Joint beamforming aided over-the-air computation systems relying on both BS-side and user-side reconfigurable intelligent surfaces
Joint beamforming aided over-the-air computation systems relying on both BS-side and user-side reconfigurable intelligent surfaces
Over-the-air computation (AirComp) has received substantial attention, given its ability to aggregate massive amounts of data from distributed wireless devices (WDs). However, the computation accuracy at the fusion center (FC) may be severely affected by receiving data corrupted by the poor channel conditions. To mitigate this issue, we consider the employment of reconfigurable intelligent surfaces (RISs) in the AirComp system considered for improving the quality of received data, and hence improve the computation accuracy. However, most previous contributions on RIS-assisted AirComp systems only employ a single RIS in the resultant single-RIS-assisted (SRISassisted) AirComp systems. We develop this concept further for mitigating the deleterious channel effects by conceiving a double-RIS-assisted (DRIS-assisted) AirComp system, where one of the RISs is located near the WDs and the other in the vicinity of the FC. We theoretically prove that the DRIS-assisted AirComp system outperforms its SRIS-assisted counterpart in terms of the resultant computation mean-squared-error (MSE). Furthermore, we propose a pair of algorithms for jointly optimizing the transmit power at the WDs, the receive beamforming vector at the FC, and the passive beamforming matrices at the RISs for minimizing the computational MSE. Specifically, the transmit power is updated by exploiting the Lagrange duality method, while the receive beamforming vector is optimized by utilizing the first-order optimality condition. Furthermore, a pair of techniques are developed for optimizing the passive beamforming matrices at the RISs based on semidefinite relaxation (SDR) and penalty-duality-decomposition (PDD), respectively. Both the complexity and the convergence of the proposed algorithms are analyzed. Finally, simulation results are provided for quantifying the overall performance of the resultant DRIS-assisted AirComp system.
Zhai, Xiongfei
09f300f9-73e1-430a-b157-a553dc299b25
Han, Guojun
ebab520d-a799-438e-9f6b-296bb4b54d1c
Cai, Yunlong
ed1440c3-10af-4b6c-9295-4b355d409a16
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
Zhai, Xiongfei
09f300f9-73e1-430a-b157-a553dc299b25
Han, Guojun
ebab520d-a799-438e-9f6b-296bb4b54d1c
Cai, Yunlong
ed1440c3-10af-4b6c-9295-4b355d409a16
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
Zhai, Xiongfei, Han, Guojun, Cai, Yunlong and Hanzo, Lajos
(2022)
Joint beamforming aided over-the-air computation systems relying on both BS-side and user-side reconfigurable intelligent surfaces.
IEEE Transactions on Wireless Communications.
(In Press)
Abstract
Over-the-air computation (AirComp) has received substantial attention, given its ability to aggregate massive amounts of data from distributed wireless devices (WDs). However, the computation accuracy at the fusion center (FC) may be severely affected by receiving data corrupted by the poor channel conditions. To mitigate this issue, we consider the employment of reconfigurable intelligent surfaces (RISs) in the AirComp system considered for improving the quality of received data, and hence improve the computation accuracy. However, most previous contributions on RIS-assisted AirComp systems only employ a single RIS in the resultant single-RIS-assisted (SRISassisted) AirComp systems. We develop this concept further for mitigating the deleterious channel effects by conceiving a double-RIS-assisted (DRIS-assisted) AirComp system, where one of the RISs is located near the WDs and the other in the vicinity of the FC. We theoretically prove that the DRIS-assisted AirComp system outperforms its SRIS-assisted counterpart in terms of the resultant computation mean-squared-error (MSE). Furthermore, we propose a pair of algorithms for jointly optimizing the transmit power at the WDs, the receive beamforming vector at the FC, and the passive beamforming matrices at the RISs for minimizing the computational MSE. Specifically, the transmit power is updated by exploiting the Lagrange duality method, while the receive beamforming vector is optimized by utilizing the first-order optimality condition. Furthermore, a pair of techniques are developed for optimizing the passive beamforming matrices at the RISs based on semidefinite relaxation (SDR) and penalty-duality-decomposition (PDD), respectively. Both the complexity and the convergence of the proposed algorithms are analyzed. Finally, simulation results are provided for quantifying the overall performance of the resultant DRIS-assisted AirComp system.
Text
Final_version-3
- Accepted Manuscript
More information
Accepted/In Press date: 26 June 2022
Identifiers
Local EPrints ID: 467923
URI: http://eprints.soton.ac.uk/id/eprint/467923
ISSN: 1536-1276
PURE UUID: 650f13f1-c6f3-46c6-87af-83e898604c2f
Catalogue record
Date deposited: 26 Jul 2022 16:32
Last modified: 17 Mar 2024 02:35
Export record
Contributors
Author:
Xiongfei Zhai
Author:
Guojun Han
Author:
Yunlong Cai
Author:
Lajos Hanzo
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics