MU-MIMO User Selection via Iterative Channel Correlation Exclusion
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Solution Overview
Problem
Current methods for selecting users in MU-MIMO communication face high computational complexity and suboptimal performance due to exhaustive search and predefined threshold-based approaches, especially in scenarios with highly correlated users, leading to reduced sum-rate and increased outage probability.
Innovation Solution
A method involving successive iterations to determine channel correlation metrics and exclude users based on these metrics, calculating performance metrics, and selecting users for MU-MIMO communication, which reduces computational complexity and eliminates the need for predefined thresholds, thereby improving performance and reducing outage probability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If exhaustive search is applied to find optimal user dropping strategy, then performance metric is improved, but computational complexity increases extremely high
Solution Approach 1:
The exhaustive search space of all possible user subset combinations is segmented into a sequential iterative process. Instead of evaluating all combinations simultaneously, the algorithm processes users one by one through successive iterations, determining channel correlation metrics and excluding users step-by-step until a stopping criterion is met, thereby dividing the computational task into manageable segments
Solution Approach 2:
The user exclusion criterion dynamically adapts during the iterative process based on calculated channel correlation metrics rather than using a fixed predefined threshold. The stopping criterion and exclusion decisions are adjusted in each iteration based on performance metric comparisons, allowing the system to optimize user selection dynamically rather than statically
2Device complexity
If predefined threshold-based user dropping is applied, then computational complexity is reduced, but performance metric becomes suboptimal
Solution Approach 1:
The fixed predefined threshold parameter is replaced with dynamically calculated channel correlation metrics that change based on actual channel conditions. The exclusion criterion evolves from a static threshold to adaptive metric-based decisions, allowing the system to respond to varying channel correlations and achieve near-optimal performance without exhaustive search
Solution Approach 2:
The algorithm incorporates feedback loops where performance metrics are calculated after each user exclusion, and these results feed back into subsequent exclusion decisions. The stopping criterion uses feedback from performance metric comparisons to determine when to terminate the iterative process, ensuring near-optimal performance while controlling computational complexity
Data Source
AI summary
A method is disclosed of selecting users for multi user multiple-input multiple-output (MU-MIMO) communication from an initial set of potential users. The method comprises repeating the following steps in successive iterations until a stopping criterion is met: determining a channel correlation metric for each user in the set of potential users, reducing the set of potential users by exclusion of a user based on the channel correlation metric, and calculating a performance metric of the set of potential users. The method also comprises selecting users corresponding to one of the sets of potential users, wherein the selection is based on a comparison of the calculated performance metrics of the sets of potential users. The channel correlation metric for a user may comprise one or more of: a channel filter norm for the user, a channel norm for the user, a channel gain for the user, pair-wise correlations between the user and one or more other users of the set of potential users, and a channel eigenvalue for the user. The performance metric may comprise