研究简介

       6G发展的新趋势为无线传输技术的发展提供了新的机遇。具体而言，大规模MIMO向超大规模MIMO的演进显著提升了空间分辨率；毫米波和太赫兹高频信道在空间域和时间域呈现稀疏特性；通过融合超大规模MIMO，定位通信感知一体化技术能够提供超高精度的定位和感知能力。通过挖掘超大规模MIMO的超高空间分辨率和毫米波/太赫兹高频信道的稀疏性，时延多普勒对齐调制（DDAM，Delay-Doppler Alignment Modulation）通过空间-时延-多普勒域信号处理，能够实现多普勒和信道时延扩展的联合调控。DDAM的核心思想是时延-多普勒补偿和逐径波束赋形。通过在发射机或接收机端引入匹配各条路径的时延和多普勒相移，结合逐径波束赋形，可以消除每条径的多普勒效应，并且所有多径信号分量能够同时到达接收机。从而，DDAM可以将时频双选信道转化为时不变无ISI信道，无需进行复杂的信道均衡或多载波传输。对于时不变频率选择性信道，DDAM退化为时延对齐调制（DAM，Delay Alignment Modulation）。

超大规模天线DDAM传输示意图

研究特点

       针对时不变频率选择性信道，通过建立可调控信道时延扩展的DAM处理架构，能够实现高效的单载波和多载波传输。完美DAM可以将频率选择性信道转化为无ISI信道。当完美DAM不可行时，可以通过普适DAM调控信道时延扩展，同时充分利用多径信号分量。

稀疏多径信道

完美DAM

普适DAM

DAM通信优势

• 可调控信道时延扩展

• 无符号间干扰单载波和多载波通信的统一架构，免于复杂的信道均衡

• 降低保护间隔开销

• 低信号处理时延和低复杂度接收机

DAM感知优势

• 低峰旁比

• 抗多普勒频偏

       此外，针对时频双选信道，基于空间-时延-多普勒域处理的DDAM能够联合调控多普勒和信道时延扩展，可以将时频双选信道转化为时不变的无符号间干扰信道，无需进行复杂的信道均衡或多载波传输。

研究成果

期刊

[1] H. Lu and Y. Zeng, “Delay alignment modulation: Enabling equalization-free single-carrier communication,” IEEE Wireless Commun. Lett., vol. 11, no. 9, pp. 1785–1789, Sep. 2022.

论文链接：https://ieeexplore.ieee.org/document/9790794

[2] H. Lu and Y. Zeng, “Delay alignment modulation: Manipulating channel delay spread for efficient single- and multi-carrier communication,” IEEE Trans. Commun., vol. 71, no. 11, pp. 6316–6331, Nov. 2023. 

论文链接：https://ieeexplore.ieee.org/document/10225416

[3] Z. Xiao and Y. Zeng, “Integrated sensing and communication with delay alignment modulation: Performance analysis and beamforming optimization,” IEEE Trans. Wireless Commun., vol. 22, no. 12, pp. 8904–8918, Dec. 2023. 

论文链接：https://ieeexplore.ieee.org/document/10105893

[4] H. Lu, Y. Zeng, S. Jin, and R. Zhang, “Single-carrier delay alignment modulation for multi-IRS aided communication,” IEEE Trans. Wireless Commun., 2023. 

论文链接：https://ieeexplore.ieee.org/document/10231105

[5] H. Lu and Y. Zeng, “Delay-Doppler alignment modulation for spatially sparse massive MIMO communication,” IEEE Trans. Wireless Commun., 2023.

论文链接：https://ieeexplore.ieee.org/document/10314448

会议

[1] Z. Xiao and Y. Zeng, “Integrated sensing and communication with delay alignment modulation,” in Proc. IEEE Int. Conf. Commun. (ICC), May 2022, pp. 793–798.

论文链接：https://ieeexplore.ieee.org/document/9839117

[2] Z. Xiao, Y. Zeng, D. W. K. Ng, and F. Wen, “Exploiting double timescales for integrated sensing and communication with delay-Doppler alignment modulation,” in Proc. IEEE Int. Conf. Commun. (ICC), May 2023, pp. 5731–5736. 

论文链接：https://ieeexplore.ieee.org/document/10279599

[3] X. Wang, H. Lu, and Y. Zeng, “Multi-user delay alignment modulation for millimeter wave massive MIMO,” in Proc. IEEE Global Commun. Conf. (GLOBECOM), Dec. 2023, pp. 6970-6975. 

论文链接：https://ieeexplore.ieee.org/document/10437061

[4] D. Ding, Y. Zeng, and D. Wang, “Channel estimation for delay alignment modulation,” in Proc. IEEE Wireless Commun. Netw. Conf. Workshops (WCNCW), 2024. 

论文链接：https://arxiv.org/abs/2206.09339

[5] J. Zhang and Y. Zeng, “Delay alignment modulation with hybrid beamforming for spatially sparse communications,” in Proc. IEEE Wireless Commun. Netw. Conf. (WCNC),  2024. 

论文链接：https://arxiv.org/abs/2307.08210

[6] Z. Zhou, Z. Xiao, and Y. Zeng, “Fractional delay alignment modulation for spatially sparse wireless communications,” in Proc. IEEE Wireless Commun. Netw. Conf. (WCNC),  2024. 

论文链接：https://arxiv.org/abs/2403.19951

预印本

[1] X. Wang, H. Lu, Y. Zeng, X. Xu,  and J. Xu, “Achievable rate region and path-based beamforming for multi-user single-carrier delay alignment modulation,” arXiv:2309.00391, 2023. 

