AI for CSI Prediction in 5G-Advanced and Beyond

Artificial intelligence (AI) is pivotal in advancing fifth-generation (5G)-Advanced and sixth-generation systems, capturing substantial research interest. Both the 3rd Generation Partnership Project (3GPP) and leading corporations champion AI's standardization in wireless communication. This piece delves into AI's role in channel state information (CSI) prediction, a sub-use case acknowledged in 5G-Advanced by the 3GPP. We offer an exhaustive survey of AI-driven CSI prediction, highlighting crucial elements like accuracy, generalization, and complexity. Further, we touch on the practical side of model management, encompassing training, monitoring, and data gathering. Moreover, we explore prospects for CSI prediction in future wireless communication systems, entailing integrated design with feedback, multitasking synergy, and predictions in rapid scenarios. This article seeks to be a touchstone for subsequent research in this burgeoning domain.

AdapCsiNet: Environment-Adaptive CSI Feedback via Scene Graph-Aided Deep Learning

Accurate channel state information (CSI) is critical for realizing the full potential of multiple-antenna wireless communication systems. While deep learning (DL)-based CSI feedback methods have shown promise in reducing feedback overhead, their generalization capability across varying propagation environments remains limited due to their data-driven nature. Existing solutions based on online training improve adaptability but impose significant overhead in terms of data collection and computational resources. In this work, we propose AdapCsiNet, an environment-adaptive DL-based CSI feedback framework that eliminates the need for online training. By integrating environmental information -- represented as a scene graph -- into a hypernetwork-guided CSI reconstruction process, AdapCsiNet dynamically adapts to diverse channel conditions. A two-step training strategy is introduced to ensure baseline reconstruction performance and effective environment-aware adaptation. Simulation results demonstrate that AdapCsiNet achieves up to 46.4% improvement in CSI reconstruction accuracy and matches the performance of online learning methods without incurring additional runtime overhead.

Prompt-Enabled Large AI Models for CSI Feedback

Artificial intelligence (AI) has emerged as a promising tool for channel state information (CSI) feedback. While recent research primarily focuses on improving feedback accuracy through novel architectures, the underlying mechanisms of AI-based CSI feedback remain unclear. This study investigates these mechanisms by analyzing performance across diverse datasets and reveals that superior feedback performance stems from the strong fitting capabilities of AI models and their ability to leverage environmental knowledge. Building on these findings, we propose a prompt-enabled large AI model (LAM) for CSI feedback. The LAM employs powerful transformer blocks and is trained on extensive datasets from various scenarios. To further enhance reconstruction quality, the channel distribution -- represented as the mean of channel magnitude in the angular domain -- is incorporated as a prompt within the decoder. Simulation results confirm that the proposed prompt-enabled LAM significantly improves feedback accuracy and generalization performance while reducing data collection requirements in new scenarios.

Efficient Deep Learning-based Cascaded Channel Feedback in RIS-Assisted Communications

In the realm of reconfigurable intelligent surface (RIS)-assisted communication systems, the connection between a base station (BS) and user equipment (UE) is formed by a cascaded channel, merging the BS-RIS and RIS-UE channels. Due to the fixed positioning of the BS and RIS and the mobility of UE, these two channels generally exhibit different time-varying characteristics, which are challenging to identify and exploit for feedback overhead reduction, given the separate channel estimation difficulty. To address this challenge, this letter introduces an innovative deep learning-based framework tailored for cascaded channel feedback, ingeniously capturing the intrinsic time variation in the cascaded channel. When an entire cascaded channel has been sent to the BS, this framework advocates the feedback of an efficient representation of this variation within a subsequent period through an extraction-compression scheme. This scheme involves RIS unit-grained channel variation extraction, followed by autoencoder-based deep compression to enhance compactness. Numerical simulations confirm that this feedback framework significantly reduces both the feedback and computational burdens.

Deep Learning-based CSI Feedback in Wi-Fi Systems

In Wi-Fi systems, channel state information (CSI) plays a crucial role in enabling access points to execute beamforming operations. However, the feedback overhead associated with CSI significantly hampers the throughput improvements. Recent advancements in deep learning (DL) have transformed the approach to CSI feedback in cellular systems. Drawing inspiration from the successes witnessed in the realm of mobile communications, this paper introduces a DL-based CSI feedback framework, named EFNet, tailored for Wi-Fi systems. The proposed framework leverages an autoencoder to achieve precise feedback with minimal overhead. The process involves the station utilizing the encoder to compress and quantize a series of matrices into codeword bit streams, which are then fed back to the access point. Subsequently, the decoder installed at the AP reconstructs beamforming matrices from these bit streams. We implement the EFNet system using standard Wi-Fi equipment operating in the 2.4 GHz band. Experimental findings in an office environment reveal a remarkable 80.77% reduction in feedback overhead compared to the 802.11ac standard, alongside a significant boost in net throughput of up to 30.72%.

