Radio resource coordination and scheduling scheme in ultra-dense cloud-based small cell networks

Transcription

1 Chen et al. EURASIP Journal on Wreless Communcatons and Networkng (2018) 2018:137 RESEARCH Open Access Rado resource coordnaton and schedulng scheme n ultra-dense cloud-based small cell networks Zengxan Chen 1,SaZou 1, Yulang Tang 1*, Xaojang Du 2 and Mohsen Guzan 3 Abstract In a 5G ultra-dense network, dynamc network topology and traffc patterns lead to excessve system overhead and complex rado resource conflcts. The cloud rado access network and the fog computng have the advantages of hgh computaton capabltes and low transmsson delays. Therefore, by takng full advantage of these two characterstcs, ths study proposes a novel rado resource coordnaton and schedulng scheme n an ultra-dense cloud-based small cell network. Interference among small cells (or remote rado heads) can be avoded by mplementng centralzed cooperatve processng n the base band unt pool n advance. Resource sharng n coordnaton and transfer depend on fog computng to releve the overloaded cloud processng platform and reduce transmsson delays, thereby maxmzng resource utlzaton and mnmzng system overhead when the network topology and number of users change dynamcally. The smulaton shows that the proposed scheme can ncrease the system throughput by 20% compared wth the clusterng-based algorthm; t can also ncrease system throughput by 33% compared wth the graph colorng algorthm, decrease the sgnalng overhead by about 50%, and mprove network s qualty of servce. Keywords: Ultra-dense network (UDN), The cloud rado access network (Cloud-RAN), Fog computng, Resource coordnaton, 5th generaton (5G), Small cell 1 Introducton Gven current demand, broadband moble data s expected to be ubqutously avalable. The ndustry has predcted a 1000-fold ncrease n moble data traffc wthn the next decade. Ultra-dense network (UDN) deployment appears promsng for mprovng network capacty [1, 2]. Tradtonal operators must deploy more nfrastructure to address myrad challenges assocated wth data prolferaton, whch ncreases total costs sgnfcantly [3, 4]. Rado resource management and schedulng n UDNs comprse an mportant means of network capacty promoton; however, because a larger number of small cells are needed to promote hgher data capacty, problems related to nterference and system overhead n the current UDN are much more severe than those n exstng cellular moble communcaton networks. The *Correspondence: tyl@xmu.edu.cn Equal contrbutors 1 Department of Communcaton Engneerng, Xamen Unversty, Xamen , Chna Full lst of author nformaton s avalable at the end of the artcle nterference problem ncludes rado resource conflct and cell nterference, whereas the system overhead problem affects delay, sgnalng nteracton, and computatonal abltes. The ultra-dense cloud-based small cell network combnes cloud computng and fog computng n the development of a large number of small cells. The cloud rado access network (Cloud-RAN) presents a promsng method for allevatng both captal and nterference n UDNs, whle provdng hgh energy effcency and capacty. Vrtualzed network technology s a key technology of resource management and schedulng n the ffth generaton (5G) moble communcaton networks [5, 6]. Vrtualzed network technology allows why Cloud-RAN to ntegrate centralzaton and vrtualzaton nto ts archtecture. The resources can be better managed and dynamcally coordnated on demand on a pool level because Cloud-RAN centralzes all base band unts (BBU) to form a pool, and remote rado heads (RRH) provde basc wreless sgnal coverage. In practce, the front haul and back haul of Cloud-RAN are often constraned by capacty and TheAuthor(s). 2018Open Access Ths artcle s dstrbuted under the terms of the Creatve Commons Attrbuton 4.0 Internatonal Lcense ( whch permts unrestrcted use, dstrbuton, and reproducton n any medum, provded you gve approprate credt to the orgnal author(s) and the source, provde a lnk to the Creatve Commons lcense, and ndcate f changes were made.

