ho strategies

Cost-aware Handover Decision Algorithm for Cooperative Cellular Relaying Networks

Tong Wu 1,2, Jing Huang 1,2, Xinmin Yu 1,2, Xinchun Qu 1, Ying Wang 1,2

1: Wireless Technology Innovation Institute, Beijing University of Posts and Telecommunications 2: Key Laboratory of Universal Wireless Communications, Ministry of Education

P.O. Box 92, BUPT, 100876 Beijing, P.R. China

Email: [email protected]

Abstract–The cooperative cellular relaying network is expected to achieve the higher capacity and enlarge the coverage. In this paper, a novel cost-aware handover decision algorithm (CHDA) for cooperative cellular relaying networks is proposed. Two cost functions, namely the triggering and priority decision cost functions are exploited, which involves the signal transmission quality, the handover signaling cost, the handover latency and the interference estimation. Simulation results show that the signaling overhead and the handover delay decrease significantly by utilizing the CHDA scheme. It is also proved that the CHDA strategy is an efficient method to achieve the tradeoff among the QoS requirements and the system overheads, which can remarkably enhance the system performance.

Keywords- cooperative relay, cost-aware handover decision, cost function, quality of service.

I. INTRODUCTION

The concept of relaying has emerged as a feasible option for challenging the tradeoff between the transmission range and the end-to-end data rate transmission [1]. Relay-enabled standards have already been considered in the IEEE 802 family such as IEEE 802.11s-WLAN, IEEE 802.16j-WMAN and IEEE 802.20-MBWA. Additionally, the WINNER project is also developing a relay-enabled deployment concept for the next generation broadband mobile radio access. Therefore, cooperative cellular relaying network is a promising solution for the future wireless communication.

However, the deployment of relay nodes (RNs) introduces several issues. One of the most challenging issues is the handover (HO) protocol, which can naturally be regarded as a routing problem in relaying networks essentially. Recently, some literatures have studied the effect of the routing protocol for the system capacity in the hybrid relaying networks. Ref.[2] presents the single-relay selection algorithms based on the pathloss. The routing algorithms for cellular relaying networks with hotspot cell are discussed in [3], in which the mobile terminal in hotspot cell can have access to another cell via free channels. Hsien-Po Shiang proposes a distributed dynamic routing scheme, called the self-learning policy, which selects the routing relays for wireless mesh networks in [4]. A joint routing and re-routing control strategy is studied in Centralized Architecture (CA) and Decentralized Architecture (DA) for relaying networks in [5]. However, most researches have not involved the HO decision scheme, and the routing

selection strategy is mainly based on the quality of link while ignores the other aspects. Moreover, [6] investigates the inter-cell HO scheme utilizing cell ID in the multi-hop networks based on the IEEE 802.16e system, but the evaluations have not involved the HO latency and signaling cost.

This paper focuses on a two-hop cooperative cellular relaying system with fixed relays and investigates the problem of how to guarantee the QoS in a mobile and fading environment through proper HO schemes. The contributions of this paper are: 1) Firstly, we propose a modified relay channel allocation strategy aiming to improve the resource utilization. 2) An effective cost-aware HO decision algorithm (CHDA) is presented based on novel cost functions, by considering the average received signal quality, the HO signaling cost, and the HO latency.

The rest of the paper is organized as follows. In section II, the network architecture and the relaying channel allocation strategy are described. The detailed CHDA algorithm is illustrated in section III. In section IV, the performance evaluation criteria are presented. The dynamic simulation results are given in section V. Finally, the conclusions are drawn in Section VI.

II. SYSTEM MODEL

A. Architechture of Cellular Relaying Networks The fixed two-hop cellular relaying network is described in

Figure 1. Each BS is located at the center of the cell and six fixed relay nodes (RNs) are placed uniformly around the BS. Each RN is located at 2/3 radius away from the BS to achieve the optimal performance [7].

There are two alternative ways to transmit signals in the relaying system. One is the traditional direct link and the other is a relaying approach in which the mobile station (MS) communicates with the BS via a RN. Since the deployment of RNs introduces more interference sources, the relaying channel allocation, affecting the resource utilization and interference mitigation, is discussed in the next subsection.

