Special Feature

Resource Management and QoS Control Scheme
in a Multibeam Satellite Communication System
Katsuya Nakahira†, Kiyoshi Kobayashi,
and Masazumi Ueba
Abstract
To develop large-capacity, high-quality mobile satellite communication systems in the future, we willneed to use a multibeam scheme that uses extremely high levels of frequency reuse. In this article, we
describe a resource allocation algorithm for multibeam satellite communication systems that can dynamically adapt to the maximum communication capacity without compromising quality, thus enhancing thequality of service (QoS). The algorithm combines two resource allocation schemes that enable it to copewith ever-changing user distributions and interbeam interference conditions.

1. Introduction
Mobile Internet services on terrestrial cellular networks are currently available only in densely populated areas. There is, however, a fairly large demand
for them outside these areas. Mobile satellite networks can meet these demands because satellites are
the most efficient means for reaching rural andremote areas using high-speed, ubiquitous links.
Based on our estimation of market demand, thenext-generation mobile satellite system [1] will needto have a capacity of 1 Gbit/s. A GEO (geostationary
earth orbit) satellite with a very limited payload
power (2 kW) will probably be used because of its
low capital investment requirements. Unfortunately,
the S-band, which is usually assigned to mobile satellite services, has a very limited frequency bandwidthof 35 MHz. To obtain a large capacity from limited
resources, such as bandwidth and payload power, wemust utilize a multibeam system that allows extremely high levels of frequency reuse [2]. However, in
multibeam systems, interbeam interference reduces
the communication capacity and link quality. In
mobile communication systems, there are usuallylarge fluctuations in user distribution, depending on† NTT Access Network Service Systems Laboratories
Yokosuka-shi, 239-0847 Japan
Email: nakahira.katsuya@lab.ntt.co.jp

Vol. 5 No. 1 Jan. 2007

time and geographical area. Thus, beam traffic isuneven in the service area. Unfortunately, such
uneven traffic reduces communication capacity
because a high-traffic beam tends to require higher
power, so it interferes more with the other beams.
Consequently, to provide high-capacity services
while maintaining high-quality communications, weshould ensure that system resources are meticulously
assigned to each user within each beam.
This article describes novel resource allocation
schemes that enable mobile satellite systems to copewith the ever-changing user distribution and interbeam interference conditions. The schemes focus on
a forward link that needs an extremely large capacity.
To simplify the resource allocation while maintaining
high capacity and high quality, the scheme’s algorithm combines two resource allocations, by linking
among beams and within a single beam. The first
scheme optimizes the resources among beams tominimize interference. The second scheme manages
the various required resources and adapts them to thebeam gain and interference levels at various user
locations within a single beam. These schemes arelayered, can allocate multibeam satellite resources tocope with the increasing communication demand,and improve the communication quality, thus enhancing QoS.

45

Special Feature

2. Multibeam configuration
In the multibeam satellite system, the Japanese economic zone is covered by 69 beams, as shown in Fig.
1. For greater usable frequency bandwidth, the frequency of each beam must be reused to avoid overlaps between contiguous beams. To construct one
three-beam cluster, we divide system bandwidth
Wsys into three frequency areas: F1, F2, and F3. The
boundaries of these areas vary depending on the
resource allocation scheme, as described below.
The system supports a wide range of applications,from data telemetry to broadband services. Thus, thetransmission bit rate of the user terminal needs to be
F2
F2

F1

F1
F1
F3
F2
F1
F3

F3
F2

F1
F3

F2
F1

F1
F3

F2

F3

F1

F2

F3

F3

F2

F3

F1

F3

F1
F3

F1

F1
F3

F1

F2

F3
F2

F1
F3

F2
F1

F3

F2

F2

F3

F1

F1

F2

F2

F3
F2

F1

F1

F2

F2

F3
F2

F1
F3

F2

F3
F2

F1

Variable boundary

F1

F2

3. Resource allocation
In conventional multibeam systems, for easy allocation of resources while maintaining high efficiency,
Wsys is assigned to all beams that use CDMA (codedivision multiple access) [3], [4]. Such systems only
adapt the satellite’s transmission power depending on
the beam traffic. However, in the CDMA system, it isdifficult to achieve our goal capacity of 1 Gbit/s while
keeping up with the uneven beam traffic, because ahigh traffic beam requires very high power comparedwith other systems such as ones based on FDMA (frequency division multiple access) systems. Such highpower causes more interference with the other beam.
In this way, we have proven that V-FDMA (variable
FDMA) with power adaptation is more effective atenhancing communication capacity [5]. Therefore,
the resource allocation scheme simultaneously varies
the frequency bandwidth and power depending on theuser distribution.
As shown in Fig. 3, we assumed that the users’
equipment uses adaptive modulation. In adaptive

F3

F3

F3

F1

F2

F2
F1

F1

F2

F3

F3
F2

F1

from 1 kbit/s to 100 Mbit/s. One characteristic ofmobile communication systems is that users are distributed non-uniformly in the service area. For simplicity, we assumed beams of identical shape, a parabolic satellite antenna, and radially symmetrical gain,as shown in Fig. 2. The side-lobe of one beam has
considerable influence on the other beams; hence,
interference from the adjacent beams diminishes
communication capacity and quality. The beam edge
gain is set at the most general value for antenna architecture: –3 dB relative to the beam center.

F3

System bandwidth Wsys

Fig. 1. Multibeam satellite communication system.
020
Practical values when BER = 10–5
Curve fit

–10

15
Beam edge

16QAM 7/8
16QAM 3/4
8PSK 7/8
8PSK 3/4

10–30

ρ (dB)

Relative gain (dB)

–20

–40

QPSK 7/8
QPSK 3/4

5

QPSK 1/2–50
0

BPSK 1/2–60

BPSK 1/3
–5

–70
–80
–5

–4

–3 –2 –1
0
1
2
3
Beam angle normalized by radiusFig. 2. Satellite antenna beam pattern.

46

4

5

–10

0

1

2

3

4

5

Spectrum efficiency (bit/s)

Fig. 3. Adaptive satellite modem performance.

NTT Technical Review

Special Feature

modulation, a spectrum efficiency η, determined by
the forward error correction coding rate and the number of modulation levels, is appropriately chosenfrom the received power ratio ρ, which is defined byρ = C/(N + I),(1)

where C is the carrier power, N is the thermal noise,and I is the interbeam interference.
This section describes a combination of two
resource allocation schemes. One involves allocating
resources among the beams to achieve the lowest possible interbeam interference and the other involves
allocating resources within a single beam to adapt tothe user’s location. Both schemes are layered toincrease communication capacity and improve communication quality.
3.1 Resource allocation among beams
Excessive beam power increases interbeam interference, and as a result, reduces total communication
capacity. Therefore, the main purpose of the scheme
is to determine the beam bandwidth that will bestminimize the total transmission beam power, thus
minimizing interference. For this purpose, we applied
the most promising optimization programming technique, SQP (sequential quadratic programming) [6],to adjust the bandwidth and power. However, it is still
difficult to properly adjust these resources, and there
are a lot of dependent optimization parameters.
To simplify the problem while minimizing interference, we introduce new constraints for the optimization. In general, the power can be minimized by using
the lowest spectrum efficiency. This is because thelower the spectrum efficiency, the lower the power, as
shown in Fig. 3. To lower the spectrum efficiency
while retaining the frequency reuse pattern, we apply
the following two new constraints on bandwidth to
SQP.
(1) Three beams in any cluster should use up thesystem frequency bandwidth and(2) Each beam in the same frequency area shouldhave the same frequency bandwidth.
As shown in Fig. 4, using no constraints, whichallows narrow or overlapping frequency bandwidth
beams, requires more power, and thus increases interference. Conversely, by using constraints, we can
avoid the previous bandwidth allocations, such that
constraint (1) widens the frequency bandwidth up to
the system limit and constraint (2) never causes overlapping of the bandwidth for any of the beams.
In addition, the number of optimization parameters
Vol. 5 No. 1 Jan. 2007

