User-Centric OFDMA Cloud Radio Access Networks with Fronthaul Capacity Constraints

Zehong Lin; Yuan Liu

doi:10.1109/GLOCOM.2017.8254716

User-Centric OFDMA Cloud Radio Access Networks with Fronthaul Capacity Constraints

Zehong Lin, Yuan Liu

Research output: Contribution to journal › Conference article published in journal › peer-review

Abstract

This paper studies resource allocation in a user-centric cloud radio access network (CRAN), where remote radio heads (RRHs) communicate with users using orthogonal frequency-division multiple-access (OFDMA). We aim to maximize the system throughput through joint RRH selection and SC allocation under fronthaul capacity constraints. We formulate the problem as a non-convex problem which is NP-hard. To solve this complex problem efficiently, we propose a near-optimal algorithm based on gradient method to achieve a suboptimal solution. Simulation results show that the proposed joint resource allocation scheme can attain significant throughput gains compared to other benchmark schemes.

Original language	English
Pages (from-to)	1-6
Number of pages	6
Journal	Proceedings - IEEE Global Communications Conference, GLOBECOM
Volume	2018-January
DOIs	https://doi.org/10.1109/GLOCOM.2017.8254716
Publication status	Published - 2017
Externally published	Yes
Event	2017 IEEE Global Communications Conference, GLOBECOM 2017 - Singapore, Singapore Duration: 4 Dec 2017 → 8 Dec 2017

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© 2017 IEEE.

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title = "User-Centric OFDMA Cloud Radio Access Networks with Fronthaul Capacity Constraints",

abstract = "This paper studies resource allocation in a user-centric cloud radio access network (CRAN), where remote radio heads (RRHs) communicate with users using orthogonal frequency-division multiple-access (OFDMA). We aim to maximize the system throughput through joint RRH selection and SC allocation under fronthaul capacity constraints. We formulate the problem as a non-convex problem which is NP-hard. To solve this complex problem efficiently, we propose a near-optimal algorithm based on gradient method to achieve a suboptimal solution. Simulation results show that the proposed joint resource allocation scheme can attain significant throughput gains compared to other benchmark schemes.",

author = "Zehong Lin and Yuan Liu",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE Global Communications Conference, GLOBECOM 2017 ; Conference date: 04-12-2017 Through 08-12-2017",

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doi = "10.1109/GLOCOM.2017.8254716",

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AU - Liu, Yuan

PY - 2017

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N2 - This paper studies resource allocation in a user-centric cloud radio access network (CRAN), where remote radio heads (RRHs) communicate with users using orthogonal frequency-division multiple-access (OFDMA). We aim to maximize the system throughput through joint RRH selection and SC allocation under fronthaul capacity constraints. We formulate the problem as a non-convex problem which is NP-hard. To solve this complex problem efficiently, we propose a near-optimal algorithm based on gradient method to achieve a suboptimal solution. Simulation results show that the proposed joint resource allocation scheme can attain significant throughput gains compared to other benchmark schemes.

AB - This paper studies resource allocation in a user-centric cloud radio access network (CRAN), where remote radio heads (RRHs) communicate with users using orthogonal frequency-division multiple-access (OFDMA). We aim to maximize the system throughput through joint RRH selection and SC allocation under fronthaul capacity constraints. We formulate the problem as a non-convex problem which is NP-hard. To solve this complex problem efficiently, we propose a near-optimal algorithm based on gradient method to achieve a suboptimal solution. Simulation results show that the proposed joint resource allocation scheme can attain significant throughput gains compared to other benchmark schemes.

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JF - Proceedings - IEEE Global Communications Conference, GLOBECOM

T2 - 2017 IEEE Global Communications Conference, GLOBECOM 2017

Y2 - 4 December 2017 through 8 December 2017

ER -

User-Centric OFDMA Cloud Radio Access Networks with Fronthaul Capacity Constraints

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