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
This paper proposes a novel integrated sensing, communication, and computing (ISCC) framework over a cloud-edge-device collaborative architecture, where passive sensing is enabled by reusing uplink offloading signals to extract sensing information directly at the edge without incurring additional transmission overhead. Nevertheless, such signal reuse introduces an inherent tradeoff between communication efficiency and sensing coverage. To address this challenge, we adopt a hybrid beamforming architecture under practical hardware constraints. In addition, the integration of sensing tasks creates significant resource contention at the mobile edge computing (MEC) server, where latency-sensitive device tasks and computation-intensive sensing inference tasks compete for limited processing capacity. To alleviate this computation burden, we introduce a split inference mechanism that strategically partitions intelligent sensing tasks between the edge and the cloud. Building upon this framework, we formulate a joint optimization problem to minimize the average computation latency of all device tasks subject to strict sensing performance constraints. To tackle the high non-convexity of the formulated problem, we develop an efficient alternating optimization algorithm. In particular, we design a two-layer framework to jointly determine the optimal DNN splitting point and computation resource allocation and employ a weighted minimum mean square error (WMMSE)-based approach with manifold optimization for hybrid beamforming design. Numerical results demonstrate that the proposed framework achieves a superior tradeoff between sensing accuracy and computation latency compared to the benchmark schemes.
Peng Liu, Zesong Fei, Xinyi Wang et al.· 0 citations
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