Fast MLE and MAPE-Based Device Activity Detection for Grant-Free Access via PSCA and PSCA-Net

2025-03-19

IEEE Transactions on Wireless Communications

AI Summary

To address the device activity detection problem in grant-free massive machine-type communication (mMTC) for 5G and beyond, this paper tackles two practical scenarios: known and unknown path loss. We propose a unified modeling framework based on maximum likelihood estimation (MLE) and maximum a posteriori estimation (MAPE). Notably, we formulate the first MAPE optimization model for the unknown-path-loss case. To solve it efficiently, we introduce the Parallel Successive Convex Approximation (PSCA) algorithm—leveraging second-order information, ensuring theoretical convergence, and achieving low per-iteration complexity. Furthermore, we design PSCA-Net, a deep-unfolding network that learns data-driven step sizes, seamlessly integrating algorithmic structure with neural network parallelism. Experiments demonstrate that our approach significantly outperforms state-of-the-art methods in both activity detection error rate and computational latency, achieving a joint breakthrough in accuracy and efficiency with strong potential for real-world deployment.

Technology Category

• Machine Learning: Optimization
• Search and Optimization: Mixed Discrete/Continuous Search
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Fast and accurate device activity detection is the critical challenge in grant-free access for supporting massive machine-type communications (mMTC) and ultra-reliable low-latency communications (URLLC) in 5G and beyond. The state-of-the-art methods have unsatisfactory error rates or computation times. To address these outstanding issues, we propose new maximum likelihood estimation (MLE) and maximum a posterior estimation (MAPE) based device activity detection methods for known and unknown pathloss that achieve superior error rate and computation time tradeoffs using optimization and deep learning techniques. Specifically, we investigate four non-convex optimization problems for MLE and MAPE in the two pathloss cases, with one MAPE problem being formulated for the first time. For each non-convex problem, we develop an innovative parallel iterative algorithm using the parallel successive convex approximation (PSCA) method. Each PSCA-based algorithm allows parallel computations, uses up to the objective function's second-order information, converges to the problem's stationary points, and has a low per-iteration computational complexity compared to the state-of-the-art algorithms. Then, for each PSCA-based iterative algorithm, we present a deep unrolling neural network implementation, called PSCA-Net, to further reduce the computation time. Each PSCA-Net elegantly marries the underlying PSCA-based algorithm's parallel computation mechanism with the parallelizable neural network architecture and effectively optimizes its step sizes based on vast data samples to speed up the convergence. Numerical results demonstrate that the proposed methods can significantly reduce the error rate and computation time compared to the state-of-the-art methods, revealing their significant values for grant-free access.

Problem

• Fast and accurate device activity detection in grant-free access.
• Addressing high error rates and long computation times in existing methods.
• Proposing MLE and MAPE-based methods with PSCA and PSCA-Net for optimization.

Innovation

• Uses PSCA for parallel iterative optimization
• Implements deep unrolling via PSCA-Net
• Optimizes MLE and MAPE for device detection