B. Relaying Channel Allocation

In two-hop cellular relaying networks, the first and second hop should occupy different resources due to the half-duplex constraint. In order to aviod excessive interference, a RN is

This paper is financed by Ericsson Company, and also supported by National Natural Science Foundation of China (60496312).

978-1-4244-1645-5/08/$25.00 ©2008 IEEE 2446

Figure 1. Two-hop cooperative cellular relaying network model

not allowed to reuse any channel in the same cell, but can reuse a part of the channels from the cell farthest from it in one cluster. Furthermore, all channels in a certain cell are equally divided into several groups and each relay can only reuse channels from one group. In [3], the channels in one cell are divided into N groups (N is the number of RN in one cell), and the channels in the neighbor RNs can not be shared, which reduces the channel utilization efficiency, especially when the traffic is non-uniformly distributed. For the sake of solving the above problem, the three neighbor RNs should share the same group of channels with each other so as to balance the traffic load. Referring to Figure 1, a certain channel group is shared by the three RNs in one triangle area. For example, the RNs in triangle1 and triangle2 will share the same channels with the RN1, RN2, RN3 and RN4 in cell 3, 4, 6 and 7 respectively. Thus, the problem of load balance can be solved effectively and the frequency utilization will be improved.

III. COST-AWARE HANDOVER DECISION ALGORITHM

Radio resource management (RRM) is very important to exploit the advantage of the cellular relaying networks, especially for the HO problem. An improperly designed HO algorithm can result in unacceptably high level bouncing or high probability of the connection termination or packet lost.

Figure 2. Handover types in cellular relaying networks

Compared to the traditional cellular networks, the integrated cellular relaying networks induce more HO types due to the deployment of relays (Shown in Figure 2). There are totally seven HO types in two-hop relaying system (See Table I). Different HO modes involve different signal quality, HO delay, signaling costs and the interference influence.

A novel HO scheme named CHDA is proposed in this section. It contains two steps, namely the HO triggering strategy and the HO priority decision process. In each step, a

novel cost function is developed.

A. Handover Triggering Strategy in CHDA In two-hop cellular relaying networks, for each MS, the

novel HO triggering cost function 1f is defined as:

1 wtf S P Tα β γ= − + , (1)

where S denotes the signaling cost induced by the handover, P denotes the received power and wtT denotes the HO latency time. α , β andγ are the weight factors, where 1α β γ+ + = .

The value of S is determined by the signaling cost of the different HO types. There are three types of intra-cell HO schemes and four inter-cell HO cases in relaying networks. In order to calculate the signaling cost for each HO type, we define the signaling messages as follows:

BSCh_REQ: the BS channel setup request in the other cell. BSCh_ACK: the BS channel setup acknowledge. BSChRL_REQ: the BS channel release request. BSChRL_ACK: the BS channel release acknowledge. RNCh_REQ: the RN channel setup request. RNCh_ACK: the RN channel setup acknowledge. RNChRL_REQ: the RN channel release request. RNChRL_ACK: the RN channel release acknowledge. BS_REQ: the BS setup request in the home cell. BS_ACK: the acknowledge of the BS. Reroute_REQ: the routing table update request. Reroute_ACK: the routing table update acknowledge.

Due to the limited length of this paper, only the flowcharts of intra-cell HO from RN to BS and the inter-cell HO from RN to RS are given to explain how to calculate S . See Fig.3 and Fig.4, where GW_CP and GW_UP represent the control plane and user plane of the Gateway respectively.

The value of mS ( 1,2,...7m = ) is then determined according to the flowcharts. For example, the value of 1S is 6 on account of the number of signaling, which denotes the intra-cell HO from RN to BS (Shown in Figure 3). For convenience of the manipulation, S needs to be represented in a log form as:

10 logdB mm

BM

SS

S= , (2)

where 1BMS S= for benchmark. According to (2), all the signaling costs of different type of HO scenarios are shown in Table I. Accordingly, the parameter P is also defined as:

10logdB ii

BM

PP

P= , (3)

where iP is the received power from the i th target node. BMPrepresents the lowest power received from the target set Ω .