2 Chen et al. EURASIP Journal on Wreless Communcatons and Networkng (2018) 2018:137 Page 2 of 15 delays. Fog computng appears to be charged wth most computaton assgnments and servces from the cloud processng platform n the network s edge, whch eases overload [7]. Clearly, Cloud-RAN wth the better computatonal capablty (10 tmes larger than that of the fog RAN) and the fog computng wth a lower transmsson delay are complementary n UDNs. The man ssue faced by resource management and schedulng s the reducton of nter-cell nterference n 5G UDNs. Interference ncludes nter-small cell nterference (ISI) and macro-small cell nterference (MSI) [8]. MSI can be solved by usng dfferent frequences; for nstance, the macro cell uses a low frequency, and the small cell uses a hgh frequency [9]. The thrd generaton partnershp project (3GPP) R8 and R9 mnmzes nter-cell nterference n the macro and maxmzes spectrum effcency through frequency reuse plannng. Due to the unplanned deployment of small cells, operators have lttle control over small cells poston and thus cannot devse frequency plans n advance. Therefore, the reasonable allocaton of rado resources n small cells, such as frequency, has become the focus of current research to reduce the nterference. Coordnated mult-pont (CoMP) s a key method of mtgatng nter-cell nterference that can be appled to the network archtecture, such as control/data plane separaton archtecture and Cloud-RAN [10]. Gven growng demand for moble data capacty, especally n urban areas, network densfcaton s nevtable [11]. The user equpment n ultra-dense small cells has a tdal effect; the authors of [12] proposed a moblty-aware uplnk nterference model for 5G heterogeneous networks to solve the uplnk nterference wth tme varaton. The authors of [13] presented a completely dstrbuted channel allocaton algorthm based on game theory. Although these methods mproved rado resource utlzaton, they also created larger system overheads. To mnmze ISI and ensure users qualty of servce, some academcs have used small cell clusterng to coordnate cluster members resource allocaton. Consderng graph theory, a clusterng-based nterference coordnaton heurstc algorthm was proposed n [14 16], whch forms clusters dynamcally and dvdes resource allocaton nto three phases to mnmze ISI: (1) cluster formaton; (2) ntracluster resource allocaton and admsson control; and (3) nter-cluster resource contenton resoluton, n order to mnmze ISI. It s worth notng that n ths model, the formatonofagvenclustersupdatedonlywhenthe nterference caused by the changes n the user number exceeds a gven threshold. In [17], authors devsed a clusterng and resource allocaton for dense femtocells n a two-ter cellular orthogonal frequency dvson multplexng access (OFDMA) network. Ths soluton converts the nterference problem nto a mxed nteger nonlnear optmzaton model, and the smulaton results found ths algorthm to be less complex. Accordng to the clustered small cells and predcted sgnal to nterference plus nose rato(sinr),theauthorsof[18] proposed a power control scheme appled to the downlnk n the small cell network and found ths method to lower the lkelhood of an outage whle mprovng throughput sgnfcantly compared to prevous methods. However, these methods requre consderable sgnalng nteracton between small cells to complete the resource allocaton. To adapt to the varous demands of rado traffc, network vrtualzaton and UDNs represent key technologes for future 5G wreless networks. In the vrtualzaton archtecture of 5G wreless networks, sgnfcant changes occur n the sgnalng nteracton of vrtual cells and the management mode of mult-dmenson wreless networks. Based on nterference coordnaton n tradtonal cells, the current network needs a more flexble and effcent sharng of rado resources to realze ther coordnated allocaton, ncludng n the tme, frequency, spatal, and power domans [19, 20]. The wreless network vrtualzaton technology forms a vrtual network n solaton for dfferent users by sharng the network s physcal nfrastructure and rado resources to acheve effcent use n [5, 6]. A centralzed resource pool must be bult to acheve dynamc sharng and coordnaton. The resource pool provdes the dstrbuted functon unt a logcally centralzed rado resource and processng resource to decouple resource and network functons, to realze resource sharng and dstrbuton n dfferent network enttes, and to mprove resource utlzaton overall [21, 22]. In [23, 24], a study conducted on nterference coordnaton n a heterogeneous wreless vrtualzaton network, usng power control to reduce nterference between small cells and thereby mprovng coverage and ndoor small cells capacty. However, the nterference and system overhead remans more severe n 5G UDNs due to dynamc traffc and the random ncrease or mgraton of small cells. Current resource allocaton algorthms for small cells are unable to manage poor stuatons, an urgent problem that requres an nnovatve soluton. Inspred by the method of collaboraton between small cells n COMP, a resource coordnaton approach was proposed n [25] tosolvetheproblem. Consderng Cloud-RAN and fog computng, ths study presents a novel resource coordnaton and schedulng scheme n ultra-dense cloud-based small cells. The rest of ths paper s organzed as follows. Secton 2 ntroduces the methods of ths paper. Secton 3 descrbes the system model of Cloud-RAN and fog computng. Secton 4 detals the resource coordnaton and schedulng scheme n ultra-dense small cells. Secton 5 descrbes a resource schedulng example and correspondng analyses of sgnalng overheads. Scenaro settng of smulaton are presented n Secton 6, and the results and dscusson

3 Chen et al. EURASIP Journal on Wreless Communcatons and Networkng (2018) 2018:137 Page 3 of 15 of ths paper are proposed n Secton 7. Fnally,theconcluson and endng remarks are gven n Secton 8. 2 Methods Consderng Cloud-RAN and fog computng, ths study presents a novel resource coordnaton and schedulng scheme n ultra-dense cloud-based small cells to mprove system throughput and reduce system sgnalng overhead. After network ntalzaton s accomplshed, a global resource allocaton algorthm s used n the cloud processng platform to allocate resources for each fog devce accordng to the global sgnalng request. When the tdal effect occurred or resources are defcent, the fog devces n the edge of the cloud executes the partal resource optmzaton algorthm or resource coordnaton and schedulng scheme to adjust the number of resources between each fog devce. Resource coordnaton between fog devces can be dvded nto three stuatons: (1) when some small cells power off, the cloud-based system wll obtan resource of the fog devce that these small cells belong to; (2) when some small cells power on, the cloud adjusts the resources between the fog devce, whose small cells power on, and ts adjacent fog devce to ensure a mnmal amount of physcal resource block (PRB) swtchng n subsequent processes; (3) when fog devce resources are nsuffcent, the fog devce lackng resources needs to ether send a request for resources to the system resource pool n the cloud or borrow resources from adjacent fog devces, a process that also ams to mnmze the amount of PRB swtchng. 3 Systemmodel Multple small cells are deployed n the area wth a densty of 177 cells per km 2, and any two cells whose sgnals nterfere wth each other are termed adjacent cell. Ths study uses the dffer-frequency development scenaros of macro-small cell 3GPP TR proposed; there s no co-channel nterference between the macro cell and small cells due to the dffer-frequency network. Based on the archtecture of Cloud-RAN and fog computng, small cells are splced as BBU and RRH, and the fog devce connects the cloud processng platform va the back haul network and connects the correspondng small cells va front haul network. As shown n Fg. 1, a BBU pool s located n a centralzed cloud processng platform that s used to manage the rado resources n the system resource pool. The cloud processng platform manages all fog devces, and dfferent fog devces manage small cells n varous traffc areas. There may be more than two fog devces n system; small cells wth the same characterstc (dependng Back haul BBU pool Cloud processng platform Gateway Core Network ISI Data RRH Fog devce Fog devce Front haul Small cell Fg. 1 System network model

