Optimal Processing Allocation to Minimize Energy and Bandwidth Consumption in Hybrid CRAN
Cloud radio access network (CRAN) architecture is proposed to save energy, facilitate coordination between radio units, and achieve scalable solutions to improve radio network's performance. However, stringent delay and bandwidth constraints are incurred by fronthaul in CRAN [the network segmen...
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| Veröffentlicht in: | IEEE transactions on green communications and networking 2018-06, Vol.2 (2), p.545-555 |
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| creator | Alabbasi, Abdulrahman Wang, Xinbo Cavdar, Cicek |
| description | Cloud radio access network (CRAN) architecture is proposed to save energy, facilitate coordination between radio units, and achieve scalable solutions to improve radio network's performance. However, stringent delay and bandwidth constraints are incurred by fronthaul in CRAN [the network segment connecting RUs and digital units (DUs)]. Therefore, we propose a hybrid cloud radio access network architecture, where a DU's functionalities can be virtualized and split at several conceivable points. Each split option results in two-level deployment of the processing functions (central site level and remote site level) connected by a transport network, called midhaul. We study the interplay of energy efficiency and midhaul bandwidth consumption under optimal processing allocation. We jointly minimize the power and midhaul bandwidth consumption in H-CRAN, while satisfying network constraints, i.e., processing and midhaul bandwidth capacity. We enable power saving functionalities by shutting down different network components. The proposed model is formulated as a constraint programming problem. The proposed solution shows that 42 percentile of midhaul bandwidth savings can be achieved compared to the fully centralized CRAN; and 35 percentile of power consumption saving can be achieved compared to the case where all the network functions are distributed at the edge. |
| doi_str_mv | 10.1109/TGCN.2018.2802419 |
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However, stringent delay and bandwidth constraints are incurred by fronthaul in CRAN [the network segment connecting RUs and digital units (DUs)]. Therefore, we propose a hybrid cloud radio access network architecture, where a DU's functionalities can be virtualized and split at several conceivable points. Each split option results in two-level deployment of the processing functions (central site level and remote site level) connected by a transport network, called midhaul. We study the interplay of energy efficiency and midhaul bandwidth consumption under optimal processing allocation. We jointly minimize the power and midhaul bandwidth consumption in H-CRAN, while satisfying network constraints, i.e., processing and midhaul bandwidth capacity. We enable power saving functionalities by shutting down different network components. The proposed model is formulated as a constraint programming problem. The proposed solution shows that 42 percentile of midhaul bandwidth savings can be achieved compared to the fully centralized CRAN; and 35 percentile of power consumption saving can be achieved compared to the case where all the network functions are distributed at the edge.</description><identifier>ISSN: 2473-2400</identifier><identifier>EISSN: 2473-2400</identifier><identifier>DOI: 10.1109/TGCN.2018.2802419</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Bandwidth ; Bandwidth constraint ; Bandwidth consumption ; Bandwidths ; Base bands ; Baseband ; Cloud computing ; Cloud radio access networks ; cloud RAN ; Computer architecture ; Computer programming ; Constraint modelling ; Constraint programming ; Constraint theory ; Distributed computer systems ; Electric power utilization ; Energy conservation ; Energy consumption ; Energy efficiency ; Microprocessor chips ; Microprocessors ; network architecture ; network function split ; Power consumption ; Power consumption savings ; Power demand ; Power demands ; Radio ; Radio access networks ; Shutdowns ; Transportation networks ; virtualized cloud RAN</subject><ispartof>IEEE transactions on green communications and networking, 2018-06, Vol.2 (2), p.545-555</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2018</rights><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c331t-590d18ee492986d4c6574f0a407b9861cc79f180592c0254a0cffd80c398ffa23</citedby><cites>FETCH-LOGICAL-c331t-590d18ee492986d4c6574f0a407b9861cc79f180592c0254a0cffd80c398ffa23</cites><orcidid>0000-0002-6614-5208</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/8280524$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>230,314,776,780,792,881,27901,27902,54733</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/8280524$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttps://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-314299$$DView record from Swedish Publication Index$$Hfree_for_read</backlink></links><search><creatorcontrib>Alabbasi, Abdulrahman</creatorcontrib><creatorcontrib>Wang, Xinbo</creatorcontrib><creatorcontrib>Cavdar, Cicek</creatorcontrib><title>Optimal Processing Allocation to Minimize Energy and Bandwidth Consumption in Hybrid CRAN</title><title>IEEE transactions on green communications and networking</title><addtitle>TGCN</addtitle><description>Cloud radio access network (CRAN) architecture is proposed to save energy, facilitate coordination between radio units, and achieve scalable solutions to improve radio network's performance. However, stringent delay and bandwidth constraints are incurred by fronthaul in CRAN [the network segment connecting RUs and digital units (DUs)]. Therefore, we propose a hybrid cloud radio access network architecture, where a DU's functionalities can be virtualized and split at several conceivable points. Each split option results in two-level deployment of the processing functions (central site level and remote site level) connected by a transport network, called midhaul. We study the interplay of energy efficiency and midhaul bandwidth consumption under optimal processing allocation. We jointly minimize the power and midhaul bandwidth consumption in H-CRAN, while satisfying network constraints, i.e., processing and midhaul bandwidth capacity. We enable power saving functionalities by shutting down different network components. The proposed model is formulated as a constraint programming problem. The proposed solution shows that 42 percentile of midhaul bandwidth savings can be achieved compared to the fully centralized CRAN; and 35 percentile of power consumption saving can be achieved compared to the case where all the network functions are distributed at the edge.</description><subject>Bandwidth</subject><subject>Bandwidth constraint</subject><subject>Bandwidth consumption</subject><subject>Bandwidths</subject><subject>Base bands</subject><subject>Baseband</subject><subject>Cloud computing</subject><subject>Cloud radio access networks</subject><subject>cloud RAN</subject><subject>Computer architecture</subject><subject>Computer programming</subject><subject>Constraint modelling</subject><subject>Constraint programming</subject><subject>Constraint theory</subject><subject>Distributed computer systems</subject><subject>Electric power utilization</subject><subject>Energy conservation</subject><subject>Energy consumption</subject><subject>Energy efficiency</subject><subject>Microprocessor chips</subject><subject>Microprocessors</subject><subject>network architecture</subject><subject>network function split</subject><subject>Power consumption</subject><subject>Power consumption savings</subject><subject>Power demand</subject><subject>Power demands</subject><subject>Radio</subject><subject>Radio access networks</subject><subject>Shutdowns</subject><subject>Transportation networks</subject><subject>virtualized cloud RAN</subject><issn>2473-2400</issn><issn>2473-2400</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpNkFFPwjAQxxejiQT5AMaXJj4Pr1032kecCCYIxqCJT83oOijCOtstBD-9xRniy93l7nf_3P2D4BpDH2Pgd4txOusTwKxPGBCK-VnQIXQQhYQCnP-rL4OecxsAIDzGCY86wce8qvUu26IXa6RyTpcrNNxujcxqbUpUG_SsS73T3wqNSmVXB5SVObr3Ya_zeo1SU7pmV_3CukSTw9LqHKWvw9lVcFFkW6d6f7kbvD2OFukknM7HT-lwGsoownUYc8gxU4pywlmSU5nEA1pARmGw9A0s5YAXmEHMiQQS0wxkUeQMZMRZUWQk6gZhq-v2qmqWorL-H3sQJtPiQb8PhbEr8VmvRYQp4dzzty1fWfPVKFeLjWls6U8UxM9xQhgBT-GWktY4Z1Vx0sUgjqaLo-niaLr4M93v3LQ7Wil14pkfx4RGP1dIfGQ</recordid><startdate>20180601</startdate><enddate>20180601</enddate><creator>Alabbasi, Abdulrahman</creator><creator>Wang, Xinbo</creator><creator>Cavdar, Cicek</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>8FD</scope><scope>L7M</scope><scope>ADTPV</scope><scope>AOWAS</scope><scope>D8V</scope><orcidid>https://orcid.org/0000-0002-6614-5208</orcidid></search><sort><creationdate>20180601</creationdate><title>Optimal