3GPP LTE Random Access Channel Self-Optimization

This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TVT.2013.2296494, IEEE Transactions on Vehicular Technology 1

3GPP LTE Random Access Channel Self-Optimization Mehdi Amirijoo, Fredrik Gunnarsson Member, IEEE , and Filip Andr´en

I. Introduction The random access (RA) procedure is a key procedure in mobile networks, enabling user equipments (UE) to initiate communications and time align to a base station. This paper is focused on self-optimization of the random access procedure in LTE, which is considered as one of the SON use cases in 3GPP [1], [2]. The random access optimization requirements [2] includes access delay requirements that can be configured by the operator. In the literature, the random access channel configuration and optimization has been discussed based on simulations of different parameter configurations to assess favorable settings, or on self-optimization schemes, where the objectives are cell specific configurations, e.g., [3]–[11]. In contrast to the existing literature we have addressed the problem of controlling random access parameters to meet access delay requirements as specified by 3GPP [2], where the random access performance objective is formulated differently compared to that addressed in previous work. The outline of the rest of the paper is as follows. Section II gives a short LTE random access procedure tutorial, while Section III addresses relevant performance specification aspects and modeling and adequate simulator modelling is discussed in Section IV. Section V considers access probability and delay estimation schemes, and the performance of access delay estimation schemes, respectively. Section VI provides a sensitivity analysis to understand impact from controller parameters to access delay. M. Amirijoo and F. Gunnarsson are with Ericsson Research, Link¨ oping, Sweden (e-mail: [email protected], [email protected]). F. Andr´ en is with the Austrian Institute of Technology, Vienna, Austria (e-mail: [email protected]). Copyright (c) 2013 IEEE. Personal use of this material is permitted. However, permission to use this material for any other purposes must be obtained from the IEEE by sending a request to [email protected].

Controller design and evaluation is in focus in Sections VII and VIII, before Section IX concludes the paper. II. Random Access Procedure in LTE There are numerous occasions to perform the LTE random access procedure, e.g., initial access of an idle mobile, re-establishment after radio link failure, and handover to a different cell. Prior to sending the random access preamble, the mobile acquires information about the random access procedure, some of which are described in the following subsections (for details we refer to [12]–[16]). In LTE the reserved time-frequency resources for random access - the random access opportunities - are slotted, and the mobile selects an opportunity at random among the available opportunities, see Figure 1. The considfrequency

PRACH PUSCH 1 RB

Abstract—This paper addresses self-optimization of the random access channel (RACH) in the 3GPP Long Term Evolution (LTE). Ensuring satisfactory random access performance in terms of access delay is crucial for reducing the delays associated with initial access and handovers. The feasibility of self-optimization is investigated by means of simulations, where the coupling between several parameters and the RACH performance in terms of access delay, is provided. We present two candidate feedback control algorithms that automatically adjust key RACH parameters in order to meet specified access delay targets.

time 1 ms

Fig. 1. Time-frequency structure of the uplink in LTE, with PRACH and PUSCH sharing the available resources.

ered resources for the physical random access channel (PRACH), can also be allocated to the physical uplink shared channel (PUSCH) used for scheduled uplink data transmission. Therefore, the resource allocation needs to consider the balance between the PRACH and PUSCH demands. The specification allows a periodicity of 1 to 20 ms, with configuration 14 representing an RA opportunity every ms, configurations {12,13} an RA opportunity every other ms, {9,10,11} six RA opportunities per 20 ms, {6,7,8} four RA opportunities per 20 ms, {3,4,5} two RA opportunities per 20 ms and configurations {0,1,2} representing an RA opportunity every 20 ms. Configurations in the same set, e.g. {0,1,2} have the same number of RA opportunities per 20 ms, but the allowed opportunities differ for different configurations and correspond to mutually orthogonal opportunity patterns. For more details we refer to [14]. The contention-based random access procedure can be applied to all random access events. It is possible that at least two mobiles select the same resources (preamble

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and opportunity) for random access, and therefore the contention situation needs to be resolved. The procedure is below. Step 1 - Random Access Preamble: The mobile selects a signal waveform (a preamble) from a specied set, and an opportunity at random and determines the preamble transmission power by estimating the downlink path loss P L from the downlink reference signal (pilot signal) and using the broadcasted parameters P0 P RACH (the desired received power) and ∆P RACH (the power ramping step). The preamble transmission power of the transmission attempt number m = (1, 2, . . . ) is PP RACH = min (Pmax , P0

P RACH − P L+ (m − 1)∆P RACH + ∆P reamble ).

