This document provides a tutorial on self-optimization of the random access procedure in 3G Long Term Evolution (LTE) networks. It describes the key components and steps of the random access procedure, including random access preambles, formats, opportunities, and the contention-based procedure. It also addresses relevant performance metrics like access probability, detection miss probability, and contention probability. The paper discusses modeling radio network simulations and collecting performance data to enable self-optimization of random access parameters to meet specified requirements. The goal is to present relevant self-optimization problems rather than complete solutions.

1. On Self-Optimization of the Random Access Procedure
in 3G Long Term Evolution
Mehdi Amirijoo, Pål Frenger, Fredrik Gunnarsson, Johan Moe, Kristina Zetterberg
Wireless Access Networks,
Ericsson Research, Ericsson AB, Sweden.
{mehdi.amirijoo, pal.frenger, fredrik.gunnarsson, johan.moe, kristina.zetterberg}@ericsson.com
Abstract—Operationally efficient radio networks typically feature a
high degree of self-organization. This means less planning efforts
and manual intervention, and a potential for better radio resource
utilization when network elements adapts its operation to the
observed local conditions. The focus in this paper is self-
optimization of the random access channel (RACH) in the 3G Long
Term Evolution (LTE). A comprehensive tutorial about the RACH
procedure is provided to span the complexity of the self-
optimization. Moreover, the paper addresses RACH key
performance metrics and appropriate modeling of the various steps
and components of the procedure. Finally, some coupling between
parameters and key performance metrics as well as self-
optimization examples are presented together with a feasibility
discussion. The main ambition with this workshop paper is to
present and define a relevant set of self-optimization problems,
rather than to provide a complete solution.
Keywords – 3GPP; LTE; Self-Organization; Random
Access;RACH; E-UTRAN; Self-Tuning; Self-Optimization
I. INTRODUCTION
There is a strong momentum for Self-Organizing Network
(SON) features in wireless communication networks, both via
requirements from operators and through standardization work.
The Next Generation Mobile Network (NGMN) association of
operators brings forward requirements on management simplicity
and cost efficiency [1]. The vision is that algorithms automate
tasks that currently require significant planning efforts. In
parallel, the 3rd
Generation Partnership Program (3GPP) works
on specifications for 3G Long Term Evolution (LTE), and SON
is central in the network management and optimization
discussions [2]. SON can mean vastly different things, but three
components are central
• Self-configuration, i.e. plug and play functionality
where network elements are configured (identity
allocation, software upgrade, communication link
establishment, etc) automatically.
• Self-optimization, i.e. a more or less continuous
adaptation of parameters to meet specified
requirements, typically specified at a high level.
• Self-healing, i.e. algorithms to handle disruptive
events and to minimize negative consequences on
services.
The random access procedure in LTE can benefit from self-
optimization. A mobile (User Equipment, UE in LTE) in an idle
state is essentially unknown to the network (except for some
rough localization information). In order to establish a relation to
the network, for example to initiate some service, the mobile
scans the carrier frequencies to determine the most suitable cell
and associated base station (eNodeB or eNB in LTE) for
communication. The broadcast information from this base station
provides the mobile with cell-specific random access procedure
details. Optimal random access performance is central to obtain
intended coverage and low delays, while avoiding excessive
interference to communication links in other cells and
maintaining a desired balance in the radio resource allocation
between random access and data services.. The considered delays
include call setup delays, session resuming delays, handover
delays, etc. The challenge is to balance the resource allocation
between random access and other communication needs, while
adapting to local radio characteristics, cell size and variations in
terms of traffic in the cell and neighboring cells.
One approach to random access procedure configuration is to
use a set of standard parameter values in all base stations,
typically based on extensive simulations. This may, however,
result in a suboptimal performance since the cell-specific
characteristics are not catered for. Another approach is to – by
means of simulation, prediction, or field trials – evaluate a wide
range of random access parameters and choose those cell-specific
settings that satisfy given requirements. The drawbacks include
the need for extensive simulation, planning and/or field trial
efforts. Furthermore, it is difficult to be responsive to variations
in the radio network, not the least due to gradual deployment of
additional network elements. Therefore, self-optimization of the
random access procedure has great potential.
