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Frontiers of Information Technology & Electronic Engineering  2022 Vol.23 No.1 P.31-46


Beamforming and fronthaul compression design for intelligent reflecting surface aided cloud radio access networks

Author(s):  Yu ZHANG, Xuelu WU, Hong PENG, Caijun ZHONG, Xiaoming CHEN

Affiliation(s):  College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China; more

Corresponding email(s):   yzhang@zjut.edu.cn, ph@zjut.edu.cn, caijunzhong@zju.edu.cn, chen_xiaoming@zju.edu.cn

Key Words:  Cloud radio access network (C-RAN), Intelligent reflecting surface (IRS), Transmit beamforming, Fronthaul compression

Yu ZHANG, Xuelu WU, Hong PENG, Caijun ZHONG, Xiaoming CHEN. Beamforming and fronthaul compression design for intelligent reflecting surface aided cloud radio access networks[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(1): 31-46.

@article{title="Beamforming and fronthaul compression design for intelligent reflecting surface aided cloud radio access networks",
author="Yu ZHANG, Xuelu WU, Hong PENG, Caijun ZHONG, Xiaoming CHEN",
journal="Frontiers of Information Technology & Electronic Engineering",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T Beamforming and fronthaul compression design for intelligent reflecting surface aided cloud radio access networks
%A Xuelu WU
%A Hong PENG
%A Caijun ZHONG
%A Xiaoming CHEN
%J Frontiers of Information Technology & Electronic Engineering
%V 23
%N 1
%P 31-46
%@ 2095-9184
%D 2022
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2100307

T1 - Beamforming and fronthaul compression design for intelligent reflecting surface aided cloud radio access networks
A1 - Xuelu WU
A1 - Hong PENG
A1 - Caijun ZHONG
A1 - Xiaoming CHEN
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 23
IS - 1
SP - 31
EP - 46
%@ 2095-9184
Y1 - 2022
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2100307

Owing to the inherent central information processing and resource management ability, the cloud radio access network (C-RAN) is a promising network structure for an intelligent and simplified sixth-generation (6G) wireless network. Nevertheless, to further enhance the capacity and coverage, more radio remote heads (RRHs) as well as high-fidelity and low-latency fronthaul links are required, which may lead to high implementation cost. To address this issue, we propose to exploit the intelligent reflecting surface (IRS) as an alternative way to enhance the C-RAN, which is a low-cost and energy-efficient option. Specifically, we consider the uplink transmission where multi-antenna users communicate with the baseband unit (BBU) pool through multi-antenna RRHs and multiple IRSs are deployed between the users and RRHs. RRHs can conduct either point-to-point (P2P) compression or Wyner-Ziv coding to compress the received signals, which are then forwarded to the BBU pool through fronthaul links. We investigate the joint design and optimization of user transmit beamformers, IRS passive beamformers, and fronthaul compression noise covariance matrices to maximize the uplink sum rate subject to fronthaul capacity constraints under P2P compression and Wyner-Ziv coding. By exploiting the Arimoto-Blahut algorithm and semi-definite relaxation (SDR), we propose a successive convex approximation approach to solve non-convex problems, and two iterative algorithms corresponding to P2P compression and Wyner-Ziv coding are provided. Numerical results verify the performance gain brought about by deploying IRS in C-RAN and the superiority of the proposed joint design.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


[1]Blahut R, 1972. Computation of channel capacity and rate-distortion functions. IEEE Trans Inform Theory, 18(4):460-473. doi: 10.1109/TIT.1972.1054855

[2]Cover TM, Thomas JA, 2006. Elements of Information Theory (2nd Ed.). John Wiley & Sons, Inc., Hoboken, New Jersey, USA.

[3]Cui YS, Yin HF, 2019. An efficient CSI acquisition method for intelligent reflecting surface-assisted mmWave networks. https://arxiv.org/abs/1912.12076v1

[4]Del Coso A, Simoens S, 2009. Distributed compression for MIMO coordinated networks with a backhaul constraint. IEEE Trans Wirel Commun, 8(9):4698-4709. doi: 10.1109/TWC.2009.081148

[5]Grant MC, Boyd SP, 2014. CVX: Matlab Software for Disciplined Convex Programming. http://cvxr.com/cvx/ [Accessed on May 1, 2021].