论文链接：https://arxiv.org/abs/2309.00391

[2] Z. Xiao, Y. Zeng, D. W. K. Ng, and F. Wen, “Integrated sensing and channel estimation by exploiting dual timescales for delay-Doppler alignment modulation,” arXiv:2310.11326, 2023. 

论文链接：https://arxiv.org/abs/2310.11326

研究简介

什么是信道知识地图？

（1）以移动节点物理或虚拟位置为主要索引的数据库

（2）直接反映特定位置的本地化信道特征，与收发机活动无关，可融合区域内所有终端的海量历史数据

（3）直接基于位置提前获取信道先验信息

（4）提升当地无线环境的认知能力，实现信道知识的快速实时预测推理

（5）避免重复的在线环境感知及信道获取

（6）由环境未知通信与感知迈向环境认知通信与感知的使能技术

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研究方向

1. 信道知识地图应用

信道知识地图在环境认知通信与感知拥有广泛的应用前景，其典型应用场景包括：

（1）未到达位置的信道：主动切换、网联无人机或网联机器人路径规划

（2）非合作节点间的信道：认知无线电：次发射机到主接收机; 物理层安全：发射机到窃听者

（3）大维信道：大规模MIMO、超大规模MIMO (XL-MIMO)

（4）硬件/信号处理受限的信道：混合波束赋形、低分辨率模数转换器、可重构智能表面（RIS）

2. 信道知识地图构建

为了实现基于信道知识地图的的通信与感知，首先需要以一种有效的方式构建信道知识地图。信道知识地图构建的本质是基于在少数位置获得的数据来重构出环境中所有感兴趣位置的信道信息，其主要方法可分为数据驱动和模型驱动两类。其中，数据驱动构建方法包括克里金法、张量补全、深度学习等；模型驱动构建方法包括空间损失场模型、虚拟障碍物模型等。

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研究成果

期刊

Y.  Zeng  and  X.  Xu, “Toward  environment-aware  6G  communicationsvia  channel  knowledge  map,”IEEE Wireless Commun.,  vol.  28,  no.  3,pp. 84–91, Jun. 2021. (首次提出了信道知识地图概念）

Y.  Zeng,  J.  Chen,  J.  Xu,  D.  Wu,  X.  Xu,  S.  Jin,  X.  Gao,  D.  Gesbert, S. Cui, and R. Zhang,“A tutorial on environment-aware communicationsvia channel knowledge map for 6G,”IEEE Commun. Surv. Tutor., Feb. 2024. (首篇信道知识地图综述论文）

D. Wu, Y. Zeng, S. Jin, and R. Zhang, “Environment-aware hybrid beamforming by leveraging channel knowledge map,” IEEE Trans. Wireless Commun., 2023.

Y. Zeng, X. Xu, S. Jin, and R. Zhang, “Simultaneous navigation and radio mapping for cellular-connected UAV with deep reinforcement learning,” IEEE Trans. Wireless Commun., vo. 20, no. 7, pp. 4205-4220, Jul. 2021.

H. Li, P. Li, G. Cheng, J. Xu, J. Chen, and Y. Zeng, “Channel knowledge map (CKM)-assisted multi-UAV wireless network: CKM construction and UAV placement,” Journal of Commun. and Inf. Netw., vol. 8, no. 3, pp. 256-270, Sep. 2023.

会议

D. Wu, Y. Zeng, S. Jin, and R. Zhang, “Environment-aware and training-free beam alignment for mmWave massive MIMO via channel knowledgemap,”in Proc. IEEE Int. Conf. Commun. Workshops (ICC Workshops), Jun. 2021, pp. 1–7.

D. Ding, D. Wu, Y. Zeng, S. Jin, and R. Zhang, “Environment-aware beam selection for IRS-aided communication via channel knowledge map,” IEEE GLOBECOM 2021.

K. Li, P. Li, Y. Zeng, and J. Xu, “Channel knowledge map forenvironment-aware communications: EM algorithm for map construc-tion,” in 2022 IEEE Wireless Communications and Networking Confer-ence (WCNC), 2022, pp. 1659–1664.

H Li, P Li, J Xu, J Chen and Y Zeng, “Derivative-free placement optimization for multi-UAV wireless networks with channel knowledge map”, in Proc. IEEE Int. Conf. Commun. Workshops (ICC Workshops), May. 2022, pp. 1029–1034.

Y. Long, Y. Zeng, X. Xu, and Y. Huang,“Environment-Aware Wireless Localization Enabled by Channel Knowledge Map,” IEEE Globecom 2022.

S. Zeng, X. Xu, Y. Zeng, and F. Liu, “CKM-assisted LoS identificationand  predictive  beamforming  for  cellular-connected  UAV,”  inProc.IEEE ICC, 2023.

D. Wu and Y. Zeng, “Environment-Aware Coordinated Multi-Point mmWave Beam Alignment via Channel Knowledge Map,” IEEE ICC 2023.

W. Xie, X. Xu, Z. Dai, and Y. Zeng, "On the Construction of Channel Gain Map: Model-Based or Model-Free Approach?", IEEE VTC20224-Spring Workshop, Singapore

预印本

X. Xu and Y. Zeng, “How much data is needed for channel knowledgemap construction?”IEEE Trans. Wireless Commun., Major Revision, [Online] Available: http://arxiv.org/abs/2312.06966.

