User Association Scheme in Cloud-RAN based Small ... - IEEE Xplore

IEEE INFOCOM 2015 Workshop on Mobile Cloud and Virtualization

User Association Scheme in Cloud-RAN based Small cell Network with Wireless Virtualization Heli Zhang, Weidong Wang, Xi Li, Hong Ji Beijing University of Posts and Telecom., P.R. China E-mail: [email protected], [email protected], [email protected], [email protected] Abstract—Cloud radio access network based small cell network (Cloud-RAN SCN) has been promising to meet the ever-increasing capacity demand of the future mobile networks. Within this network, since large amount of small cells exist and users move frequently, the reselection of access small cell for users becomes a great challenge. To encounter this challenge, in this paper, we study the user association scheme in the Cloud-RAN SCN scenario. Firstly, we establish a user association optimization problem with the aim of minimizing the network latency. The latency is deduced by minimal potential delay fairness function (MPDF). Moreover, considering the rising CO2 emission and to guarantee the network throughput, energy saving and interference limitation are also incorporated in the optimization. To solve the user association optimization problem, three phrase search algorithm (TPSA) is proposed using the concept of Pareto Optimality. Under the help of TPSA, appropriate small cells and physical resources are chosen for users while minimizing the overall energy consumption and reducing the network interference. Various simulation results are shown to demonstrate the effectiveness of the proposed scheme. Keywords- Wireless Virtualization; Cloud-RAN based Small Cell architecture; User Association Optimization; Three Phrase Search Algorithm;

I.

INTRODUCTION

Recently, Cloud-RAN based Small Cell architecture has attracted much attention due to the availability of sustaining a higher traffic volume [1]. Researchers believed that this architecture can play a key role in future 5G mobile networks. Wireless network virtualization, with the advantage of enabling abstraction and sharing of infrastructure and radio spectrum resources, reducing capital expenditure (CAPEX) and operational expenditure (OPEX) of the network, can be applied to enhance the C-RAN architecture and is becoming a promising research direction [2]. Large amounts of works have been done for wireless network virtualization. To satisfy the requirement of efficient resource utilization of virtualization, authors in [3] proposed a resource negotiation based solution which is applied to Long Term Evolution-Advanced (LTE-A) environments with numerous small cells. In this solution, wireless resources are mapped to radio virtualization elements and are allocated reasonably to achieve higher throughput. With the same objective, authors of [4] investigated the virtualization of wireless resource in LTE systems, where a shared radio access network connected to multiple Mobile Network Operators (MNOs) and users was put forward. Kamel et al. [5] also paid

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much attention to the virtualization of wireless access networks and propose a dynamic resource allocation scheme for the schedule of radio resource blocks, and meanwhile, the fairness between cell-edge users and cell-center users are considered. Different from previous works focusing on only one physical wireless network, authors of [6] studied the resource utilization problem under multiple physical networks. An opportunistic spectrum sharing method was put forward to increase spectrum allocation efficiency. Besides of efficient resource utilization, isolation is also important to virtualization. In view of this, D. Garlisi et al. [7] presented a virtualization solution for wireless local area networks, which also achieved flexible resource partitioning based on the concept of MAClets. In [8], authors separated the infrastructure providers (InPs) and service providers (SPs) in a virtualized framework, the interaction between InPs and SPs was modeled by the stochastic game and the prices charged by InPs and SPs are determined while taking the quality-of-service (QoS) of the users into account. To meet mobile requirements of future network, Hoffmann et al. [9] concerned three elements that are virtualized physical resources, virtual resource manager and virtual network controller, respectively. Furthermore, one service specific algorithm is showed to adapt the variation of mobile networks. In [10], one converged infrastructure of next generation that supporting cloud and mobile cloud computing services was presented, where the virtualization of heterogeneous networks and related challenges were discussed. Although some excellent works have been done on wireless network virtualization, when combing it with Cloud-RAN based small cell network, most works pay attention to the construction of architecture, the virtualization and allocation of wireless resources, less focuses on the design of user association technology. User association which determines appropriate small cells for users has many advantages such as balancing the load, etc. Since the small cells are densely deployed and the mobile users’ positions change rapidly, disassociation happens frequently. Therefore, investigating user association scheme is necessary in Cloud-RAN SCN. In this paper, we first construct a user association optimization problem in Cloud-RAN SCN with mobile users. When mobility is considered in the network, one major limitation is the latency experienced by users in reaching the cloud center. Users are acutely sensitive to delay: as latency increases, interactive response suffers. Since the interaction times foreseen in 5G systems are quite small (in the order of milliseconds) [11], a strict latency control must be somehow incorporated. Thus the aim of the objective function in the user association optimization problem is to constrain the overall network latency. The function of latency is deduced from user’s transmission rate. Moreover, since increasing

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them as the licensed users of SP1. For other users, we call them unlicensed users). The price of access is determined by the accessed SP. Small cells are usually deployed overlay a macrocell. To avoid the interference generated from small cells to the macrocell, the physical channels occupied by the macrocell should be unavailable for small cells. Thus, under this interference avoidance rule, the cloud part is responsible of determining the available physical channels for small cells. Two SPs denoted by sp1 and sp 2 coexist in the network. The small cell vector is defined as S  {1,  , S} and small cell n  {1, , S } is served by sp1 . The small cells within set 2 {S +1, , S } are managed by sp 2 . Let the user set be U . 2 Assume the vector of orthogonal channels for small cell n be Ln  1, l , Ln  , n  S , where the width of the each is w . In

CO2 emissions can damage the environment and interference can lower the throughput, energy saving and interference limitation are also incorporated as two targets. In the CloudRAN based small cell network with wireless virtualization, small cells can belong to different SPs. To achieve the target of efficient resource utilization, we assume that users are free to access different SPs when appropriate prices are charged. We summarize our contributions in the following: 1) Formally define a user association optimization problem and present a formulation which combines the network latency and prices charged by SPs for users’ free access to small cells, 2) Energy saving and interference limitation are incorporated as two objectives when establishing the optimization problem, 3) Present a three phase search algorithm based on the rule of multicriterion multimodal assignment problem (MMAP) to solve the proposed problem and search the appropriate small cells for users. The rest of the paper is organized as follows. Section II provides a description of the user association optimization problem and the system model. The problem is analyzed and in Section III, moreover, the proposed three phase search approach is also discussed. The numerical results and their discussion are presented in Section IV followed by conclusions in Section V. II.

order of limiting interference to the macrocell, the maximum power transmitted on the corresponding channel is constrained. Then, the power vector of nth small cell provided by the cloud part can be pn  { pn1 , pnl , , pnLn } . pnl means that the power on the l th block of the nth small cell. This symbol also signifies that once the physical channel is allocated to the user, the transmit power is determined. Ti ,nl  t  is the user association index, if at time t user i access to small cell n , then the value of the index is designated to 1, otherwise is 0.

SYSTEM MODEL

B. Physical Link Model Assume the channel state information can be obtained through channel estimation in BBU, the SINR of user i in small cell n utilizing the channel l can be written as:

 in,l   



hin,l   pin,l  

k  n , k S

gik,l   pik,l    I l  N 0

(1)

pin,l   , pik,l    pn

where hin,l   and pin,l   denote the channel gain and power between the small cell n and user i . gik,l   is the channel gain between the neighboring small cell k and the user i located at small cell n . pik,l   is the power generated by small cell k . gik,l   pik,l   denotes the interference from

Fig.1 Architecture of the Cloud-RAN based Small Cell Network

neighboring small cell. N 0 denotes the white Gaussian noise. I l is the inter-channel interference which happens due to

A. Network Description The Cloud-RAN based Small Cell architecture with wireless virtualization [12] is shown in Fig.1, which includes the cloud part and the access part. The cloud part is comprised of BBU pools implementing most of the computation algorithms such as digital baseband processing, mobility management, resource allocation, user association, etc. The access part is formed by small cells, which are managed by two different SPs, called SP1 and SP2, respectively. Users are also belonged to the two SPs. Different from traditional network that SPs only allow the licensed user to access, in this architecture, unlicensed users can also be served by the SP only if some money is paid (If a group users belong to SP1, we call

Ln

1 vf , f d  c . v and c j 1, j  l j  l f c are users’ travel speed and channel central frequency.

users’ mobility that I l 

( f d )2

2



Assume vin,l  t  be the transmission rate gained by user i at time [t  1, t ] , then the following function can be obtained：

vin,l  t   w log 1   in,l    [bps / Hz ]