(1)

Step 2 - Random Access Response: Upon detection of a preamble in an opportunity, the base station signals an uplink resource allocation, and all mobiles that used the specific preamble in the specific random access opportunity considers this information. If no response is obtained within a configured time window, the mobile increases the preamble transmission attempt number m and returns to step 1) unless the max number of attempts has been reached. Step 3 - Scheduled Transmission: Using the allocated uplink resource, the mobile transmits an identity that uniquely identifies the mobile in the base station. Step 4 - Contention Resolution: During step 3 of the random access procedure, several mobiles that have sent the same preamble may respond. The base station chooses one of the mobile identities and responds with the identity of the selected mobile, and only this mobile acknowledge the reception of the contention resolution. III. LTE RACH Optimization Requirements in 3GPP This section addresses RACH self-optimizing requirements [2]. The main objective with the random access procedure is to provide prompt and reliable access. One way of expressing the target is via the access delay (AD), which is defined as the time from when the UE initiates the random access procedure until it is completed. Let T0 define the time when the UE decides to perform a random access. Let T4 define the time when step 4 is executed and access has been granted. Then access delay is defined by AD = T4 − T0 . Access delay requirements are specified in terms of a percentile for a given access delay, for example that 90% of the accesses shall be completed within a certain delay [2]. In this paper we use the following notation for expressing the access delay requirements. The performance specification is specified with an AD percentile and its target, namely, TAD = P R , where AD at indicated percentile [P1 , . . . , PN ] must be less or equal to the reference [R1 , . . . , RN ]. For example, assuming that the 50th AD percentile should be at 30ms and the 99th AD percentile

should be at 70ms, then theperformance specification is 0.5 30 denoted as TAD = . 0.99 70 One important observation can be made from the random access optimization requirements. It is up to each base station to meet the requirements, and it is natural to consider meeting the requirements tightly to avoid excessive interference to other base stations. IV. Simulator Overview and Scenario In this paper we use a simulator with time correlation. The length of a time step is one subframe (1ms) and the following steps are performed at each step. First a set of UEs that are to perform a random access are created in this step, where the number of UEs created follows the Poisson process with a mean arrival intensity LoadRACH (number of UEs/second/cell). The generated UEs are uniformly distributed over the simulated area. In order to accurately model random access, a set of different timing parameters [15], [16] have been modeled. The first timing factor is the time a UE waits for an RA response after it has sent its preamble, called raresponseWindowsSize. The second timing factor is the time a UE has to wait after the received RA response until it can send its unique identity. The third factor is the time a UE waits for a contention resolution response after it has sent its unique identity and is controlled by the parameter mac-ContentionResolutionTimer. We define the PUSCH load (denoted LoadP U SCH ) as the ratio of resource blocks (RBs) that are scheduled for PUSCH to the total number of RBs available during a subframe (1 ms). Let PRACH RBs denote those RBs where PRACH is scheduled, see Figure 1. We assume that the probability of scheduling PUSCH on PRACH RBs is a linear function of LoadP U SCH , hence, the probability of scheduling PUSCH on the PRACH RBs increases with LoadP U SCH . PUSCH power control is based on [15] and simplified to open-loop power control, where P0 P U SCH denotes is the desired target received power. The UEs execute random access consisting of the selection and the transmission of a preamble with the power level according to (1), where P L is subject to a 3 dB standard deviation Gaussian noise. The received preambles are processed, where the SIN Rp,c of each preamble p received P gp,c at cell c is computed according to, SIN Rp,c = IPpc +N where Pp is the transmission power of the UE transmitting preamble p, gp,c is the path gain from the UE to the eNodeB of cell c, N is the thermal noise power over the PRACH RBs, and IPc is the received interference from PUSCH at cell c. The SIN Rp,c is then mapped to a preamble detection probability [6]. If several UEs transmit the same preamble in a cell, then contention resolution is carried out by randomly choosing a preamble (UE) among the detected preambles. As mentioned above we model the uplink inter-cell interference from PUSCH and its impact on PRACH. The impact of PRACH on PUSCH in neighboring cells depends various parameters, e.g., P0 P RACH , period of the random



