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This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the WCNC 2008 proceedings.

An Adaptive Energy Saving Mechanism in the Wireless Packet

Access Network 1

Mugen Peng1,2, Wenbo Wang 1,2 Wireless Signal Processing & Network Lab, Beijing University of Posts and Telecommunications, 2 Key Laboratory of Universal Wireless Communication (BUPT), Ministry of Education P.O.Box 93, No.10 Xi Tu Cheng Road, Beijing 100876, China. Tel: +86-10-62282977. Email: [email protected]

Abstract—The energy saving mechanism is very important for supporting the mobility and extending the battery life in the wireless packet access network. For example, the sleep-mode has been standardized in both IEEE 802.16e and 3G long term evolution (LTE) systems. According to the definition in the standards, an adaptive energy saving mechanism (AESM) for enhancing the standardized energy saving mechanism (ESM) is proposed, in which the sleep interval is determined by both the system load and traffic properties. The proposed AESM determines the initial sleep interval and the period sleep interval adaptively according to the proposed parameters γ and ψ , which are the multiplying step size of the sleep interval and the traffic's property respectively. In order to demonstrate the proposal validity, the theoretical analysis based on the Markov chain model is presented, and the total energy consumption for the heavy load and the light load is compared. Simulation results illustrate that the proposed scheme can achieve a good balance between the energy saving efficiency and the transmission response time delay. Index Terms—Energy consumption, Wireless packet access network, Sleep mode, 3G LTE, IEEE 802.16e

I. INTRODUCTION he next generation wireless communication systems will be based on the “all-IP” structure and the wireless packet access network (WPAN) will have a dominant future. Mobility is one of main features in WPANs. In order to support the mobility, not only the efficient handover is necessary, but also the energy saving is very important to extent the limited battery life and decrease the interference. The IEEE 802.16e based mobile WiMAX system [1] and the third generation wireless communication system long term evolution (3G LTE) [2] adopt the sleep-mode to save the transmission energy. Sleep-mode is a state in which the mobile subscriber station (MSS) conducts pre-negotiated periods of absence from the serving base station (BS). There are two key parameters to determine the performance for the sleep-mode: sleep and listening intervals. Increasing the sleep interval is used to control the absence periods, and reduce the unnecessary energy consumption. However, the sleep interval should be reasonably decided because the trade-off between the traffic response delay and the energy saving efficiency needs to be balanced. The bigger sleep interval can save more energy while bringing out the longer response time to the queuing data flows, whereas the smaller sleep interval means the efficient transmission but with more energy consumption.

T

This work was supported in part by National Natural Science Foundation of China under No. 60602058, National advanced technologies researching and developing programs. (China 863 programming, NO.: 2006AA01Z257).

Many published papers and literatures have addressed the energy saving mechanisms. Yang Xiao [3] analytically modeled the sleep-mode scheme in the IEEE 802.16e wireless network, and demonstrated that some key parameters have great impacts on performances of sleep-mode. A sleep-mode interval control algorithm that considers the downlink traffic pattern and terminal mobility to maximize the energy-efficiency is presented in [4]. Among all these published papers related to the energy saving schemes, the Energy Saving Mechanism (ESM) scheme is popular and researched deeply. Furthermore, it was accepted and standardized in IEEE 802.16e standard. The ESM scheme requires that the initial-sleep window is the minimum sleep interval, which will result in excessive listening intervals. Consequently, Junfeng Xiao [5] proposed an Enhanced Energy Saving Mechanism (EESM), and the main idea of EESM is to reduce the listening operation while to monitor the arrival of traffic data effectively. Differing from ESM, the initial sleep interval is not the minimum sleep interval Tmin in EESM. A MSS uses half of the last sleep interval when it exits from the previous sleep-mode operation as the initial sleep interval in the next sleep-mode operation. However, in the real mobile wireless access systems, the traffic load is dynamical and the traffic arrival accords with some probability distribution. The single parameter optimization method in [5] will result in the unfitted operation for varies situations under different traffic load. Furthermore, the same sleep interval size isn’t adaptive to the different non-real time service. To our best knowledge, most published papers didn’t adjust the sleep interval according to the system load and the traffic property adaptively. In this paper, an Adaptive Energy Saving Mechanism (AESM) is proposed to adaptively save the power consumption and guarantee the spectrum efficiency under the consideration of the system load and the traffic characteristic. Note that the proposal is fully compatible with the he Energy Saving Mechanism (ESM) suggested in the IEEE 802.16e [1] and the EESM in [5]. The difference between them is that AESM is an adaptive enhancement and can have a higher energy saving gain with the allowable transmission response time delay. Additionally, since EESM is an enhancing of ESM, it is also named as ESM for the simplicity of describing. If there is no special noticed, the ESM in the following contents is specified as the EESM in[5]. In order to accommodate various situations, AESM divides the whole process into three scenarios: heavy- load scenario, medium-load and light-load scenario. For these three different scenarios, the proposed AESM can be utilized to provide better performance than ESM does because ESM mainly focused on