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Demo展示

信道知识地图辅助高效波束对齐原型系统

视频链接：https://www.bilibili.com/video/BV1A8411o7Hv/?spm_id_from=333.999.0.0

研究简介

随着未来6G网络的发展，更大的天线孔径以及更高频段的使用（毫米波及太赫兹等），使得近场特性变得日益显著，传统的远场平面波假设不再适用。在近场技术领域，电磁波传播特性不再可以简单地近似为平面波，而需被视作球面波。这种新的物理特性带来了诸如空间非平稳性、波束分裂等多种新电磁效应。因此，许多传统通信以及感知算法在6G近场场景下的性能会严重下降，或无法充分利用这些新特性。

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研究特点

1. 超大规模MIMO近场信道建模

精确表征不同阵元间信号幅度，相位和方向图的差异，建立了适用于远近场的超大规模MIMO信道模型。以此为基础，探明了单用户近场信噪比缩放定律和渐近特性，分析了超大规模MIMO近场多用户波束赋形性能。同时，推导了考虑信号方向的瑞利距离和均匀功率距离，提出了相应的远近场精细划分准则。

超大规模MIMO近场球面波模型                                   远近场划分准则

2. 超大规模MIMO空间相关性研究

基于近场非均匀球面波和部分可视特性，建立了超大规模天线阵列下近场相关性模型，比较远近场空间相关性模型，体现了表征超大规模天线阵列通信近场空间相关性建模重要性。

超大规模近场空间非平稳特性

3. 超大规模MIMO智能反射面

针对超大规模智能反射面辅助通信系统，在近场非均匀球面波信道模型的基础上，精确表征反射单元方向性，推导了其性能缩放定律与渐近特性。

超大规模智能反射面辅助通信系统                 信噪比与智能反射面尺寸的变化关系

4. 模块化超大规模MIMO通信

针对模块化超大规模MIMO近场通信，研究模块化超大规模MIMO通信近场建模与性能分析、模块化超大规模MIMO码本设计和近场波束训练，以及模块化超大规模天线阵列的近场信道测量与验证。

模块化超大规模天线阵列

5. 超大规模MIMO感知

建立超大规模感知系统的一般模型，针对超大规模MIMO雷达与相控阵雷达，分别推导了近场场景下感知接收信噪比和克拉美罗下界的闭式表达式。

超大规模MIMO近场感知系统模型                                单站近场感知的角度克拉美罗界

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研究成果

• 期刊论文

[1] H. Lu, Y. Zeng, C. You, Y. Han, J. Zhang, Z. Wang, Z. Dong, S. Jin, C.-X. Wang, T. Jiang et al., “A tutorial on near-field XL-MIMO communications towards 6G,” IEEE Commun. Surv. Tuts., 2024. 

论文链接：https://arxiv.org/pdf/2310.11044.pdf

[2] H. Lu and Y. Zeng, “Communicating with extremely large-scale array/surface: Unified modeling and performance analysis,” IEEE Trans. Wireless Commun., vol. 21, no. 6, pp. 4039–4053, Jun. 2022.

论文链接：https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9617121

[3] H. Lu and Y. Zeng, “Near-field modeling and performance analysis for multi-user extremely large-scale MIMO communication,” IEEE Commun. Lett., vol. 26, no. 2, pp. 277–281, Feb. 2021.

论文链接：https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9620081

[4] Z. Dong and Y. Zeng, "Near-Field Spatial Correlation for Extremely Large-Scale Array Communications," IEEE Commun. Lett., vol. 26, no. 7, pp. 1534-1538, July 2022.

论文链接：https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9763525

[5] Z. Dong, X. Li, Y. Zeng, “Characterizing and Utilizing Near-Field Spatial Correlation for XL-MIMO Communication,” submitted to IEEE Trans. Commun, 2024.

[6] C. Feng, H. Lu, Y. Zeng, T. Li, S. Jin and R. Zhang, “Near-field modelling and performance analysis for extremely large-scale IRS communications,” IEEE Trans. Wireless Commun., 2023.

论文链接：https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10287779

[7] X. Li, H. Lu, Y. Zeng, S. Jin, and R. Zhang,“Near-field modelling and performance analysis of modular extremely large-scale array communications,” IEEE Commun. Lett., vol. 26, no. 7, pp. 1529-1533, Jul. 2022.

论文链接：https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9767808

[8] X. Li, H. Lu, Y. Zeng, S. Jin, and R. Zhang,“Modular extremely large-scale array communication: Near-field modelling and performance analysis,” China Commun., vol. 20, no. 4, pp. 132-152, Apr. 2023.

论文链接：https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10110335

[9] X. Li, Z. Dong, Y. Zeng, S. Jin, and R. Zhang,“ Multi-user modular XL-MIMO communications: Near-field beam focusing pattern and user Grouping,” submitted to IEEE Trans. Wireless Commun., 2023.

论文链接：https://arxiv.org/pdf/2308.11289.pdf

[10] H. Wang, Z. Xiao and Y. Zeng, "Cramér-Rao Bounds for Near-Field Sensing With Extremely Large-Scale MIMO," IEEE Trans. Signal Process., vol. 72, pp. 701-717, 2024.

论文链接：https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10388218

• 会议论文

[1] H. Lu and Y. Zeng, “How does performance scale with antenna number for extremely large-scale MIMO?” in Proc. IEEE Int. Conf. Commun. (ICC), Jun. 2021, pp. 1–6.