If the HO strategy is implemented without considering signaling cost, too much signaling cost will cause the performance degradation of the relaying network. The novel cost function will make the tradeoff between the signal quality gain and the HO signaling cost first.

Besides, when the HO target is overloaded, the HO request to

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Figure 3. Handover triggering process of intra-cell RN-BS HO

TABLE I. EVALUATION OF HANDOVER COST

HO types Source-Target Signaling cost (S) Release gain (G)Intra HO BS-RN 6 (0dB) 1/2 (-3dB) Intra HO RN-BS 6 (0dB) 2/1 (3dB) Intra HO RN-RN 8 (1.25dB) 2/2 (0dB) Inter HO BS-BS 8 (1.25dB) 1/1 (0dB) Inter HO BS-RN 10 (2.22dB) 1/2 (-3dB) Inter HO RN-BS 10 (2.22dB) 2/1 (3dB) Inter HO RN-RN 12 (3dB) 2/2 (0dB)

the target is keeping on a failed state until the HO target has free channels. It may lead to intolerable delay. Therefore, the HO latency time wtT is considered in the cost function of the HO triggering process. Similarly, the latency wtT is also defined as:

10 logdB wt iwt i

BM

TT

T−

− = , (4)

where BMT represents the lowest latency time w.r.t. all the potential target nodes. Thereby resorting to the cost function, the HO latency time of the HO process can be controlled. Therefore, the serving node whose cost function has the minimum value is chosen as the HO target. The HO triggering strategy is described as follows:

1. The number of the RN and BS in the candidate set Ω is initialized. The candidate set is chosen based on the average received power, then (1) ( ) ( )

1 1 1 , , , , i If f fΩ = .2. The cost of each link in the candidate set is calculated

based on the cost function defined in (1). 3. The cost of the current link was calculated based on the

equation below:

1_ localf P= − , (5)

where P is the average received power of the current link. 4. The hysteretic threshold value klag of each link is

calculated based on the HO type, where 1k = denotes the Intra-cell HO, 2k = denotes the Inter-cell HO.

5. BS (or RN) with the minimum cost function is chosen. ( ) ( )

1 1 1_ ,i if min min f f= ∈Ω (6) ( )

1_ ii min arg min f= . (7) If 1 1_ _kf min lag f local+ < , _i min is chosen as the HO

target. Then the available channel is scanned. If there exist free channels to establish the new link, the MS will handover to the target node. Else, the MS will keep the current link.

B. Handover Priority Decision Process in CHDA

In the heavy traffic networks, many MSs may send out HO requests to the same target at the same time leading to the

Figure 4. Handover triggering process of inter-cell RN-RN HO

performance degradation. For the sake of the efficient HO process, a priority decision scheme is proposed with two aspects. One is the quality of the signal of the current link, the other is the potential interference produced by the link shift. According to the transmission model in Section II, relay users bring more interference than single hop users due to the reuse operation for the second hops. We define a resource release gain G to describe the channel resource utilization difference before and after the HO, which is written as:

10logdB i bei

i af

lG

l−

−

= , (8)

where i bel − and i afl − are the link number of the user before and after the HO operation. The value of, G corresponding to different HO scenarios, is also shown in Table I. Hence the cost function 2f for priority decision can be expressed as:

2 (1 ) rel curf SIR Gη η−= − − , (9)

where η is the weight factor which is changing with the different weight of the two parameters. rel curSIR − denotes the relative SIR gain between the current link ( curSIR ) and the link with the highest SIR ( BMSIR ) in the HO candidate set

10log currel cur

BM

SIRSIR

SIR− = . (10)

The priority of HO process is based on the cost function at every instant. That is to say, all the HO requests are queuing according to the cost function, and the HO request with lowest cost has the top-priority.

