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Multi-Task Communication Resource Allocation for MIMO-Based Vehicular Fog Computing
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DOI：10.1109/TVT.2023.3311011.png)
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摘要
En 中文
In this article, we study the multi-task allocation design for vehicular fog computing (VFC) systems, where multiple vehicles offload multiple tasks simultaneously to enhance performance. The data, often originating from multiple sources, must be transmitted at a high data rate in parallel. For catering the thriving demands for transmitting thriving data generated by vehicles, multiple input multiple output (MIMO) antennas are mounted on all the vehicles. However, with an ordinary communcation strategy, the data rate in MIMO-empowered VFC systems may decrease due to the interference generated from MIMO antennas. On the other hand, more antennas may lead to higher energy consumption, which is antagonistic to vehicles powered by limited electricity. To address all these challenges, we aim to offload multiple tasks simultaneously with higher data rates and lower power consumption in MIMO-empowered VFC, while taking into account the mobility of vehicles and communication interference. This design is a mixed integer non-convex optimization problem that is challenging to solve. Therefore, a Fountain system is proposed, which is a novel multi-task allocation strategy adopting deep learning and convex optimization, that transforms the multi-task allocation optimization problem into a convex solvable one for achieving an effective sub-optimal solution. For the purpose to utilize Fountain in the real world, the effectiveness of Fountain is evaluated based on real-world vehicle trajectories. Compared with other schemes, simulation results illustrate that our proposed Fountain improves the average transmission data rate by up to 163% and reduces the average power consumption by up to 40%.
Keyword:
Vehicular fog computing (VFC)
task allocation
multi-input multi-output (MIMO)
hybrid precoding
deep learning
convex optimization
期刊
IF:
7.1
论文数:
1.8W
被引数:
6.6W
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