In multiple-input multiple-output (MIMO) systems, beamforming is a core enabler for concentrating signal power toward targeted users to enhance spectral efficiency and network capacity. However, realizing the full potential of beamforming critically relies on accurate channel state information (CSI). Conventional CSI acquisition requires extensive pilot transmissions and continuous feedback, incurring high overhead. To overcome this limitation, inspired by the novel concept of channel knowledge map (CKM), we propose an environment-aware beamforming prediction (EA-BFP) framework that directly maps environmental topology and transceiver locations to optimal beamforming vectors. The framework employs a dual-branch neural network: a modified ResNet extracts spatial features from the environmental map, while fourier position encoding (FoPE) provides precise high-dimensional coordinate embedding, which enables zero-shot beam decisions. Simulation results demonstrate that our approach achieves lower prediction errors and higher achievable rates than baseline models, showcasing superior main lobe alignment, data efficiency, and cross-scenario generalization under limited fine-tuning samples.
Yi-Ning Wang, Ha Nan, Le Zhao et al.· IEEE Wireless Communications...· 0 citations
End-edge collaborative inference has emerged as an important trend for deploying deep learning applications on resource-constrained end devices. However, the continuous data interaction between end devices and edge server inevitably raises privacy concerns. Fully homomorphic encryption (FHE) provides a privacy-preserving solution by enabling inference directly on encrypted data, but deploying full FHE inference on the edge suffers from high latency. To address these issues, we propose FHE-EESI, an FHE-based end-edge collaborative split inference framework. In FHE-EESI, the end device executes the initial layers on plaintext, encrypts intermediate features using the residue number system Cheon-Kim-Kim-Song (RNS-CKKS) scheme, and transmits them to the edge server for the remaining FHE inference. To construct an FHE-compatible inference network and enable flexible partitionability, we adopt a stage-wise key management strategy and the Chebyshev polynomial approximation. Furthermore, considering dynamic channel conditions and heterogeneous computational capabilities, we design a dueling double deep Q-network (D3QN)-based dynamic split mechanism to adaptively determine the optimal split point. Experimental results on the CIFAR-10 dataset show that FHE-EESI achieves 83.3% inference accuracy and 183.1 s total latency, effectively providing privacy-preserving inference while maintaining inference efficiency.
Haiyue Zhang, Yiming Liu, Jing Jin et al.· IEEE Transactions on Cogniti...· 0 citations
A ComBERT-driven service-based RAN UP decoupling method, specifically targeting the functional coupling and redundancy between the PDCP and RLC sublayers is proposed, providing a crucial foundation for on-demand service orchestration in 6G networks tailored to agent services.
Hai-Yu Ding, Shang-Yuan Du, Xin Sun et al.· Italian National Conference...· 0 citations
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