Gabriel M. Almeida, Joao F. Santos, Walid Saad, Nicola Marchetti, Kleber V. Cardoso
原文摘要
The disaggregation of base stations into radio access network (RAN) functions introduces new vulnerabilities to mobile networks, as disruptions in cloud locations can interrupt multiple RAN functions and trigger cascading failures, leading to widespread outages. In this paper, we propose a criticality-aware RAN function placement solution to improve the robustness of disaggregated mobile networks against random failures and targeted attacks. Our solution optimizes the placement of centralized units (CUs) and distributed units (DUs) across cloud locations to reduce functional dependencies and limit the criticality of cloud locations, while preserving the cost benefits of RAN function centralization. We formulate the placement as an optimization model that jointly minimizes functional dependency and placement cost, subject to criticality constraints, as well as computing and transport requirements. We numerically evaluated our approach on a real-world mobile network topology across multiple disruption scenarios and demonstrated that our solution improves robustness, yielding up to 75% more operational radio units (RUs) after disruptions than existing approaches.
Integrated sensing and communication (ISAC) enables wireless infrastructure to jointly support communication and environmental sensing, but also introduces new physical-layer security threats. A malicious reconfigurable intelligent surface (RIS) can manipulate its time-varying reflection phases to generate spoofing echoes and distort the sensing results of a roadside unit (RSU). Existing trajectory-level detectors rely on reconstructed sensing parameters and vehicle trajectories, making their performance dependent on upstream estimation accuracy. In this paper, we propose a signal-level Green Learning (GL) framework that directly detects RIS spoofing from matched-filtered RSU echoes. The proposed method combines two-stage Subspace Approximation with Adjusted Bias (SAAB), Discriminant Feature Test (DFT), and eXtreme Gradient Boosting (XGBoost) for spatial-temporal representation learning, feature selection, and classification, respectively. Simulation results show 99.335\% detection accuracy for a 32-element malicious RIS at $-80$ dBm receiver noise power. GL remains competitive with Long Short-Term Memory (LSTM) and bidirectional LSTM (BiLSTM) baselines while requiring only 0.1119 million floating-point operations (FLOPs), and also generalizes to previously unseen RIS sizes.
Po-Heng Chou, Wei-Chen Lin, Wan-Jen Huang, Walid Saad, Ronald Y. Chang
原文摘要
Hybrid precoding reduces the radio-frequency (RF) hardware burden in wideband massive MIMO-OFDM systems, but conventional optimization and deep neural network (DNN)-based approaches can incur high online complexity. This paper proposes XGB-SVD, a lightweight hybrid precoding framework that maps compact ray-tracing path parameters directly to analog beamforming codeword indices using cascade eXtreme Gradient Boosting (XGBoost). The cascade captures sequential beam-selection dependencies and transmit--receive propagation correlation, while the digital precoder and combiner are obtained from the reduced-dimensional equivalent channel using singular value decomposition (SVD). Evaluations over five DeepMIMO scenarios spanning sub-6-GHz and millimeter-wave bands show that XGB-SVD consistently outperforms the considered DNN baselines and approaches simultaneous orthogonal matching pursuit (SOMP) in several environments. Cross-scenario results further indicate improved transfer from propagation-diverse training environments, while XGB-SVD provides lower online computational and training costs than the DNN baselines.