can be dramatically decreased from 69 to 2 because
only two frequency boundaries need to be optimized
in a 3-beam cluster. Generally, reducing the parameters is highly effective at avoiding the local minimum
solution. Therefore, the clarified constraints canachieve the lowest interference. We call this scheme
ORA (optimal resource allocation) for the multibeam
system.
3.1.1 Performance of ORA
Since a single beam can cover a densely populatedarea such as Tokyo, we assumed an unequal traffic
pattern in which one single beam had heavy traffic
while the other beams had very little. From thisassumption, we define a traffic concentration ratio Tr
as being when the heavy-traffic beam has Tr times the
traffic of the other beams.
The results of the resource allocation using ORA
with Tr = 5 are shown in Fig. 5. The x-axis is the beam
number when beam 1 has heavy traffic. As can beseen from the constraints, Fig. 5(a) shows that beams
in the same frequency areas are allocated the same
bandwidth, while beams within a cluster use up thesystem bandwidth. Hence, the total bandwidth ofbeams 1, 2, and 3 is 35 MHz. As shown in Fig. 5(b),the power density of each beam is allocated to carry
unequal traffic simultaneously.
3.2 Resource allocation within a beam
Resource allocation among the beams as clarified
above did not take into account individual user
requirements, such as the receiver antenna size andradio propagation due to inner-beam location. This
No constraints → more interference
Wsys
Cluster A
Cluster B

F1

F2

F3

Narrow bandwidth
Cluster C
Overlapping bandwidth
Constraints → minimum interference
Wsys
Cluster A
Cluster B

F1

F2

F3

(2) Same bandwidth
Cluster C(1) Using up Wsys

Fig. 4. Bandwidth constraints for the optimization algorithm.

47

Special Featuresection describes how to manage the differencesamong users located in the inner-beam area.
The beam gain and interference levels change
depending on the location within the inner beam.
Thus, depending on their locations, users should beallocated resources to provide required bit rate Rreq
and required ρreq, given as the prescribed communication quality, such as BER (bit error rate).
20

F1

18

F2

F3

Bandwidth (MHz)

16
14
12
10
Total 35 MHz

8
6
4
2
0

0

15
10
Beam number(a) Bandwidth allocation.

For individual users, bandwidth W, power density
Pr, and spectrum efficiency η can generally be chosen
as parameters. However, because W is determined by
Rreq/η, those can be reduced to two parameters, Prand η. From these, two resource allocation schemes
can be proposed:(1) Variable Pr with fixed η for a variable power
density (VPD) and(2) Variable η with fixed Pr for a fixed power density (FPD).
3.2.1 Variable power density
Since the beam gain and interference levels change
within a beam, ρ is estimated for the user’s location
as shown in Fig. 6. Thus, the VPD varies Pr to satisfy the requirement for ρ while factoring in the innerbeam location. VPD then sets an identical η for allusers in a beam. However, such Pr differences cause
the following problems.
As shown in Fig. 7(a), users a and c are allocated a20

5

Beam gain(main lobe)

Inter-beam interference(side-lobe)

–45

100

Power density (dBW/Hz)

Bit rate (Mbit/s)

I

C

–50

80

–55

60

–60

40

–65

20

Beam A

Beam CBeam B

User teminals
Estimated ρ = C/(N + I )
W
W

–70

0

5

10
Beam number

0
20

15

Pr

W

Pr

Pr

8PSK 1/3 16QAM 3/4

BPSK 1/2(b) Power allocation.

Fig. 5. Optimal resource allocation among beams at Tr = 5.

Pr

Fig. 6. Radio propagation within a beam.

MuchPr
interference
withother users Fixed Pr

Variable Pr
User aUser bUser cUser aPreq

Preq

Fixed ηVariable ηUser aUser bUser c(a) VPD scheme

Constant
interference

User aUser bUser cUser bUser c(b) FPD scheme

Fig. 7. Resource allocation within a beam.

