# Activity Detection for Massive Connectivity in Cell-free Networks with Unknown Large-scale Fading, Channel Statistics, Noise Variance, and Activity Probability: A Bayesian Approach

arXiv: https://arxiv.org/abs/2401.16775 · HTML: https://mlsanity.com/paper/2401.16775/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 52 references that are in the map.
- First posted on arXiv 30 Jan 2024.

## Builds on (most cited first, 12 of 12)

- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [Compressive Sensing Based Adaptive Active User Detection and Channel Estimation: Massive Access Meets Massive MIMO](https://mlsanity.com/paper/1906.09867/) (2019)
- [Heterogeneous Transformer: A Scale Adaptable Neural Network Architecture for Device Activity Detection](https://mlsanity.com/paper/2112.10086/) (2021)
- [Towards Flexible Sparsity-Aware Modeling: Automatic Tensor Rank Learning Using The Generalized Hyperbolic Prior](https://mlsanity.com/paper/2009.02472/) (2020)
- [Ubiquitous Cell-Free Massive MIMO Communications](https://mlsanity.com/paper/1804.03421/) (2018)
- [Clustering-Based Activity Detection Algorithms for Grant-Free Random Access in Cell-Free Massive MIMO](https://mlsanity.com/paper/2111.15378/) (2021)
- [Joint Activity Detection and Channel Estimation in Cell-Free Massive MIMO Networks with Massive Connectivity](https://mlsanity.com/paper/2112.13013/) (2021)
- [Asynchronous Activity Detection for Cell-Free Massive MIMO: From Centralized to Distributed Algorithms](https://mlsanity.com/paper/2210.00451/) (2022)
- [Sparsity Learning Based Multiuser Detection in Grant-Free Massive-Device Multiple Access](https://mlsanity.com/paper/1807.10911/) (2018)
- [EP-based Joint Active User Detection and Channel Estimation for Massive Machine-Type Communications](https://mlsanity.com/paper/1810.02026/) (2018)
- [Covariance-Based Joint Device Activity and Delay Detection in Asynchronous mMTC](https://mlsanity.com/paper/2110.05815/) (2021)
- [Activity Detection in Distributed MIMO: Distributed AMP via Likelihood Ratio Fusion](https://mlsanity.com/paper/2208.03070/) (2022)

## Built on by (most cited first, 3 of 3)

- [Bayesian-based Online Label Shift Estimation with Dynamic Dirichlet Priors](https://mlsanity.com/paper/2511.18615/) (2025)
- [Mixture of Experts-augmented Deep Unfolding for Activity Detection in IRS-aided Systems](https://mlsanity.com/paper/2502.20183/) (2025)
- [Fast MLE and MAPE-Based Device Activity Detection for Grant-Free Access via PSCA and PSCA-Net](https://mlsanity.com/paper/2503.15259/) (2025)

## Abstract (arXiv)

Activity detection is an important task in the next generation grant-free multiple access. While there are a number of existing algorithms designed for this purpose, they mostly require precise information about the network, such as large-scale fading coefficients, small-scale fading channel statistics, noise variance at the access points, and user activity probability. Acquiring these information would take a significant overhead and their estimated values might not be accurate. This problem is even more severe in cell-free networks as there are many of these parameters to be acquired. Therefore, this paper sets out to investigate the activity detection problem without the above-mentioned information. In order to handle so many unknown parameters, this paper employs the Bayesian approach, where the unknown variables are endowed with prior distributions which effectively act as regularizations. Together with the likelihood function, a maximum a posteriori (MAP) estimator and a variational inference algorithm are derived. Extensive simulations demonstrate that the proposed methods, even without the knowledge of these system parameters, perform better than existing state-of-the-art methods, such as covariance-based and approximate message passing methods.