4 Chen et al. EURASIP Journal on Wreless Communcatons and Networkng (2018) 2018:137 Page 4 of 15 on traffc and locaton) connect to the assocated fog devce va a front haul network. Dfferent fog devces can communcate when resource coordnaton s requred. A table of notatons for ths paper s provded n Table 2. Set S = {s 1,..., s m } as the set of small cells n the area G; m N, N represents the set of postve ntegers. B = {b 1,..., b n } donates PRBs n the system resource pool, and n N. U = {u 1,..., u o } s a set of user equpment (UE) n a small cell, o N. Due to the downlnk nterference, the rate R,k,j UE u k obtaned based on PRB b j n small cell s s R,k,j = W log 2 (1 + Snr,k,j ), (1) where W s the bandwdth of PRB, and Snr,k,j represents the SINR UE u k receved based on PRB b j n s, whch can be calculated by Snr,k,j = p,k,j g,k,j, (2) m p l,k,j g l,k,j + n 0 l=1 l = where p,k,j ndcates the transmsson power from servng small cell s to UE u k based on PRB b j, p l,k,j ndcates the nterference power from ntruder small cell s l to UE u k based on PRB b j, n 0 stands for the nose power, and g,k,j s the channel gan, and the channel gan s composed of lnk loss and the antenna gan. Set BS = {b 1 s, b 2 s,...} as the avalable PRB set of small cell s. For any UE u k n the network, R k s a constant used to ndcate the mnmum rate demand of UE u k.the constrant that the UE u k can access the small cell s at a certan poston s b j s BS R,k,j R k, (3) where the summaton b j s BS R,k,j stands for the current rate that the UE u k obtaned when all the PRBs n small cells s are allocated to the UE u k, whch must be greater than or equal to the mnmum rate demand R k of the UE u k. 4 Resource coordnaton and schedulng scheme n small cells The problem of resource allocaton n ths paper s smlar to graph colorng problem (GCP). The same PRB cannot be assgned to adjacent nodes no matter whch node the resource allocaton starts from, untl the PRB resource allocaton of all nodes has completed. Moreover, the problems n our paper are dfferent from GCP. Each node has only one color n GCP, but each nodes needs to be allocated one or more PRBs n our problems, so t not only belongs to GCP but also belongs to subset selecton problem. GCP s one of 33 NP-hard problems, and subset selecton problem has been proven to be NP-hard problem n [26]. Therefore, heurstc algorthm s needed to solve the problem of resource allocaton n ths paper. A novel rado resource coordnaton and schedulng scheme n small cells (ncludng three heurstc algorthms) are proposed to solve the resource allocaton problem. A specfc functon dagram of the scheme sshownnfg.2. Global resource allocaton and partal resource allocaton are executed n a cloud processng platform wth ncreased computatonal capabltes. The resource coordnaton and schedulng s processed n a fog devce wth larger storage. Frst, the cloud processng platform allocates PRB for each fog devce to try to meet the pre-demands of small cells usng mnmum PRB n the system resource pool, thereby reducng the sgnalng overheads and ensurng nterference optmzaton n the whole network. Then, the cloud processng platform updates real-tme nterference graphs (mentoned n Secton 3.1.2) wth topology changes, and a partal resource allocaton algorthm s used to adjust the number of allocated PRBs n each fog devce. A resource request Cloud computng Global resource allocaton Partal resource optmzaton Coordnated resource schedulng Adopt allocated PRB n servng small cell Fog computng Use remanng PRB n system resource pool Adopt unused PRB n adjacent cells Use transferred PRB from adjacent cells Rate s compensated Fg. 2 Functon block dagram of the proposed scheme

5 Chen et al. EURASIP Journal on Wreless Communcatons and Networkng (2018) 2018:137 Page 5 of 15 to the fog devce wll be sent as UE s added or swtched n a small cell f resources are lackng. If there are no avalable dle resources n the system resource pool or n adjacent small cells, then a PRB transfer process wll be launched for the small cell. The adjacent small cells transfer ther PRBs to the small cell followng certan rules, and the rate compensaton s executed n adjacent small cells; that s, the fog devce fnds avalable PRBs for the small cells wth rate loss. Ths process ams to acheve mnmal PRB swtchng whle facltatng user access. 4.1 Global resource allocaton algorthm Global resource allocaton model Based on the system model n Fg. 1, the cloud processng platform allocates a certan number of PRBs for each fog devce, and the fog devce has a PRB lst of small cells. Ths process avods nterference to reduce user access delay and sgnalng overheads. The problem of global resource allocaton can be transformed nto an optmzaton model, whch s amed at the maxmum number of remander PRBs n the system resource pool after each allocaton, as shown n formula (4). arg max m =1 subject to n Y = 1 + m ( ) n Y, (4a) l=1 l = I,l (4b) b j BS, I,l = 1, b j / BS l, (4c) (4) where n represents the total number of PRBs n the system resource pool n ts ntal state, n denotes the total number of PRBs n the system resource pool before each allocaton, n n, andn Y represents the remanng PRBs n the system resource pool after allocatng for small cell s. I s an nterference matrx of small cells. Any two cells whose sgnals nterfere wth each other are termed adjacent cells. If s and s l are adjacent cells, accordng to the nterference weght, I,l, I l, (0, 1]; otherwse, I,l = I l, = 0. A knd of demand functon Y s proposed and shown n formula(4b) for nterference optmzaton and farness n the whole network. Y means that the total PRB n the system resource pool s shared equally by a small cell and ts adjacent cells. In other words, the more adjacent cells a small cell has, the more nterference t causes, and the Y s smaller. represents roundng down to the nearest nteger. The constrant (4c) means that two small cells wth nterference cannot be allocated to the same PRB Soluton of global resource allocaton model In ths secton, a global resource allocaton optmzaton algorthm s proposed to solve the above optmzaton model. The algorthm s as follows: 1. Each small cell scans the feld strength and forms an nterference dagram of ts neghbor cells. Meanwhle, the small cell reports the nterference dagram to the cloud processng platform. 2. The cloud processng platform generates the global nterference graph shown n Fg. 3 and forms the nterference matrx. The vertex V of SC1 SC2 SC3 SC8 SC7 SC6 Fg. 3 The global nterference graph SC5 SC4