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performance optimization with the average system load. Accurately, the size of the next sleep interval is not just twice longer than that of the current sleep interval, and the γ times is instead in our work. The parameter γ is bigger than 2 if the system load is light and the serving traffic is non-real time. Otherwise, the parameter γ is not more than 2 if the system load is heavy or the traffic arrival is similar with the real-time service. Furthermore, the initial sleep interval is not fixed as ESM, and it can be changed and be determined by the average initial sleep interval and the current service status. The rest of this paper is organized as follows. In Section , the traditional sleep-mode operation for the energy saving is introduced, and the main idea in AESM is described. The theoretical analysis model of energy saving mechanisms according to the Markov chain is introduced in section III. The simulation results and the related analysis results are provided in section IV. The finial section V shows the conclusions.

II. PROPOSED MODEL The modes related to the power management can be categorized as wake-mode and sleep-mode. After a certain amount of frames in which there is no data traffic, MSS would send request to the BS and wait for BS’s approval before goes to sleep. After getting approval, the MSS goes into the sleep-mode. When the MSS enter the sleep-mode, it gets sleep for an interval, and then wakes up to check whether there are buffered data for it. If there are data for the MSS, the MSS goes to wake-mode. Otherwise, the MSS is still in the sleep-mode and keeps the state for another interval. Here we call the interval from the beginning of the initial sleep cycle to the end of the last cycle as a Sleep Operation Interval (SOI), which is described as Fig. 1. At the first sleep interval, a minimum sleep interval SWmin is used, where SWmin denotes the size of the sleep interval. Then each sleep interval is doubled (2iSWmin), until the maximum sleep interval SWmax is reached, and the sleep interval keeps SWmax, where i stands for the i-th sleep interval. After the sleep operation interval, the MSS temporarily wakes up for a short time interval to listen to the traffic indication message broadcasted from the BS, and the message includes the information inducing MSSs has buffered packet to be sent. Such procedures should be negotiated beforehand between the desired MSS and the corresponding serving BS. Parameters such as SWmin and SWmax are included the sleep request message sent form the MSS to the BS before getting asleep. Furthermore, the MSS can terminate the sleep-mode if there is an out-going frame, mostly because of the user’s manual interaction. Though the sleep-mode is defined in both WiMAX and 3G LTE, however, only the basic principle and simple mechanisms are introduced. Accurately, the sleep interval is sensitive to the traffic properties and the system load. In order to save more energy, the sleep interval should be adaptive to the different non-real time services and system load. In this paper, the non-real

time services are further categories as the heavy-load and light-load according to the current system load, which is executed in the BS. For the proposed AESM proposal, the sleep interval is adaptive to the system load and the traffic properties, in which the initial sleep interval and the period sleep interval is not in subjection with the binary exponential distribution. The core scheme is that the next sleep interval should be the last one multiplied with the parameter γ (i.e., the multiplying step size of the sleep interval). For the ESM scheme, the parameter γ is always equal to 2. However, the parameter γ will changes with the system load and traffic properties. For the non-real time service, the parameter γ is bigger than 2 if the system load is less than the threshold (for example, 50%), otherwise it is equal to or less than 2. For the real time service, the parameter γ should be equal to 1. For the non-real time traffic, the parameter γ should be bigger than 1 but less than 2. Meanwhile, the initial sleep interval is SWlim instead of SWmin. The new parameter SWlim is adaptively determined according to the last and the mean sleep interval in the previous SOI. For the first SOI, SWlim is assumed as SWmin. According to the mentioned above, we can have the conclusion: z If the serving traffic is time sensitive, the parameter γ is always equal to 1. z If the traffic is non-real time, and the current system load ( η ) is less than the threshold ( η th ), the parameter γ is determined by min {2η th / η + ψ , MAX } , where the function of min{.} means the minimum value, and the constant value of MAX means the allowable maximum value. Note that the parameter ψ donates the traffic‘s property related to the traffic arrival distribution, the traffic packet duration, packet size, and etc. In our work, MAX is 4, and ψ is set 0 because the multi-service is not considered in this work. z If the traffic is non-real time, and the current system load ( η ) is bigger than the threshold (η th ), the parameter γ is determined by max {2η / η th + ψ , MIN } , where the function of max{.} means the maximum value, and the constant value of MIN means the allowable minimum value. In our work, MIN is 1, and ψ is set 0. For the initial sleep interval, the different traffic has the corresponding value of SWmin. If assuming the basement of the initial sleep interval is SWbase, then SWmin= 2ψ *SWbase. About the traffic property, it is simply categorized as three types recommended for connections of Unsolicited Grant Service (UGS), real time polling service (rtPS), extended rtPS (ertPS), non real time polling service (nrtPS), and best effort (BE) types in the IEEE 802.16e standard. Only the nrtPS and BE services can support the changeable sleep interval size and the parameter γ is fixed as 2.

