Mapped ADMM: A Robust Algorithm for 1-Bit mMIMO Detection
2026-07-22 • Information Theory
Information Theory
AI summaryⓘ
The authors explored a way to improve signal detection in one-bit massive MIMO systems by treating the problem like a type of machine learning called support vector machines (SVM). They split the SVM into smaller parts that work together using a method called CADMM, which helps the parts reach agreement and makes the system more reliable. They also upgraded CADMM so that its results correspond to valid signal values, improving performance. By adjusting how they group these smaller classifiers, the authors found a good balance between accuracy and reliability. Overall, their approach performed better than other current methods for this detection task.
one-bit massive MIMOsignal detectionsupport vector machinebinary classificationdecentralized learningconsensus alternating direction method of multipliersCADMMclassifier groupingconstellation pointsrobustness
Authors
Mohammad Amin Keshmiri, Masoud Ardakani
Abstract
Recently, it has been reported that one-bit massive MIMO (mMIMO) detection is equivalent to a binary classification problem that can be solved efficiently using support vector machine (SVM). Inspired by this result, we first reformulate SVM in a decentralized form consisting of multiple classifiers. This enables the use of the consensus alternating direction method of multipliers (CADMM), a technique that can improve robustness and performance through its inherent consensus making. We further update CADMM to output only valid constellation points and achieve significantly improved detection performance. In our method, by changing the size of the grouped classifiers, we balance the number of classifiers for consensus accuracy with sufficient data per group to ensure classifier robustness. Ultimately, we demonstrate that our proposed method significantly outperforms existing practically feasible methods for one-bit mMIMO detection.