Self-organization and tuning have been previously addressed
in the literature. For an overview on autonomic communication
in networks refer to [3]. Automation of neighbor relation lists has
received some attention lately [4][5]. Several publications related
to automation in 3G networks exists, e.g., capacity and coverage
balancing [6][7], and admission control [8]. The project
29978-1-4244-3924-9/09/$25.00 c 2009 IEEE

2. SOCRATES aims at the development of self-organization
methods for future wireless access networks [9]. Papers [10][11]
address WCDMA random access optimization by means of
simulations.
The outline of this paper is as follows. Section II gives an
extensive tutorial on the random access procedure in LTE, while
Section III addresses relevant performance specification aspects
and modeling. Selected experiments in Section IV illustrate the
relation between random access parameters and traffic on one
hand and performance of the other, and Section V gives a self-
optimization example, before Section VI concludes the paper.
II. RANDOM ACCESS PROCEDURE IN LTE
The random access procedure in LTE is performed at any of
the following five events: i) initial access of an idle mobile, ii) re-
establishment after radio link failure, iii) handover to a different
cell, iv) downlink data transmission to a mobile, which is out of
time-synchronization, and v) uplink data transmission from an
out-of-synch mobile. In all cases, one objective is to establish
uplink time synchronization, while in some it also provides the
means for the mobile to notify the network about its presence,
and for network to give the mobile initial access. At events iii) to
and v), the serving base station can control the procedure to avoid
collisions and ambiguities in the random access (non-contention
based procedure). However, in the general case, the possibility of
a collision, or contention, between different users’ access
attempts needs to be handled (contention-based procedure). The
former is essentially a simpler version of the latter.
Prior to sending the random access preamble, the mobile
performs cell selection if necessary, and establishes downlink
synchronization. The mobile acquires broadcasted information
about the random access resources and procedure configuration.
These parameters are further described in the following
subsections. For further details on the random access procedure
in LTE, see [12][13][14][15][16].
A. Random Access Physical Resources
The random access physical resource consists of a set of
preambles, a set of formats, and a set of random access
opportunities.
1) Random Access Preambles
The requirements on the sequence comprising the preamble
are two-fold: good correlation properties to allow precise arrival
time estimation and low correlation with other preambles to
suppress interference from other mobiles. A sequence that has
ideal such properties is the Zadoff-Chu sequence (root sequence)
[14][17]. The periodic auto-correlation function (ACF) of a
Zadoff-Chu sequence is only non-zero at time-lag zero (and
periodic extensions) and the magnitude of the correlation with
other sequences is equal to the square-root of the sequence length
N. In LTE, the sequence length N = 839.
Multiple preamble sequences can be derived from one
Zadoff-Chu sequence by cyclically shifting the sequence. Each
cell is assigned 64 preambles [14]. For small cells up to 1.5 km
radii all 64 preambles can be derived from a single root sequence
and are therefore orthogonal to each other. In larger cells not all
preambles can be derived from a single root sequence and
multiple root sequences must be allocated to a cell. Preambles
derived from different root sequences are not orthogonal to each
other, but the cross-correlation is low.
High mobile velocities relative to the base station cause
additional correlation peaks, which lead to ambiguous timing
determination. In order to cope with this problem in LTE high-
speed mode root sequences are defined, for which certain cyclic
shift values are disabled so that transmitted preamble and round
trip time can uniquely be identified.
2) Random Access Formats
The transmitted preambles travel along different paths to the
receiver, causing a delay spread of the received preamble. By
adopting a cyclic prefix (the last part of the preamble is copied
and prefixed the preamble before transmission), the receiver can
suppress this spread. Furthermore, random access coverage is
related to the maximum transmission power of the mobile. For
large cells, some mobiles are unable to provide the receiver with
sufficient received energy for correct preamble detection due to
this limit. An alternative means to increase the received energy at
the receiver is to transmit for a longer time. Therefore, some
formats feature a repetition of the preamble.
Figure 1 illustrates the four random access formats for LTE
frequency division duplex (FDD), featuring both short and long
cyclic prefix corresponding to capabilities to handle narrow and
wide delay spreads, and single and repeated random access
preambles to enable coverage in cells with different sizes [14].
Format 0 fits within 1ms (one subframe), format 1 and 2 fits
within 2ms, while format 3 fits within 3ms.
3) Random Access Opportunities
103 μs 839 samples = 800 μs
RA sequence
time
TX
CP
a) Format 0
103 μs 839 samples = 800 μs
RA sequence
time
TX
CP
a) Format 0
684 μs 800 μs
RA sequence
time
TX
CP
b) Format 1
203 μs 1600 μs
RA sequence
time
TX
CP
c) Format 2
TX
d) Format 3
RA sequence
CP
684 μs
TX
1600 μs
RA sequence
time
RA sequence
Figure 1. LTE random access formats for FDD.