Each of the possibilities has a time delay αi associated with it. The parameters α0 until α4 are defined as below. The first attempt delay, denoted as α0 , is defined as the time in ms between a UE wants to start a random access attempt until the UE sends its first preamble. The detection delay, denoted as α1 , is defined as the time in ms from a UE sends a preamble until the time the UE gets the TABLE I RA response associated with the preamble. The detection Simulation parameters miss delay, denoted α2 , is defined as the time in ms from Parameter Value a UE sends a preamble until the time the UE sends a User distribution Uniform preamble in the next attempt, given that the UE did not Site to site distance 500 m receive a RA response. The random access finished delay, Antenna Tilt 8 degrees Pmax 23 dBm denoted α3 , is defined as the time in ms from a UE receives -89 dBm P 0 P U SCH its RA response until the UE gets the contention resolution N -109 dBm Path loss L L = 128.1+37.6log10(d), d [km] response containing the unique identity belonging to the Log-normal shadowing 8 dB standard deviation UE. The contention resolution failed delay, denoted α4 , LoadP U SCH 0.5 is defined as the time in ms from when a UE receives LoadRACH 250 preambles/cell/s its RA response until the UE sends a preamble in the PRACH Format 0 P 0 P RACH -90 dBm next attempt, given that the UE did not receive a RA ∆P RACH 2 dB contention resolution response with its unique identity. PRACH Configuration {0, 1, 2} From Figure 2 and the required number of attempts ns ra-ResponseWindowSize 5 subframes mac-ContentionResolutionTimer 24 subframes needed to for a successful random access, we can formulate Simulation Time 100 s d as the expected access delay, AD

access opportunities, frequency bandwidth of the network, and number of users performing random. Considering this, in general the impact of PRACH inter-cell interference on PUSCH is smaller compared to the corresponding PUSCH inter-cell interference and, therefore, we do not model PRACH impact on PUSCH.

Finally, the network is deployed in a hexagonal layout of 7 sites each 3-sectored and wrap-around propagation. We model slow fading caused by obstacles in the propagation path between the base station and the mobile, by assuming log-normal shadowing. Table I gives the default value of parameters used in the simulations. V. Access Probability Estimation The total AD of a UE depends on how many attempts the UE needs. For one access attempt there are three possibilities for a UE, see Figure 2. The first possibility (called possibility A) is that the access attempt fails with a Dectection Miss Probability (DMP) as a consequence of a preamble detection miss, i.e., the UE’s sent preamble was not detected by the eNB. The second possibility, B, is that the attempt fails with a Contention Probability (CP) as a consequence of a UE choosing the same preamble as another UE and loosing the contention resolution. The third and last possibility (C ) is that the UE gets access.

α0

c s) AD(n

=

α0 + α1 + α3 + ns − 1

P (A|AccessF ailed)α2 +

+P (B|AccessF ailed) α1 + α4

.

(2)

where P (A|AccessF ailed) and P (B|AccessF ailed) are the conditional probabilities that a UE goes down one of the paths A or B given the attempt was unsuccessful. The conditional probabilities are derived according to: P (A|AccessF ailed)

dP DM

=

(3)

dP )(1 − CP c) 1 − (1 − DM P (B|AccessF ailed)

dP )CP c (1 − DM

=

.

(4)

dP )(1 − CP c) 1 − (1 − DM dP and CP d . We denote the number of Next we derive DM sent preambles by ns , number of detected preambles by nd , and number of mobiles that have successfully completed random access by na . Then, we may use the following estimates of DM P and CP : nd ns > 0 dP = 1 − ns DM (5) 0, ns == 0

DMP

1-DMP

α1

α2

c= CP

1−

na nd

nd > 0

0, nd == 0

(6)

Detection miss (A)

1-CP

α3 Access (C)

Fig. 2.

CP

α4 Contention resolution failure (B)

Probability diagram for one random access attempt.

The number of detected preambles nd and number of mobiles that are granted access na are directly measurable at the base station. However, it is not possible to measure ns at the base station due to preamble detection miss. Henceforth, we assume that mobiles report the number of attempts needed to obtain access once the mobile is granted access to the network, which is contained in the UE Information Response message, triggered by a UE information request in [1]).



