无人机辅助无蜂窝大规模MIMO中的空地协同调度

doi:10.11959/j.issn.1000-0801.2022242

Abstract

Abstract:

Cell-free massive multiple-input multiple-output (MIMO) technology uses a large number of access points (AP) to provide efficient communication services for terrestrial users.However, massive APs and users increase the demand for channel state information detection, especially for users with high moving speeds.In order to reduce the consumption of pilot resources, a dual system architecture that integrates unmanned aerial vehicle-assisted (UAV-assisted) communications and cell-free massive MIMO communications was proposed.The architecture was able to predict the movement trajectories of high-speed users and use UAVs to provide them with reliable communications.A UAV trajectory design and user scheduling scheme based on deep reinforcement learning (DRL) was further proposed, which maximized the system sum rate under the premise of satisfying various constraints.Simulation results demonstrate that the proposed scheme is able to predict user trajectories and improve system sum rate compared with existing schemes.

Key words: cell-free massive MIMO, UAV-assisted communication, coordinated air-ground scheduling, DRL

CLC Number:

TN915

Danhao DENG, Chaowei WANG, Fan JIANG, Weidong WANG. Coordinated air-ground scheduling for UAV-assisted cell-free massive MIMO[J]. Telecommunications Science, 2022, 38(8): 37-44.

Figures/Tables 5

References 20

[1]	LARSSON E G , EDFORS O , TUFVESSON F ,et al. Massive MIMO for next generation wireless systems[J]. IEEE Communications Magazine, 2014,52(2): 186-195.
[2]	WU Y P , KHISTI A , XIAO C S ,et al. A survey of physical layer security techniques for 5G wireless networks and challenges ahead[J]. IEEE Journal on Selected Areas in Communications, 2018,36(4): 679-695.
[3]	ZHAO N , YU F R , LEUNG V CM . Opportunistic communications in interference alignment networks with wireless power transfer[J]. IEEE Wireless Communications, 2015,22(1): 88-95.
[4]	NGO H Q , ASHIKHMIN A , YANG H ,et al. Cell-free massive MIMO versus small cells[J]. IEEE Transactions on Wireless Communications, 2017,16(3): 1834-1850.
[5]	ZHANG J Y , BJ?RNSON E , MATTHAIOU M ,et al. Prospective multiple antenna technologies for beyond 5G[J]. IEEE Journal on Selected Areas in Communications, 2020,38(8): 1637-1660.
[6]	SHAKYA I L , ALI F H . Joint access point selection and interference cancellation for cell-free massive MIMO[J]. IEEE Communications Letters, 2021,25(4): 1313-1317.
[7]	INTERDONATO G , KARLSSON M , BJ?RNSON E , ,et al. Local partial zero-forcing precoding for cell-free massive MIMO[J]. IEEE Transactions on Wireless Communications, 2020,19(7): 4758-4774.
[8]	MATTHAIOU M , YURDUSEVEN O , NGO H Q ,et al. The road to 6G:ten physical layer challenges for communications engineers[J]. IEEE Communications Magazine, 2021,59(1): 64-69.
[9]	ZHAO N , LU W D , SHENG M ,et al. UAV-assisted emergency networks in disasters[J]. IEEE Wireless Communications, 2019,26(1): 45-51.
[10]	陈新颖, 盛敏, 李博 ,等. 面向 6G 的无人机通信综述[J]. 电子与信息学报, 2022,44(3): 781-789.
	CHEN X Y , SHENG M , LI B ,et al. Survey on unmanned aerial vehicle communications for 6G[J]. Journal of Electronics ＆Information Technology, 2022,44(3): 781-789.
[11]	PANG X W , GUI G , ZHAO N ,et al. Uplink precoding optimization for NOMA cellular-connected UAV networks[J]. IEEE Transactions on Communications, 2020,68(2): 1271-1283.
[12]	ZENG Y , ZHANG R , LIM T J . Wireless communications with unmanned aerial vehicles:opportunities and challenges[J]. IEEE Communications Magazine, 2016,54(5): 36-42.
[13]	ZENG Y , ZHANG R . Energy-efficient UAV communication with trajectory optimization[J]. IEEE Transactions on Wireless Communications, 2017,16(6): 3747-3760.
[14]	JIANG X , WU Z L , YIN Z D ,et al. Joint power and trajectory design for UAV-relayed wireless systems[J]. IEEE Wireless Communications Letters, 2019,8(3): 697-700.
[15]	ZHAO N , LI Y X , ZHANG S ,et al. Security enhancement for NOMA-UAV networks[J]. IEEE Transactions on Vehicular Technology, 2020,69(4): 3994-4005.
[16]	LIU X , LIU Y W , CHEN Y ,et al. Trajectory design and power control for multi-UAV assisted wireless networks:a machine learning approach[J]. IEEE Transactions on Vehicular Technology, 2019,68(8): 7957-7969.
[17]	ZHANG R , WANG M , CAI L X ,et al. Learning to be proactive:self-regulation of UAV based networks with UAV and user dynamics[J]. IEEE Transactions on Wireless Communications, 2021,20(7): 4406-4419.
[18]	LIU Z N , HUANG G F , ZHONG Q H ,et al. UAV-aided vehicular communication design with vehicle trajectory's prediction[J]. IEEE Wireless Communications Letters, 2021,10(6): 1212-1216.
[19]	DING R J , GAO F F , SHEN X S . 3D UAV trajectory design and frequency band allocation for energy-efficient and fair communication:a deep reinforcement learning approach[J]. IEEE Transactions on Wireless Communications, 2020,19(12): 7796-7809.
[20]	XIA X J , ZHU P C , LI J M ,et al. Joint user selection and transceiver design for cell-free with network-assisted full duplexing[J]. IEEE Transactions on Wireless Communications, 2021,20(12): 7856-7870.

Metrics

Recommended 0

No Suggested Reading articles found!

Coordinated air-ground scheduling for UAV-assisted cell-free massive MIMO

RichHTML

PDF下载

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 5

References 20

Related Articles 2

Metrics

Recommended 0

[1]	Jiawei HU, Xiaoqian LIU, Xinke TANG, Yuhan DONG. Trajectory planning of UUV-assisted UWOC systems based on DQN [J]. Telecommunications Science, 2023, 39(5): 42-47.
[2]	Yue WANG, Fangmin XU, Haiyan CAO. Pilot interference mitigation for TDD cell-free massive MIMO systems [J]. Telecommunications Science, 2022, 38(5): 87-94.