Processing Allocation to Minimize Energy and Bandwidth Consumption in Hybrid CRAN</title><author>Alabbasi, Abdulrahman ; Wang, Xinbo ; Cavdar, Cicek</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c331t-590d18ee492986d4c6574f0a407b9861cc79f180592c0254a0cffd80c398ffa23</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Bandwidth</topic><topic>Bandwidth constraint</topic><topic>Bandwidth consumption</topic><topic>Bandwidths</topic><topic>Base bands</topic><topic>Baseband</topic><topic>Cloud computing</topic><topic>Cloud radio access networks</topic><topic>cloud RAN</topic><topic>Computer architecture</topic><topic>Computer programming</topic><topic>Constraint modelling</topic><topic>Constraint programming</topic><topic>Constraint theory</topic><topic>Distributed computer systems</topic><topic>Electric power utilization</topic><topic>Energy conservation</topic><topic>Energy consumption</topic><topic>Energy efficiency</topic><topic>Microprocessor chips</topic><topic>Microprocessors</topic><topic>network architecture</topic><topic>network function split</topic><topic>Power consumption</topic><topic>Power consumption savings</topic><topic>Power demand</topic><topic>Power demands</topic><topic>Radio</topic><topic>Radio access networks</topic><topic>Shutdowns</topic><topic>Transportation networks</topic><topic>virtualized cloud RAN</topic><toplevel>online_resources</toplevel><creatorcontrib>Alabbasi, Abdulrahman</creatorcontrib><creatorcontrib>Wang, Xinbo</creatorcontrib><creatorcontrib>Cavdar, Cicek</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998–Present</collection><collection>IEEE Xplore</collection><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>SwePub</collection><collection>SwePub Articles</collection><collection>SWEPUB Kungliga Tekniska Högskolan</collection><jtitle>IEEE transactions on green communications and networking</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Alabbasi, Abdulrahman</au><au>Wang, Xinbo</au><au>Cavdar, Cicek</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Optimal Processing Allocation to Minimize Energy and Bandwidth Consumption in Hybrid CRAN</atitle><jtitle>IEEE transactions on green communications and networking</jtitle><stitle>TGCN</stitle><date>2018-06-01</date><risdate>2018</risdate><volume>2</volume><issue>2</issue><spage>545</spage><epage>555</epage><pages>545-555</pages><issn>2473-2400</issn><eissn>2473-2400</eissn><abstract>Cloud radio access network (CRAN) architecture is proposed to save energy, facilitate coordination between radio units, and achieve scalable solutions to improve radio network's performance. However, stringent delay and bandwidth constraints are incurred by fronthaul in CRAN [the network segment connecting RUs and digital units (DUs)]. Therefore, we propose a hybrid cloud radio access network architecture, where a DU's functionalities can be virtualized and split at several conceivable points. Each split option results in two-level deployment of the processing functions (central site level and remote site level) connected by a transport network, called midhaul. We study the interplay of energy efficiency and midhaul bandwidth consumption under optimal processing allocation. We jointly minimize the power and midhaul bandwidth consumption in H-CRAN, while satisfying network constraints, i.e., processing and midhaul bandwidth capacity. We enable power saving functionalities by shutting down different network components. The proposed model is formulated as a constraint programming problem. 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| subjects | Bandwidth Bandwidth constraint Bandwidth consumption Bandwidths Base bands Baseband Cloud computing Cloud radio access networks cloud RAN Computer architecture Computer programming Constraint modelling Constraint programming Constraint theory Distributed computer systems Electric power utilization Energy conservation Energy consumption Energy efficiency Microprocessor chips Microprocessors network architecture network function split Power consumption Power consumption savings Power demand Power demands Radio Radio access networks Shutdowns Transportation networks virtualized cloud RAN |
| title | Optimal Processing Allocation to Minimize Energy and Bandwidth Consumption in Hybrid CRAN |
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