Next, we address how to estimate αi . It is assumed that all events happen at a time uniformly distributed in the time window associated with the event. This means that the subframe when a UE wants to start its random access attempt is uniformly distributed in the window [0,Tconf P − 1], where Tconf P is the time between each random access slot, which depends on the PRACH configuration (see Section II. Since we are interested in the average AD the estimation of α0 , denoted as α c0 is set to αb0 =

Tconf P − 1 . 2

The subframe a UE gets its RA response in is uniformly distributed in the window [1,ra-ResponseWindowSize] and the contention resolution response comes in a subframe uniformly distributed in the window [1,macContentionResolutionTimer ]. This means that the estimations of α1 and α3 , denoted as α c1 and α c3 can be written as αb1 = 2 +

0.01 0 -0.01 0

(7) (8)

where

TrespW =ra-ResponseWindowSize and TcontW =mac-ContentionResolutionTimer. The 2 ms in (7) is a standardized waiting time in subframes before the RA response window starts and the 5 ms in (8) is a standardized waiting time before a UE can send its unique identity to the eNB [15]. The time until the next attempt after a UE does not get a response Tnext depends on the PRACH configuration and the length of TrespW and TcontW . This implies that α2 can be estimated as α c2 = 2 + TrespW + Tnext,2 , where Tnext,2 is calculated as CDF(AD); Load = 100 preambles/s/cell (9)

0.8

The estimation of α4 is done in a similar way. The possible 0.6 times from the RA attempt until the contention resolution 0.4 window is over for the UE is given in the vector traResp 0.2 and can be written as traResp = 2 +

1

2

0

100

to understand the accuracy of the AD estimation under varying conditions. First, the accuracy at different RACH loads 300 and 700 preambles/s/cell is investigated, and the results are plotted in Figure 3 which shows the relative residuals . The deviation of the residuals is within one percent, which should be considered acceptable. Second, the accuracy at different target received powers P 0 P RACH -110 and -90 dBm is evaluated and our results show that these are within 2 percent (not shown by figure). Overall, CDF(AD); Load the results show that the estimation of AD= 100 is preambles/s/cell sufficiently 1 accurate. 0.8 0.6

VI. Sensitivity Analysis 0.4 The goal of the sensitivity analysis is to understand the 0.2 impact of P0 P RACH , ∆P RACH , PRACH configuration, 0 50 100 150 RACH load, and PUSCH load 0on the access delay. Access Delay (ms)

CDF(

50 100 Access Delay (ms)

150

CDF(AD); LoadRACH = 300 preambles/s/cell


1

1

0.8

0.8

0.6

0.6

0.4

0.4

0.2

0.2

0

0


150

0

Config 0,1,2 Config 3,4,5 Config 6,7,8 Config 14 0


150


1

1

Tnext,4 = Tconf P − t0.6raResp mod Tconf P

Fig. 4. Access delay CDF for different PRACH configurations and 0.8 RACH loads.

(10)

0.4

and α4 is consequently expressed as 0.2 0 αb4 = 5 + TcontW 0 + Tnext,4 50

100 Access Delay (ms)

(11) 150

CDF(AD); Load = 900 preambles/s/cell where Tnext,4 is the mean of the vector Tnext,4 . The mean 1 in (11) is needed because it is not known when the UE did 0.8 get its RA response. 0.6 We next estimate the accuracy of the method above, by 0.4 Config 0,1,2 c . From AD measuring the relative residuals = AD− Section Config 3,4,5 AD 0.2 Config 6,7,8 VI we will learn that the RACH load in terms ofConfig random 14 0 0 50 100 150 access intensity and random access power control paramAccess Delay (ms) eters have a crucial impact on access delay. Therefore, we vary RACH load and power control parameters in order RACH

0.8 0.6 0.4 0.2 0

0

CDF(

Access Delay (ms)

TrespW + 5= + CDF(AD); LoadRACH 500T preambles/s/cell contW .

The vector Tnext,4 can then0.8be expressed as

1


A. Effects of Varying PRACH1 Configuration and Load 0.8 The goal of these simulations is to study how AD is affected by different PRACH 0.6configurations and different RACH loads. It can be noted0.4 that P0 P RACH is set to 90 dBm in order to minimize 0.2the effects of detection miss 0 on AD. 0 50 100 150

···

80

Fig. 3. Relative residuals for different RACH loads 300 (thick) and 700 (thin) preambles/s/cell. Load_RACH 700 preambles/s/cell; Config 0,1,2

RACH

Tnext,2 = Tconf P − (2 + 1TrespW ) mod Tconf P .

