Cross-Attention Transformer for Joint Multi-Receiver Uplink Neural Decoding

We propose a cross-attention Transformer for joint decoding of uplink OFDM signals received by multiple coordinated access points. A shared per-receiver encoder learns time-frequency structure within each received grid, and a token-wise cross-attention module fuses the receivers to produce soft log-likelihood ratios for a standard channel decoder, without requiring explicit per-receiver channel estimates. Trained with a bit-metric objective, the model adapts its fusion to per-receiver reliability, tolerates missing or degraded links, and remains robust when pilots are sparse. Across realistic Wi-Fi channels, it consistently outperforms classical pipelines and strong convolutional baselines, frequently matching (and in some cases surpassing) a powerful baseline that assumes perfect channel knowledge per access point. Despite its expressiveness, the architecture is compact, has low computational cost (low GFLOPs), and achieves low latency on GPUs, making it a practical building block for next-generation Wi-Fi receivers.

Lightweight Learning for Grant-Free Activity Detection in Cell-Free Massive MIMO Networks

Grant-free random access (GF-RA) is a promising access technique for massive machine-type communications (mMTC) in future wireless networks, particularly in the context of 5G and beyond (6G) systems. Within the context of GF-RA, this study investigates the efficiency of employing supervised machine learning techniques to tackle the challenges on the device activity detection (AD). GF-RA addresses scalability by employing non-orthogonal pilot sequences, which provides an efficient alternative comparing to conventional grant-based random access (GB-RA) technique that are constrained by the scarcity of orthogonal preamble resources. In this paper, we propose a novel lightweight data-driven algorithmic framework specifically designed for activity detection in GF-RA for mMTC in cell-free massive multiple-input multiple-output (CF-mMIMO) networks. We propose two distinct framework deployment strategies, centralized and decentralized, both tailored to streamline the proposed approach implementation across network infrastructures. Moreover, we introduce optimized post-detection methodologies complemented by a clustering stage to enhance overall detection performances. Our 3GPP-compliant simulations have validated that the proposed algorithm achieves state-of-the-art model-based activity detection accuracy while significantly reducing complexity. Achieving 99% accuracy, it demonstrates real-world viability and effectiveness.

Robust Learning-Based Sparse Recovery for Device Activity Detection in Grant-Free Random Access Cell-Free Massive MIMO: Enhancing Resilience to Impairments

Massive MIMO is considered a key enabler to support massive machine-type communication (mMTC). While massive access schemes have been extensively analyzed for co-located massive MIMO arrays, this paper explores activity detection in grant-free random access for mMTC within the context of cell-free massive MIMO systems, employing distributed antenna arrays. This sparse support recovery of device activity status is performed by a finite cluster of access points (APs) from a large number of geographically distributed APs collaborating to serve a larger number of devices. Active devices transmit non-orthogonal pilot sequences to APs, which forward the received signals to a central processing unit (CPU) for collaborative activity detection. This paper proposes a simple and efficient data-driven algorithm tailored for device activity detection, implemented centrally at the CPU. Furthermore, the study assesses the algorithm's robustness to input perturbations and examines the effects of adopting fixed-point representation on its performance.

Deep Learning-Based Approach for User Activity Detection with Grant-Free Random Access in Cell-Free Massive MIMO

Modern wireless networks must reliably support a wide array of connectivity demands, encompassing various user needs across diverse scenarios. Machine-Type Communication (mMTC) is pivotal in these networks, particularly given the challenges posed by massive connectivity and sporadic device activation patterns. Traditional grant-based random access (GB-RA) protocols face limitations due to constrained orthogonal preamble resources. In response, the adoption of grant-free random access (GF-RA) protocols offers a promising solution. This paper explores the application of supervised machine learning models to tackle activity detection issues in scenarios where non-orthogonal preamble design is considered. We introduce a data-driven algorithm specifically designed for user activity detection in Cell-Free Massive Multiple-Input Multiple-Output (CF-mMIMO) networks operating under GF-RA protocols. Additionally, this study presents a novel clustering strategy that simplifies and enhances activity detection accuracy, assesses the resilience of the algorithm to input perturbations, and investigates the effects of adopting floating-to-fixed-point conversion on algorithm performance. Simulations conducted adhere to 3GPP standards, ensuring accurate channel modeling, and employ a deep learning approach to boost the detection capabilities of mMTC GF-RA devices. The results are compelling: the algorithm achieves an exceptional 99\% accuracy rate, confirming its efficacy in real-world applications.