48

NTT Technical Review

Special Feature

higher power density than user b because of thesmaller beam gain. Thus, the users of other beams
located in the beam center experience more interference if they are allocated the same power as a or c. In
other words, interference changes depending on frequency, so the received power ratio sometimes falls
below ρreq.
3.2.2 Fixed power density
As shown in Fig. 7(b), to enable the problem thatarises using VPD to be overcome, FPD keeps Pr constant within the beam while adjusting η to the user’s
location. Therefore, the allocated power depends on
only the bandwidth because the power density
remains constant. Thus, when Wsys is used up, theinterference density for one user is independent of theassigned frequency. Consequently, interference fluctuations, such as those in VPD do not occur.
3.3 Two-layer resource allocation
A two-layer allocation flow-chart is shown in Fig.
8. The first scheme allocates the resources among
beams satisfying the beam traffic, and then the second scheme allocates the resources among beams
depending on the various user locations.
Earlier, ORA was described for the first scheme and
VPD and FPD were described for the second scheme.
However, it should be noted that ORA determines ηfor each beam, even though FPD adjusts η for eachuser. Therefore, when FPD is used as the second
scheme, the two-layer process is iterated to converge
η for each beam [7].
FRA (fixed resource allocation) is added to the first
scheme, which fixes 1/3 of the system bandwidth of
all beams, while adjusting the power to satisfy theuneven beam traffic.
4. Performance of the resource allocation
schemes
Finally, we evaluate the performance of the combination of the previously described schemes. We firstevaluate the communication quality for each user andthen evaluate the communication capacity in various
traffic situations. The simulation parameters are summarized in Table 1.
4.1 Communication quality
VPD and FPD using ORA are compared in Fig. 9 toshow the difference between received ρ and ρreq foreach user. They were calculated for beams 1 and 2
when the frequency was randomly selected for eachuser.
Vol. 5 No. 1 Jan. 2007

Start
Set beam traffic ratio.
Allocate resources amongst beams.
Set user location.
Allocate resources within a beam.
Determine each user resource.

Have resources converged.

No

Yes
End

Fig. 8. Flow-chart of a two-layered resource allocation.

Table 1. Simulation parameters.
Beam number

69

System bandwidth Wsys

35 MHzSatellite transmission power

600 W

User terminal G/T–11.3 dB/K

Required BER

10–5

Required bit rate Rreq

1 Mbit/s

User location

Beam center or edge

Users in a low-traffic beam

10

Users in a high-traffic beam

10 × TrIn the ORA + VPD shown in Fig. 9(a), some users
who received ρ did not reach ρreq because of thelarge amount of interference generated by users who
were allocated high power density, as previously
mentioned. This consequently degraded communication quality.
However, in ORA + FPD, because the interference
power density remained constant regardless of thefrequency assignment, a stable ρ was supplied for theuser locations shown in Fig. 9(b).
These results show that ORA + FPD can guarantee
the prescribed communication quality for all users,
meaning that it is superior to ORA + VPD in enhancing QoS.
4.2 Communication capacity
We examined the maximum communication capacities using up both the system bandwidth and satellite
transmission power for the four different allocations.
49

Special Feature

Beam 1

Beam 2Required76

ρ (dB)

5
4
3
2
1
0

0

5

10
User number

15

20

Maximum communicaton capacity (Mbit/s)

1200

8

1100
1000

Beam 1

800
700
600
500
400
300
0

(a) ORA + VPD
8900

ORA + FPDFRA + FPD
ORA + VPD
FRA + VPD
2

4

6

8

10

Traffic concentration ratio Tr

Beam 2

Fig. 10. Communication capacity.

Required7

munication quality and enhanced system capacity
regardless of user distributions in multibeam systems.
In this respect, resource allocation using ORA + FPD
proved superior to the other allocation schemes.

6ρ (dB)

54

5. Conclusion

3
2
1
0

0

5

10
User number

1520(b) ORA + FPD

Fig. 9. Received ρ and required ρ of each user.

Capacity was calculated by summing for users who
received adequate ρreq. The results are shown in Fig.
10. The x-axis is traffic concentration ratio Tr when
the users concentrate on the Tokyo beam.
The figure representing ORA + FPD shows that it
offers higher capacities than the others when Tr isdenser. Specifically, it achieves our goal: a capacity
higher than 1 Gbit/s. Also, ORA + FPD has up to double the capacity that FRA + VPD has when Tr ≥ 7 isapplied. This is because FRA cannot cope with thevarying traffic conditions because the bandwidth ofeach beam is fixed. However, ORA enhances thecapacity by optimally coping with the bandwidth
allocated to each beam. In addition, FPD enhances
the capacities more than VPD, because VPD lowers ρ
to below the expected value. As a result, some users
do not reach ρreq.
Based on the evaluations in this section, it is clearthat ORA + FPD both attained the prescribed com50