IV. PERFORMANCE EVALUATION CRITERIA

A. Handover Latency and Signaling Cost

Define HOD to be the average delay (second per user) and totalS as the total signaling overhead. Assume that ,m nt is the

HO latency time in m th HO type of n th MS, 1,2,...7m = .Moreover, mu denotes the number of the users in the m th HO type, and mS is the signaling overhead exchanging for each HO type. It is easy to get these relations as follows:

, , , ,

, , ,

() / _

HO inter BS BS inter BS RN inter RN BS inter RN RN

intra BS RN intra RN BS intra RN RN

D D D D D

D D D user num− − − −

− − −

= + + ++ + +

7 7

,1 1 1

/mu

m n mm n m

t u= = =

= , (11)

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total inter intraS S S= +7

1m m

m

u S=

= ⋅ . (12)

B. Signaling Cost to Capcity Ratio(SCR)

Suppose that dΓ is the signal-to-interference ratio (SIR) of the direct link, and 1Γ , 2Γ represent the SIR of the first hop and the second hop of the relaying users respectively. The capacity of the k th user adopting decode-and-forward (DF) mode can be expressed as below [7].

2

2 1 2 2

log (1 )1 1min log (1 ), log (1 )2 2

k d

kk k

B k DC

B B k R

+ Γ ∈=

+ Γ + Γ ∈,(13)

where kB is the bandwidth allocated to the k th user. k D∈means it is a direct user while k R∈ means relaying user.

In order to evaluate the fairness of CDHA strategy, the signaling cost to capacity ratio (SCR) is proposed as follows:

7

1_ m mm

kk

u SSignaling cost

SCRCapacity C

== = . (14)

V. SIMULATION RESULTS

A. Propagation Model The average large-scale pathloss is adopted as

20 10

0

4 10cL

d f dP

c d

α ξπ= , (15)

where cf is the carrier frequency in Hz, c is the speed of light given in meters/s, α is the pathloss exponent, and 0d is the reference distance at free space. ξ is a Gaussian distributed random variable with zero mean and standard deviation σ . The auto-correlation function of shadowing is exponential [8],

21 2 1 2( ( ) ( )) exp( / )cE d d d d dξ ξ σ= − − , (16)

where cd determines how fast the correlation decays with the distance.

B. Simulation Results and Analysis

The dynamic simulation is utilized to evaluate the performance of the proposed CHDA algorithm and the wrapa round method is adopted to avoid the edge effect. The system under investigation consists of 7 clusters, each of which contains 4 cells, as shown in Figure 1. The main simulation parameters are listed in Table II. Besides, the priority decision strategy is fixed and the weight factor η is assumed to be 0.5. We mainly focus on the impact of the triggering strategy with various α , β andγ .

Figure 5 gives the result of the channel capacity under different traffic load. It can be seen that when CHDA strategy does not consider the effect of the signaling ( 0α = ), the channel capacity is almost the same no matter the HO latency

TABLE II. SIMULATION PATAMETERS

Parameter Values Reuse Factor 4Cell radius 500 m RN radius 500*(2/3) m

Number of RNs in One Circle 6 Standard Deviation of Shadowing 8 dB Pathloss Exponent for Home Cell

(BS-MS; BS-RN; RN-MS) (4; 2.5; 3.5)

Pathloss Exponent for Other Cells 4 Carrier Frequency f 2 GHz

Reference Distance d0 10 m Downlink Max BS/RN Power 1 watt / 0.2 watt

Noise power each channel -132 dBmChannels per Cell 24 MS average speed 5 m/s

Transmission bandwidth 2 MHz Hysteretic threshold of inter-/ intra- 3 dB / 2 dB

time is considered ( 1/ 2γ = ) or not ( 0γ = ). This indicates that the channel capacity is not sensitive to the HO latency time scheme. It is easy to find that the channel capacity will decrease when considering the signaling overhead, which is because that the scheme with 0γ ≠ tends to achieve the tradeoff between signal quality and signaling overhead. Therefore, the capacity performance may be sacrificed in order to decrease the signaling overhead. In addition, the HO latency time affects the capacity obviously. Figure 6 shows the average HO delay under different traffic load. It can be seen that the average HO delay decreases a lot when 0γ ≠ . It denotes that the HO delay performance is quite sensitive to HO latency time in triggering cost function.