0

60 40 Time (s)

RACH

1 + TrespW 2

1 + TcontW αb3 = 5 + 2

20

0.6 0.4Figure 4 illustrates access delay CDFs from the sim0.2 ulations. The reason for the stair-wise increase of the CDFs is due to the number of needed random accesses. 0 0 50 100 150 Delay (ms) For RACHAccessload of 900 preambles/s/cell and PRACH configuration{0,1,2}, about 85% of the attempts succeed during the first attempt, about 97% of the attempts succeed during the second attempt, and close to 99% of the attempts succeed during the third attempt. It can be seen that the changes in number of sent preambles coincides with the sharp changes in the corresponding access delay CDFs. The conclusion from these simulations is that AD can be controlled by the PRACH configuration. Altering the


1 0.8 0.6 0.4 0.2 0

0


configuration leads in general to small changes in AD, except for the higher percentiles. B. Effects of Varying Power Control Parameters

CDF(AD); ∆ PRACH = 0 dB


1

P0_PRACH = −120 dBm

0.6

0.4


0.4

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0.6

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200 300 400 Access Delay (ms)

500

600


1

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Fig. 5.

50


200

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P0_PRACH = − 90 dBm 0


0.8

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0.6

0.6

0.4

0.4

0.2

50


200

150

CDF(AD); LoadPUSCH = 0.6


0

150


0

0.8

0.6

0.6

0.4

0.4


150

CDF(AD); LoadPUSCH = 1

1

0.2 0


0

150

0

Fig. 6. Access delay CDF for different P0



0.2 0

0.8

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0

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0

0

150


1

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The goal of these simulations is to study the effects of P0 P RACH and ∆P RACH on AD, where LoadRACH = 100 preambles/s/cell, PRACH Configuration is set to {9, 10, 11} and LoadP U SCH = 0.5. 1


1


P RACH

150

and PUSCH loads.

VII. RACH Self-Optimization Controller Structures P0_PRACH = −120 dBm P0_PRACH = −105 dBm P0_PRACH = − 90 dBm 0

Access delay CDF for different P0

50


P RACH

200

and ∆P RACH .

The results are given in Figure 5. We note that the difference between the CDFs for different P0 P RACH decreases when ∆P RACH increases. As such the impact of P0 P RACH on AD depends on ∆P RACH . The conclusion is that it is possible to control AD using P0 P RACH and ∆P RACH . Both parameters P0 P RACH and ∆P RACH make it possible to decrease AD by decreasing the number of attempts needed by the UEs to get access. C. Effects of Varying PUSCH Load The goal of these simulations is to study the effects of P0 P RACH and PUSCH load on AD. The PUSCH load has a direct effect on the inter-cell interference on PRACH and it is therefore interesting to study the its impact. The following assumptions are made: LoadRACH = 100 preambles/s/cell, PRACH Configuration is set to {9, 10, 11}, and ∆P RACH = 2 dB. The results are given in Figure 6. In general the AD is negatively affected when the PUSCH load is increased, however for P0 P RACH ≥ −100 dBm there are minor differences to the CDFs when the PUSCH load is changed. This suggest that it is possible to reduce the negative effects a high PUSCH load has on the AD.

The basic controller principle is shown in Figure 7. The control structures aim at controlling access delay in terms of tuning various parameters in order to achieve a certain reference value for access delay TAD (see Section III). The choice of parameters are based on the insights derived from the previous section where we studied impact of parameters on access delay. Below we introduce two different control structures, namely, the the mid-range controller and the double-percentile controller. The two controllers differ in number of access delay percentile references they can handle (essentially the number of rows in TAD ). Even r AD

Fig. 7.

-

Controller

P0_RACH ∆ RACH Config.

RACH

AD

Basic principle of the RACH behaviour under control.

if the main purpose of this paper is to control the AD of the random access procedure, it is still important not to forget that the PRACH and the PUSCH share resources. The most important priority of the control structures are to meet the AD requirements, and if these can be satisfied, then as much resources as possible are given to PUSCH. A. Modeling for Control Design In this paper we have considered an autoregressive (ARX) model [17] A(q)y(k) = B(q)u(k)+e(k), where u(k) and y(t) represent the input and the output respectively and q represents the shift-operator which means that



q −1 u(k) = u(k − 1). The term e(t) represents white noise. The rational functions A(q) and B(q) can be written as A(q) = 1 + a1 q −1 + . . . + ana q −na and B(q) = b1 + b2 q −1 + . . . + bnb q −nb+1 where ai and bi are model parameters, which collectively can be gathered in the parameter vector θ. Given a data set of samples t = 1 . . . N , the estimated model is selected as the one minimizing the cost function VN (θ) =

