Hu, X;
Masouros, C;
Wong, K;
(2021)
Removing Channel Estimation by Location-Only Based Deep Learning for RIS Aided Mobile Edge Computing.
In:
ICC 2021 - IEEE International Conference on Communications.
IEEE: Montreal, QC, Canada.
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Abstract
In this paper, we investigate a deep learning architecture for lightweight online implementation of a reconfigurable intelligent surface (RIS)-aided multi-user mobile edge computing (MEC) system, where the optimized performance can be achieved based on user equipment’s (UEs’) location-only information. Assuming that each UE is endowed with a limited energy budget, we aim at maximizing the total completed task-input bits (TCTB) of all UEs within a given time slot, through jointly optimizing the RIS reflecting coefficients, the receive beamforming vectors, and UEs’ energy partition strategies for local computing and computation offloading. Due to the coupled optimization variables, a three-step block coordinate descending (BCD) algorithm is first proposed to effectively solve the formulated TCTB maximization problem iteratively with guaranteed convergence. The location-only deep learning architecture is then constructed to emulate the proposed BCD optimization algorithm, through which the pilot channel estimation and feedback can be removed for online implementation with low complexity. The simulation results reveal a close match between the performance of the BCD optimization algorithm and the location-only data-driven architecture, all with superior performance to existing benchmarks.
| Type: | Proceedings paper |
|---|---|
| Title: | Removing Channel Estimation by Location-Only Based Deep Learning for RIS Aided Mobile Edge Computing |
| Event: | International Conference on Communications (ICC), 2021 IEEE |
| Location: | Virtual / Montreal, Canada |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/ICC42927.2021.9500961 |
| Publisher version: | https://doi.org/10.1109/ICC42927.2021.9500961 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Deep learning, Simulation, Channel estimation, Computer architecture, Benchmark testing, Partitioning algorithms, Complexity theory |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10130206 |
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