6 Chen et al. EURASIP Journal on Wreless Communcatons and Networkng (2018) 2018:137 Page 6 of 15 the nterference graph represents the small cell n area G.TheedgeE of the nterference graph stands for the conflctng relatonshp between any two small cells that need to be avoded n nterference. I,l and I l, ndcates the weght of edge connectng small cells s and s l. I,l depends on the SINR level n small cell s consderng the nterference of small cells s l,andi l, depends on the SINR level n small cell s l consderng the nterference of small cell s. In other words, the larger SINR s, the smaller the correspondng nterference weght s. Here, we normalzed the nterference weghts, I,l, I l, [0,1]. Any two vertexes wth an edge cannot be allocated to the same PRB. 3. Accordngtothe formula (4b), the cloud processng platform calculates the number of allocated PRBs for each small cell n the set of S. 4. The cloud processng platform allocates PRBs for small cell s and chooses the PRB wth the smallest seral number n the system resource pool untl the PRB number reaches Y, and the chosen PRB has not been allocated to ts adjacent cells. The pseudo code of algorthm 1 s as follows: Algorthm 1 Global allocaton for small cells Requre: c = 0, j = 1; //c represents the PRB number allocated for s ; j stands for the ndex of PRB. Ensure: BS ; //BS represents the allocated PRB lst of s. 1: for = 1; <= m; ++do 2: whle c <= Y do; //allocate PRB for s to meet ts demand Y. 3: f prb j / BS and I,l = 1 then; // allocate PRB for s dfferent from ts neghbors. 4: BS = BS + b j; 5: c = c + 1; 6: else 7: j = j + 1; 8: end f 9: end whle 10: end for 4.2 Partal resource optmzaton algorthm When the network topology changes, a partal resource optmzaton algorthm s proposed, whch s used to reduce amount of PRB swtchng caused by twce global allocaton, thus mprovng user satsfacton. When the number of small cells n the network declnes, such as when a small cell powers off, the system resource pool wll recover the PRB from the used PRB lst of the powered-off small cell. If the network adds small cell s, the mathematcal optmzaton model amed at the mnmum amount of PRB swtchng s subject to arg mn m T. ( sze sze =1 ) BS Y. ( B ) sze ( B ). (5a) (5b) (5c) Here, the amount of PRB swtchng s defned as the sum of the number of PRB changes used by the cell after optmzaton. T stands for the extent of PRB swtchng caused by small cell s.sze(bs ) represents the total number of allocated PRBs n small cells after global allocaton. sze(b ) ndcates the total number of PRBs n the system resource pool before partal resource optmzaton, whle sze(b ) s the total number of PRBs n the system resource pool thereafter. The constrant (5b) ndcates that the total number of practcally allocated PRBs for small cell s sequaltoornearthenumberoftheoretcally allocated PRBs for small cell s. The constrant (5c) shows that the number of remanng PRBs n the system resource pool s unchanged after resource optmzaton. To solve problem (5), the specfc process of the resource allocaton algorthm s as follows: (5) 1. A new small cell scans the feld strength and formats an nterference dagram of neghbor cells. Meanwhle, the new small cell reports the nterference dagram to the cloud processng platform. 2. The cloud processng platform updates the nterference graph of adjacent cells accordng to the report on the relatonshp of the new small cell s neghbors. 3. The cloud processng platform calculates the number of allocated PRB for new small cell usng formula (4b). 4. BZ s stands for the dle PRB lst belongng to adjacent cells of small cell s.ifb j BZ s and b j / B, the cloud processng platform allocates PRB b j to small cell s. 5. If there are more than two adjacent cells wth dle allocated PRB, the cloud processng platform adjusts the PRB allocaton of adjacent cells accordng to farness prncples and tres ts best to meet the demand of PRB allocaton for small cell s. Meanwhle, the cloud processng platform deletes the correspondng PRB allocated for small cell s from the dle PRB lst of adjacent cells.