This article described a resource allocation algorithm for multibeam satellite systems that can simultaneously control satellite resources, such as transmission power, frequency bandwidth, and modulation parameters, while factoring in interbeam interference and user location. The results demonstratethat applying an optimal algorithm under new constraint conditions can result in optimal solutions thatminimize interbeam interference and offer better
capacity than conventional systems.
Furthermore, the performance of allocation
schemes that can cope with user locations by applying this optimal algorithm was analyzed, and theresults showed that a fixed power density with adaptive modulation could guarantee communication
quality independent of the center frequency allocation of the user bandwidth.
References
[1]

[2]

[3]

M. Ueba, K. Ohata, J. Mitsugi, and M. Umehira, “Broadband andscalable mobile satellite communication system for future access network,” AIAA-2004-3154, May 2004.
Y. Nakasuga, J. Mitsugi, K. Ohata, and M. Ueba, “Communication
Capacity Enhancement with Adaptive Resource Assignment forMulti-beam Mobile Satellite Communication Systems,” AIAA-20043147, May 2004.
J. Romero-Garcia and R. de Gaudenzi, “On Antenna Design andCapacity Analysis for the Forward Link of a Multibeam Power Con-NTT Technical Review

Special Feature

[4]

[5]

[6]
[7]

trolled Satellite CDMA Network,” IEEE J. Select. Areas Commun.,Vol. 18, No. 7, Jul. 2000.
K. S. Gilhousen, I. M. Jacobs, R. Padovani, and L. A. Weaver,“Increased Capacity Using CDMA for Mobile Satellite Communication,” IEEE J. Select. Areas Commun., Vol. 8, No. 4, May 1990.
K. Nakahira, K. Kobayashi, K. Ohata, and M. Ueba, “A Study on an
Optimal Resource Allocation Algorithm for Next Generation Mobile
Satellite Communication Systems,” Technical Report of IEICE, JCSAT2004-351, Oct. 2004.
R. Fletcher, “Practical Methods of Optimization,” John Wiley andSons, 1987.
K. Nakahira, K. Kobayashi, and M. Ueba, “Communication Capacity
and Quality Enhancement Using a Two-layered Adaptive Resource
Allocation Scheme for Multi-beam Mobile Satellite Communication
Systems,” IEICE Trans. Commun., E89-A, No. 7, pp. 1930-1939,
July 2006.

Katsuya Nakahira
Research Engineer, Satellite Communication
Systems Group, Wireless Access Systems Project, NTT Access Network Service Systems Laboratories.
He received the B.S. and M.S. degrees in physical science from Kochi University, Kochi, in
1989 and 1991, respectively. Since beginning towork at NTT Radio Communications Systems
Laboratories in 1991, he has mainly been
engaged in research on satellite communication
network management and the development ofsatellite earth station equipment. His current
interest is a QoS architecture for mobile satellite
communication systems.

Kiyoshi Kobayashi
Senior Research Engineer, Supervisor, Satellite Communication Systems Group, WirelessAccess Systems Project, NTT Access Network
Service Systems Laboratories.
He received the B.E., M.E., and Ph.D. degrees
in electrical engineering from Tokyo Universityof Science, Tokyo, in 1987, 1989, and 2004,
respectively. He joined NTT Radio Communication Systems Laboratories in 1989. Since then, he
has been engaged in R&D of digital signal processing algorithms and their implementation
techniques including modulation/demodulation,synchronization control, and diversity for satellite and personal wireless communication systems. He is currently working on future mobile
satellite communication systems. He is a memberof IEEE and the Institute of Electronics, Information and Communication Engineers (IEICE)of Japan.

Masazumi Ueba
Senior Research Engineer, Supervisor, Group
Leader, Satellite Communication Systems
Group, Wireless Access Systems Project, NTT
Access Network Service Systems Laboratories.
He received the B.E. and M.S. degrees in aeronautical engineering, and the Dr.Eng. degree forwork on the design methodology of highly accurate antenna pointing system from the Universityof Tokyo, Tokyo, in 1982, 1984, and 1996,
respectively. He joined the Yokosuka ElectricalCommunication Laboratories of Nippon Telegraph and Telephone Public Corporation (nowNTT) in 1984. He has been engaged in researchon the dynamics of antenna pointing control systems of satellites and shape control systems forlarge antenna reflectors. He is currently researching technologies for next-generation mobile
satellite communication systems. He is a memberof IEICE, the Japan Society for Aeronautical andSpace Sciences, and the American Institute of
Aeronautics and Astronautics.

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51