" Fig. 1 Sleep Mode Operation Interval

"


Actually, the different traffics in the same scheduling service should have different values of the parameter γ. The advantage of the proposed AESM scheme is to reduce the listening operation as few as possible while to monitor the arrival of new packets effectively. Actually, if the serving system is medium-load and only one kind of traffics is served, our proposal can be regarded as an adaptive ESM scheme [1][3]. Our proposal jointly considers the different system load and the traffic properties of the multi-services to adaptively save the consumption energy.

According to Fig.2, each state has two “next state”, one for the traffic arrival, denoted by real lines; and the other for nonetraffic arrival, denoted by dashed lines. Let di denote the event that there is at least one data frame arrival during the sleep cycle i. Note that it is the sleep-mode in the listening intervals. We have:

Pr ( di = true ) = 1 − e

− λ ( SWi + L )

= δi

(4)

Pr ( di = false ) = 1 − δ i = δî

III. ANALYTICAL MODEL Since the energy saving mechanisms are more important and necessary for the non-real time service, this paper only focuses on the situations where the parameter γ is bigger than 1. The analytical model and simulation results are thoroughly based on the non-real time traffic and only one type of traffic is considered. Note that our proposal is available for non-real time multi-services, in which the parameter ψ should be optimized carefully. In order to demonstrate the validation of the proposed AESM scheme, the analytical model is described as following. The traffic data arrival process from networks is assumed to follow the Possion process with rate λ. The traffic arrival time follows an exponential distribution with mean 1/λ. A discrete time model is used as unit time, where time is partitioned into intervals of fixed duration, called frames. Define the sleep time (ST) as the time period in the sleep mode including one or more sleep cycles and each sleep cycle includes one sleep interval and one listening interval. We assume that the listening interval (L) is a fixed length, during which period the BS broadcasts traffic indication messages. Let SWi denote the length of the ith sleep interval. Then the sleep cycle (Ci) is a sum of a sleep interval and a listening interval and denoted as SWi+L.

 2i SWmin 0 ≤ i ≤ lim  SWi = γ i −lim SWlim lim < i ≤ max  SW i > max  max

(1)

where

lim = log 2 SWlim SWmin max = lim + logγ SWmax SWlim

(2)

γ denotes the increasing step size of sleep intervals. Let n denote the number of sleep cycles in a SOI, then the average sleep time number in a SOI can be described as: n  n = ST SWi m = 0 or lim ∑ m  i =m   Ln = ( n - m + 1) L n ≥ m  m

(3)

where m and n denotes the first and last sleep cycle of the SOI respectively. In Fig. 2, the embedded Markov chain is adopted, where Si denotes the state that a MSS is in the sleep cycle i, and the corresponding sleep interval is SWi.

χ 00

S0

χ lim −1,0

χ 20

χ10 ϕ12

ϕ01

S1

S2

Slim-1

…

χ max −1,lim

χ max,lim

Smax ϕ m ax,m ax

χ lim,0

ϕlim −1,lim

ϕ 23

Smax-1

ϕ max −1,max

ϕlim,lim +1

Slim

χ lim + 2,lim

…

χ lim +1,lim

Slim+2

ϕlim + 2,lim + 3

Slim+1

ϕlim +1,lim + 2

Fig. 2 State Transition Model of AESM

Let φij and χij denote the transition probability form Si to Sj for the traffic and the none-traffic arrivals respectively:

δî 0 ≤ i ≤ max − 1, j = i + 1  ϕij = δˆmax i = j = max 0 others  0 ≤ i ≤ lim, j = 0  δ i or lim < i ≤ max, j = lim χ ij =  0 others

(5)

(6)

