Fronthaul-Aware Design for Cloud Radio Access Networks

doi:10.1017/9781316771655.004

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Print publication year: 2017
Online publication date: April 2017

3 - Fronthaul-Aware Design for Cloud Radio Access Networks

from Part I - Communication Network Architectures for 5G Systems

By Liang Liu, University of Toronto, Canada, Wei Yu, University of Toronto, Canada, Osvaldo Simeone, New Jersey Institute of Technology, USA

Edited by Vincent W. S. Wong, University of British Columbia, Vancouver, Robert Schober, Derrick Wing Kwan Ng, University of New South Wales, Sydney, Li-Chun Wang, National Chiao Tung University, Taiwan
Publisher: Cambridge University Press
https://doi.org/10.1017/9781316771655.004
pp 48-75

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Summary

Introduction

The cloud radio access network (C-RAN) is an emerging paradigm for fifth generation (5G) wireless cellular networks, according to which the traditional physical layer base station (BS) transmission and reception infrastructure is virtualized using cloud computing techniques. The virtualization of wireless access also enables centralized control and management of wireless access points, which provides significant benefit from a transmission spectral efficiency perspective. In the current third generation (3G) and fourth generation (4G) cellular networks, each user, also called the user equipment or UE, is served solely by its own BS. This traditional single-cell processing paradigm, shown in Figure 3.1, suffers from considerable intercell interference, especially for cell edge users. In the C-RAN paradigm shown in Figure 3.2, as the BSs are coordinated centrally from the cloud, they can potentially transmit and receive radio signals to/from users jointly, thereby creating the possibility for interference cancellation, which can significantly improve the overall network throughput.

In a C-RAN architecture, the traditional BSs essentially become remote radio heads (RRHs) that serve to relay information between the mobile users and the central processor (CP) in the cloud. Baseband processing, together with its associated decoding/encoding complexities, is implemented in the cloud rather than taking place locally at each BS as in traditional 3G/4G networks. As the RRHs in C-RAN require only rudimentary wireless access capabilities, they are much more cost-effective to deploy, therefore allowing the C-RAN architecture to be more easily scaled geographically, leading to denser deployment of remote antennas and the ability of the network to support many more users. Furthermore, as baseband units (BBUs) are now implemented centrally at the CP, the C-RAN architecture allows the pooling of computational resources across the entire network, leading to better utilization of the computational units and higher energy efficiency for the network.

Because of both the distributed nature of wireless antenna placement and the centralized nature of cloud computing resources in C-RAN, the communication links between the RRHs and the CP are of central importance in C-RAN design. These links are often referred to as fronthaul links, as they connect the radio front end with the BBUs implemented in the cloud (in contrast to the backhaul links between the traditional 3G/4G BSs and the backbone network).

[1] C., Zhu and W., Yu, “Stochastic analysis of user-centric network MIMO,” in Proc. of IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Jul. 2016.

[2] Y., Zhou and W., Yu, “Optimized backhaul compression for uplink cloud radio access network,” IEEE J. Sel. Areas Commun., vol. 32, no. 6, pp. 1295–1307, Jun. 2014.

[3] Y., Zhou and W., Yu, “Fronthaul compression and transmit beamforming optimization for multi-antenna uplink C-RAN,” IEEE Trans. Signal Process., vol. 64, no. 16, pp. 4138–4151, Aug. 2016. Available at http://arxiv.org/abs/1604.05001v1.

[4] L., Liu and R., Zhang, “Optimized uplink transmission in multi-antenna C-RAN with spatial compression and forward,” IEEE Trans. Signal Process., vol. 63, no. 19, pp. 5083–5095, Oct. 2015.

[5] S. H., Park, O., Simeone, O., Sahin, and S., Shamai, “Joint precoding and multivariate backhaul compression for the downlink of cloud radio access networks,” IEEE Trans. Signal Process., vol. 61, no. 22, pp. 5646–5658, Nov. 2013.

[6] B., Dai and W., Yu, “Sparse beamforming and user-centric clustering for downlink cloud radio access network,” IEEE Access, vol. 2, pp. 1326–1339, 2014.

[7] P., Patil, B., Dai, and W., Yu, “Performance comparison of data-sharing and compression strategies for cloud radio-access networks,” in Proc. of European Signal Processing Conf. (EUSIPCO), Sep. 2015.

[8] L., Liu and W., Yu, “Joint sparse beamforming and network coding for downlink multi-hop cloud radio access networks,” in Proc. of IEEE Global Communications Conf. (Globecom), Dec. 2016.

[9] E., Dahlman, S., Parkvall, J., Skold, and P., Bemin, 3G Evolution: HSPA and LTE for Mobile Broadband, Academic Press, 2nd edn., 2008.

[10] NGMN Alliance, “Further study on critical C-RAN technologies,” Tech. Rep., Mar. 2015.

[11] A., Checko, H., Christiansen, Y., Yan, L., Scolari, G., Kardaras, M., Berger, and L., Dittmann, “Cloud RAN for mobile networks – A technology overview,” IEEE Commun. Surv. Tutor., vol. 17, no. 1, pp. 405–426, Mar. 2015.

[12] O., Simeone, A., Maeder, M., Peng, O., Sahin, and W., Yu, “Cloud radio access network: Virtualizing wireless access for dense heterogeneous systems,” J. Commun. Netw., vol. 18, no. 2, Apr. 2016.

[13] A., Mohamed, O., Onireti, M., Imran, A., Imran, and R., Tafazolli, “Control-data separation architecture for cellular radio access networks: A survey and outlook,” IEEE Commun. Surv. Tutor., vol. 18, no. 1, pp. 446–465, Jan. 2016.

[14] China Mobile, “C-RAN: The road towards green RAN,” White Paper, Oct. 2011.

[15] M., Nahas, A., Saadani, J., Charles, and Z., El-Bazzal, “Base stations evolution: Toward 4G technology,” in Proc. of International Conf. on Telecommunications (ICT), Apr. 2012.

[16] U., Dotsch, M., Doll, H. P., Mayer, F., Schaich, J., Segel, and P., Sehier, “Quantitative analysis of split base station processing and determination of advantageous architectures for LTE,” Bell Labs Tech. J., vol. 18, no. 1, pp. 105–128, Jun. 2013.

[17] P., Rost and A., Prasad, “Opportunistic hybrid ARQ-enabler of centralized-RAN over nonideal backhaul,” IEEE Wireless Commun. Lett., vol. 3, no. 5, pp. 481–484, Oct. 2014.

[18] G., Caire and D., Tuninetti, “The throughput of hybrid-ARQ protocols for the Gaussian collision channel,” IEEE Trans. Inform. Theory, vol. 47, no. 5, pp. 1971–1988, Jul. 2001.

[19] W., Yang, G., Durisi, T., Koch, and Y., Polyanskiy, “Quasi-static MIMO fading channels at finite blocklength,” Apr. 2014. Available at http://arxiv.org/pdf/1311.2012v2.pdf.

[20] Y., Polyanskiy, H., Poor, and S., Verdú, “Channel coding rate in the finite blocklength regime,” IEEE Trans. Inf. Theory, vol. 56, no. 5, pp. 2307–2359, May 2010.

[21] S., Khalili and O., Simeone, “Uplink HARQ for distributed and cloud RAN via separation of control and data planes,” Dec. 2015. Available at http://arxiv.org/pdf/1508.06570v3.pdf.

Key Technologies for 5G Wireless Systems

3 - Fronthaul-Aware Design for Cloud Radio Access Networks

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