N X

1 N

(y(k) − yˆ(k|θ))2

(12)

k=1

with respect to θ, na and nb. For details, see [17]. The modeling of AD with respect to P0 P RACH is based on data from simulations with step-wise random changes of P0 P RACH (the signal u(t) below) . A parameter vector θ is determined for each model structure (na, nb). The minimum cost function (12) value is obtained for na = 2 and nb = 1, but the difference to the simpler model na = 0 and nb = 1 is minor, so the system from P0 P RACH to AD is therefore modelled as y(k) = b1 u(k) + c + e(k), where b1 = −0.63 and c = −44.44 are constants. An analogous modeling for ∆P RACH (where u = ∆P RACH ) and the PRACH configuration gives cost function minimum for na = 2 and nb = 4, but minor difference to the simple model with b1 = −6.60 and c = 89.20. Hence, for all isolated control parameter changes, a static network model i sufficient for controller design. In order to remove steady-state errors, integrating controllers are preferred. Since simple models are sufficient, already simple controller structures give enough freedom, where

u(k) = min( max u(k − 1) + KI T yref (k) − y(t) , umin , umax (13)

where yref (k) is the control reference, and umin and umax are control parameter limitations. We have used poleplacement [18] to tune the controller (13), where the pole −KI b1 is set to -0.3 and -0.2 when controllring P0 P RACH and ∆P RACH , respectively.

The wanted reference point for PRACH configuration rConf ig is based on the PUSCH load. Define RBavailable as the amount of resource block available for PUSCH and let RBneeded be the amount of resource blocks needed by the scheduler over a period of time. If RBneeded > RBavailable , then rConf ig is decreased one step (resulting in increasing random access opportunity period Tconf P ) and opposite if RBneeded < RBavailable . An internal feed-forward connection is also included to keep the PRACH configuration from varying too much. The expression for the feed-forward control, named Ff f in the figure, is expressed as u1,f f (k) = u1 (k) + Kf f u2 (k − 1) − u2 (k) (14) where u1,f f , u1 and u2 are the signals as defined in Figure 8. The tuning parameter Kf f in (14) represents the gain of the feed-forward connection. C. Double-Percentile Controller As the name suggests this controller has two access delay percentiles as reference (see Section III). The control structure ensures that both AD percentiles fullfill their targets (R1 and R2 ). The controller can be seen in Figure 9. From the simulations in Section VI-B it could be seen that ∆P RACH affects the higher percentiles more than the lower percentiles. This controller is therefore designed to control a lower percentile with P0 P RACH and a higher percentile with ∆P RACH , where R2 > R1 . In contrast to the midrange controller, what is lost in range of possible AD values from not using both control parameters together is gained with the possibility to use two percentiles as reference.

R1

B. Mid-Range Controller

rConfig R1

Fig. 8.

-

FP

-

FConfig

P

RACH

u2 u1 Config

Mid-Range Controller

AD Fff

u1,ff

RACH

FP

P0_

0_

R2 The mid-range controller used in this case can be seen in Figure 8. The reason why this structure has been chosen, is that both PRACH configuration and P0 P RACH are used to control the AD. This leads to a wider range of possible values for AD. In Figure 8 the PRACH configuration is used to control one percentile of AD and P0 P RACH is used to keep the PRACH configuration close to its reference point rConf ig .

-

Fig. 9.

RACH

AD

Alarm

-

FΔ

Δ

RACH

Double-Percentile Controller

The controllers in Figure 9 operate separately from each other. This means that the effect from one controller will be seen as a disturbance by the other. Because of this the controllers operate with different sample times. The controller for P0 P RACH operates faster than the controller for ∆P RACH . Outside of these two controllers is a PUSCH controller that controls the assigned RBs for PUSCH by altering the PRACH configuration, similar to the one used in the mid-range controller. If RBneeded > RBavailable , then the random access opportunity period Tconf P is decreased one step and vice versa. The dotted line in Figure 9 named Alarm is a communication possibility for the controllers to raise an alarm if



150

1

AD (ms)

LoadPUSCH

0.8 0.6

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0.4 0

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12,13 9,10,11 6,7,8 3,4,5

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0,1,2

300 200

−120

100 0

0

50

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250 Time (s)

300

350

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Fig. 10. Simulation scenario with varying PUSCH and RACH loads.