30 2009 IFIP/IEEE Intl. Symposium on Integrated Network Management — Workshops

3. In the classical random access scheme Slotted Aloha, access
attempts are restricted to slots to avoid partial overlap between
users. Similarly, 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 2. The considered
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. The latter channel
can also carry uplink control information as an alternative to the
less flexible and capable physical uplink control channel
(PUCCH). Therefore, the resource allocation needs to consider
the balance between the PRACH and PUSCH demands. The
plausible random access opportunities [14] dictate both the
opportunity period and the timing, for example enabling non-
overlapping opportunities for three cells at the same site.
Furthermore, the opportunity selection is also related to the
random access format, since the opportunities needs to be sparse
enough to fit the length of the selected format (1, 2 or 3 ms),
while avoiding preamble overlaps.
B. Contention-Based Random Access
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 and opportunity) for
random access, and therefore the contention situation needs to be
resolved. The procedure is outlined in Figure 3, and the steps are
further described below.
1) Random Access Preamble
The mobile selects a preamble and an opportunity at random,
and determines the format based on the broadcasted system
information. In addition, the mobile determines the preamble
transmission power by estimating the downlink path loss PL from
the downlink reference signal (pilot signal) and using the
broadcasted parameters P0_RACH (the desired received power),
ΔRACH (the power ramping step) and ΔPreamble (the preamble-based
offset equal to 0 dB for formats with a single preamble, i.e.,
formats 0 and 1 and equal to – 3dB for formats with duplicate
preambles, i.e., format 2 and 3). The mobile also monitors the
preamble transmission attempt number m. For the initial
preamble transmission, m=1, and the preamble transmission
power is set according to
})1(,min{ _0max PreambleRACHRACHRACH mPLPPP Δ+Δ−+−= (1)
Finally, the selected preamble is sent with the determined
power level and format in the selected opportunity.
2) Random Access Response
The base station correlates the received signal in each random
access opportunity with all possible preamble sequences. Figure
4 illustrates the detector and the corresponding round trip time
estimation subject to noise and interference. Upon detection of a
preamble in an opportunity, the base station signals timing
adjustment information, and 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.
3) Scheduled Transmission
Using the allocated uplink resource, the mobile transmits an
identity that uniquely identifies the mobile in the base station.
4) Contention Resolution
During step 3 of the random access procedure, several
mobiles that have sent the same preamble may respond. The base
Figure 3. Contention-based random access procedure for LTE with four
steps.
6 RB
1 RB
20 ms
time
frequency
1 ms
PRACH
PUSCH
PUCCH
Figure 2. Example of random access opportunities. The opportunities
repeats every 20 ms.
Correlation
Correlation zone
Detection threshold
Noise and interference
time
TCS
preamble
Detection threshold
Roundtrip time estimate
Correlation
Correlation zone
Detection threshold
Noise and interference
time
TCS
preamble
Detection threshold
Roundtrip time estimate
Figure 4. Random access preamble correlation detector, and round trip
time estimation for cyclic shift length TCS.
2009 IFIP/IEEE Intl. Symposium on Integrated Network Management — Workshops 31

4. 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.
C. Non-Contention-Based Random Access
This procedure is applicable when the base station can signal
a reserved random access preamble to the mobile, i.e. at handover
and uplink synch failure. In this case, all necessary information
(essentially timing) is acquired at the random access response.
III. REQUIREMENTS AND MODELING
This section addresses both key performance metrics and
their observation, and well as more general radio network
simulation modeling.
A. Performance Requirements and Observations
The main objective with the random access procedure is to
provide prompt and reliable access. Therefore, the access
probability is of interest, i.e. the probability that a mobile
acquires access upon an attempt. It is likely that an operator or
vendor would like to specify the access probability APm at
attempt m, i.e. the probability that the UE has access after attempt
m (1 m M), and then use autonomous algorithms to adjust the
random access related parameters accordingly. For example, AP1
= 0.8 and AP3 = 0.99. Furthermore, it is instructive to express the
access probability APm as a function of the detection miss
probability and the contention probability.
