Double-RIS Assisted Over-the-Air Computation

Over-the-air computation (AirComp) is a promising multiple-access scheme that seamlessly integrates the computation and communication by leveraging the waveform superposition properly of a multiple access channel. To unleash the full potential of AirComp, in this paper, we present a double-RIS assisted multiple-input multiple-output AirComp system, where two RISs are deployed to extend the effective coverage area and enhance the channel quality of randomly distributed sensors. We consider the cooperative passive beamforming design at RISs to further improve the performance. Our objective is to minimize the mean-squared error of AirComp by jointly optimizing the receive beamforming, denoising factor, transmit power control, cooperative passive beamforming design. The formulated problem is challenging to be optimally solved due to the coupled cooperative passive beamforming and transceiver variables. To this end, we propose an efficient algorithm based on the block coordinate descent method and semidefinite relaxation to obtain the receive beamforming at the AP and the cooperative passive beamforming at RISs after deriving the closed-form solutions for denoising factor and transmit power control. The numerical results validate that the double-RIS assisted AirComp system achieves a better performance compared to the single-RIS baseline in various system settings.

[1]  H. Vincent Poor,et al.  Energy-Efficient Wireless Communications With Distributed Reconfigurable Intelligent Surfaces , 2020, IEEE Transactions on Wireless Communications.

[2]  Zhibin Wang,et al.  Wireless-Powered Over-the-Air Computation in Intelligent Reflecting Surface-Aided IoT Networks , 2021, IEEE Internet of Things Journal.

[3]  Changsheng You,et al.  Intelligent Reflecting Surface-Aided Wireless Communications: A Tutorial , 2020, IEEE Transactions on Communications.

[4]  Slawomir Stanczak,et al.  Max-Consensus Over Fading Wireless Channels , 2020, IEEE Transactions on Control of Network Systems.

[5]  Yuanming Shi,et al.  Reconfigurable Intelligent Surface Empowered Downlink Non-Orthogonal Multiple Access , 2019, IEEE Transactions on Communications.

[6]  Ahmed Alkhateeb,et al.  Enabling Large Intelligent Surfaces With Compressive Sensing and Deep Learning , 2019, IEEE Access.

[7]  Yuanming Shi,et al.  Stochastic Beamforming for Reconfigurable Intelligent Surface Aided Over-the-Air Computation , 2020, GLOBECOM 2020 - 2020 IEEE Global Communications Conference.

[8]  Shuowen Zhang,et al.  Cooperative Double-IRS Aided Communication: Beamforming Design and Power Scaling , 2020, IEEE Wireless Communications Letters.

[9]  Jun Zhang,et al.  Communication-Efficient Edge AI: Algorithms and Systems , 2020, IEEE Communications Surveys & Tutorials.

[10]  Xiaojun Yuan,et al.  Matrix-Calibration-Based Cascaded Channel Estimation for Reconfigurable Intelligent Surface Assisted Multiuser MIMO , 2019, IEEE Journal on Selected Areas in Communications.

[11]  Zhiguo Ding,et al.  A Simple Design of IRS-NOMA Transmission , 2019, IEEE Communications Letters.

[12]  Shuowen Zhang,et al.  Intelligent Reflecting Surface Meets OFDM: Protocol Design and Rate Maximization , 2019, IEEE Transactions on Communications.

[13]  Kaibin Huang,et al.  Optimal Power Control for Over-the-Air Computation , 2019, 2019 IEEE Global Communications Conference (GLOBECOM).

[14]  Zhi Ding,et al.  Federated Learning via Over-the-Air Computation , 2018, IEEE Transactions on Wireless Communications.

[15]  Tao Jiang,et al.  Over-the-Air Computation via Intelligent Reflecting Surfaces , 2019, 2019 IEEE Global Communications Conference (GLOBECOM).

[16]  Wei Chen,et al.  The Roadmap to 6G: AI Empowered Wireless Networks , 2019, IEEE Communications Magazine.

[17]  Qingqing Wu,et al.  Intelligent Reflecting Surface Enhanced Wireless Network via Joint Active and Passive Beamforming , 2018, IEEE Transactions on Wireless Communications.

[18]  Kaibin Huang,et al.  MIMO Over-the-Air Computation for High-Mobility Multimodal Sensing , 2018, IEEE Internet of Things Journal.

[19]  Slawomir Stanczak,et al.  Nomographic Functions: Efficient Computation in Clustered Gaussian Sensor Networks , 2013, IEEE Transactions on Wireless Communications.

[20]  Wotao Yin,et al.  A Block Coordinate Descent Method for Regularized Multiconvex Optimization with Applications to Nonnegative Tensor Factorization and Completion , 2013, SIAM J. Imaging Sci..

[21]  Zhi-Quan Luo,et al.  Semidefinite Relaxation of Quadratic Optimization Problems , 2010, IEEE Signal Processing Magazine.