Journal of Guangdong University of Technology ›› 2024, Vol. 41 ›› Issue (02): 116-121.doi: 10.12052/gdutxb.220181

• Comprehensive Studies • Previous Articles    

An Optimized Transceiver Design for Wireless Powered Over-the-air Computation Systems

Hong Ze-bin, Wang Feng   

  1. School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China
  • Received:2022-12-02 Published:2024-04-23

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Abstract: A wireless powered over-the-air computation (AirComp) system is studied, where one separately-located energy transmitter (ET) is deployed to charge multiple low-power sensors simultaneously via energy beamforming, and these sensors rely on the harvested energy for sequential data sensing and functional computation along with the access point (AP) . A harvest-then-sense-and-transmit protocol is considered. Under this system setup, an energy-efficient AirComp design is pursued to minimize the transmit energy of the ET, subject to the sensor energy harvesting constraints and the computational mean squared error (MSE) constraints. The energy beamforming vectors of the ET, the receive beamforming vectors of the AP, and the transmit coefficients at the sensors are jointly optimized. Due to the complicated variable coupling, the resultant energy minimization problem is non-convex. As such, an alternating optimization method is presented to obtain a near-optimal design solution in an iteration manner. Numerical results are provided to show the fast convergence performance and the merit of the proposed design solution.

Key words: over-the-air computation (AirComp), energy harvesting, computational MSE, alternating optimization

CLC Number: 

  • F224.32
[1] MAO Y Y, YOU C S, ZHANG J, et al. A survey on mobile edge computing: the communication perspective [J]. IEEE Communications Surveys & Tutorials, 2017, 19(4): 2322-2358.
[2] CHEN M Z, CHALLITA U, SAAD W, et al. Artificial neural networks-based machine learning for wireless networks: a tutorial [J]. IEEE Communications Surveys & Tutorials, 2019, 21(4): 3039-3071.
[3] 陈力, 卫国. 未来无线网络下的空中计算技术[J]. 中兴通讯技术, 2019, 25(1): 29-34.
CHEN L, WEI G. Over-the-air computation for future networks [J]. ZTE Technology Journal, 2019, 25(1): 29-34.
[4] 王丰, 李宇龙, 林志飞, 等. 基于计算吞吐量最大化的能量采集边缘计算系统在线资源优化配置[J]. 广东工业大学学报, 2022, 39(4): 17-23.
WANG F, LI Y L, LIN Z F, et al. An online resource allocation design for computation capacity maximization in energy harvesting mobile edge computing systems [J]. Journal of Guangdong University of Technology, 2022, 39(4): 17-23.
[5] ZHOU Z, CHEN X, LI E, et al. Edge intelligence: paving the last mile of artificial intelligence with edge computing [J]. Proceedings of the IEEE, 2019, 107(8): 1738-1762.
[6] WANG F, XU J, DING Z G. Multi-antenna NOMA for computation offloading in multiuser mobile edge computing systems [J]. IEEE Transactions on Communications, 2018, 67(3): 2450-2463.
[7] GOLDENBAUM M, BOCHE H, STANCZAK S. Harnessing interference for analog function computation in wireless sensor networks [J]. IEEE Transactions on Signal Processing, 2013, 61(20): 4893-4906.
[8] SHI G X, GUO S S, YE J, et al. Multiple parallel federated learning via over-the-air computation [J]. IEEE Open Journal of the Communications Society, 2022, 3: 1252-1264.
[9] WANG S, HONG Y C, WANG R, et al. Edge federated learning via unit-modulus over-the-air computation [J]. IEEE Transactions on Communications, 2022, 70(5): 3141-3156.
[10] NAZER B, GASTPAR M. Computation over multiple-access channels [J]. IEEE Transactions on Information Theory, 2007, 53(10): 3498-3516.
[11] GOLDENBAUM M, STANCZAK S. Robust analog function computation via wireless multiple-access channels [J]. IEEE Transactions on Communications, 2013, 61(9): 3863-3877.
[12] GOLDENBAUM M, BOCHE H, STANCZAK S. Nomographic functions: efficient computation in clustered gaussian sensor networks [J]. IEEE Transactions on Wireless Communications, 2014, 14(4): 2093-2105.
[13] CHEN L, ZHAO N, CHEN Y F, et al. Over-the-air computation for IoT networks: computing multiple functions with antenna arrays [J]. IEEE Internet of Things Journal, 2018, 5(6): 5296-5306.
[14] WANG F, XU J, LAU V K N, et al. Amplify-and-forward relaying for hierarchical over-the-air computation [J]. IEEE Transactions on Wireless Communications, 2022, 21(12): 10529-10543.
[15] WANG F, LAU V K N. Multi-level over-the-air aggregation of mobile edge computing over D2D wireless networks [J]. IEEE Transactions on Wireless Communications, 2022, 21(10): 8337-8353.
[16] LIU W C, ZANG X, LI Y H, et al. Over-the-air computation systems: optimization, analysis and scaling laws [J]. IEEE Transactions on Wireless Communications, 2020, 19(8): 5488-5502.
[17] ZHU G X, HUANG K B. MIMO over-the-air computation for high-mobility multimodal sensing [J]. IEEE Internet of Things Journal, 2019, 6(4): 6089-6103.
[18] HU L, WANG Z B, ZHU H B, et al. RIS-assisted over-the-air federated learning in millimeter wave MIMO networks [J]. Journal of Communications and Information Networks, 2022, 7(2): 145-156.
[19] CAO X W, ZHU G X, XU J, et al. Optimized power control for over-the-air computation in fading channels [J]. IEEE Transactions on Wireless Communications, 2020, 19(11): 7498-7513.
[20] BASARAN S T, KURT G K, CHATZIMISIOS P. Energy-efficient over-the-air computation scheme for densely deployed IoT networks [J]. IEEE Transactions on Industrial Informatics, 2020, 16(5): 3558-3565.
[21] VISSER H J, VULLERS R J M. RF energy harvesting and transport for wireless sensor network applications: principles and requirements [J]. Proceedings of the IEEE, 2013, 101(6): 1410-1423.
[22] WANG F, XU J, CUI S G. Optimal energy allocation and task offloading policy for wireless powered mobile edge computing systems [J]. IEEE Transactions on Wireless Communications, 2020, 19(4): 2443-2459.
[23] LI X Y, ZHU G X, GONG Y, et al. Wirelessly powered data aggregation for IoT via over-the-air function computation: beamforming and power control [J]. IEEE Transactions on Wireless Communications, 2019, 18(7): 3437-3452.
[24] WANG Z B, SHI Y M, ZHOU Y, et al. Wireless-powered over-the-air computation in intelligent reflecting surface-aided IoT networks [J]. IEEE Internet of Things Journal, 2021, 8(3): 1585-1598.
[25] FARAJZADEH A, ERCETIN O, YANIKOMEROGLU H. Mobility-assisted over-the-air computation for backscatter sensor networks [J]. IEEE Wireless Communications Letters, 2020, 9(5): 675-678.
[26] BOYD S, VANDENBERGHE L. Convex optimization[M]. UK: Cambridge University Press, 2004.
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