Fluid Antenna for Mobile Edge Computing

In the evolving environment of mobile edge computing (MEC), optimizing system performance to meet the growing demand for low-latency computing services is a top priority. Integrating fluidic antenna (FA) technology into MEC networks provides a new approach to address this challenge. This letter proposes an FA-enabled MEC scheme that aims to minimize the total system delay by leveraging the mobility of FA to enhance channel conditions and improve computational offloading efficiency. By establishing an optimization problem focusing on the joint optimization of computation offloading and antenna positioning, we introduce an alternating iterative algorithm based on the interior point method and particle swarm optimization (IPPSO). Numerical results demonstrate the advantages of our proposed scheme compared to traditional fixed antenna positions, showing significant improvements in transmission rates and reductions in delays. The proposed IPPSO algorithm exhibits robust convergence properties, further validating the effectiveness of our method.

Achievable Rate Analysis and Optimization of Double-RIS Assisted Spatially Correlated MIMO with Statistical CSI

Reconfigurable intelligent surface (RIS) is a novel meta-material which can form a smart radio environment by dynamically altering reflection directions of the impinging electromagnetic waves. In the prior literature, the inter-RIS links which also contribute to the performance of the whole system are usually neglected when multiple RISs are deployed. In this paper we investigate a general double-RIS assisted multiple-input multiple-output (MIMO) wireless communication system under spatially correlated non line-of-sight propagation channels, where the cooperation of the double RISs is also considered. The design objective is to maximize the achievable ergodic rate based on full statistical channel state information (CSI). Specifically, we firstly present a closed-form asymptotic expression for the achievable ergodic rate by utilizing replica method from statistical physics. Then a full statistical CSI-enabled optimal design is proposed which avoids high pilot training overhead compared to instantaneous CSI-enabled design. To further reduce the signal processing overhead and lower the complexity for practical realization, a common-phase scheme is proposed to design the double RISs. Simulation results show that the derived asymptotic ergodic rate is quite accurate even for small-sized antenna arrays. And the proposed optimization algorithm can achieve substantial gain at the expense of a low overhead and complexity. Furthermore, the cooperative double-RIS assisted MIMO framework is proven to achieve superior ergodic rate performance and high communication reliability under harsh propagation environment.

Auto-CsiNet: Scenario-customized Automatic Neural Network Architecture Generation for Massive MIMO CSI Feedback

Deep learning has revolutionized the design of the channel state information (CSI) feedback module in wireless communications. However, designing the optimal neural network (NN) architecture for CSI feedback can be a laborious and time-consuming process. Manual design can be prohibitively expensive for customizing NNs to different scenarios. This paper proposes using neural architecture search (NAS) to automate the generation of scenario-customized CSI feedback NN architectures, thereby maximizing the potential of deep learning in exclusive environments. By employing automated machine learning and gradient-descent-based NAS, an efficient and cost-effective architecture design process is achieved. The proposed approach leverages implicit scene knowledge, integrating it into the scenario customization process in a data-driven manner, and fully exploits the potential of deep learning for each specific scenario. To address the issue of excessive search, early stopping and elastic selection mechanisms are employed, enhancing the efficiency of the proposed scheme. The experimental results demonstrate that the automatically generated architecture, known as Auto-CsiNet, outperforms manually-designed models in both reconstruction performance (achieving approximately a 14% improvement) and complexity (reducing it by approximately 50%). Furthermore, the paper analyzes the impact of the scenario on the NN architecture and its capacity.

Knowledge-driven Meta-learning for CSI Feedback

Accurate and effective channel state information (CSI) feedback is a key technology for massive multiple-input and multiple-output systems. Recently, deep learning (DL) has been introduced for CSI feedback enhancement through massive collected training data and lengthy training time, which is quite costly and impractical for realistic deployment. In this article, a knowledge-driven meta-learning approach is proposed, where the DL model initialized by the meta model obtained from meta training phase is able to achieve rapid convergence when facing a new scenario during target retraining phase. Specifically, instead of training with massive data collected from various scenarios, the meta task environment is constructed based on the intrinsic knowledge of spatial-frequency characteristics of CSI for meta training. Moreover, the target task dataset is also augmented by exploiting the knowledge of statistical characteristics of wireless channel, so that the DL model can achieve higher performance with small actually collected dataset and short training time. In addition, we provide analyses of rationale for the improvement yielded by the knowledge in both phases. Simulation results demonstrate the superiority of the proposed approach from the perspective of feedback performance and convergence speed.

Demo: Reconfigurable Distributed Antennas and Reflecting Surface (RDARS)-aided Integrated Sensing and Communication System

Integrated sensing and communication (ISAC) system has been envisioned as a promising technology to be applied in future applications requiring both communication and high-accuracy sensing. Different from most research focusing on theoretical analysis and optimization in the area of ISAC, we implement a reconfigurable distributed antennas and reflecting surfaces (RDARS)-aided ISAC system prototype to achieve the dual-functionalities with the communication signal. A RDARS, composed of programmable elements capable of switching between reflection mode and connected mode, is introduced to assist in uplink signal transmission and sensing. The developed RDARS-aided ISAC prototype achieves reliable user localization without compromising the communication rate, showcasing its potential for future 6G systems.