MMSense: Adapting Vision-based Foundation Model for Multi-task Multi-modal Wireless Sensing

Large AI models have been widely adopted in wireless communications for channel modeling, beamforming, and resource optimization. However, most existing efforts remain limited to single-modality inputs and channel-specific objec- tives, overlooking the broader potential of large foundation models for unified wireless sensing. To bridge this gap, we propose MMSense, a multi-modal, multi-task foundation model that jointly addresses channel-centric, environment-aware, and human-centered sensing. Our framework integrates image, radar, LiDAR, and textual data by transforming them into vision- compatible representations, enabling effective cross-modal align- ment within a unified feature space. A modality gating mecha- nism adaptively fuses these representations, while a vision-based large language model backbone enables unified feature align- ment and instruction-driven task adaptation. Furthermore, task- specific sequential attention and uncertainty-based loss weighting mechanisms enhance cross-task generalization. Experiments on real wireless scenario datasets show that our approach outper- forms both task-specific and large-model baselines, confirming its strong generalization across heterogeneous sensing tasks.

Industrial Viewpoints on RAN Technologies for 6G

6G standardization is to start imminently, with commercial deployments expected before 2030. Its technical components and performance requirements are the focus of this article. Our emphasis is on the 6G radio access, especially MIMO, AI, waveforms, coding, signal constellations and integration with non-terrestrial networks. Whilst standardization has not yet formally started, the scope of the 6G study items has been defined. Our predictions in this paper are speculative as there are no results of the study yet, but our views are guided by implementation and deployment aspects. We expect that the views here will guide researchers and industry practitioners.

AI-RAN: Transforming RAN with AI-driven Computing Infrastructure

The radio access network (RAN) landscape is undergoing a transformative shift from traditional, communication-centric infrastructures towards converged compute-communication platforms. This article introduces AI-RAN which integrates both RAN and artificial intelligence (AI) workloads on the same infrastructure. By doing so, AI-RAN not only meets the performance demands of future networks but also improves asset utilization. We begin by examining how RANs have evolved beyond mobile broadband towards AI-RAN and articulating manifestations of AI-RAN into three forms: AI-for-RAN, AI-on-RAN, and AI-and-RAN. Next, we identify the key requirements and enablers for the convergence of communication and computing in AI-RAN. We then provide a reference architecture for advancing AI-RAN from concept to practice. To illustrate the practical potential of AI-RAN, we present a proof-of-concept that concurrently processes RAN and AI workloads utilizing NVIDIA Grace-Hopper GH200 servers. Finally, we conclude the article by outlining future work directions to guide further developments of AI-RAN.

Towards Energy Efficient RAN: From Industry Standards to Trending Practice

As 5G deployments continue throughout the world, concerns regarding its energy consumption have gained significant traction. This article focuses on radio access networks (RANs) which account for a major portion of the network energy use. Firstly, we introduce the state-of-the-art 3GPP and O-RAN standardization work on enhancing RAN energy efficiency. Then we highlight three unique ways for enabling energy optimization in telecommunication networks, including full stack acceleration, network functions consolidation, and shared infrastructure between communication and artificial intelligence. These network design strategies not only allow for considerable overall reduction in the energy footprint, but also deliver several added benefits including improved throughput, reduced cost of ownership, and increased revenue opportunities for telcos.

The Bridge Toward 6G: 5G-Advanced Evolution in 3GPP Release 19

The 3rd generation partnership project (3GPP) initiated 5G-Advanced in Release 18, laying a solid foundation for the further evolution of 5G-Advanced. Release 19-the next wave of 5G-Advanced-will primarily focus on commercial deployment needs while serving as a bridge toward 6G. In this article, we provide an in-depth overview of the 5G-Advanced evolution in 3GPP Release 19. We not only delve into the key technology components and their corresponding use cases in 5G-Advanced evolution but also shed light on initial 3GPP studies toward 6G.

Deep Learning for Joint Design of Pilot, Channel Feedback, and Hybrid Beamforming in FDD Massive MIMO-OFDM Systems

This letter considers the transceiver design in frequency division duplex (FDD) massive multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems for high-quality data transmission. We propose a novel deep learning based framework where the procedures of pilot design, channel feedback, and hybrid beamforming are realized by carefully crafted deep neural networks. All the considered modules are jointly learned in an end-to-end manner, and a graph neural network is adopted to effectively capture interactions between beamformers based on the built graphical representation. Numerical results validate the effectiveness of our method.

An Overview of the 3GPP Study on Artificial Intelligence for 5G New Radio

Air interface is a fundamental component within any wireless communication system. In Release 18, the 3rd Generation Partnership Project (3GPP) delves into the possibilities of leveraging artificial intelligence (AI)/machine learning (ML) to improve the performance of the fifth-generation (5G) New Radio (NR) air interface. This endeavor marks a pioneering stride within 3GPP's journey in shaping wireless communication standards. This article offers a comprehensive overview of the pivotal themes explored by 3GPP in this domain. Encompassing a general framework for AI/ML and specific use cases such as channel state information feedback, beam management, and positioning, it provides a holistic perspective. Moreover, we highlight the potential trajectory of AI/ML for the NR air interface in 3GPP Release 19, a pathway that paves the journey towards the sixth generation (6G) wireless communication systems that will feature integrated AI and communication as a key usage scenario.

Hardware Acceleration for Open Radio Access Networks: A Contemporary Overview

Radio access networks (RAN) are going through a paradigm shift towards interoperable, intelligent, software-defined, and cloud-native open RAN solutions. A key challenge towards the adoption and deployment of open RAN at scale is performance. Hence, it is critical to leverage the power of hardware acceleration to offload compute-heavy RAN workloads to specialized hardware devices to enable accelerated compute for open RAN deployments. In this article, we provide a state-of-the-art overview of hardware acceleration for open RAN in the fifth generation (5G) wireless networks. We also present a practical implementation of inline hardware acceleration for open RAN layer 1 processing and identify several areas for future exploration towards the sixth generation (6G) wireless networks.

Deep Reinforcement Learning for Interference Management in UAV-based 3D Networks: Potentials and Challenges

Modern cellular networks are multi-cell and use universal frequency reuse to maximize spectral efficiency. This results in high inter-cell interference. This problem is growing as cellular networks become three-dimensional with the adoption of unmanned aerial vehicles (UAVs). This is because the strength and number of interference links rapidly increase due to the line-of-sight channels in UAV communications. Existing interference management solutions need each transmitter to know the channel information of interfering signals, rendering them impractical due to excessive signaling overhead. In this paper, we propose leveraging deep reinforcement learning for interference management to tackle this shortcoming. In particular, we show that interference can still be effectively mitigated even without knowing its channel information. We then discuss novel approaches to scale the algorithms with linear/sublinear complexity and decentralize them using multi-agent reinforcement learning. By harnessing interference, the proposed solutions enable the continued growth of civilian UAVs.

Artificial Intelligence in 3GPP 5G-Advanced: A Survey

Industries worldwide are being transformed by artificial intelligence (AI), and the telecom industry is no different. Standardization is critical for industry alignment to achieve widespread adoption of AI in telecom. The 3rd generation partnership project (3GPP) Release 18 is the first release of 5G-Advanced, which includes a diverse set of study and work items dedicated to AI. This article provides a holistic overview of the state of the art in the 3GPP work on AI in 5G-Advanced, by presenting the various 3GPP Release-18 activities on AI as an organic whole, explaining in detail the design aspects, and sharing various design rationales influencing standardization.