论文链接：https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9500972

[2] Z. Dong, X. Li, Y. Zeng, S. Jin and T. Jiang, "Near-Field Spatial Correlation for Multi-Path XL-Array Communications with Partial Visibility," in Proc. IEEE Global Commun. Conf. (GLOBECOM), Dec. 2023.

论文链接：https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10437851

[3] C. Feng, H. Lu, Y. Zeng, S. Jin and R. Zhang, "Wireless Communication with Extremely Large-Scale Intelligent Reflecting Surface," in Proc. IEEE/CIC Int. Conf. Commun. China (ICCC Workshops), Jul., 2021.

论文链接：https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9538864

[4] X. Li, Z. Dong, Y. Zeng, S. Jin, and R. Zhang,“Near-field beam focusing pattern and grating lobe characterization for modular XL-array,”in Proc. IEEE Global Commun. Conf. (GLOBECOM), Dec. 2023.

论文链接：https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10436773

[5] H. Wang and Y. Zeng, "SNR Scaling Laws for Radio Sensing with Extremely Large-Scale MIMO," in Proc. IEEE Int. Conf. Commun. Workshops (ICC Workshops), May, 2022.

论文链接：https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9814501

研究简介

    共生无线通信由于其在频谱和能源方面的互惠共享特性，有望成为解决频谱资源短缺和能源供应匮乏的有效解决方案，也被认为是6G的关键候选技术之一。它的核心思想是利用认知后向散射通信，将无源的后向散射设备（Backscatter Device, BD）与有源的主发射机（primary transmitter, PT）相结合，具体来说，BD通过被动后向散射来自PT的入射信号来调制自己的信息，而不进行主动信号处理，从而实现高效节能地频谱和能量共享。

研究方向

• 1.大维共生无线通信性能研究

提出利用系统固有的大维潜在被动式用户协作增强被动式通信性能，同时实现对主动式通信的互惠增强。以实现更高的谱效与能效。通过推导大维被动式用户下的主被动式通信速率渐进值，切实揭示了大维场景下主被动式通信间的互惠增强趋势。

• 2.无蜂窝共生通信系统研究

建立了无蜂窝共生无线通信系统模型，利用无定形架构的分布式接入增益，融合主被动传输方式共生，实现了高宏分集的被动式通信。提出了适用于无蜂窝共生无线通信系统的二阶段信道估计方法并表征了可达速率区域，揭示了主被动式通信相互折中关系。

• 3.共生无线通信能量效率研究

分析了共生无线通信系统效能与资源配置的关系，表征了主被动式通信能效区域的帕累托边界，并推导了主被动式通信能效渐进表达式。进一步探究了共生系统主被动通信能效与资源配置的相互关系，以期实现高能效的大维共生通信系统。

研究成果

• 期刊论文

[1].  Z. Dai, J. Xu, Y. Zeng*, S. Jin and T. Jiang, “Characterizing the rate region of active and passive communications with RIS-based cell-free symbiotic radio,” IEEE Internet Things J., 2023, doi: 10.1109/JIOT.2023.3308970.

论文链接：https://ieeexplore.ieee.org/abstract/document/10231349

[2]. J. Xu, Z. Dai and Y. Zeng*, “MIMO symbiotic radio with massive backscatter devices: asymptotic analysis and precoding optimization,” IEEE Trans. Commun., vol. 71, no. 9, pp. 5487-5502, Sep. 2023.

论文链接：https://ieeexplore.ieee.org/document/10146297

[3]. Z. Dai, R. Li, J. Xu, Y. Zeng* and S. Jin, “Rate-region characterization and channel estimation for cell-free symbiotic radio communications, IEEE Trans. Commun., vol. 71, no. 2, pp. 674-687, Feb. 2023.

论文链接：https://ieeexplore.ieee.org/document/9973339

• 会议论文

[1] S. Wang, J. Xu, and Y. Zeng, "Characterizing the Energy-Efficiency Region of Symbiotic Radio Communications," IEEE WCSP 2022.

论文链接：https://ieeexplore.ieee.org/document/10039158

[2] Z. Dai, R. Li, J. Xu, Y. Zeng and S. Jin, "Cell-Free Symbiotic Radio: Channel Estimation Method and Achievable Rate Analysis," 2021 IEEE/CIC International Conference on Communications in China (ICCC Workshops), Xiamen, China, 2021, pp. 25-30.

论文链接：https://ieeexplore.ieee.org/document/9538923

[3] J. Xu, Z. Dai and Y. Zeng, "Enabling Full Mutualism for Symbiotic Radio with Massive Backscatter Devices," 2021 IEEE Global Communications Conference (GLOBECOM), Madrid, Spain, 2021, pp. 1-6.

论文链接：https://ieeexplore.ieee.org/document/9686018

研究简介

随着人类对空间资源的深入探索利用、重大突发事件应急响应需求的不断提升、以及无人化、自动化和智能化等新业务需求的不断涌现，网络发展面临着由传统地面蜂窝通信迈向未来三维立体通信的重大变革机遇。特别是无人机行业的高速发展，使得无人机呈现出与蜂窝通信技术紧密结合的发展趋势，形成网联无人机。网联无人机不仅可以实现超远距离遥控和高速率信息传输，还能够满足低成本空域融合的通信需求。然而，无人机在带来便利的同时，也造成了“黑飞”事件频发，严重危害公共安全和个人隐私。雷达感知系统可以充分发挥基站广泛和密集部署等优势，一方面对覆盖范围内移动目标进行全空域连续探测，定位、识别并跟踪侵入到范围内无人机，防范非合作无人机导致的各类事故；另一方面又能满足低空无人机的大带宽通信需求，实现载荷数据的实时回传和在网无人机的实时控制，融合多源感知实现在网无人机飞状态识别、路径规划和避障等功能。同时，由于通信和感知共享基站，可以降低感知功能的部署成本，并实现无人机通信感知一体化设计。