In Figure 7, the total signaling overhead is equal to the product of the handover rate of each HO type and the signaling overhead of each type. The HO signaling overhead of each type is set to be the number of the signaling exchanged among the nodes, including MS, serving nodes and target nodes. The result shows that when the CHDA strategy considers the effect of the signaling and the HO latency time, the HO signaling overhead decreases obviously.

The performance comparisons of SCR are shown in Figure 8. The values of SCR decrease when either the signaling cost or the HO latency time is considered, and the performance is more sensitive to the HO latency time than the signaling. When the signaling and the HO latency time are both considered in the CHDA strategy, the system has the best performance with lowest SCR among the different schemes.

VI. CONCLUSION

This paper has addressed the handover problem in the cooperative cellular relaying system. In the HO procedure, not only average received power, but also the HO signaling overhead, HO latency time cost and interference influence are considered. The simulation results present that the system HO

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14 15 16 17 18 19 20 21 221.1

1.2

1.3

1.4

1.5

1.6

1.7

1.8

1.9

Number of users per cell

Cap

acity

(bps

/Hz)

RP-based : α=0, β=1, γ=0Cost f-based : α=0, β=1/2, γ=1/2Cost f-based : α=1/2, β=1/2, γ=0CHDA : α=1/4, β=1/2, γ=1/4

Figure 5. Average channel capacity ( 1/ 2η = )

14 15 16 17 18 19 20 21 220

0.02

0.04

0.06

0.08

0.1

0.12


The

aver

age

dela

y of

the

syst

em(s

)

RP-based : α =0, β=1, γ=0Cost f-based : α=0, β=1/2, γ=1/2Cost f-based : α=1/2, β=1/2, γ=0CHDA : α =1/4, β=1/2, γ=1/4

Figure 6. The average handover delay ( 1/ 2η = )

signaling overhead decreases significantly when the signaling and HO latency time cost are considered. Furthermore, the HO delay decreases obviously and the MSs which handover to the overload cell can be efficiently transferred to other free cells when the HO latency time cost is considered. The system has the best performance in terms of SCR when the CHDA strategy takes into account both the signaling overhead and the HO latency time. Therefore, the CHDA algorithm with cost function can efficiently guarantee MSs’ QoS requirements.

REFERENCES

[1] Pabst R, Walke B H, et al., “Relay-based deployment concepts for wireless and mobile broadband radio,” IEEE Communications Magazine, vol. 42, pp. 80-89, Sept. 2004.

[2] Sreng V, et al., “Relayer Selection Strategies in Cellular Networks with Peer-to-Peer Relaying”, IEEE Vehicular Technology Conference, vol. 3, pp. 1949-1953, Orlando, Oct. 2003.

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HO

sig

nalin

g ov

erhe

ad(t

imes

/cel

l/s)


Figure 7. System handover signaling overhead ( 1/ 2η = )

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Figure 8. The SCR vs number of users per cell ( 1/ 2η = )

[3] Zhang Jingmei, Shen Xiaodong, et al., “Call Routing and Admission Control for Two-hop TDMA Cellular System”, IEEE Vehicular Technology Conf., vol. 1, pp. 407-411, Dallas, Sept. 2005.

[4] Shiang, Hsien-Po, et al., “Quality-aware Video Streaming over Wireless Mesh Networks with Optimal Dynamic Routing and Time Allocation”, IEEE ACSSC '06, pp. 969-973, California, Oct.-Nov. 2006.

[5] Shen Xiaodong, Tang Mei, Wang Ying, Liu Baoling, Zhang Ping, “Joint Routing and Re-routing Control in Two-hop Cellular Relaying System”, IEEE APCC’ 06, Busan Korea, pp. 1-5, Aug. 2006.

[6] Ji Hyun Park, et al., “Reducing Inter-Cell Handover Events based on Cell ID Information in Multi-hop Relay Systems”, IEEE Vehicular Technology Conf., pp. 743-747, Dublin, April 2007.

[7] Proakis J G, Digital communications Fourth Edition, published by: MCGRAW-HILL, ISBN: 0-07-232111-3, 2001.

[8] Gudmundson M. “Correlation model for shadow fading in mobile radio system”, IEEE Electron Letters, vol. 27, no. 23, pp. 2145-2146, Nov. 1991.

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