P0_PRACH

LoadRACH (preambles/s/cell)

50% percentile 99% percentile Target

100

−130 −140 −150

their measured AD percentile is higher than their reference, causing the other controller to increase the control parameter. If, for example, P0 P RACH = −90 dBm and the measured AD percentile is higher than R1 , than the controller FP0 P RACH will send an alarm to the controller F∆P RACH , resulting in F∆P RACH to increase ∆P RACH with 2 dB if possible. If R1 still cannot be reached then ∆P RACH is increased with another 2 dB and so on. If both P0 P RACH and ∆P RACH have reached their highest limit and one of the target R1 or R2 still cannot be reached an alarm will be sent to the PUSCH controller that causes the PUSCH controller to decrease the random access opportunity period. VIII. Control Evaluations by Simulations The controllers are evaluated in order to determine whether they are capable of keeping AD according to specified levels TAD as defined in Section III. The scenario (Figure 10) that has been simulated represents gradual increases in number of connecting UEs and PUSCH load as these factors were proven to have an impact on AD according to Section VI. A. Double-Percentile Controller Figure 11 shows the simulation results. For the first 100 seconds we observe a 50 and 90-percentile AD below their respective targets, even though P0 P RACH and ∆P RACH are at their lowest values (-120 dBm and 0 dB). This is explained by the PRACH configuration during this time, i.e., since the PUSCH load is low the PRACH configuration with the shortest Tconf P can be used. At 150 s the PUSCH load increases to 100%, which can be seen in Figure 10. This has the affect that the PRACH configuration is decreased to {0, 1, 2}. After this the two controllers have difficulties to reach their targets. This will trigger an alarm according to Section VII-C, which explains why the PRACH configuration is increased one step at 200 s and one more step at around 225 s. Moreover, during the time interval from 270 s to 350 s the PRACH configuration remains fixed, and the controller

∆ PRACH

6 4 2 0

Fig. 11.

Simulation results of the double-percentile controller.

system aims at meeting the requirements by only adjusting power parameters. The conclusion is that the double-percentile controller together with the PUSCH-controller manage to keep AD close to or below their respective targets. Because of the opportunity to specify the target of two percentiles the operator gets a more detailed insight in how well the random access procedure is carried out.

B. Mid-Range Controller Here we assume the following TAD = 0.99 70 and an inter-site distance of 5000 m. Figure 12 shows the simulation results. During the first 100 s the PUSCH load is low enough for the PUSCH-controller to keep rConf ig at PRACH configuration {14}. Because of this P0 P RACH is set to its lowest value (-90 dBm) to drive the PRACH configuration up towards the value of rConf ig . Since the PRACH configuration controller only is focused on reaching the AD target the PRACH configuration will only be set to rConf ig if the AD target is reachable with this configuration. In general the AD follows its target well except for some high peaks with ADs over 100 ms, but the interesting thing to notice here is the cooperation between PRACH configuration and P0 P RACH in Figure 12. Since this simulation was done with an ISD of 5000 m, the uplink inter-cell interference is much lower than with an ISD of 500 m. For this reason the altering of P0 P RACH has less effect on AD, thus forcing the control of the PRACH configuration to step in. Overall, it is shown that the controller manages to keep AD close to its reference point.



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Fig. 12.

Simulation results of the mid-range controller.

IX. Conclusion Operators are seeking to increase cost efficiency through simplifying network management processes by means of automation. In this paper, we have introduced a method for automatically configuring and tuning random access in LTE that meets stated random access requirements specified by 3GPP. We have analyzed the RACH optimization problem thoroughly, from the 3GPP requirements in terms of access delay. A method to estimate access delay has been introduced given available measurements and a sensitivity analysis is performed to see the capability of the RACH parameters to affect the access delay. Given this insight two controllers have been designed that tunes RACH parameters in order to meet requirements on access delay. The results indicate that it is feasible to self-optimize the RACH performance to meet stated targets in terms of access delay.