研究特点

1) 空地融合移动通信: 针对无人机通信，提出了无人机空中基站辅助通信和网联飞行器通信两大空地融合范式，揭示了空地融合移动通信系统的通信容量与飞行器轨迹及无线资源配置的内在关系，综合空气动力学及信息通信理论，在国际上首次提出了空地融合通信系统的能量效率度量指标，建立了空地融合通信能量效率理论，提出了容量最大化及能效最大化的飞行器轨迹优化方法。通过仿真验证和实验测量相结合，对网联无人机的可行性验证及性能进行评估，提出了基于垂直扇区划分和三维波束成形的干扰控制方案，采用了适用于网联无人机通信的基站侧三维天线模型，并利用随机几何方法分析了小区关联概率和通信中断率等性能。

图1 无人机通信场景

2) 无人机空间环境感知: 在无人机感知方面，针对移动通信网络空中覆盖不连续的问题，提出了基于图论和凸优化算法的覆盖感知无人机路径优化方法，解决低空覆盖盲区问题。此外，利用通感一体化网络的环境学习及认知能力，挖掘三维空间物理和射频环境的高度相关性，提出了三维物理环境地图和信道知识地图同构框架，采用了智能化深度强化学习方法，获取空中障碍物位置信息以避免通信阻塞，并实现网络资源配置和空中飞行器节点控制的联合设计。

  a) 无人机感知覆盖增强 

        b)三维信道知识地图构建          

图2 无人机感知场景

专著：

[B1] Yong Zeng, Ismail Guvenc, Rui Zhang, Giovanni Geraci, and David W. Matolak (Editors), UAV Communications for 5G and Beyond, Wiley, 2020. https://ieeexplore.ieee.org/book/9295056

期刊：

[J1] Li, P. Li, G. Cheng, J. Xu, J. Chen, and Y. Zeng, “Channel Knowledge Map (CKM)-Assisted Multi-UAV Wireless Network: CKM Construction and UAV Placement,” Journal of Commun. and Inf. Netw., vol. 8, no. 3, pp. 256-270, Sep. 2023. https://ieeexplore.ieee.org/document/10272353

[J2] R. Li, Z. Xiao and Y. Zeng, “Towards seamless sensing coverage for cellular multi-static integrated sensing and communication,” IEEE Trans. Wireless Commun., 2023. https://ieeexplore.ieee.org/document/10304081

[J3] B. Li, Q. Li, Y. Zeng, R. Yue, and R. Zhang, "3D trajectory optimization for energy-efficient UAV communication: a control design perspective," IEEE Trans. Wireless Commun., vol. 21, no. 6, pp. 4579-4593, June 2022. https://ieeexplore.ieee.org/document/9652043

[J4] Q. Wu, J. Xu, Y. Zeng, D. W. K. Ng, N. A. Dhahir, R. Schober, and A. L. Swindlehurst, “A comprehensive overview on 5G-and-beyond networks with UAVs: From communications to sensing and intelligence,” IEEE J. Sel. Commun., vol. 39, no. 10, pp. 2912-2945, Oct. 2021. https://ieeexplore.ieee.org/document/9456851

[J5] Y. Zeng, X. Xu, S. Jin, and R. Zhang, “Simultaneous navigation and radio mapping for cellular-connected UAV with deep reinforcement learning,” IEEE Trans. Wireless Commun., vo. 20, no. 7, pp. 4205 - 4220, Jul. 2021. https://ieeexplore.ieee.org/document/9354009

[J6] H. Lu, Y. Zeng*, S. Jin, and R. Zhang, "Aerial intelligent reflecting surface: joint placement and passive beamforming design with 3D beam flattening," IEEE Trans. Wireless Commun., vol. 20, no. 7, pp. 4128 - 4143 , Jul. 2021. https://ieeexplore.ieee.org/document/9351782

[J7] N. Gao, Y. Zeng* J. Wang, D. Wu, C. Zhang, Q. Song, J. Qian, and S. Jin, "Energy model for UAV communications: experimental validation and model generalization," accepted by China Communications., available on arXiv https://arxiv.org/abs/2005.01305

[J8] J. Zhang, Y. Zeng, and R. Zhang, "Multi-Antenna UAV Data Harvesting: Joint Trajectory and Communication Optimization," Journal of Communications and Information Networks, vol. 5, no. 1, pp. 86-99, Mar., 2020. https://ieeexplore.ieee.org/document/9055113

[J9] Q. Song, Y. Zeng, J. Xu and S. Jin, “A Survey of prototype and experiment for UAV Communications,” accepted by Science China Information Sciences. https://link.springer.com/article/10.1007/s11432-020-3030-2

[J10] C. Zhan and Y. Zeng, "Energy-efficient data uploading for cellular-connected UAV systems," IEEE Trans. Wireless Commun., vol. 19, no. 11, pp. 7279-7292, Nov. 2020. https://ieeexplore.ieee.org/document/9149835

[J11] L. Xie, J. Xu, and Y. Zeng, "Common Throughput Maximization for UAV-Enabled Interference Channel With Wireless Powered Communications," IEEE Trans. Commun., vol. 68, no. 5, pp. 3197-3212, May, 2020. https://ieeexplore.ieee.org/document/8982086