385

(2)

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gim,l   pim,l   is the interference generated from the mth

C. Utility Definition Utilities of small cells play an important role in determining whether one user is allowed to associate with the small cell. Through carefully designing the utility function, we intend to reach that when one users’ association request decreases the utility of the small cell, then the user will be refused to access. Here, the utility is related to two elements, see the network latency and the price charged by the SP. In the following, we describe the two elements in detail. Network latency: Latency in this paper refers to the time needed to transmit unit bit on the bandwith of w . The minimal potential delay fairness function (MPDF) [13] with the advantage of guaranteeing the network throughput and users’ fairness is applied to deduce the network latency. This function can be denoted as the reciprocal function of user’s transmission rate, and with which the latency for one certain user can be obtained. In the following, we show the latency of user i : 1 1 (3)  n vi ,l  t  w log 1   in,l   

small cell to the i th user located at the nth small cell. gim,l   and pim,l   are the channel gain and transmitting power from the mth small cell to user i . Combining with the network utility, the optimization problem can be formulated as the following: min U n

s.t. min   pin,l   Ti ,nl  t  nS iU lLn

min    gim,l   pim,l   Ti ,nl  t  mS iU lLn

C.1

(7)

  T t   1 nS lLn

n i ,l

C.2 Ti ,nl  t   0,1 Where C .1 means at time t every user can only access to one small cell. Within this optimization problem, the most optimal small cells are selected out for users with lower network latency, less energy consumption and interference influence.

The network latency is an assembly of all users’ latency, which can be written as: 1 Ti ,nl  t  n (4)  vi ,l  t  nS iU Access price: As described in the network description, the users should pay the money if they want to access to the small cell without a license. The price is determined by the SP and it should be a constant in this paper, we define the mathematical symbol as  . With network latency and access price, the utility function is formulated by the weighted sum approach. Thus, in the following we give the network utility represented by the U n for small cell n :

THE THREE PHASE SEARCH ALGORITHM

III.

A. Problem Analysis The user association optimization problem shown in (7) is equal to a multicriterion multimodal assignment problem (MMAP), where multiple objectives should be achieved. To solve the MMAP, weighted sum method and delamination sequence method are commonly utilized. The former merges the multiple objectives to one and the user-smallcell pair can be figured out with Newton algorithm. The later transform the MMAP to several sub-problems, where each has one or two objectives, and then the user-smallcell pair is found out through solving the sub-problems in sequence. In this paper, based on the work of Pedersen et al. [15], we utilize the delamination sequence method and propose a three phase search algorithm. The user association optimization problem is divided into two sub-problems which are bicriterion multimodal assignment problem (BiMMP, see function (8)) and optimal search subproblem (OSP, see function (9)), respectively.

  1 (5)    U n     Ti ,nl  t  n   vi ,l  t  nS iU   where  and  are two parameters to adjust the order of magnitude of the latency and price. From the above function, it’s obvious that the utility will decrease when the network latency increases, then the user will not be permitted to the cell n , and vice versa. For the access price, it has the same influence on the user association.

min   pin,l   Ti ,nl  t  nS iU lLn

D. Optimization Problem In this section, we formulate the user association problem while guaranteeing the network utility. Furthermore, considering the energy and interference problems, energy saving and interference limitation are also regarded as two objectives in the user association optimization problem. With the two targets, the energy consumed by the network and the interference suffered from other small cells should be minimized, which are shown in the following:

 min   pin,l   Ti ,nl  t   nS iU lLn  m m n  min    g i ,l   pi ,l   Ti ,l  t  mS iU lLn 

pin,l    pn

pin,l    pn

min    gim,l   pim,l   Ti ,nl  t  mS iU lLn

C.1

  T t   1 nS lLn

(8)

n i ,l

C.2 Ti ,nl  t   0,1

min

Un

(9) s.t. Ti ,nl   Different from the single objective optimization, the BiMMP should satisfy multiple criterions. Then the feasible solution is not unique. Thus, through solving the problem of BiMMAP, a group of feasible solutions denoted by  can be found. By solving the problem of (9), the most optimal solution

(6)

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is selected from  . Based on this most optimal solution, whether the user is allowed to access to the small cell is determined.

3.

 :  f UL , f DR  .

