Green Learning for STAR-RIS mmWave Systems with Implicit CSI

• URL: http://arxiv.org/abs/2509.06820v1
• Date: Mon, 08 Sep 2025 15:56:06 GMT
• Title: Green Learning for STAR-RIS mmWave Systems with Implicit CSI
• Authors: Yu-Hsiang Huang, Po-Heng Chou, Wan-Jen Huang, Walid Saad, C. -C. Jay Kuo,
• Abstract summary: Green learning (GL)-based precoding framework is proposed for simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-aided millimeter-wave (mmWave) broadcasting systems.<n>Motivated by the emphasis on environmental sustainability in future 6G networks, this work adopts a transmission framework for scenarios where multiple users share identical information, improving spectral efficiency and reducing redundant transmissions and power consumption.
• Score: 53.03358325565645
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, a green learning (GL)-based precoding framework is proposed for simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-aided millimeter-wave (mmWave) MIMO broadcasting systems. Motivated by the growing emphasis on environmental sustainability in future 6G networks, this work adopts a broadcasting transmission architecture for scenarios where multiple users share identical information, improving spectral efficiency and reducing redundant transmissions and power consumption. Different from conventional optimization methods, such as block coordinate descent (BCD) that require perfect channel state information (CSI) and iterative computation, the proposed GL framework operates directly on received uplink pilot signals without explicit CSI estimation. Unlike deep learning (DL) approaches that require CSI-based labels for training, the proposed GL approach also avoids deep neural networks and backpropagation, leading to a more lightweight design. Although the proposed GL framework is trained with supervision generated by BCD under full CSI, inference is performed in a fully CSI-free manner. The proposed GL integrates subspace approximation with adjusted bias (Saab), relevant feature test (RFT)-based supervised feature selection, and eXtreme gradient boosting (XGBoost)-based decision learning to jointly predict the STAR-RIS coefficients and transmit precoder. Simulation results show that the proposed GL approach achieves competitive spectral efficiency compared to BCD and DL-based models, while reducing floating-point operations (FLOPs) by over four orders of magnitude. These advantages make the proposed GL approach highly suitable for real-time deployment in energy- and hardware-constrained broadcasting scenarios.

