Statistical Joint Precoding for Multi-Cell MU-MIMO Capacity
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In coordinated multi-point (CoMP) multi-user multiple-input multiple-output (MIMO) systems, the complexity of joint precoding approaches limits the achievement of maximum rates due to the need for comprehensive channel state information feedback and the increase in processing complexity, especially in centralized architectures where geographically distributed base stations jointly precode downlink transmissions.
Innovation Solution
The system employs comprehensive statistical channel feedback to determine optimal downlink input covariances by leveraging the duality between downlink and uplink MU-MIMO channels, using a network node with a communications interface, computation circuit, adjustment circuit, and joint precoding circuit to compute uplink input covariances that maximize capacity, while accounting for individual transmit power constraints and uplink bandwidth limitations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If comprehensive statistical channel feedback is used to determine optimal joint precoding, then the capacity of downlink MU-MIMO channels is maximized, but the uplink bandwidth for feedback increases and processing complexity increases
Solution Approach 1:
The patent applies duality theory to invert the downlink precoding problem into an equivalent uplink problem. By transforming the downlink MU-MIMO channel with joint precoding into a dual uplink MU-MIMO channel with equivalent capacity, the solution computes uplink input covariances that maximize the dual channel capacity, then maps these to optimal downlink input covariances. This inversion simplifies the non-convex downlink optimization into a convex uplink optimization problem, reducing processing complexity while achieving maximum capacity.
Solution Approach 2:
The patent changes the optimization parameters from downlink input covariances to uplink input covariances. By formulating the capacity maximization problem in terms of uplink input covariances subject to global transmit power constraints, the solution transforms a non-convex problem into a convex optimization problem. This parameter transformation enables the use of efficient convex optimization techniques to compute optimal joint precoding matrices.
2Productivity
If comprehensive statistical channel feedback is used to determine optimal joint precoding, then the capacity of downlink MU-MIMO channels is maximized, but the uplink bandwidth for feedback increases
Solution Approach 1:
The patent extracts only the essential statistical channel information needed for capacity optimization. Instead of requiring full channel state information matrices, the solution extracts and uses only the channel mean and covariance matrices at each mobile terminal. This extraction of minimal sufficient statistics reduces the feedback bandwidth requirement while maintaining the ability to compute optimal joint precoding that maximizes downlink capacity.
3Productivity
If joint precoding is implemented in centralized CoMP architecture, then achievable rates increase, but the complexity increases and maximum rates are not reached
Solution Approach 1:
The patent uses duality theory to invert the centralized joint precoding problem into an equivalent distributed uplink problem. By transforming the downlink joint precoding with geographically distributed base stations into a dual uplink problem with equivalent capacity, the solution enables each base station to independently compute local precoding matrices based on local channel statistics. This inversion maintains the rate benefits of centralized joint precoding while eliminating the complexity of centralized coordination.
Solution Approach 2:
The patent segments the joint precoding computation into independent local computations at each base station. By using the duality transformation, each base station can independently compute its local precoding matrix based on local channel mean and covariance information, without requiring centralized coordination. This segmentation of the precoding computation reduces overall system complexity while maintaining the achievable rates through the equivalence of downlink and uplink capacity regions.
Data Source
AI summary
A network node jointly precodes multi-user (MU) multiple-input multiple-output (MIMO) transmissions simultaneously sent from geographically distributed base stations to a plurality of mobile terminals over associated downlink MU-MIMO channels. The node receives feedback that describes statistics of the downlink MU-MIMO channels, including channel mean and covariance. The node then computes, based on the channel means and covariances, uplink input covariances for the mobile terminals that would collectively maximize a first or second-order approximation of the ergodic capacity of dual uplink MU-MIMO channels, subject to a global transmit power constraint that comprises the sum of individual transmit power constraints for the base stations. Notably, the node adjusts the uplink input covariances as needed to satisfy the individual transmit power constraints for the base stations, maps the uplink input covariances to corresponding downlink input covariances, and jointly precodes MU-MIMO transmissions sent over the downlink MU-MIMO channels based on those downlink input covariances.