7 Chen et al. EURASIP Journal on Wreless Communcatons and Networkng (2018) 2018:137 Page 7 of 15 The farness prncples are as follows: set P(C ) to be the probablty that PRB b j s capable of beng borrowed, and set P(D ) to be the probablty that PRB swtchng s generated n adjacent cells after borrowng PRB b j.theres an adverse effect on subsequent resource allocaton after borrowng some certan PRBs; thus, the cloud processng platform chooses the PRB wth the smallest posteror probablty P(C D ) to allocate to the new small cell. Because P(C D) = P(D C ) P(D ) and P(D ) does not depend on C, the cloud processng platform selects the PRB wth the smallest posteror probablty to transfer to small cell s s shown n formula (6). log P(C, D ) = log(p(d C )) + logp(c ). (6) log P(C, D ) s a type of Bayesan score. The pror dstrbuton for parameter θ c s P(θ c C ). P(D C ) n the formula (6)sexpandedtogettheformula(7). P(D C ) = + P(D C, θ c ) P(θ c C )dθ c. (7) The frst term of the ntegral n the formula (7) s deconstructed to obtan the next formula. P (D C, θ c ) = ( N θ,j,k,j,k) j k ( ) (8) θ N,j,k,j,k, j k ( ) where θ,j,k = P x k aj s the condtonal probablty where x s equal to k n the case such that the parent node of the th node x s j. Therefore, the pror dstrbuton for parameter θ c canbebrokendownntothefollowng formula: P(θ c C ) = ( ) P θ,j,k j j Apply the formula (8)and(9)totheformula(7). P(D C ) = + j k ( ) P θ,j,k. (9) ( N θ,j,k) ( ),j,k P θ,j,k dθ,j,k (10) Assumng that the ntal varables N,j,k and the transfer varables N,j,k have been selected and the observed data are complete, the problem can be solved usng a heurstc algorthm (e.g., the greedy algorthm, genetc algorthm, tabu search algorthm, or ant colony algorthm). 4.3 Resource coordnaton and schedulng algorthm In the system, each small cell has an dle allocated PRB lst and a used PRB lst. When a new UE or swtchng UE ntates an access request to the servng small cell, the smallcellneedstogetthereusableprbftsdleprb s unable to satsfy the rate demand of UE. Ths process may cause PRB swtchng n adjacent cells. In order to guarantee mnmal PRB swtchng n the whole network, and to ensure the network throughput does not drop, a mathematcal optmzaton model s depcted as (11). arg mn m T (11a) =1 (11) subject to O downstream > O downstream. (11b) The system downlnk throughput O downstream n certan transmsson tme t s calculated as O downstream = R k (t t delay), (12) t u k U where t delay s a certan constant. O downstream represents the system downlnk throughput before each resource coordnaton, whle O downstream represents the system downlnk throughput after each resource coordnaton. The constrant (9b) means that the system downlnk throughput wll be ncreased after resource coordnaton. To solve problem (11), a small cell resource coordnaton and schedulng algorthm (RCS) s proposed n fog devce. The specfc mplementaton process of RCS algorthm s as follows: 1. The fog devce counts the small cells wth new PRB demands for } the formaton of the set S = {s 1, s 2, If the remanng PRB of the system resource pool can meet the new PRB demands, the fog devce allocates the PRB to small cell s and deletes the PRB from the system resource pool. Then, the process jumps to step 7, where the resource pool wll recover the PRB when the user communcaton s completed; otherwse, the fog devce solves unmet PRB demands of small cell s n the next step. 3. If the adjacent cells have dle PRBs, the fog devce dstrbutes these PRBs to the small cell s. If the PRB requrements have been met, the process proceeds to step 7. Otherwse, the process proceeds to step If small cell s never ntates the PRB transfer process but the transfer wll mprove the system throughput, the fog devce counts the PRB adjacent cells used and calculates the PRB value usng the formula shown n (13). The PRB wth the maxmum value s prohbted from transferrng. The fog devce selects the PRB wth the mnmum value to transfer to meet the demand of small cell s. In partcular, f multple PRBs have equal values, the fog devce chooses the PRB wth the smaller ndex to transfer; otherwse, the process proceeds to step 7.

8 Chen et al. EURASIP Journal on Wreless Communcatons and Networkng (2018) 2018:137 Page 8 of 15 V j = s l Z s w j,l (13) sze(bs l ). Here, V j stands for the value of PRB b j n adjacent cell s l,andz s represents the adjacent. The fog devce forms the PRBs set BS l by sortng the PRB n ascendng order based on the use value n adjacent cells. PRB s use value depends on the actual rate obtaned by UE who allocated ths PRB n small cell. w j,l stands for the ndex of PRB b j n BS l. 5. If the PRB small cell s needs have been met, the cell set of small cell s small cell s exts the resource schedulng process and does not partcpate n subsequent coordnaton, the process proceeds to step 6. Otherwse, the process proceeds to step The fog devce calculates the rate loss of a small cell after transfer based on Eq. (14)andsorts small cells n descendng order accordng to the rate loss. The ordered results wll be sorted nto the set S, prortzng the resource schedulng process to those small cells whose new PRB demands have not yet been met. L l = b j s l TS l j=1 R l,k,j b j s l BS l j=1 R l,k,j (14) In ths equaton, L l represents the rate loss of adjacent cell s l, TS l denotes the set of transferred PRBs n adjacent cell s l,andbs l stands for the PRB lst of adjacent cell s l before the transfer. 7. If the PRB demands n the set S have been solved, the algorthm ends; otherwse, the process proceeds to step 2. 5 Resource schedulng example Ths secton ntroduces a PRB schedulng example of small cells to better explan the mplementaton process of the proposed scheme. Small cells belong to the same fog devce as they are deployed n the adjacent offce of the same buldng. Seven small cells s 1, s 2,..., s 7 power on, whle s 8 s n a shutdown state. After a certan perod of tme, s 8 powers on and can no longer accommodate a new access request. In ths stuaton, a system resource allocaton conflct occurs. As s shown n Fg. 4, n actual network operaton, there s an example of resource schedulng n managed area of a fog devce. In ths example, the j standsforb j havng been used; j ndcates PRB b j has not been used. The schedulng case s prmarly dvded nto four processes: 1. The cloud processng platform allocates PRB for the powered-on small cells based on the global resource allocaton algorthm. 2. After small cell s 8 powers on, the fog devce borrows PRB b 3 and b 4 from the dle PRB lst n adjacent cells to allocate for small cell s 8 based on the partal resource optmzaton algorthm. In ths case, b 9 and b 10 reman n system resource pool. 3. Small cell s 8 has fve new PRB demands and uses the allocated b 3 and b 4. The new PRB demand of s 8 has not been met. At ths tme, a system resource allocaton conflct occurs. The fog devce fnds avalable PRBs usng a resource coordnaton and schedulng algorthm. The specfc steps of ths process are as follows: (a) The fog devce allocates PRB b 9 and b 10 n the system resource pool to small cell s 8 and deletes the correspondng PRB from the resource pool. The new PRB demand of small cell s 8 has not been satsfed, and adjacent cells have no dle PRB. The fog devce launches the transfer process for small cell s 8 and uses the formula (13)to calculate the value of PRB used n adjacent cells. The PRB wth the maxmum value s prohbted from transferrng. The fog devce selects the PRB b 5 wth the mnmum value to transfer to small cell s 8. The PRB demands of small cell s 8 have been met, and small cell s 8 exts the resource schedulng process and does not partcpate n subsequent coordnaton. The rate loss caused by the transfer of small cell s 1 s 1 2, and that caused by the transfer of small cell s 4 s 1 3. (b) The fog devce launches the rate compensaton for small cell s 1,andsmallcell s 1 uses ts own allocated PRB b 7 to meet demands. Then, the fog devce ntates the rate compensaton for small cell s 4 and allocates the unused PRB b 8 n adjacent cells to small cell s 4. Once the resource requrements of all small cells n the system have been met, the algorthm ends. Assume there s no UE access to small cell s 8 for an extended perod of tme, and the allocated PRBs of small cell s 8 are all borrowed to ts adjacent cells. Conversely, when there are new accessed users n small cell s 8,thesystem wll ntate the PRB transfer process. As shown n Fg. 5, there are two new PRB demands n the poweredon small cell s 8. In order to allocate PRB for new users of small cell s 8, the PRB transfer wll occur among the other small cells. As a result, small cell s 8 wll get enough

