Joint Hybrid Backhaul and Access Links Design in Cloud-Radio Access Networks

Oussama Najeeb Dhifallah, Hayssam Dahrouj, Tareq Y. Al-Naffouri, Mohamed-Slim Alouini

Research output: Chapter in Book/Report/Conference proceedingConference contribution

20 Scopus citations


The cloud-radio access network (CRAN) is expected to be the core network architecture for next generation mobile radio systems. In this paper, we consider the downlink of a CRAN formed of one central processor (the cloud) and several base station (BS), where each BS is connected to the cloud via either a wireless or capacity-limited wireline backhaul link. The paper addresses the joint design of the hybrid backhaul links (i.e., designing the wireline and wireless backhaul connections from the cloud to the BSs) and the access links (i.e., determining the sparse beamforming solution from the BSs to the users). The paper formulates the hybrid backhaul and access link design problem by minimizing the total network power consumption. The paper solves the problem using a two-stage heuristic algorithm. At one stage, the sparse beamforming solution is found using a weighted mixed 11/12 norm minimization approach; the correlation matrix of the quantization noise of the wireline backhaul links is computed using the classical rate-distortion theory. At the second stage, the transmit powers of the wireless backhaul links are found by solving a power minimization problem subject to quality-of-service constraints, based on the principle of conservation of rate by utilizing the rates found in the first stage. Simulation results suggest that the performance of the proposed algorithm approaches the global optimum solution, especially at high signal-to-interference-plus-noise ratio (SINR).
Original languageEnglish (US)
Title of host publication2015 IEEE 82nd Vehicular Technology Conference (VTC2015-Fall)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
ISBN (Print)9781479980918
StatePublished - Jan 28 2016

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01


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