4.

f  : f UL , f  : f DR .

B. The Three Phase Search Algorithm For single-criterion optimization, the concept of optimality is well defined. Respecting common practice in the field of multi-criterion optimization, the Pareto concept of optimality is deployed [14]. The solutions in  should meet the Pareto Optimization. That f1  min   pin,l   Ti ,nl  t   PT

5.

nS iU lLn

f 2  min    g mS iU lLn

m i ,l

  p   T  t   ΓT m i ,l

n i ,l

   f , f   , Obtain the optimal solution T* from (13) and f *   f1* , f 2*  , If f  *    f1  f 2  , then



    T*  . } Else

} } End While

END Through the step 1, the initial feasible solution TUL and T are gained. Based on the two solutions, supported nonf UL and f LR can be found. dominated points DR

Assume f  : f UL , f  : f DR , the search direction can be

(11)

obtained with function (14) and     f  , f   . In step 5, we

obtain point f *   f1* , f 2*  and the optimal solution T* . If

exist and we have f 1   f11 , f 21  , f 2   f12 , f 22  ,

then we have:

    f 1 , f 2  :

f f

1 2

2 1

 f 22   f11 

f *   f1  f 2 , then the point is supported non-dominated. After repeating step 5, the supported non-dominated solution set  can be found. The solutions calculated in phase I are only part of the solution space, other solutions are found out in phase II by searching the supported non-extreme and the unsupported nondominated points.

(12)

The parameter function of (13) can be solved by many algorithms such as genetic algorithm. Assume T* be one solution of (13), then  PT* , ΓT*  is a supported non-

Proposition 1: If two unsupported non-dominated points f  and f  satisfy function (15), then none unsupported nondominated points exist in the triangle area constructed by the two points f1  f1  1 or f 2  f 2  1 (13)

dominated point. In the follow the detail of the algorithm is shown： Phase I: Search unsupported non-dominated points 1. Initialization f UL :  PTUL , ΓTUL  , where TUL is the optimal solution of lex min   PT, ΓT  .

f

DR

:  PT , ΓT DR

DR

of lex min   ΓT, PT  .

 , where T



DR

Assume solution space   T , T UL

When f  and f  fail to meet proposition 1, then phase II can be implemented. In phase II, the triangle search area   f  , f   is constructed firstly. The vertexes of the triangle are  f1 , f 2  ,

is the optimal solution DR

{ f  : f  ,

f  : Next   , f   .

s.t. T   where  is the slope of the two points, which can also be regarded as the search direction. Assume T1   and T2  



{     f * ,

(10)

The two phase approach presented in [14] is efficient for solving the BiMMAP. Combing the phase for solving the OSP, a three phase search algorithm is introduced to search the user association solution for the network. Just as the name implies, the algorithm includes three stages: firstly, in phase I find the supported non-dominated points and record the corresponding user association solutions. Secondly, in phase II, searching the supported non-extreme and the unsupported non-dominated points with the corresponding user association solutions. Finally, through phase III, constructing the feasible solution set based on the forgoing two phases and selecting out the most optimal user association solution from the solution set. Before describing the algorithm, we firstly define the parameter function f   T  , min f   T     P  Γ  T

While f   f  , do {

 f , f  and  f , f  , respectively. The search direction is   f , f  . The order of parameter f  T  can be obtained by  1

 2



.

2. If f UL =f DR , then the algorithm stops （Only one non-dominated solution exists in the solution space).

387

 1



 2



KBest algorithm. When f   T  reaches the upper bound

UB :  f1  f 2 , the search stops, and meanwhile the most

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} End While Out the optimal solution T0 . END

optimal solution Tt and the corresponding parameter f t  f   Tt  is returned by KBest method. Through substituting this parameter to function NonDom  f t  , if

satisfies, then f t can be the supported non-dominated point. Then solution Tt can be included in the solution space  , the upper bound can be updated by UB  max  f1  f 2 ,  f  f 2  .



Phase II: Search the supported non-extreme and the unsupported non-dominated points 1.

Input parameter and construct the triangle   f  , f   .

2.

    f  , f   ,  : f  , f   .

3.

t  1 , LB :  f  f , UB : updateUB  

4.



 1

 2

.