9 Chen et al. EURASIP Journal on Wreless Communcatons and Networkng (2018) 2018:137 Page 9 of 15 a) allocate PRB for SC 1 b) SC8 s on, small cells. SC 2 allocate PRB 3,4,5,6,7 for SC8. SC 3 5,6,7 1,2 SC 8 SC 7 off 3,4 SC 6 3,4,6,7,8 SC 5 1,2,5 SC 6 3,4,6,7,8 SC 5 1,2,5 SC 4 5,6,7 SC 2 6,7 SC 3,5 1,2 SC 7 3,4 Fg. 4 Resource schedulng example SC 1 6,7 SC 4 8,6,7 SC 8 3,4,9,10,5(e xt) SC 5 1,2,5 SC 6 3,4,6,7,8 c2) Compensaton for SC1 and SC4. SC 2 6,7,5 SC 7 3,4 SC 2 7,6,5 SC 5 1,2,5 SC 6 3,4,6,7,8 SC 3 1,2 SC 7 3,4 SC 4 5,6,7 SC 1 5,6,7 SC 3 1,2 SC 8 3,4 SC 1 6,7 (loss) SC 4 6,7 (loss) PRB pool 9,10 c1) Fve new PRB demands n SC8. SC 8 3,4,9,10,5(e xt) PRB pool null PRB for ts new users, whle the used PRB from other small cells wll need to make addtonal PRB swtches. The system overhead and transmsson delay caused by PRB swtchng should be mnmzed as much as possble. Here, we gve a schedulng case comparson of the clusterngbased nterference coordnaton algorthm [16] andthe graph colorng algorthm [27] to our algorthm based on the allocaton n Fg. 5 to analyze and contrast transmsson delay and sgnalng overheads. The clusterng-based nterference coordnaton algorthm was descrbed n the ntroducton, and the graph colorng algorthm s a type of greedy algorthm. The latter s a heurstc method of solvng the nter-cell nterference problem and nvolves two man stages: () the formaton of nterference graph and () the resource allocaton soluton. The resource allocaton soluton means that each nodes must be allocated a preset number of colors, and no two adjacent nodes may have any colors n common. The objectve s to complete ths usng the fewest possble number of colors. PRB swtchng occurs three tmes n the proposed algorthm, sx tmes n the clusterng-based algorthm, and nne tmes n the graph colorng algorthm. The global resource allocaton can reduce the delay of PRB allocaton for new user access. In ths case, small cell s 8 s the worst case, and t does not fully leverage the advantages of allocaton. However, the algorthm stll reduces the amount of PRB swtchng caused by resource conflcts and lowers the system overheads. 6 Smulaton 6.1 Scenaro settng In the smulaton scenaro, 64 small cells were deployed wthn a macro cell coverage range. The macro and small cells used dffer-frequency. UE randomly arrved at a small cell at the arrval rate of 30 user/s, and ts transmsson rate demands range from 1 to 1000 kb/s. There were 25 total PRBs n the system. The specfc smulaton parameter wll be wrtten as shown n Tables 1 and 2. To determne the performance of the proposed algorthm, we compared t to two other algorthms wth regard to the followng: system throughput, average user SINR, user satsfacton, and sgnalng overheads.