[6]Guo HY, Liang YC, Chen J, et al., 2019. Weighted sum-rate maximization for intelligent reflecting surface enhanced wireless networks. IEEE Global Communications Conf, p.1-6. doi: 10.1109/GLOBECOM38437.2019. 9013288

[7]Hua M, Wu QQ, Ng DWK, et al., 2021. Intelligent reflecting surface-aided joint processing coordinated multipoint transmission. IEEE Trans Commun, 69(3):1650-1665. doi: 10.1109/TCOMM.2020.3042275

[8]Huang SC, Ye Y, Xiao M, et al., 2021. Decentralized beamforming design for intelligent reflecting surface-enhanced cell-free networks. IEEE Wirel Commun Lett, 10(3):673-677. doi: 10.1109/LWC.2020.3045884

[9]Kim S, Lee H, Cha J, et al., 2021. Practical channel estimation and phase shift design for intelligent reflecting surface empowered MIMO systems. https://arxiv.org/abs/2104.14161v1

[10]Najafi M, Jamali V, Ng DWK, et al., 2019. C-RAN with hybrid RF/FSO fronthaul links: joint optimization of fronthaul compression and RF time allocation. IEEE Trans Commun, 67(12):8678-8695. doi: 10.1109/TCOMM.2019.2940183

[11]Pan CH, Ren H, Wang KZ, et al., 2020. Intelligent reflecting surface aided MIMO broadcasting for simultaneous wireless information and power transfer. IEEE J Sel Areas Commun, 38(8):1719-1734. doi: 10.1109/JSAC.2020.3000802

[12]Park SH, Simeone O, Sahin O, et al., 2013a. Joint precoding and multivariate backhaul compression for the downlink of cloud radio access networks. IEEE Trans Signal Process, 61(22):5646-5658. doi: 10.1109/TSP.2013.2280111

[13]Park SH, Simeone O, Sahin O, et al., 2013b. Robust and efficient distributed compression for cloud radio access networks. IEEE Trans Veh Technol, 62(2):692-703. doi: 10.1109/TVT.2012.2226945

[14]Park SH, Simeone O, Sahin O, et al., 2014. Fronthaul compression for cloud radio access networks: signal processing advances inspired by network information theory. IEEE Signal Process Mag, 31(6):69-79. doi: 10.1109/MSP.2014.2330031

[15]Park SH, Lee KJ, Song C, et al., 2016. Joint design of fronthaul and access links for C-RAN with wireless fronthauling. IEEE Signal Process Lett, 23(11):1657-1661. doi: 10.1109/LSP.2016.2612192

[16]Park SH, Lee KJ, Song C, et al., 2017a. Compressed cooperative reception for the uplink of C-RAN with wireless fronthaul. Int Symp on Wireless Communication Systems, p.211-215. doi: 10.1109/ISWCS.2017.8108112

[17]Park SH, Song C, Lee KJ, 2017b. Inter-cluster design of wireless fronthaul and access links for the downlink of C-RAN. IEEE Wirel Commun Lett, 6(2):270-273. doi: 10.1109/LWC.2017.2671431

[18]Peng MG, Sun YH, Wang WB, 2020. Intelligent-concise radio access networks in 6G: architecture, techniques and insight. J Beijing Univ Posts Telecomm, 43(3):1-10 (in Chinese). doi: 10.13190/j.jbupt.2020-079

[19]Pizzinat A, Chanclou P, Saliou F, et al., 2015. Things you should know about fronthaul. J Lightw Technol, 33(5):1077-1083. doi: 10.1109/JLT.2014.2382872

[20]Potra FA, Wright SJ, 2000. Interior-point methods. J Comput Appl Math, 124(1-2):281-302. doi: 10.1016/S0377-0427(00)00433-7