The detection miss probability DMPm at attempt m is defined
as the probability of a preamble, transmitted at attempt m, not
being detected at the base station. Moreover, the contention
probability CP is defined as the probability that a UE is not
granted access due to a preamble collision, conditional that the
preamble of the UE is detected. The access probability at attempt
m can therefore be expressed as
( )∏=
×−+−=
m
i
iim CPDMPDMPAP
1
)1(1 . (2)
The corresponding observables are denoted access ratio,
detection miss ratio, and contention ratio. Assume that random
access procedure data is collected over time intervals of length T,
and let n(k) denote the counter value gathered over the time
interval [(k-1)T,kT]. In particular, denote the number of sent
preambles by ns(k), number of detected preambles by nd(k), and
number of mobiles that have successful random access by na(k).
An additional subscript m may be used to denote a particular
attempt number. For example, ns,m(k) gives the number of sent
preambles for attempt number m during the time interval [(k-
1)T,kT]. Hence, the preamble detection miss ratio for attempt m
is given by,
=
>−
=
0)(,0
0)(,
)(
)(
1
)(
,
,
,
,
kn
kn
kn
kn
kDMR
ms
ms
ms
md
m
The contention ratio is defined as,
=
>−
=
0)(,0
0)(,
)(
)(
1
)(
kn
kn
kn
kn
kCR
d
d
d
a
Finally, the access ratio is obtained via DMR and CR and (2).
The number of detected preambles nd and number of mobiles that
are granted access na are directly measurable at the base station
and it is therefore tractable to estimate CR. However, it is not
possible to measure ns at the base station unless this is reported
by the mobiles. An undetected preamble is simply a correlation
peak below the detection threshold (see Figure 4), which is
classified as noise at the base station detector. Henceforth, we
assume that mobiles report the number of attempts needed to
obtain access once the mobile is granted access to the network
(see the PREAMBLE_TRANSMISSION_COUNTER in [15]).
These reports from the mobile enable the derivation of DMRm.
Note that this particular mobile report is not yet standardized for
LTE at the moment of writing.
B. Radio Network and Random Access Modeling
The simulation work in this paper is based on a semi-static
simulator with random drops of mobiles without mobility
modeling, but with time correlations (e.g. a failed random access
attempt at one time instant will result in a retransmission by the
same mobile at later time). It models the random access
procedure in a multi-cell scenario with interfering uplink data
traffic. The network is deployed in a hexagonal layout of 7 sites
each 3-sectored and wrap-around propagation. The path loss
predictions are adopted from [18] and the antenna models from
[19].
The number of created mobiles at each drop that initiate the
random access procedure follows a Poisson process with the
mean arrival intensity LoadRACH (number of mobiles/second/cell),
and they are uniformly distributed over the simulated area.
Uplink data traffic is modeled by the PUSCH load (denoted
LoadPUSCH), defined as the fraction of the frequency band that is
used for PUSCH during a sumframe of 1ms (c.f. Fig. 4.). A cell
with no random access opportunity at a specific subframe
randomizes (depending on the PUSCH load) whether uplink data
is scheduled in the frequency band where random access is
configured. If uplink data is scheduled then a PUSCH user is
randomized in the cell. Moreover, PUSCH power control is based
on [16] and simplified to open-loop power control,
dBm},min{ _0max PLPPP PUSCHPUSCH += .
where P0_PUSCH is the desired target received power, PL is the
path loss estimated by the UE based on the downlink reference
signal, and Pmax is the maximum transmission power.
Random access mobiles select a preamble (randomly) and an
opportunity (typically the next available), and transmit at a power
given by (1). However, the path loss estimate PL is measured for
the downlink and not the uplink, and therefore additive white
32 2009 IFIP/IEEE Intl. Symposium on Integrated Network Management — Workshops

5. Gaussian measurement noise with standard deviation σEE is
considered.
The received preambles are processed and the signal to
interference and noise ratio SINRp,c of each preamble p received
at cell c is computed according to,
NII
gP
SINR
cRACHcPUSCH
cpp
cp
++
=
,,
,
,
where Pp is the transmission power of the mobile transmitting
preamble p, gp,c is the path gain from the mobile to the based
station of cell c, N is the thermal noise power over the random
access frequency band, IPUSCH,c is the received interference power
from PUSCH at cell c from users in other cells, and IRACH,c is the
received interference power from random access preambles
transmitted by other mobiles in the same cell (typically zero if all
preambles originates from the same root sequence). The SINRp,c
is then mapped to a preamble detection probability (see [21]). If
several mobiles transmit the same preamble in the same
opportunity in a cell, then contention resolution is carried out by
randomly choosing a preamble (i.e. mobile) among the detected
preambles. Table I summarizes central parameter values used in
the simulations.