10 Chen et al. EURASIP Journal on Wreless Communcatons and Networkng (2018) 2018:137 Page 10 of 15 The allocaton of PRB at a certan tme. 7,3,8 4,9, ,4 7,3,8 5,6 3,4 4,9,10 1,2,10 null 2,3,4,7 The proposed algorthm. SC 3 9,6 Clusterng-based algorthm. 5,2,10(ext) 2,8,4,7 1,3 (ext) 4,5,6 1 Graph colorng algorthm. 2 1,2,10 4,7,8 5,6 3,4 3,4 1,7,8 3,4 1,5,6 SC 4 4,7,8,9 5,6 1,2,3 1,7,8,9 3,4 Fg. 5 Comparson schedulng case wth other two algorthms Table 1 System parameter Parameters Value Smulaton area sze 600m*600m Bandwdth of network 5MHz/2GHz Antenna gan of small cell 5dB Maxmum transmt power of BS 24dBm Radus of the small cell 40m Maxmum user transmt power 23dBm Handover delay 20ms Whte nose 174dBm/Hz Shadow fadng standard devaton 3dB Number of User[ntal value: step: fnal value] [30:30:750] Buffer model Full buffer 7 Results and dscusson 7.1 Analyss of throughput and SINR The smulaton curves n ths study are zgzagged because the user arrval rate (30 user/s) caused the performance ndex to change sgnfcantly. As shown n Fg. 6, when the user number s less than 210, the system resources are abundant and can meet users demands, and the system throughput ncreases overall. The system throughput reaches a maxmum about 121 Mbps when the user number s equal to 210. The proposed algorthm satsfed the resource demands of users n servng small cells by borrowng resources from adjacent cells. Some users were assgned the PRB wth a low rate and hgh reusablty, whch scarfed the beneft for users assgned ths PRB, and clearly mproved the throughput of a new user obtanng ths PRB; therefore, the system throughput s mproved sgnfcantly. When the user number gradually

11 Chen et al. EURASIP Journal on Wreless Communcatons and Networkng (2018) 2018:137 Page 11 of 15 Table 2 Table of notatons Symbol Meanng S = {s 1,..., s m } Set of small cells n the whole network, m N,and N represents the set of postve ntegers. B = {b 1,...b n } Set of PRBs n the system resource pool, n N. U = {u 1,..., u o } Set of users (UE) of a small cell, o N. R,j,k The rate user u k obtaned based on PRB b j n small cell s. W Bandwdth of per PRBs. Snr,j,k SINR u k recevedbasedonprbsb j n small cell s. g,j,k Channel gan. p Transmtted power of small cell. n 0 Nose power. BS = {b 1 s, b 2 s,..., } The set of avalable PRBs n small cell s. R k A constant to ndcate the rate demand of user u k. n Total number of PRB n the system resource pool n ntal state. n Total number of PRBs n the system resource pool before each pre-allocaton, n n. { 1 m}, m s the total number of small cells. n Y The remander PRBs n the system resource pool after pre-allocaton for s. I An nterference matrx of small cells. Y { } The number of allocated PRBs n small cell s. S = s 1,..., s m The set of small cells n the whole network n descendng order based on the degree of vertex n nterference graph. BS The set of PRBs n small cell s after global preallocaton. B B BZ s Z s O downstream O downstream Total number of PRBs n the system resource pool before resource optmzaton. Total number of PRBs n the system resource pool after resource optmzaton. The dle PRB lst belongng to adjacent cells of small cell s. The adjacent cell set of small cell s. System downlnk throughput. System downlnk throughput after each resource coordnaton. Average processng tme caused by PRB swtchng. t delay V j The value of PRB b j. } {s 1, s 2,... The set of small cells wth new PRB demands. S = BS l The set of PRBs n ascendng order based on the value n the adjacent cell s l of small cell s l. O downstream System downlnk output after each resource coordnaton. w j,l The order of PRB b j n BS l. L l The rate loss of the adjacent cell s l of small cell s. TS l The set of transferred PRBs n small cell s l. ncreases to above 210 and the overall trend becomes saturated, the algorthm s convergent. New users cannot access the network when there are more than 210 users. The throughput of the other two algorthms are lower than the proposed algorthm, and the proposed algorthm tended to be saturated more quckly; hence, the proposed algorthm clearly acheved the desred results of mprovng the system throughput. Through the coordnaton of small cells, ths algorthm demonstrated better performance compared to the other two algorthms for mtgatng nter-cell nterference and mprovng SINR. As llustrated n Fg. 7, when the number of users ncreased, the other two algorthms showed a sharp ncrease n nterference, whle the proposed algorthms shows a slow ncrease and ts SINR tended to converge quckly. 7.2 Analyss of user satsfacton User satsfacton s a comprehensve evaluaton of users successful access and contnuous communcaton. The user s unable to access to the network untl the small cell meets ts rate demand, and the probablty of successful access s P access. Wth ncreasng user numbers, the PRB used n small cells wll be transferred to the new users to ensure throughput maxmzaton when the wreless resource s scarce, whch wll cause some communcatng users of beng dropped. The probablty to be dropped s P drop. User satsfacton wth the network servces measures the network s performance. Here, the user satsfacton s defned as P [28]: P = 0.1 P access (1 P drop ). (15) From the user s experence wth network servces, the user prefers to be dened access rather than to be dropped. 1 P drop stands for the probablty of not beng dropped; therefore, n ths formula, the weght factor of successful access s 0.1, and the weght factor of not beng dropped s 0.9. As s shown n Fg. 8,wthanncreasenusernumbers, user satsfacton starts to declne when the user number reaches 150. User satsfacton decreases slowly and gradually trends plateaus when the user number s greater than 150. Compared to the other two algorthms, the proposed algorthmobtansabetterusersatsfacton.becausethe algorthm solves the small cell resource allocaton conflct harmoncally n the local area, t reduces the PRB swtchng and users outage probablty. 7.3 Analyss of sgnalng overheads Fgure 9 offers a smple example to ndcate that the proposed algorthm has less sgnalng than the other algorthms on the bass of two stuatons.

