Joint Resource Allocation and RIS Beamforming for Enhanced Computational Efficiency in Multi-RIS Assisted MEC Networks
Liu, Tong Zhang, Shilong Chen, Jinhua Yu, Keping Niyato, Dusit Guizani, Mohsen Tolba, Amr M.
Liu, Tong
Zhang, Shilong
Chen, Jinhua
Yu, Keping
Niyato, Dusit
Guizani, Mohsen
Tolba, Amr M.
Supervisor
Department
Machine Learning
Embargo End Date
Type
Journal article
Date
2026
License
Language
English
Collections
Research Projects
Organizational Units
Journal Issue
Abstract
Mobile edge computing (MEC) has become a key technique for enhancing wireless devices in terms of computation performance by enabling task offloading to proximate edge servers. However, the performance of MEC networks is often limited by unfavorable wireless propagation conditions, which degrade the reliability of communication links required for efficient task offloading. Reconfigurable intelligent surfaces (RIS) have recently been proposed as a promising solution to mitigate these challenges by intelligently reconfiguring the wireless environment through passive beamforming, where the phase shifts of reflecting elements are adjusted to reshape the propagation channel. Nevertheless, single-RIS deployments generally provide limited signal coverage, which is insufficient to support spatially distributed users. To overcome this limitation, this paper explores a MEC framework supported by multiple RISs, where several surfaces are strategically placed to jointly improve network connectivity and computational efficiency. A joint optimization framework is established to enhance the system-wide computation efficiency by simultaneously optimizing user-RIS association, RIS passive beamforming, and the allocation of communication and computation resources. To tackle this highly non-convex problem, we propose an efficient hierarchical optimization framework, termed hierarchical computational efficiency resource allocation (H-CERA). This framework adopts an outer-loop algorithm based on the Dinkelbach method to transform the original sum-of-ratios objective into a tractable form, and an inner-loop alternating optimization procedure to iteratively update the decoupled variable blocks. Comprehensive simulation experiments confirm that the proposed H-CERA framework is effective, showing substantial improvements in computational efficiency over representative baseline schemes. The results further demonstrate its robustness and scalability under diverse system configurations.
Citation
T. Liu et al., "Joint Resource Allocation and RIS Beamforming for Enhanced Computational Efficiency in Multi-RIS Assisted MEC Networks," in IEEE Transactions on Vehicular Technology, doi: 10.1109/TVT.2025.3648963
Source
IEEE Transactions on Vehicular Technology
Conference
Keywords
Computational efficiency, Fractional programming, Hierarchical optimization framework, Multiple RISs, Task offloading
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Source
Publisher
IEEE