TABLE I. SIMULATION PARAMETERS
Parameter Value
User distribution Uniform
Site to site distance 500 m (5000m Section IV.E)
Antenna Tilt 8 degrees
PMAX 23 dBm W
P0_PUSCH -89 dBm
N -109 dBm W
σEE 3 dB
Path loss L L = 128.1+37.6log10(d), d [km]
Log-normal shadowing 8 dB standard deviation
TABLE II. DEFAULT EXPERIMENT SETUP
Parameter Default Value
LoadPUCSH 0.5
LoadRACH 250 preambles/cell/s
RACH Format 0
RACH Opp interval 5 ms
P0_RACH -120 dBW
ΔRACH 2 dB
M 8
Simulation Time 180 s
IV. EXPERIMENTS
The experiment objectives are to illustrate the coupling
between various tunable parameters, the performance of random
access, and the interference caused by random access. Table II
gives the standard parameters used in all experiments (if not
otherwise stated).
Note that the standard value for LoadRACH may seem too high.
Since at the time of the writing LTE has not been deployed in
large scale and typical loads are not yet available, we assume a
wide range of RACH loads in the simulations. The default
RACH load has been selected such that CR = 0.01 for one
random access opportunity per 5 ms [14]. Also a higher RACH
load enables shorter simulations times since more data is
gathered compared to a lower RACH load.
A. Effects of Varying PUSCH Load
The goal of this experiment is to study the effects of P0_RACH
and LoadPUSCH on DMR and CR. Recall that P0_RACH dictates the
received signal power and LoadPUSCH determines the interference
on RACH. The parameters are altered according to LoadPUSCH =
{0.0,0.2,…,1.0} and P0_RACH = [-150,-120] dBW in steps of 10
dBW.
As shown in Fig. 5(a) the DMR of the first attempt (DMR1)
increases with increasing LoadPUSCH and decreasing P0_RACH. The
DMR of attempts 2-8 show similar behavior. Fig. 5(a) indicates
that some P0_RACH values result in very low DMR and robustness
to varying LoadPUSCH and interference. Consequently it seems
that setting P0_RACH to, e.g., -130 dBW, will give a satisfactory
RACH performance. However, these results hold only for the
deployment used and the models and assumptions of, e.g.,
propagation, PUSCH and RACH. There may be cases where a
P0_RACH lower (or higher) than -130 dBW should be used
depending on prevailing conditions.
0 0.2 0.4 0.6 0.8 1
0
0.5
1
PUSCH Load
DMR
Attempt Nr 1
(a)
0 0.2 0.4 0.6 0.8 1
0
0.005
0.01
0.015
0.02
PUSCH Load
CR
-120dBW
-130dBW
-140dBW
-150dBW
0 0.2 0.4 0.6 0.8 1
0
0.005
0.01
0.015
0.02
PUSCH Load
CR
-120dBW
-130dBW
-140dBW
-150dBW
(b)
Figure 5. DMR and CR as a function of LoadPUSCH and P0_RACH.
2009 IFIP/IEEE Intl. Symposium on Integrated Network Management — Workshops 33

6. Fig. 5(b) shows that CR increases as LoadPUSCH increases and
P0_RACH decreases. This is a result of an increasing number of
preamble retransmissions (due detection misses) causing a higher
contention probability.
In conclusion, the PUSCH load and the induced PUSCH
interference heavily affect DMR of all attempts. To counteract
this, the power control parameter P0_RACH can be adjusted to an
appropriate setting. Further, DMR and CR are coupled, meaning
that an increase in DMR results in an increase in CR.
B. Effects of Varying Power Control Parameters
The goal of this experiment is to study the effects of P0_RACH
and ΔRACH on DMR and to establish whether a given performance
specification in terms of DMR for each attempt number can be
satisfied. The parameters are altered according to P0RACH = {-
120,-125,…,-150} dBW and ΔRACH = {0,2,4,6} dB.
