Cross Layer Power Allocation by Graph Neural Networks in Heterogeneous D2D Video Communications

IF 3.4 3区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS IEEE Access Pub Date : 2025-03-06 DOI:10.1109/ACCESS.2025.3548854
Shu-Ming Tseng;Sz-Tze Wen;Chao Fang;Mehdi Norouzi
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Abstract

The massive connectivity trend was set to shape B5G/6G networks. Device-to-device (D2D) communications play a crucial role in massive connectivity in the context of Internet of Things (IoT) applications. Recently, a heterogeneous interference graph neural network (HIGNN) was proposed for resource allocation in heterogeneous networks. The HIGNN captured the spatial information hidden in heterogeneous network topology and was scalable. However, existing methods primarily focused on resource allocation at the physical layer only and did not adequately address the cross-layer issues involved in video transmission. Therefore, in this paper, we propose the video-optimized heterogeneous interference graph neural network (VD-HIGNN) as a cross-layer D2D resource allocation method for video transmission, which introduces the following contributions: 1) joint source encoder rate and beamforming/power control, 2) incorporating video rate distortion function parameters from the application layer into the node features, and 3) changing the loss function from data rate to Peak-Signal-to-Noise-Ratio (PSNR), a function of video rate distortion and a metric of video quality. Simulation results demonstrate that our proposed VD-HIGNN outperforms two physical layer baseline schemes: the iterative fractional programming method by 0.53 dB and HIGNN by approximately 2 dB for video transmission. Moreover, when scaled to larger problems with 2-12 times the number of nodes within a fixed area size, the VD-HIGNN achieves 94% or more of the performance of a retrained model, showcasing its scalability and generalization ability.
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IEEE Access
IEEE Access COMPUTER SCIENCE, INFORMATION SYSTEMSENGIN-ENGINEERING, ELECTRICAL & ELECTRONIC
CiteScore
9.80
自引率
7.70%
发文量
6673
审稿时长
6 weeks
期刊介绍: IEEE Access® is a multidisciplinary, open access (OA), applications-oriented, all-electronic archival journal that continuously presents the results of original research or development across all of IEEE''s fields of interest. IEEE Access will publish articles that are of high interest to readers, original, technically correct, and clearly presented. Supported by author publication charges (APC), its hallmarks are a rapid peer review and publication process with open access to all readers. Unlike IEEE''s traditional Transactions or Journals, reviews are "binary", in that reviewers will either Accept or Reject an article in the form it is submitted in order to achieve rapid turnaround. Especially encouraged are submissions on: Multidisciplinary topics, or applications-oriented articles and negative results that do not fit within the scope of IEEE''s traditional journals. Practical articles discussing new experiments or measurement techniques, interesting solutions to engineering. Development of new or improved fabrication or manufacturing techniques. Reviews or survey articles of new or evolving fields oriented to assist others in understanding the new area.
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