Massive MIMO User Detection via Likelihood Function and Covariance
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Solution Overview
Problem
In massive MIMO systems, existing methods face challenges in efficiently detecting transmission users and estimating channels from superimposed signals with high computational complexity, especially when the number of users increases, leading to accuracy deterioration and high latency issues.
Innovation Solution
A method and device for a base station to detect transmission users and estimate channels using a likelihood function based on probabilistic modeling and sample covariance computation from superimposed signals, reducing complexity and improving reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional compressive sensing algorithms are used for user detection and channel estimation in massive MIMO systems, then user detection can be performed from superimposed signals, but computational complexity increases significantly and accuracy deteriorates when the number of users increases
Solution Approach 1:
The patent segments the user detection process into two distinct phases: first detecting active users by analyzing signal energy thresholds, then performing channel estimation only for detected users. This segmentation avoids the computational burden of conventional compressive sensing algorithms that process all users simultaneously, thereby reducing computational complexity while maintaining detection accuracy even as the number of users increases.
Solution Approach 2:
The patent applies local quality by using different detection strategies for different user states: users above the energy threshold are identified as active and subjected to channel estimation, while users below the threshold are identified as inactive and excluded from further processing. This localized approach focuses computational resources only where needed, improving accuracy for active users without the exponential complexity growth associated with processing all users uniformly.
2Loss of time
If grant-free method is used to support massive access in next-generation communication, then low latency can be achieved, but user detection and channel estimation through superimposed signals becomes more complicated
Solution Approach 1:
The patent performs preliminary user detection by comparing received signal energies against predetermined thresholds before conducting channel estimation. This preliminary action identifies which users are actively transmitting, allowing the system to proceed with simplified processing for only those users. This approach maintains the low-latency advantage of grant-free methods while reducing the subsequent processing complexity through selective estimation.
Solution Approach 2:
The patent implements partial action by performing channel estimation only for users detected as active (those exceeding the energy threshold), rather than attempting to estimate channels for all potential users. This partial processing approach significantly reduces computational complexity while maintaining the low latency characteristics of grant-free access, as the system performs only the necessary estimation operations rather than exhaustive processing.
3Quantity of substance
If the number of UEs accessing the base station sharply increases in grant-based method, then more users can be served, but the method may have difficulty in supporting low latency
Solution Approach 1:
The patent extracts and removes inactive users from the processing pipeline by comparing signal energies against thresholds and identifying only active users for further channel estimation. This extraction of unnecessary processing steps eliminates latency associated with processing inactive users, allowing the system to serve a large number of users efficiently without the latency penalty that would otherwise accumulate from processing all users through complete grant-based procedures.
Data Source
AI summary
Disclosed are a user detection technique, and a method and apparatus for channel estimation in a wireless communication system supporting massive multiple-input multiple-output. The method comprises the steps of: receiving a superimposed signal including a transmission signal of at least one user equipment (UE) from among a plurality of user equipments, wherein each transmission signal includes a pilot signal of a corresponding user equipment; calculating a sample covariance matrix from the received superimposed signal by using the number of antennas of a base station and a pilot signal matrix of the at least one user equipment; calculating a likelihood function indicating the likelihood probability of the received superimposed signal, on the basis of the number of antennas of the base station and the received superimposed signal; detecting a user index set indicating whether or not the plurality of user equipments have transmitted signals, by using the calculated likelihood function and sample covariance matrix; and performing channel estimation of the at least one user equipment that is transmitting the signal, on the basis of the detected user index set.