The results for DMR1, DMR3 and DMR5 are given in Fig. 6. In
general, the DMR of all attempts decreases nonlinearly with
increasing P0_RACH. As expected, for the first attempt the DMR
does not vary over ΔRACH. As such, the only way to control DMR
for the first attempt is to set P0_RACH. For attempts greater than
one, DMR varies over both P0_RACH and ΔRACH. The amount by
which DMR decreases when increasing ΔRACH depends on the
attempt number. This implies that for low attempt numbers there
are limits for how much DMR can be altered by using ΔRACH. As
such, it may be necessary to alter P0_RACH to not only satisfy the
first attempt, but also to satisfy attempt numbers greater than one.
The conclusion of this experiment is that it is possible to
control DMR by using P0_RACH and ΔRACH. The parameter P0_RACH
can be set according to the DMR requirements of the first
attempt, whereas ΔRACH can be tuned to satisfy DMR
requirements for the other attempts. In some cases the latter may
not be possible and in such circumstances P0_RACH must be
adjusted as well.
C. Effects of Varying RACH Load and Configuration
The goal of this experiment is to study the effects of RACH
load and RACH configuration on CR. RACH load is altered
according to LoadRACH = {100,300,…,900} preambles/cell/s.
RACH configuration corresponds to random access opportunity
intervals (RAOI) of 20, 5, 2 , and 1 ms. Note that P0_RACH = -120
dBW, which results in the majority of the preambles to be
detected at the first attempt.
As expected CR increases with increasing LoadRACH and
increasing random access opportunity period (determined by the
RACH configuration), as shown in Fig. 7. The conclusion of this
experiment is that it is possible to control CR by altering the
configuration.
D. Interference on PUSCH by Random Access Preambles
The goal of this experiment is to study the interference on
PUSCH generated by random access preamble transmissions.
The idea is to show whether there is a benefit of adjusting P0_RACH
in order to reduce the interference on PUSCH (compared to
setting P0_RACH = -120 dBW). The generated interference is a
function of the preamble transmission power and number
preamble transmissions. For this reason we vary P0_RACH and the
RACH load. The parameters are altered according to LoadRACH =
{1,5,10,25,50,100,200,300} preambles/cell/s and P0_RACH =
{-120,-130,-140,-150} dBW. Define the PUSCH noise rise as,
0 200 400 600 800
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
RACH Load
CR
RAOI = 20ms
RAOI = 5ms
RAOI = 2ms
RAOI = 1ms
0 200 400 600 800
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
RACH Load
CR
RAOI = 20ms
RAOI = 5ms
RAOI = 2ms
RAOI = 1ms
Figure 7. Effect of RACH load and random access opportunity interval
(RAOI) on CR.
-150 -145 -140 -135 -130 -125 -120
0
0.2
0.4
0.6
0.8
P0_RACH
DMR
Attempt Nr 1
ΔRACH
=0
ΔRACH
=2
ΔRACH
=4
ΔRACH
=6
-150 -145 -140 -135 -130 -125 -120
0
0.2
0.4
0.6
0.8
P0_RACH
DMR
Attempt Nr 3
-150 -145 -140 -135 -130 -125 -120
0
0.2
0.4
0.6
0.8
P0_RACH
DMR
Attempt Nr 5
Figure 6. DMR as a function of P0_RACH and ΔRACH
34 2009 IFIP/IEEE Intl. Symposium on Integrated Network Management — Workshops

7. RB
RBtotRACH
N
NI
NR
+
= ,
where IRACH,tot denotes the received RACH inter-cell interference
power (on PUSCH) and NRB -146 dBW is the noise power over
one resource block (corresponding to a 180 kHz frequency band).
The noise rise over all simulated cells is given in Fig. 8. The
noise rise increases as a result of increasing LoadRACH and
P0_RACH. The noise rise is substantial for some preamble
transmissions when P0_RACH = -120 dBW and very small when
P0_RACH = -140 dBW. Although, mobiles close to the base station
may not suffer significantly from inter-cell RACH interference,
mobiles at the cell edge may, and the result may be a decreased
PUSCH coverage and/or performance. As such, there may be a
benefit in decreasing the interference on PUSCH by lowering
P0_RACH. The conclusion of this experiment is that RACH may
cause interference on PUSCH for high P0_RACH and RACH load
and this may be alleviated by appropriately setting P0_RACH (given
the performance requirements are satisfied). Note, the need of
adjusting P0_RACH only is valid during excessive RACH load.