[21]Scutari G, Facchinei F, Lampariello L, et al., 2014. Distributed methods for constrained nonconvex multi-agent optimization—part I: theory. https://arxiv.org/abs/1410.4754v1

[22]Sengijpta SK, 1995. Fundamentals of statistical signal processing: estimation theory. Technometrics, 37(4):465-466. doi: 10.1080/00401706.1995.10484391

[23]Sidiropoulos ND, Davidson TN, Luo ZQ, 2006. Transmit beamforming for physical-layer multicasting. IEEE Trans Signal Process, 54(6):2239-2251. doi: 10.1109/TSP.2006.872578

[24]Wang ZR, Liu L, Cui SG, 2020. Channel estimation for intelligent reflecting surface assisted multiuser communications: framework, algorithms, and analysis. IEEE Trans Wirel Commun, 19(10):6607-6620. doi: 10.1109/TWC.2020.3004330

[25]Weinberger K, Ahmad AA, Sezgin A, et al., 2021. Synergistic benefits in IRS- and RS-enabled C-RAN with energy-efficient clustering. https://arxiv.org/abs/2105.05619

[26]Wu QQ, Zhang R, 2018. Intelligent reflecting surface enhanced wireless network: joint active and passive beamforming design. IEEE Global Communications Conf, p.1-6. doi: 10.1109/GLOCOM.2018.8647620

[27]Wu QQ, Zhang R, 2020. Towards smart and reconfigurable environment: intelligent reflecting surface aided wireless network. IEEE Commun Mag, 58(1):106-112. doi: 10.1109/MCOM.001.1900107

[28]Yu D, Park SH, Simeone O, et al., 2020. Optimizing over-the-air computation in IRS-aided C-RAN systems. IEEE 21st Int Workshop on Signal Processing Advances in Wireless Communications, p.1-5. doi: 10.1109/SPAWC48557.2020.9154243

[29]Zeng M, Li XW, Li G, et al., 2021. Sum rate maximization for IRS-assisted uplink NOMA. IEEE Commun Lett, 25(1):234-238. doi: 10.1109/LCOMM.2020.3025978

[30]Zhang P, Peng MG, Cui SG, et al., 2022. Theory and techniques for "intellicise" wireless networks. Front Inform Technol Electron Eng, 23(1):1-4. doi: 10.1631/FITEE.2210000

[31]Zhang Y, Zhong CJ, Zhang ZY, et al., 2020. Sum rate optimization for two way communications with intelligent reflecting surface. IEEE Commun Lett, 24(5):1090-1094. doi: 10.1109/LCOMM.2020.2978394

[32]Zhang Y, He XX, Zhong CJ, et al., 2021. Fronthaul compression and beamforming optimization for uplink C-RAN with intelligent reflecting surface-enhanced wireless fronthauling. IEEE Commun Lett, 25(6):1979-1983. doi: 10.1109/LCOMM.2021.3062861

[33]Zhang ZJ, Dai LL, 2021. A joint precoding framework for wideband reconfigurable intelligent surface-aided cell-free network. IEEE Trans Signal Process, 69: 4085-4101. doi: 10.1109/TSP.2021.3088755

[34]Zhou G, Pan CH, Ren H, et al., 2020. A framework of robust transmission design for IRS-aided MISO communications with imperfect cascaded channels. IEEE Trans Signal Process, 68: 5092-5106. doi: 10.1109/TSP.2020.3019666

[35]Zhou YH, Yu W, 2014. Optimized backhaul compression for uplink cloud radio access network. IEEE J Sel Areas Commun, 32(6):1295-1307. doi: 10.1109/JSAC.2014.2328133

[36]Zhou YH, Yu W, 2016. Fronthaul compression and transmit beamforming optimization for multi-antenna uplink C-RAN. IEEE Trans Signal Process, 64(16):4138-4151. doi: 10.1109/TSP.2016.2563388

[37]Zhu YX, Zheng G, Wong KK, 2020. Stochastic geometry analysis of large intelligent surface-assisted millimeter wave networks. IEEE J Sel Areas Commun, 38(8):1749-1762. doi: 10.1109/JSAC.2020.3000806

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