While LB  UB , do {  f t , Tt  : KBest  t ,  

If NonDom  f t  , then {



IV. SIMULATION RESULTS In this part, the effectiveness of the proposed user association scheme will be demonstrated by simulation. Within the simulation, we compare the performance of our scheme with several other schemes from several aspects. The simulation parameters are given as follows. One macrocell and 100 small cells are included in the CRAN SCN scenario. The coverage area of the macrocell is 1000m while that of small cell is 20m. Other related simulation parameters are defined as: v  30km/h , c  3  108 m/s , w  1MHz , N 0  100dB .



 :   f t  ,     Tt  , UB : updateUB  



} End If LB :  f1t  f 2t , t  t  1 . } End While

END All the feasible solutions for BiMMAP have been got in phase I and II. Below, we search the most optimal solution for the sub-problem of OSP. Assume 1 g   Ti ,nl  t  n  T , phase III is shown as: nS iU

Fig.2 Network latency under the method of Max-SINR, QOSA and the proposed TPSA

 i ,l  

In phase III, all the solutions selected in phase I and phase II are substituted to the function (9). After comparing the correspond latency values by the bubble approach of step 3, the solution T0 is chosen as the most optimal result for the user association problem.

Phase III: Search the most optimal solution 1．Initialization 2．Assume the number of the feasible solutions be K , the number of search time t  1 , T 0  Tt , g 0  T 0  0 , 3．While t  K , do { t  t  1 ； g t  Tt  t ；

Fig.3 Interference suffered by the small cell under the method of Max-SINR, QOSA and the proposed TPSA

If g  g { t

0

In Fig. 2, the lines related to the network latency under the proposed three phase search algorithm (TPSA) and two other candidate association schemes are plotted. The candidate schemes are maximum SINR method (Max-SINR) with which

g 0  g t , T 0  Tt } End If

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users chooses the small cell with maximum SINR and QoSbased association algorithm (QOSA) that users access to the small cell that guaranteeing the minimum transmission rate. From the graph, it’s obvious that with the TPSA, the network latency is lower than the other schemes, it can be concluded that users the proposed scheme is more adapt to the mobile network scenario. Moreover, the network latency has an increasing tend when the number of small cells becomes larger. The results presented in Fig 3 show the comparison of MaxSINR, QOSA and the proposed TPSA from the view of interference. From the graph, it can be obtained that as the number of small cells increase, the interference also becomes larger. The interference value increases until to an acme and then becomes steady. This happens because the maximum interference suffered by the macrocell is limited. When the interference generated from the small cells increases to a threshold, no more small cell are allowed to serve the users. Moreover, with our user association scheme, less interference is consumed.

minimizing the overall energy consumption and reducing the network interference. In this paper, we consider the transmit latency for users. Since the algorithm computation time and software implementation time in the cloud are also crucial to the network latency. In the future work, we will consider them in the user association scheme.

VI.

THE RESEARCH IS SUPPORTED BY NATIONAL SCIENCE AND TECHNOLOGY MAJOR PROJECT OF THE MINISTRY OF SCIENCE AND TECHNOLOGY AND PROJECT NAME IS “WIRELESS MOBILE SPECTRUM RESEARCH AND VERIFICATION FOR WRC15” WITH 2014ZX03003013-004 REFERENCES [1]

[2]

[3]

[4]

[5]

[6] [7]

[8]

Fig.4 Energy consumption under the method of Max-SINR, QOSA and the proposed TPSA

[9]

The energy consumption of small cells is shown by the lines delineated in Fig.4. The schemes compared are the same with Fig.2 and Fig.3 that are Max-SINR, QOSA and the proposed TPSA. It can be obtained that with TPSA, small cell save more energy than other schemes. Moreover, the saved power shows a rising trend as more small cells enter the network.

[10]

[11] [12]

V.

ACKNOWLEDGE

CONCLUSION [13]

In this paper, to choose appropriate small cells for users, we study the user association scheme in a Cloud-RAN SCN scenario. Combing the network latency, we construct a user association optimization problem. What’s more, considering the rising CO2 emission and the necessary network throughput, energy saving and interference limitation are also incorporated in the optimization problem. To solve the user association optimization problem, a three phrase search algorithm is proposed combing the concept of Pareto Optimality. With this algorithm, appropriate small cells and physical resources are chosen for users while minimizing the network latency,

[14]

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