E. Random Access Format Coverage Implication
The goal of this experiment is to study the effect of RACH
format on RACH coverage performance. Therefore, the P0_RACH
and format are varied for a much sparser deployment with 5000
meters inter-site distance. From Fig. 9, it is evident that formats
with repeated preambles improve the random access coverage.
Furthermore, some mobiles in bad coverage spots will still have
insufficient power to succeed with the random access, and raising
the P0_RACH will not change this fact. A format with a repeated
preamble will help some, but not all.
V. SELF-OPTIMIZATION EXAMPLE
In order to exemplify how the information and models in this
paper can form the basis for self-optimization, we illustrate that
DMR1 can be controlled to meet a given performance
specification by automatically adjusting P0_RACH. Recall from
above that DMR is heavily affected by the PUSCH load.
Therefore, LoadPUSCH is varied according to Fig. 10. Although the
stepwise changes in LoadPUSCH may not be realistic, this gives the
worst-case interference change on RACH and, allows us to study
the performance of the controller under extreme conditions. The
initial value of P0_RACH is -120 dBW. Detection miss probability
for the first attempt should be 0.01. An integrating controller (I
controller)
))(01.0()1()( 1_0_0 kDMRKkPkP IRACHRACH −+−=
is used where KI is a tunable parameter, and sampling period is
1s.
The results are given in Fig. 10, where the average over all
cells is shown for LoadPUSCH, DMR1, and P0_RACH. We can see
that the controller is capable of adjusting P0_RACH so that DMR1
tracks its target value (0.01). At time 45s and 80s, the PUSCH
load increases significantly resulting in DMR1 overshoots. The
overshoots cannot be avoided unless a mechanism that predicts
the increase in LoadPUSCH is available. One conclusion is that
using a simple I controller it is possible to control P0_RACH such
0 5 10 15 20
0
0.2
0.4
0.6
0.8
1
CDF
Noise Rise (dB)
0 5 10 15 20
0
0.2
0.4
0.6
0.8
1
CDF
Noise Rise (dB)
(a) (b) (c)
Figure 8. Noise rise distributions as functions of P0_RACH and LoadRACH: (a) P0_RACH = -120 dBW (b) P0_RACH = -130 dBW (c) P0_RACH = -140 dBW.
0 5 10 15 20
0
0.2
0.4
0.6
0.8
1
CDF
Noise Rise (dB)
LoadRACH = 300
LoadRACH = 1
-150 -145 -140 -135 -130 -125 -120
0
0.05
0.1
P0_RACH
DMR
Attempt Nr 1
Format=0
Format=2
Format=3
-150 -145 -140 -135 -130 -125 -120
0
0.02
0.04
0.06
0.08
P0_RACH
DMR
Attempt Nr 2
-150 -145 -140 -135 -130 -125 -120
0
0.05
0.1
P0_RACH
DMR
Attempt Nr 1
Format=0
Format=2
Format=3
-150 -145 -140 -135 -130 -125 -120
0
0.02
0.04
0.06
0.08
P0_RACH
DMR
Attempt Nr 2
Figure 9. DMR for first and second attempt and 5000 m inter-site distance.
2009 IFIP/IEEE Intl. Symposium on Integrated Network Management — Workshops 35

8. that DMR1 satisfies a given performance specification in terms of
a given target value.
VI. CONCLUSION
In this paper we have argued that there is an increasing need
for self-organization in future wireless access networks. To meet
these expectations there has been a significant effort carried out
in academia, standardization bodies (e.g., 3GPP), and industry.
One aspect that benefits from automation is RACH optimization,
and the random access procedure is thoroughly described. We
have studied the feasibility of RACH self-optimization in LTE
by providing simulation results showing the impact of a set of
key parameters on the RACH performance. Further an algorithm
was presented, which tunes the RACH power control parameters
such that the detection miss probability of transmitted preambles
tracks given requirements. In order to automate the optimization
of RACH power control parameters there is, however, a need for
the UEs to report the number of sent preambles. These reports
enable the derivation of preamble detection miss probability,
which can be used for controlling the network access delay.
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0 50 100 150
0
0.5
1
Time (s)
LoadPUSCH
0 50 100 150
0
0.05
Time (s)
DMR1
DMR
Target
0 50 100 150
-130
-120
Time (s)
P0RACH
[dB]
Figure 10. Result of a self-tuning algorithm that autmatically sets P0_RACH
based on observed DMR1.
36 2009 IFIP/IEEE Intl. Symposium on Integrated Network Management — Workshops