Evolution of UE in Massive MIMO Systems for 6G: From Passive to Active

2026-01-01

arXiv.org

AI Summary

This work addresses the fundamental limitations of conventional centralized gNB-based massive MIMO architectures in meeting the stringent latency, reliability, and highly dynamic channel requirements of 6G. To overcome these challenges, the paper proposes redefining the user equipment (UE) from a passive transceiver into an intelligent entity actively participating in system optimization. It systematically reviews the evolution of UE capabilities across 3GPP Releases 15–19 and integrates key enabling technologies, including AI/ML-enhanced CSI feedback, UE-autonomous beam management, multi-panel UE (MPUE) architectures, and terminal energy efficiency optimization. Validation on a digital twin platform demonstrates that the proposed paradigm significantly enhances throughput and link robustness, transcending the constraints of network-centric designs and laying the groundwork for distributed intelligent access in 6G.

Technology Category

• Intelligent Robots: Embodied AI
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Humans and AI: Intelligent User Interfaces

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search

Abstract

As wireless networks continue to evolve, stringent latency and reliability requirements and highly dynamic channels expose fundamental limitations of gNB-centric massive multiple-input multiple-output (mMIMO) architectures, motivating a rethinking of the user equipment (UE) role. In response, the UE is transitioning from a passive transceiver into an active entity that directly contributes to system-level performance. In this context, this article examines the evolving role of the UE in mMIMO systems during the transition from fifth-generation (5G) to sixth-generation (6G), bridging third generation partnership project (3GPP) standardization, device implementation, and architectural innovation. Through a chronological review of 3GPP Releases 15 to 19, we highlight the progression of UE functionalities from basic channel state information (CSI) reporting to artificial intelligence (AI) and machine learning (ML)-based CSI enhancement and UE-initiated beam management. We further examine key implementation challenges, including multi-panel UE (MPUE) architectures, on-device intelligent processing, and energy-efficient operation, and then discuss corresponding architectural innovations under practical constraints. Using digital-twin-based evaluations, we validate the impact of emerging UE-centric functionalities, illustrating that UE-initiated beam reporting improves throughput in realistic mobility scenarios, while a multi-panel architecture enhances link robustness compared with a single-panel UE.

Problem

• massive MIMO
• user equipment
• 6G
• beam management
• channel state information

Innovation

• active UE
• AI/ML-based CSI
• UE-initiated beam management
• multi-panel UE
• digital twin