancellation significantly improves the signal quality and reduces the MSE performance.
Solution Approach 2:
The patent implements feedback by iteratively optimizing the power allocation factor and precoding matrices based on channel state information. The base station receives feedback about channel conditions and adjusts the precoding and power allocation accordingly to maximize interference cancellation effectiveness. This feedback mechanism ensures continuous improvement of both interference cancellation and MSE performance.
Data Source
AI summary
This invention provides a joint power allocation, precoding, and decoding method and a base station thereof. They are applicable to multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) systems. The method includes: (1) decomposing the precoder for each cluster into a first precoder and a second precoder; (2) obtaining the mean-squared error (MSE) functions of the decoded signals for all user equipment devices in each cluster; (3) calculating the power allocation factors for each cluster in the case of minimizing the maximum of all the MSE functions in each cluster; and (4) obtaining the second precoder and the decoders for each cluster in the case of minimizing a sum of the MSE functions of the decoded signals for all user equipment devices in all clusters under a total power constraint according to the power allocation factors.