Related papers

• Efficient Beamforming Optimization for STAR-RIS-Assisted Communications: A Gradient-Based Meta Learning Approach [29.972987396713126]
  STAR-RIS is a promising technology to realize full-space coverage and spectral efficiency in next-generation wireless networks.<n>We develop a gradient-based meta learning (GML) that directly optimization feeds into neural networks, thereby removing the need for pre-training and enabling fast adaptation.
  arXiv  Detail & Related papers  (2025-12-09T16:28:15Z)
• Generative MIMO Beam Map Construction for Location Recovery and Beam Tracking [67.65578956523403]
  This paper proposes a generative framework to recover location labels directly from sparse channel state information (CSI) measurements.<n>Instead of directly storing raw CSI, we learn a compact low-dimensional radio map embedding and leverage a generative model to reconstruct the high-dimensional CSI.<n> Numerical experiments demonstrate that the proposed model can improve localization accuracy by over 30% and achieve a 20% capacity gain in non-line-of-sight (NLOS) scenarios.
  arXiv  Detail & Related papers  (2025-11-21T07:25:49Z)
• Capacity-Net-Based RIS Precoding Design without Channel Estimation for mmWave MIMO System [14.025055154083104]
  Capacity-Net is a novel unsupervised learning approach aimed at maximizing the achievable rate in millimeter-wave (mmWave) systems.<n>To combat severe channel fading of the mmWave spectrum, we optimize the phase-shifting factors of the reflective elements in the RIS to enhance the achievable rate.<n>Instead of channel estimation, the Capacity-Net is proposed to establish a mapping among the received pilot signals, optimized RIS phase shifts, and the resultant achievable rates.
  arXiv  Detail & Related papers  (2025-09-30T01:57:33Z)
• Fluid Antenna System-assisted Physical Layer Secret Key Generation [64.92952968689636]
  This paper investigates physical-layer generation (PLKG) in multiant base station systems by leveraging a fluid antenna system (FAS) to dynamically radio environments.<n>We propose an assisted PLKG model that integrates transmit beamforming and port selection under independent and spatially correlated environments.<n>It is shown that the sliding window-based port selection method introduced in this paper achieves higher KGR with fewer chains through dynamic port selection.
  arXiv  Detail & Related papers  (2025-09-19T03:01:29Z)
• CSIYOLO: An Intelligent CSI-based Scatter Sensing Framework for Integrated Sensing and Communication Systems [18.244924566345027]
  ISAC is regarded as a promising technology for next-generation communication systems, enabling simultaneous data transmission and target sensing.<n> scatter sensing plays a crucial role in exploiting the full potential of ISAC and supporting applications such as autonomous driving and low-altitude economy.<n>We propose CSIYOLO, a framework that performs scatter localization using estimated CSI from a single base station-user equipment pair.
  arXiv  Detail & Related papers  (2025-09-15T06:46:39Z)
• Channel Fingerprint Construction for Massive MIMO: A Deep Conditional Generative Approach [65.47969413708344]
  We introduce the concept of CF twins and design a conditional generative diffusion model (CGDM)<n>We employ a variational inference technique to derive the evidence lower bound (ELBO) for the log-marginal distribution of the observed fine-grained CF conditioned on the coarse-grained CF.<n>We show that the proposed approach exhibits significant improvement in reconstruction performance compared to the baselines.
  arXiv  Detail & Related papers  (2025-05-12T01:36:06Z)
• Integrated Sensing and Communications for Low-Altitude Economy: A Deep Reinforcement Learning Approach [20.36806314683902]
  We study an integrated sensing and communications (ISAC) system for low-altitude economy (LAE)<n>The expected communication sum-rate over a given flight period is maximized by jointly optimizing the beamforming at the GBS and UAVs' trajectories.<n>We propose a novel LAE-oriented ISAC scheme, referred to as Deep LAE-ISAC (DeepLSC), by leveraging the deep reinforcement learning (DRL) technique.
  arXiv  Detail & Related papers  (2024-12-05T11:12:46Z)
• Hybrid Knowledge-Data Driven Channel Semantic Acquisition and Beamforming for Cell-Free Massive MIMO [6.010360758759109]
  This paper focuses on advancing outdoor wireless systems to better support ubiquitous extended reality (XR) applications. We propose a hybrid knowledge-data driven method for channel semantic acquisition and multi-user beamforming in cell-free massive multiple-input multiple-output (MIMO) systems.
  arXiv  Detail & Related papers  (2023-07-06T15:35:55Z)
• Model-based Deep Learning Receiver Design for Rate-Splitting Multiple Access [65.21117658030235]
  This work proposes a novel design for a practical RSMA receiver based on model-based deep learning (MBDL) methods. The MBDL receiver is evaluated in terms of uncoded Symbol Error Rate (SER), throughput performance through Link-Level Simulations (LLS) and average training overhead. Results reveal that the MBDL outperforms by a significant margin the SIC receiver with imperfect CSIR.
  arXiv  Detail & Related papers  (2022-05-02T12:23:55Z)
• Deep Learning-based Resource Allocation For Device-to-Device Communication [66.74874646973593]
  We propose a framework for the optimization of the resource allocation in multi-channel cellular systems with device-to-device (D2D) communication. A deep learning (DL) framework is proposed, where the optimal resource allocation strategy for arbitrary channel conditions is approximated by deep neural network (DNN) models. Our simulation results confirm that near-optimal performance can be attained with low time, which underlines the real-time capability of the proposed scheme.
  arXiv  Detail & Related papers  (2020-11-25T14:19:23Z)
• Optimization-driven Deep Reinforcement Learning for Robust Beamforming in IRS-assisted Wireless Communications [54.610318402371185]
  Intelligent reflecting surface (IRS) is a promising technology to assist downlink information transmissions from a multi-antenna access point (AP) to a receiver. We minimize the AP's transmit power by a joint optimization of the AP's active beamforming and the IRS's passive beamforming. We propose a deep reinforcement learning (DRL) approach that can adapt the beamforming strategies from past experiences.
  arXiv  Detail & Related papers  (2020-05-25T01:42:55Z)
• Millimeter Wave Communications with an Intelligent Reflector: Performance Optimization and Distributional Reinforcement Learning [119.97450366894718]
  A novel framework is proposed to optimize the downlink multi-user communication of a millimeter wave base station. A channel estimation approach is developed to measure the channel state information (CSI) in real-time. A distributional reinforcement learning (DRL) approach is proposed to learn the optimal IR reflection and maximize the expectation of downlink capacity.
  arXiv  Detail & Related papers  (2020-02-24T22:18:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.