Then the transition matrixes Φ for the sleep cycle transition and Χ for next initial sleep window selection could be expressed as:

0 ϕ01 0 0 0 ϕ 12  Φ ij =  # # #  0 0 0 0 0 0

Χi ,0

    0 ≤ i, j ≤ max % #  " ϕ max −1 max  " ϕmax max  " "

0 0

(7)

 χ 00   χ lim +1 lim   χ   #   0 ≤ i ≤ max =  10  , Χ i ,lim =   #   χ max-1 lim       χ lim 0   χ max lim 

(8)

[]

In this paper, E ⋅ is used to stand for the mean/average function. Let ES and EL denote the energy consumption units per unit of time in the sleep interval and listening interval respectively. The total energy consumption in a SOI is:

{

Energysleep = Pr ( S0 ) E  STbusy  ES + E  Lbusy  EL

} (9)

+ Pr ( Slim ) { E [ STidle ] ES + E [ Lidle ] EL }


In order to determine the energy saving efficiency, the time length of different status should be calculated. Since the time length changes with the system load, these values are analyzed according to the different system load. 1) Heavy Load Situation If the system load is bigger than the threshold, the energy consumption is toughly related to the sleeping and listening interval time length. Assuming that an MSS enters an SOI after a certain amount of frames (Nenter) in which there is no traffic data arrival. Then the SOI starts follow:

Pr ( SOI ) = e − λ Nenter

(10)

During the heavy load scenario, the SOI begins from the state S0 with the probability:

 − λ ∑ ( SWi + L )    Pr ( S 0 ) = Pr ( SOI ) × 1 − e i=0     lim = e − λ Nenter 1 − δˆ lim

(

∏

i =0

i

(11)

)

The sleeping and listening time lengths in a SOI can be derived by: max

E  STbusy  = SW0 ⋅ δ 0 + ∑ ST0i Pr ( Si | S0 ) δ i i =1

+

∞

∑

(12)

ST0 Pr ( S max | S0 ) ϕ i

i = max +1

+

∑ ( i + 1) Pr ( S

max

i = max +1

Pr ( Si | Slim ) = ϕlim,lim +1 …ϕi −1,i lim < i ≤ max

Note that the light load situations aforementioned low- and medium-loads.

include

(18) the

IV. PERFORMANCE EVALUATION Firstly, we evaluate the AESM scheme with the theoretical analysis and the simulation results. The parameters for the performance evaluation are listed as follows: L=1, Tmin=1, Tlim=16, Tmax=1024, Es=1, EL=10. γ is set to be 4. Fig.3 shows both simulation results and analytical results of energy consumption over the traffic arrival rate λ. As illustrated in the figure, the simulation results match analytical results pretty well. In order to evaluate the performances of proposed energy saving mechanisms, an advanced dynamic system level simulator, developed by the OPNET tool, is developed for the performance evaluation of IEEE 802.16e system. In our simulation, the hexagonal macro scenario is considered. There are 57 sectors, and all the BSs are located in the crossing points of three sectors, in which each sector will be allocated the same frequency, and the frequency’s band width is 10 MHz. Every cell’s radius is assumed to be 2km. Additionally, the wrap around technique is adopted.

δ

i -max max,max i

max  E  Lbusy  = δ 0 + ∑ ( i + 1) Pr ( Si | S0 ) δ i i =1  ∞

Where

| S0 ) ϕ

 δ L 

(13)

i -max max,max i

where

Pr ( Si | S0 ) = ϕ01ϕ12 …ϕi −1,i

0 < i ≤ max

(14)

2) Light Load Situation If the system load is detected to be under the light load, i.e, the load is less than the load threshold, the probability of the SOI beginning from the state Slim can be described as :

Fig. 3 Simulation results vs. Analytical results

The propagation model is applicable for the test scenarios in urban and suburban areas outside of the high raise core where the buildings are of nearly uniform height. The pathloss Pr ( Slim ) = Pr ( SOI ) − Pr ( S0 ) calculation formula with the 3.4GHz frequency band is: lim (15) − λ ∑ ( SWi + L ) Pathloss = 149.63+35.63*log10(d) (19) lim = e − λ Nenter × e i=0 = e − λ Nenter i =0 δî where d is the average distance between the desired BS and the corresponding subscribers in kilometer. The shadowing fading Then the sleep interval and listening interval time length in a is generated as lognormal distribution with the standard SOI can be derived by: deviation 10dB. The Jakes fading model is adopted to involve max i the multi-path fading effects, the mobile users move at the E [ STidle ] = SWlimδ lim + STlim Pr ( Si | Slim ) δ i i = lim +1 (16) speed of 3km/h. ∞ In order to compare the performance of ESM and AESM i i -max + STlim Pr ( S max | Slim ) ϕmax,max δi schemes, some evaluation metrics should be presented, such as i = max +1 the energy saving efficiency, average listening time, average max  sleep time and average response time. During these metrics, E [ Lidle ] = δ lim + ( i + 1 − lim ) Pr ( Si | Slim ) δ i the most important is the energy saving efficiency. Accurately, i = lim +1  (17) the energy consumption without sleep-mode can be derived as: ∞