[J12] J. Zhang, Y. Zeng, and R. Zhang, “Receding horizon optimization for energy-efficient UAV communication,” IEEE Wireless Commun. Letters, vol. 9, no. 4, pp. 490-494, April, 2020. https://ieeexplore.ieee.org/document/8935101

[J13] C. Zhang and Y. Zeng, “Aerial-ground cost tradeoff for multi-UAV enabled data collection in wireless sensor networks,” IEEE Trans. Commun. vol. 68, no. 3, pp. 1937-1950, Mar. 2020. https://ieeexplore.ieee.org/document/8943326

[J14] L. Xiao, Y. Xu, D. Yang, and Y. Zeng, "Secrecy energy efficiency maximization for UAV-enabled mobile relaying," IEEE Trans. Green Commun. and Networking, vol. 4, no. 1, pp. 180-193, Mar. 2020. https://ieeexplore.ieee.org/document/8884126

[J15] C. Shen, T.-H. Chang, J. Gong, Y. Zeng, and R. Zhang, "Multi-UAV interference coordination via joint trajectory and power control," IEEE Trans. Signal. Process, vol. 68, pp. 843-858, Jan. 2020. https://ieeexplore.ieee.org/document/8961096

[J16] Y. Zeng, Q. Wu, and R. Zhang, “Accessing from the sky: a tutorial on UAV communications for 5G and beyond,’’ Proceedings of the IEEE (Invited Paper), vol. 107, no. 12, pp. 2327-2375, Dec. 2019. (ESI highly cited paper, ESI hot paper) https://ieeexplore.ieee.org/document/8918497

[J17] C. Zhan and Y. Zeng, "Completion time minimization for multi-UAV enabled data collection," IEEE Trans. Wireless Commun., vol. 18, no. 10, pp. 4859-4872, Jul., 2019. https://ieeexplore.ieee.org/document/8779596

[J18] Y. Zeng, J. Xu, and R. Zhang, “Energy minimization for wireless communication with rotary-wing UAV,” IEEE Trans. Wireless Commun., vol. 18, no. 4, pp. 2329 - 2345, Apr. 2019. (ESI highly cited paper, ESI hot paper) https://ieeexplore.ieee.org/document/8663615

[J19] Q. Song, F.-C. Zheng, Y. Zeng, and J. Zhang, "Joint beamforming and power allocation for UAV-enabled full-duplex relay", IEEE Trans. Veh. Technol., vol. 68, no. 2, pp. 1657-1671, Feb. 2019. https://ieeexplore.ieee.org/document/8586877

[J20] S. Zhang, Y. Zeng, and R. Zhang, "Cellular-Enabled UAV Communication: A Connectivity-Constrained Trajectory Optimization Perspective," IEEE Trans. Commun. (EIC Invited Paper), vol. 67, no. 3, pp. 2580-2604, Mar. 2019. (ESI highly cited paper) https://ieeexplore.ieee.org/document/8531711

[J21] G. Zhang, H, Yan, Y. Zeng, M. Cui, and Y. Liu, "Trajectory optimization and power allocation for multi-hop UAV relaying communications," IEEE Access, vol. 6, pp. 48566 - 48576, Aug. 2018. https://ieeexplore.ieee.org/document/8453022

[J22] Y. Zeng, J. Lyu, and R. Zhang, "Cellular-connected UAV: potentials, challenges and promising technologies," IEEE Wireless Commun., vol. 26, no. 1, pp. 120-127, Feb. 2019. (ESI highly cited paper) https://ieeexplore.ieee.org/document/8470897

[J23] J. Zhang, Y. Zeng, and R. Zhang, "UAV-enabled radio access network: multi-mode communication and trajectory design," IEEE Trans. Signal Process., vol. 66, no. 20, pp. 5269 - 5284, Oct. 2018. https://ieeexplore.ieee.org/document/8443133

[J24] C. Zhan, Y. Zeng, and R. Zhang, “Trajectory design for distributed estimation in UAV enabled wireless sensor network,” IEEE Trans. Veh. Technol., vol. 67, no. 10, pp. 10155 - 10159, Oct. 2018. https://ieeexplore.ieee.org/document/8419316

[J25] J. Xu, Y. Zeng, and R. Zhang, "UAV-enabled wireless power transfer: trajectory design and energy optimization," IEEE Trans. Wireless Commun., vol. 17, no. 8, pp. 5092-5106, Aug., 2018. (ESI highly cited paper) https://ieeexplore.ieee.org/document/8365881

[J26] X. Xu, Y. Zeng*, Y. L. Guan, and R. Zhang, "Overcoming Endurance Issue: UAV-Enabled Communications with Proactive Caching", IEEE J. Sel. Areas on Commmun., vol. 36, no. 6, pp. 1231-1244, Jun., 2018. https://ieeexplore.ieee.org/document/8374947

[J27] J. Lyu, Y. Zeng, R. Zhang, "UAV-aided offloading for cellular hotspot," IEEE Trans. Wireless Commun. vol. 17, no. 6, pp. 3988-4001, Jun., 2018. (ESI highly cited paper) https://ieeexplore.ieee.org/document/8329013

[J28] D. Yang, Q, Wu, Y. Zeng*, and R. Zhang, “Energy trade-off in ground-to-UAV communication via trajectory design,” IEEE Trans. Veh. Technol., vol. 67, no. 7, pp. 6721-6726, Jul. 2018. (ESI highly cited paper) https://ieeexplore.ieee.org/document/8316986

