整合感測與通訊統被視為第六代無線網路演進的關鍵技術，能夠同時向通訊用戶傳送信號並對目標進行感測。我們考慮一個雲端無線接入網絡，其中多個路邊基地台合作進行感測和通訊，並聯合服務車輛到基礎設施下行系統中的所有車輛。多個路邊基地台之間的合作有助於提高整體效能。我們考慮毫米波多輸入多輸出系統，利用大型天線陣列降低效能的路徑損耗。下一時刻的波束成型根據上一時刻的估計進行優化。我們將可達到的通訊總速率和克拉美爾-拉奧下界(CRLB)作為優化問題中的通訊和感測效能。然而，估計可能是不完美的，我們將估計錯誤視為高斯誤差，並且要求效能在高機率下大於個臨界點。因為 CRLB 是無偏差估計器的最小標準差，因此估計誤差的不確定性受到CRLB 的約束。在本文中，我們研究了在通訊總速率約束和 CRLB 約束下的功率最小化的穩健波束成型問題，並使用半定鬆弛和 Bernstein 不等式。模擬結果顯示，我們所提
出的算法對估計具有強健性，並且通訊和感測效能都能被滿足。

Integrated sensing and communication (ISAC) system is commonly regarded as a key technology in the evolution of sixth-generation (6G) wireless networks to simultaneously transmit downlink signal to communication users and perform radar sensing toward targets. We consider a cloud radio access network (C-RAN) that multiple roadside units (RSUs) perform sensing and communication cooperatively and jointly serve all the vehicles in a vehicle-to-infrastructure (V2I) downlink system. The cooperation between multiple RSUs helps increase the overall performance. We consider a millimeter wave (mmWave) multiple-input multiple-output (MIMO) system, which
leverages large antenna arrays to confront path loss that might degrade the performance. Sensing and communication are performed simultaneously and the beamformer of the next time slot is optimized based on the estimation of the previous time slot. We adopt the achievable sum rate and the cramer rao lower bound (CRLB) as the communication and sensing performance in the optimization problem. However, the estimation might be imperfect and the optimization based on the estimation might fail to combat the estimation mismatch. Therefore, we considered the estimation mismatch to be in a Gaussian error model, and the outage probability constraint requires that the
performance should be greater than a threshold with high probability. To be mentioned, the CRLB threshold is utilized as the variance of the Gaussian error model since the CRLB is the minimum variance of an unbiased estimator. Therefore, the uncertainty of the estimation mismatch is constrained by the CRLB threshold. In this paper, we investigated a robust beamforming problem of power minimization under communication sum rate constraint and CRLB constraint. Semi-definite relaxation (SDR) and Berstein- type inequalities are used to make the problem tractable. Simulation results show that the proposed algorithm is robust against the sensing and the communication performance is satisfied.

摘要
目錄
第一章-------------------------1
第二章-------------------------6
第三章-------------------------18
第四章-------------------------23
第五章-------------------------36
第六章-------------------------50

