Beamforming plays a key role in multiple-input-multiple-output (MIMO) communication systems. However, conventional beamforming design normally requires accurate instantaneous channel state information (CSI) and iterative optimization, which incur substantial pilot overhead and computational complexity. Recognizing that radio propagation is intrinsically governed by the physical geometry, we develop a 3D Gaussian splatting for environment-aware beamforming (GSBF) pipeline based on multi-modal data, which characterizes the environment through a persistent 3D Gaussian representation. Specifically, GSBF models the environmental scattering response with reciprocity-preserving bidirectional spherical Gaussian (Bi-SG) kernels and performs two-sided electromagnetic rasterization to render an angular propagator map. The rendered map is then aggregated through an over-complete array-manifold dictionary and projected to the constant-modulus beamformers, thereby synthesizing beams directly from the access point (AP) pose and user position without online instantaneous CSI. Simulations demonstrate that GSBF consistently outperforms baselines such as exhaustive beam alignment (EBA) with lower latency.
Yijie Bian, Wei Guo, Zixin Wang et al.· 0 citations
Federated Learning (FL) has emerged as a transformative distributed learning paradigm, offering a privacy-preserving solution for collaborative model training. However, its conventional cloud-centric architecture suffers from a significant communication bottleneck casused by frequent model transmissions and heterogeneous data, particularly in dynamic mobile computing environments. To address this challenge, we introduce EdgeFLow, a novel, efficient serverless FL framework that fundamentally redesigns the system architecture by replacing the central cloud server with a sequential model migration across edge nodes, while deliberately preserving the cluster-based parallel training topology. EdgeFLow targets an intermediate design point between cloud-centric parallel FL and fully decentralized sequential FL, aiming to retain the convergence stability of intra-cluster parallel aggregation while eliminating the long-distance cloud transmissions that dominate the communication overhead in realistic edge deployments. This paradigm confines both model aggregation and propagation to the edge, forming a structured learning “flow” that gives the framework its name. We provide a theoretical analysis of communication savings using queuing theory, and establish a formal convergence guarantee under non-convex objective functions with non-independent and identically distributed (non-IID) data. Building on these findings, we propose Heterogeneity-Aware EdgeFLow (HA-EdgeFLow), an enhanced algorithm that mitigates the impact of non-IID data through a gradient-based client selection mechanism. Experimental results across various configurations validate the theoretical analysis, demonstrating that our proposed framework significantly reduces communication overhead and latency while achieving comparable or superior model accuracy to traditional baselines. As a system-level architectural innovation for communication-efficient FL, EdgeFLow establishes a foundational framework for future developments in edge network learning systems.
Yuchen Shi, Qi-Jun Hou, Ping-Yi Fan et al.· IEEE Transactions on Mobile...· 0 citations
Edge inference has emerged as a promising solution for the proliferation of artificial intelligence (AI) services by deploying models at the network edge to circumvent cloud-routing latency. Existing edge inference approaches mainly focused on either cooperative inference to reduce latency or lightweight model design to fit resource-constrained devices. These solutions often address the communication and computation challenges separately, and thus struggle to achieve a balanced trade-off among transmission efficiency, on-device processing cost, and inference accuracy. To bridge this gap, this paper proposes a hybrid-precision task-oriented communication framework for edge inference to holistically balance communication, on-device computation, and utility. In this framework, a binarized front-end is deployed on the edge device to extract and transmit binary features via orthogonal frequency-division multiplexing (OFDM) signals, while a full-precision back-end on the edge server performs the final inference. To ensure model consistency, we introduce an on-device binarization method tailored for split inference and develop an integrated channel-aware transmission scheme featuring subcarrier-based feature calibration. Furthermore, a knowledge distillation (KD)-based training strategy, supported by specialized gradient estimators, is developed to optimize the end-to-end system and inherit semantic knowledge from a full-precision teacher model. Extensive experiments on the large-scale ImageNet dataset demonstrate the superiority of the proposed hybrid system. Our analysis confirms that this design achieves an optimal trade-off among communication efficiency, on-device computational cost, and inference accuracy, outperforming existing edge inference solutions.
Songjie Xie, Wei Guo, Sheng-Hui Song et al.· arXiv.org· 0 citations
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