[J29] Y. Zeng, X. Xu, and R. Zhang, "Trajectory design for completion time minimization in UAV-enabled multicasting"，IEEE Trans. Wireless Commun., vol. 17, no. 4, pp. 2233-2246, Apr., 2018. (ESI highly cited paper) https://ieeexplore.ieee.org/document/8255824

[J30] Q. Wu, Y. Zeng*, and R. Zhang, "Joint trajectory and communication design for multi-UAV enabled wireless networks," IEEE Trans. Wireless Commun., vol. 17, no. 3, 2109-2121, Mar., 2018. (ESI highly cited paper, ESI hot paper) https://ieeexplore.ieee.org/document/8247211

[J31] C. Zhan, Y. Zeng*, and R. Zhang, "Energy-efficient data collection in UAV enabled wireless sensor network," IEEE Wireless Commun. Letters, vol. 7, no. 3, pp. 328-331, Jun., 2018. (ESI highly cited paper) https://ieeexplore.ieee.org/document/9824168

[J32] H. He, S. Zhang, Y. Zeng*, and R. Zhang, "Joint altitude and beamwidth optimization for UAV-enabled multiuser communications," IEEE Commun. Letters. vol. 22, no. 2, pp. 344-347, Feb., 2018. (ESI highly cited paper) https://ieeexplore.ieee.org/document/8103781

[J33] J. Lyu, Y. Zeng, R. Zhang, and T. J. Lim, "Placement Optimization of UAV-Mounted Mobile Base Stations", IEEE Commun. Letters, vol. 21, no. 3, pp. 604-607, Mar., 2017. (ESI highly cited paper, IEEE Communications Society Heinrich Hertz Award) https://ieeexplore.ieee.org/document/7762053

[J34] Y. Zeng and R. Zhang, "Energy-Efficient UAV Communication with Trajectory Optimization," IEEE Trans. Wireless Commun., vol. 16, no. 6, pp. 3747-3760, Jun., 2017. (ESI highly cited paper, ESI hot paper, IEEE Marconi Prize Paper Award) https://ieeexplore.ieee.org/document/9674583

[J35] J. Lyu, Y. Zeng, and R. Zhang, "Cyclical multiple access in UAV-aided communications: a throughput delay tradeoff'', IEEE Wireless Commun. Letters, vol. 5, no. 6, pp. 600-603, Dec., 2016. https://ieeexplore.ieee.org/document/7556368

[J36] Y. Zeng, R. Zhang, and T. J. Lim, "Throughput maximization for UAV-enabled mobile relaying systems," IEEE Trans. Commun., vol. 64, no. 12, pp. 4983-4996, Dec., 2016. (ESI highly cited paper, ESI hot paper) https://ieeexplore.ieee.org/document/7572068

[J37] Y. Zeng, R. Zhang, and T. J. Lim, "Wireless communications with unmanned aerial vehicles: opportunities and challenges," IEEE Commun. Mag.,vol. 54, no. 5, pp. 36-42, May, 2016. (ESI highly cited paper, ESI hot paper) https://ieeexplore.ieee.org/document/7470933

会议：

[C1] Y. Yang, X. Xu and Y. Zeng, “Simultaneous radio and physical mapping for cellular-connected UAV by fusing radio and sensing data,” IEEE International Conference on Communications (ICC) Workshops, 2023, pp. 1433-1438. https://ieeexplore.ieee.org/document/10283578

[C2] S. Zeng, X. Xu, Y. Zeng, and F. Liu, "CKM-Assisted LoS Identification and Predictive Beamforming for Cellular-Connected UAV", IEEE ICC 2023. https://ieeexplore.ieee.org/document/10278702

[C3] R. Li, Z. Xiao, and Y. Zeng, “Beamforming Towards Seamless Sensing Coverage for Cellular Integrated Sensing and Communication,” IEEE ICC 2022 Workshop. https://ieeexplore.ieee.org/document/9814606

[C4] H. Li, P. Li, J. Xu, J. Chen and Y. Zeng, “Derivative-Free Placement Optimization for Multi-UAV Wireless Networks with Channel Knowledge Map,” IEEE ICC 2022 Workshop. https://ieeexplore.ieee.org/document/9814544

[C5] Y. Huang and Y. Zeng, "Simultaneous Environment Sensing and Channel Knowledge Mapping for Cellular-Connected UAV," accepted by IEEE Globecom 2021 Workshop. https://ieeexplore.ieee.org/document/9682178

[C6] X. Xu and Y. Zeng, "Time-weighted coverage of integrated aerial and ground networks for post-disaster communications," IEEE WCNC 2020 Workshop. https://ieeexplore.ieee.org/document/9124908

[C7] Y. Huang, X. Mo, J. Xu, L. Qiu, and Y. Zeng, "Online maneuver design for UAV-enabled NOMA systems via reinforcement learning," IEEE WCNC 2020. https://ieeexplore.ieee.org/document/9120492

[C8] Y. Zeng and X. Xu, "Path design for cellular-connected UAV with reinforcement learning," IEEE Globecom 2019, available online at https://arxiv.org/abs/1905.03440.