12 Chen et al. EURASIP Journal on Wreless Communcatons and Networkng (2018) 2018:137 Page 12 of 15 Fg. 6 Comparson of system throughput. Users nclude those wth access and those dened access In Fg. 9a, the PRB of the small cell s suffcent. In ntal state, the accessed UE accesses the small cell, whch needs two sgnalng (the sold lne arrows). In general, the proposed algorthm causes less access delay for two sgnalng than the other two algorthms because the global resource allocaton for the small cell managed by fog devces. When the new UE accesses the small cell, there are also two sgnalng between new UE and the small cell. The proposed algorthm has fewer nstances of PRB swtchng than the other two algorthms. There are two extra sgnalng (the dotted arrows) n the other two algorthms because they requre global resource allocaton agan once the new UE requests access, and the accessed UE n small cell may be assgned to a dfferent PRB than before. In other words, the PRB swtchng s the man reason for the ncrease n sgnalng overhead. In Fg. 9b, when the new UE requests to access the small cell β, the proposed algorthm wll execute the resource coordnaton and schedulng process f the PRB of the small cell s defcent. If the small cell α has dle avalable PRB, t can borrow PRB to small cell β. Otherwse, the small cell can transfer the PRB wth the smallest used value to the small cell β, whch may lead to sgnalng for PRB swtchng; thus, there are two extra sgnalng between small cell α and the accessed UE. The other two algorthms requre the global resource allocaton for all the UE wthn the network, so ths process may cause two sgnalng to each accessed UE. Fg. 7 Comparson of average SINR. Users nclude those wth access and those dened access

13 Chen et al. EURASIP Journal on Wreless Communcatons and Networkng (2018) 2018:137 Page 13 of 15 Fg. 8 Comparson of user satsfacton. Users nclude those wth access and those dened access Assume that the sgnalng overhead per PRB swtch s equal to 20 ms. As shown n Fg. 10, when the user number s less than 150 (.e., the PRB s suffcent), the sgnalng overhead of the proposed algorthm remans at zero and the other two algorthms gradually ncrease as the user number ncreases. When the number of users s larger than 150 (.e., the PRB s defcent), the sgnalng overhead of all three algorthms ncreases as the user number ncreases. When the user number reaches a certan value, the sgnalng overhead gradually decreases due to the number of access users becomng saturated, and the system capacty reaches ts lmt. Meanwhle, sgnalng overhead of the proposed algorthm s less than the other two algorthms, provng PRB swtchng tmes n ths algorthm s less frequent than n the other two algorthms. a Sgnalng New UE The proposed algorthm. New UE The other two algorthms. b New UE The proposed algorthm. The other two algorthms. Fg. 9 Comparson of sgnalng n access process. a The PRB of small cell s suffcent. b The PRB of small cell s defcent New UE

14 Chen et al. EURASIP Journal on Wreless Communcatons and Networkng (2018) 2018:137 Page 14 of 15 Fg. 10 Comparson of sgnalng overheads. Users nclude those wth access and those dened access 8 Conclusons The problems of nterference and system overhead n ultra-dense small cells must be resolved. They depend on the effcent and ratonal allocaton of rado resources. Ths study presents a novel rado resource coordnaton and schedulng scheme based on cloud nfrastructure n an ultra-dense small cell network. In lght of Cloud-RAN s mproved computatonal ablty, the global resource allocaton and partal resource optmzaton wth heavy computng tasks can be run on the cloud processng platform, consderng the resource utlzaton and the farness of resource allocaton between small cells n the entre network. Meanwhle, because fog computng has a lower transmsson delay, the resource coordnaton and schedulng algorthm are proposed for generally changeable small cells va the fog devce, thereby avodng excessve sgnalng overheads caused by usng cloud computng. The smulaton results show that the proposed scheme mproves user satsfacton and downlnk throughput and reduces system sgnalng overheads through the resource coordnaton of small cells. Acknowledgements Ths research was supported by the Natonal Natural Scence Foundaton of Chna (Grant No ) and the Natonal Hgh Technology Research and Development Program of Chna (Grant No. 2015AA01A705). An earler verson of ths paper was presented at the th Internatonal Conference on Computer Scence and Educaton (ICCSE). Specal thanks are dedcated to Professor Wehua Zhuang s gudance, from the Department of Electrcal and Computer Engneerng at the Unversty of Waterloo, Canada. Fundng The research n ths paper and ts publcaton are supported by the Natonal Natural Scence Foundaton of Chna (Grant No ) and the Natonal Hgh Technology Research and Development Program of Chna (Grant No. 2015AA01A705). Authors contrbutons YT proposed the research deas and drected the wrtng of the paper. ZC s the man author of ths paper; she conducted the experment and analyzed t wth the help of SZ. Other authors promoted the qualty of the entre manuscrpt. All authors read and approved the fnal manuscrpt. Competng nterests The authors declare that they have no competng nterests. Publsher s Note Sprnger Nature remans neutral wth regard to jursdctonal clams n publshed maps and nsttutonal afflatons. Author detals 1 Department of Communcaton Engneerng, Xamen Unversty, Xamen , Chna. 2 Department of Computer and Informaton Scences, Temple Unversty, Phladelpha, USA. 3 Department of Electrcal and Computer Engneerng, Unversty of Idaho, Moscow, ID, USA. Receved: 31 March 2017 Accepted: 15 May 2018 References 1. S Chen, F Qn, B Hu, X L, Z Chen, User-centrc ultra-dense networks for 5G: challenges, methodologes, and drectons. IEEE Wreless Commun. 23(2), (2016) 2. O Semar, W Saad, S Valentn, M Benns, B Maham, n Acoustcs, Speech and Sgnal Processng (ICASSP), 2014 IEEE Internatonal Conference On. Matchng theory for prorty-based cell assocaton n the downlnk of wreless small cell networks (IEEE, Florence, 2014), pp C Yang, J L, M Guzan, Cooperaton for spectral and energy effcency n ultra-dense small cell networks. IEEE Wreless Commun. 23(1),64 71 (2016)

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