∏

∑

∑

∑

+

∑ ( i + 1 − lim ) Pr ( S

i = max +1

max

 i -max | S lim ) ϕ max,max δi  L 


{

}

According to the results in Fig. 4, it can be deduced that the Energynormal = Pr ( S0 ) E  STbusy  + E  Lbusy  EN (20) proposed AESM can’t work much better than ESM. Accurately, the energy saving mechanism is inefficiency when + Pr ( Slim ) { E [ STidle ] + E [ Lidle ]} EN system load is high.

The energy saving efficient with the sleep-mode is:

ξ=

Energynormal − Energysleep Energynormal

×100%

Energy Saving Efficiency (%)

(21)

Since ESM and AESM are toughly related to the system load, we can conduct simulations for the high and light load scenarios to evaluate the system performances. The high load means that the system load is 75%, while the light load means that the system load is 30%. It is observed that the performance metrics become worse along with the increasing traffic load. As the traffic load becomes heavier, the average time length of SOIs becomes shortened. Fig.4 shows the performance comparisons between ESM and AESM when the system load is high. The performance metrics include energy saving efficiency, average listening time, average sleep time and average response time. Since AESM scheme can’t save more energy when the system is high, the performance of AESM and ESM schemes are almost same. Note that the energy saving efficiency is related to the average listening time, average sleep time, and average response time. AESM has better energy saving efficiency, which is determined by lower listening time, longer sleep time. Due to the sleep interval increases, the response time become longer, which has great bad impacts on guaranteeing the quality of service (QoS), especially for the time sensitive services. Energy Saving Efficiency (%)

Average Sleep Time

Average Listening Time

Average Listening Time

Average Response Time

Average Sleep Time

Average Response Time

Fig. 5 Performance of AESM in light load scenario

Fig.5 shows the performance comparisons between ESM and AESM when the system load is light. The proposed AESM scheme can improve the performance of energy saving efficiency greatly because the unnecessary listening time is reduced, while the sleep time is maximized in a reasonable range. Although the response time of AESM is larger than that of ESM, it is tolerable because this situation only happens on a few data packets. Considering of energy saving, it is worthy to extend the response delay reasonably to save more energy as a whole. V. CONCLUSIONS The proposed AESM can have a better energy saving efficiency than the ESM, especially when the system load is light. In order to make AESM work better, the key parameters, such as γ and Tlim, should be optimized carefully. Meanwhile, how to determine the load threshold is still a challenging. The future research work should focus on the parameter’s optimization in the AESM schemes. Note that the proposed AESM is suitable not only for the IEEE 802.16e systems, but also for the 3G LTE and beyond 3G/4G systems.

Fig. 4 Performance of AESM in heavy load scenario [3]

REFERENCES [1]

[2]

IEEE 802.16e/D6-2005, “Draft IEEE Standard for Local and Metropolitan Area Networks --- Part 16: Air Interface for Fixed and Mobile Broadband Wireless Access Systems – Amendment for Physical and Medium Access Control Layer for Combined Fixed and Mobile Operation in Licensed Bands,” Feb., 2004. 3GPP TR 25.913 Requirements for Evolved UTRA (E-UTRA) and Evolved UTRAN (E-UTRAN) (Release 7) V7.3.0 (2006-03)

[4]

[5]

Yang Xiao, “Energy Saving Mechanism in the IEEE 802.16e Wireless MAN,” IEEE Communications Letters, Vol. 9, No. 7, July 2005. Neung-Hyung Lee and Saewoong Bahk, “MAC Sleep Mode Control Considering Downlink Traffic Pattern and Mobility,”. Vehicular Technology Conference, 2005. VTC2005-Spring. 2005 IEEE 61st, Vol.3, 30 May–1 June 2005. pp: 2076-2080 Junfeng Xiao, “An Enhanced Energy Saving Mechanism in IEEE 802.16e,” IEEE GLOBECOM, 2006