[7] ——, “On self-optimization of the random access procedure in 3G long term evolution,” in Proc. IEEE Integrated Network Management (IM) Workshops, Jun. 2009, pp. 177 –184. [8] S. Choi, W. Lee, D. Kim, K.-J. Park, S. Choi, and K.-Y. Han, “Automatic configuration of random access channel parameters in LTE systems,” in Proc. IFIP Wireless Days, Oct. 2011, pp. 1 –6. [9] O. Yilmaz, J. Hamalainen, and S. Hamalainen, “Selfoptimization of random access channel in 3GPP LTE,” in Proc. Int’l Wireless Communications and Mobile Computing Conference, Jul. 2011, pp. 1397 –1401. [10] A. Giovanidis, Q. Liao, and S. Stanczak, “Measurement based self-optimization in random access communications,” arXiv:1107.1158 [cs.NI], 2011. [11] W. Lee, D. Kim, S. Choi, K.-J. Park, S. Choi, and K.-Y. Han, “Self-optimization of rach power considering multi-cell outage in 3gpp lte systems,” in Vehicular Technology Conference (VTC Spring), 2012 IEEE 75th, may 2012, pp. 1 –5. [12] E. Dahlman, S. Parkvall, and J. Sk¨ old, 4G LTE/LTE-Advanced for Mobile Broadband, ser. Academic Press. Academic Press, 2011. [13] G. T. 36.300, “E-UTRA and E-UTRAN; Overall description; stage 2, (Release 8).” [14] G. T. 36.211, “E-UTRA; Physical Channels and Modulation, (Release 8).” [15] G. T. 36.213, “E-UTRA; Physical layer procedures, (Release 8).” [16] G. T. 36.321, “E-UTRA; Medium Access Control (MAC) protocol specification, (Release 8).” [17] L. Ljung, System Identification - Theory For the User, 2nd ed. Prentice Hall, 1999. [18] K. ˚ Astr¨ om and T. H¨ agglund, Advanced PID Control. ISA-The Instrumentation, Systems, and Automation Society, 2006.

Mehdi Amirijoo received his M.Sc. and Ph.D. degrees in computer science and engineering from Link¨ oping University, Sweden, in 2002 and 2007 respectively. His interests cover self-organizing networks (SON), radio network management, and machine-to-machine (M2M) communication. Dr. Amirijoo is currently affiliated with Ericsson Research, where he is heading a radio network research group, contributing to product concepts and standardisation in 3GPP. He has published over 30 papers related to self-organisation in computing systems and mobile networks.

References [1] G. T. 36.331, “E-UTRA; Radio resource control (rrc), (Release 8).” [2] G. T. 32.522, “Telecommunication management; self-organizing network (SON) policy network resource model (NRM); integration reference point (IRP); information service (IS).” [3] J. H. Sarker and S. J. Halme, “Optimizing the use of random access channels in GSM-GPRS,” Wireless Personal Communications, vol. 22, no. 3, pp. 387–408, Sep. 2002. [4] J. Reig, O. L¨ı£¡pez-Jim¨ı£¡nez, L. Rubio, and N. Cardona, “Random Access Channel (RACH) Parameters Optimization in WCDMA Systems,” IEEE 60th Vehicular Technology Conference, VTC2004-Fall, 2004. [5] S. Kim, Y. So, D. Hong, J. Kim, S. Moon, K. Lee, and S. Oh, “Uplink Capacity Maximization based on Random Access Channel (RACH) Parameters in WCDMA,” Vehicular Technology Conference, 2006. VTC 2006-Spring. IEEE 63rd, 2006. [6] M. Amirijoo, P. Frenger, F. Gunnarsson, J. Moe, and K. Zetterberg, “Towards random access channel self-tuning in LTE,” in Proc. IEEE Vehicular Technology Conference (VTC) Spring, Apr. 2009, pp. 1 –5.

Fredrik Gunnarsson received the M.Sc. and Ph.D. degrees in electrical engineering from Link¨ oping University, Sweden, in 1996 and 2000 respectively. His research interests include signal processing and automatic control aspects of radio resource and network management, as well as mobile localization. Currently, he is active in various SON activities both academically like the initiative with the IWSON series, as well as industrially with work on SON towards 3GPP. In 2001 he joined Ericsson and is now Senior Specialist in Radio Network SON at Ericsson Research as well as an Associate Professor at Automatic Control, Link¨ oping University.



Filip Andr´ en received his M.Sc. degree in Applied Physics and Electrical Engineering, with a thematic focus on control and information systems, at Link¨ oping University in 2009. Since 2009 he is working as a scientist at Austrian Institute of Technology, Energy Department, where he has specialised in smart grids and is working with control and communication standards as well as modelling and development of grid components.


3GPP LTE Random Access Channel Self-Optimization

3GPP LTE Random Access Channel Self-Optimization

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