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Title
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Joint offloading policy and resource allocation in IRS-aided MEC for IoT users with short packet transmission
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Author
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Abstract
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| This paper focuses on leveraging a mobile edge computing (MEC) server at an access point (AP) to address the delay and reliability sensitivity requirement of multi-user machine-type communication (MTC). By offloading tasks to the MEC server, latency for low-power MTC devices can be minimized. Meanwhile, intelligent reflecting surfaces (IRSs) are supported to facilitate robust offloading, enhance spectrum efficiency, and improve coverage by influencing incident radio-frequency wave propagation via modifying the phase shifts with passive reflecting components. Therefore, we investigate joint radio resource allocation and edge offloading decision optimization in a multi-user IRS-assisted MEC network, wherein a multi-antenna AP receives information symbols from a set of Internet of Things (IoT) users with short packet transmission. In particular, we minimize the system's power utilization subject to offloading MTC-enabled IoT users' quality of service (QoS) requirements, transmit power feasibility, capacity limitation, and IRS phase shift. The non-convex nature of the formulated problem poses a challenge to solving it effectively. To address this, we propose an efficient iterative algorithm based on successive convex approximation (SCA) and a penalty-based approach for handling unit-modulus constraints in the presence of passive reflecting elements at the IRS. Simulation results demonstrate the superior performance of our algorithm compared to other baseline schemes. |
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Language
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English
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Source (book)
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98th IEEE Vehicular Technology Conference (VTC-Fall), OCT 10-13, 2023, Hong Kong, Hong Kong
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Publication
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New York, N.Y.
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IEEE
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2023
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ISBN
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979-83-503-2928-5
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DOI
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10.1109/VTC2023-FALL60731.2023.10333867
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Volume/pages
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(2023)
, p. 1-7
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ISI
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001133762500448
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Full text (Publisher's DOI)
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Full text (publisher's version - intranet only)
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