[C9] X. Xu and Y. Zeng, "Cellular-connected UAV: performance analysis with 3D antenna modelling," IEEE International Conference on Communications (ICC’19) Workshop. https://ieeexplore.ieee.org/document/8756719

[C10] M. A. Ali, Y. Zeng, and A. Jamalipour, "Delay-oriented spectrum sharing and traffic offloading in coexisting UAV-enabled cellular and WiFi networks," IEEE DySPAN 2018. https://ieeexplore.ieee.org/document/8610500

[C11] Y. Zeng, J. Xu, and R. Zhang, "Rotary-wing UAV enabled wireless network: trajectory design and resource allocation," IEEE Globecom 2018. https://ieeexplore.ieee.org/document/8647595

[C12] S. Zhang, Y. Zeng, and R. Zhang, “Cellular-enabled UAV communication: trajectory optimization under connectivity constraint”, IEEE International Conference on Communications (ICC’18). https://ieeexplore.ieee.org/document/8422584

[C13] J. Xu, Y. Zeng, and R. Zhang, “UAV-Enabled Multiuser Wireless Power Transfer: Trajectory Design and Energy Optimization”, Asia-Pacific Conference on Communications (APCC) Workshop, 2017. https://ieeexplore.ieee.org/document/8254949 

[C14] J. Xu, Y. Zeng, and R. Zhang, “UAV-enabled wireless power transfer: Trajectory design and energy region characterization”, IEEE Globecom Workshop, 2017. https://ieeexplore.ieee.org/document/8269097

[C15] J. Lyu, Y. Zeng, and R. Zhang, "Spectrum sharing and cyclical multiple access in UAV-aided cellular offloading," IEEE Globecom, 2017. https://ieeexplore.ieee.org/document/8254236

[C16] Q. Wu, Y. Zeng, and R. Zhang, "Joint trajectory and communication design for UAV-enabled multiple access," IEEE Globecom, 2017. https://ieeexplore.ieee.org/document/8304077 

[C17] J. Zhang, Y. Zeng, and R. Zhang, "Spectrum and energy efficiency maximization in UAV-enabled mobile relaying," IEEE International Conference on Communications (ICC), 2017. https://ieeexplore.ieee.org/document/7997208

一、毫米波OFDM通信感知一体化原型系统

系统简介

通信感知一体化（Integrated Sensing and Communication，ISAC）旨在在一个系统内实现无线通信和雷达感知，可以实现两个功能的硬件共用、频谱资源共享以及相互协作、实现互惠增强。目前，通信感知一体化已被ITU-R正式纳入6G的六大使用场景之一。

与此同时，正交频分复用（orthogonal frequency division multiplexing，OFDM）已被广泛应用于当今的通信系统，如4G LTE、5G NR和802.11 WLAN。可以预测，OFDM仍将是6G系统中具有竞争力的调制方案之一。 此外，随着频谱资源日益稀缺，更高频率（如毫米波和太赫兹）成为无线系统的发展趋势之一，这使得使用更大的带宽和更大规模的天线阵列成为可能。更大的带宽和更大规模的天线阵列在提升通信可达速率的同时，也更有利于提高感知的距离和角度分辨率。

本系统实现了毫米波频段OFDM调制的通信感知一体化原型。如图所示，本系统实现了单基站在为用户提供通信服务时同时对环境进行感知。

通信感知一体化场景示意图

系统设计

系统架构图

如图所示，原型系统由两个部分组成，即ISAC基站和用户设备。基站包含一台通用软件无线电设备（USRP）和两台1x16的毫米波相控阵，而用户设备包含一台USRP和一台用于接收的毫米波相控阵。

该系统的工作流程如下：在基站端，使用H.264编码器对视频流进行编码。编码后的数据通过OFDM调制器调制，并由发射相控阵传输。然后，用户设备接收信号并对其进行解调以获取传输的数据。接收到的信号输入到H.264解码器进行解码并播放。基站端的雷达接收机接收周围环境反射的信号并进行距离和角度感知。当视频比特率较低时，会传输虚拟填充帧以确保雷达刷新率。 由于雷达接收器只有一个射频链路，因此采用了切换技术来实现角度感知。

该系统支持多种切换模式。雷达接收相控阵有16个阵元，最简单的模式是在一个OFDM帧内切换到不同的阵元。然后，可以应用阵列信号处理方法来实现角度感知。另一种模式是在一个OFDM帧内切换到DFT码本中的不同波束成形矢量，然后使用IDFT来获得每个天线元素上的信号。这种方法更复杂，但利用了波束成形增益。由于切换是在一个帧内进行的，角度感知具有实时性。

对于基站发射相控阵，可以使用不同的波束形成方案。在传输数据帧时，可以设计窄波束以确保通信用户的高波束成形增益。这种方案的缺点是只有一个较小的角度范围被照亮，因而只适合于感知目标就是通信用户的场景下对用户的追踪。 波束也可以设计得更宽，以增加用于感知的角度范围，但会牺牲波束成形增益，从而影响通信性能。在进行全局感知时，波束成形方案由雷达接收相控阵切换方法而定。当使用天线切换时，波束需要尽可能宽，以覆盖大部分角度范围。当选择波束成形矢量切换时，发射相控阵的波束成形矢量与雷达接收相控阵同步切换，以最大化波束成形增益。

相关论文

[1] Zhou Z, Zhang C, Zeng Y. Prototype of Real-time Integrated Sensing and Communication with Millimeter-Wave OFDM[C]//2023 IEEE/CIC International Conference on Communications in China (ICCC). IEEE, 2023: 1-2.

相关视频

ICCC 2023 毫米波OFDM实时通感一体化原型系统_哔哩哔哩_bilibili

相关奖项

本系统获得第十七届中国研究生电子设计竞赛全国二等奖。