1. Liu, C. Masouros, A. P. Petropulu, H. Griffiths, and L. Hanzo, “Joint radar and communication design: Applications, state-of-the-art, and the road ahead,” IEEE Trans. Commun.,
vol. 68, no. 6, pp. 3834–3862, 2020.
2. A. Liu, Z. Huang, M. Li, Y. Wan, W. Li, T. X. Han, C. Liu, R. Du, D. K. P. Tan, J. Lu,
et al., “A survey on fundamental limits of integrated sensing and communication,” IEEE
Communications Surveys & Tutorials, vol. 24, no. 2, pp. 994–1034, 2022.
3. F. Liu, Y. Cui, C. Masouros, J. Xu, T. X. Han, Y. C. Eldar, and S. Buzzi, “Integrated sensing
and communications: Toward dual-functional wireless networks for 6g and beyond,” IEEE J.
Sel. Areas Commun., vol. 40, no. 6, pp. 1728–1767, 2022.
4. J. A. Zhang, X. Huang, Y. J. Guo, J. Yuan, and R. W. Heath, “Multibeam for joint communication and radar sensing using steerable analog antenna arrays,” IEEE Trans. Veh. Technol.,
vol. 68, no. 1, pp. 671–685, 2018.
5. X. Wang, Z. Fei, J. A. Zhang, and J. Xu, “Partially-connected hybrid beamforming design
for integrated sensing and communication systems,” IEEE Trans. Commun., vol. 70, no. 10,
pp. 6648–6660, 2022.
6. C. Qi, W. Ci, J. Zhang, and X. You, “Hybrid beamforming for millimeter wave mimo integrated sensing and communications,” IEEE Commun. Lett., vol. 26, no. 5, pp. 1136–1140,
2022.
7. F. Liu, W. Yuan, C. Masouros, and J. Yuan, “Radar-assisted predictive beamforming for
vehicular links: Communication served by sensing,” IEEE Trans. Wireless Commun., vol. 19,
no. 11, pp. 7704–7719, 2020.
8. C. Liu, W. Yuan, S. Li, X. Liu, H. Li, D. W. K. Ng, and Y. Li, “Learning-based predictive
beamforming for integrated sensing and communication in vehicular networks,” IEEE J. Sel.
Areas Commun., vol. 40, no. 8, pp. 2317–2334, 2022.
9. Y. Huang, Y. Fang, X. Li, and J. Xu, “Coordinated power control for network integrated
sensing and communication,” IEEE Trans. Veh. Technol., vol. 71, no. 12, pp. 13361–13365,
2022.
10. W. Jiang, Z. Wei, and Z. Feng, “Toward multiple integrated sensing and communication base
station systems: Collaborative precoding design with power constraint,” pp. 1–5, 2022.
11. U. Demirhan and A. Alkhateeb, “Cell-free isac mimo systems: Joint sensing and communication beamforming,” arXiv preprint arXiv:2301.11328, 2023.
12. Z. Behdad, O. T. Demir, K. W. Sung, E. Bj ¨ ornson, and C. Cavdar, “Power allocation for joint ¨
communication and sensing in cell-free massive mimo,” in GLOBECOM 2022-2022 IEEE
Global Communications Conference, pp. 4081–4086, IEEE, 2022.
13. P. Gao, L. Lian, and J. Yu, “Cooperative isac with direct localization and rate-splitting multiple access communication: A pareto optimization framework,” IEEE J. Sel. Areas Commun.,
2023.
14. W. Xu, Y. Cui, H. Zhang, G. Y. Li, and X. You, “Robust beamforming with partial channel
state information for energy efficient networks,” IEEE J. Sel. Areas Commun., vol. 33, no. 12,
pp. 2920–2935, 2015.
15. Y. Xu, H. Xie, Q. Wu, C. Huang, and C. Yuen, “Robust max-min energy efficiency for risaided hetnets with distortion noises,” IEEE Trans. Commun., vol. 70, no. 2, pp. 1457–1471,
2022.
16. J. Park, Y. Sung, D. Kim, and H. V. Poor, “Outage probability and outage-based robust beamforming for mimo interference channels with imperfect channel state information,” IEEE
Trans. Wireless Commun., vol. 11, no. 10, pp. 3561–3573, 2012.
17. W.-C. Li, T.-H. Chang, C. Lin, and C.-Y. Chi, “Coordinated beamforming for multiuser miso
interference channel under rate outage constraints,” IEEE Trans. Signal Process., vol. 61,
no. 5, pp. 1087–1103, 2012.
18. S. Ma, M. Hong, E. Song, X. Wang, and D. Sun, “Outage constrained robust secure transmission for miso wiretap channels,” IEEE Trans. Wireless Commun., vol. 13, no. 10, pp. 5558–
5570, 2014.
19. H. Sun, F. Zhou, R. Q. Hu, and L. Hanzo, “Robust beamforming design in a noma cognitive
radio network relying on swipt,” IEEE J. Sel. Areas Commun., vol. 37, no. 1, pp. 142–155,
2018.
20. B. Liao, X. Xiong, and Z. Quan, “Robust beamforming design for dual-function radarcommunication system,” IEEE Trans. Veh. Technol., 2023.
21. A. Bazzi and M. Chafii, “On outage-based beamforming design for dual-functional radarcommunication 6g systems,” IEEE Trans. Wireless Commun., 2023.
22. C. B. Barneto, S. D. Liyanaarachchi, M. Heino, T. Riihonen, and M. Valkama, “Full duplex
radio/radar technology: The enabler for advanced joint communication and sensing,” IEEE
Wireless Commun.
23. M. F. Keskin, H. Wymeersch, and V. Koivunen, “Mimo-ofdm joint radar-communications: Is
ici friend or foe?,” IEEE J. Sel. Top. Signal Process., vol. 15, no. 6, pp. 1393–1408, 2021.
24. Y. Liu, G. Liao, Y. Chen, J. Xu, and Y. Yin, “Super-resolution range and velocity estimations with ofdm integrated radar and communications waveform,” IEEE Trans. Veh. Technol.,
vol. 69, no. 10, pp. 11659–11672, 2020.
25. H. Godrich, A. M. Haimovich, and R. S. Blum, “Target localization accuracy gain in mimo
radar-based systems,” IEEE Trans. Inf. Theory, vol. 56, no. 6, pp. 2783–2803, 2010.
26. B. Guo, J. Liang, G. Wang, B. Tang, and H. So, “Bistatic mimo dfrc system waveform design
via fractional programming,” IEEE Trans. Signal Process., 2023.
27. O. El Ayach, S. Rajagopal, S. Abu-Surra, Z. Pi, and R. W. Heath, “Spatially sparse precoding
in millimeter wave mimo systems,” IEEE Trans. Wireless Commun., vol. 13, no. 3, pp. 1499–
1513, 2014.
28. D. Astely and B. Ottersten, “The effects of local scattering on direction of arrival estimation
with music,” IEEE Trans. Signal Process., vol. 47, no. 12, pp. 3220–3234, 1999.
29. R. Roy and T. Kailath, “Esprit-estimation of signal parameters via rotational invariance techniques,” IEEE Trans. Acoust., Speech, Signal Process., vol. 37, no. 7, pp. 984–995, 198
30. P. Stoica and A. Nehorai, “Music, maximum likelihood, and cramer-rao bound,” IEEE Trans.
Acoust., Speech, Signal Process., vol. 37, no. 5, pp. 720–741, 1989.
31. Y.-Y. Wang, J.-T. Chen, and W.-H. Fang, “Tst-music for joint doa-delay estimation,” IEEE
Trans. Signal Process., vol. 49, no. 4, pp. 721–729, 2001.
32. K.-Y. Wang, A. M.-C. So, T.-H. Chang, W.-K. Ma, and C.-Y. Chi, “Outage constrained robust transmit optimization for multiuser miso downlinks: Tractable approximations by conic
optimization,” IEEE Trans. Signal Process., vol. 62, no. 21, pp. 5690–5705, 2014.
33. Y. Chen, M. Wen, L. Wang, W. Liu, and L. Hanzo, “Sinr-outage minimization of robust
beamforming for the non-orthogonal wireless downlink,” IEEE Trans. Commun., vol. 68,
no. 11, pp. 7247–7257, 2020.