Bronzel_M_MPRG_98

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Integrated Broadband Mobile System (IBMS) Featuring Smart Antennas

M. Bronzel, J. Jelitto, M. Stege, N. Lohse, D. Hunold, and G. Fettweis

Dresden University of Technology, Germany

[email protected]

Abstract: IBMS is a concept for future mobile communication systems to provide a large range of

data rates with different degrees of mobility. The integration of heterogeneous services and

communication systems requires a common Network Access and Connectivity CHannel (NACCH)

for basic signaling to provide permanent network access. Smart Antennas are utilized to

adaptively enable a trade-off between mobility and data rate.

I. Introduction

Currently, wireless communication systems are designed for specific data rates and mobility support.

While high mobility is provided by personal communication systems at low data rates with limited

potential services, higher data rates up to 155 Mb/s will be supported by wireless ATM and/or IP systems

for users with limited mobility. However, future demands for mobile communication systems will be

dominated by the heterogeneity of broadband and narrowband services, which have to be supplied in in-

house and outdoor environments simultaneously, with varying degrees of mobility. The objective of the

Integrated Broadband Mobile System (IBMS) [1,2] is to provide a unified way for supporting a variety of

communication classes ranging from high mobility with low data rates towards portability at high data

rates as an integral feature of a wireless communication system. This requires the development of a

uniform network structure, which also enables the integration of different communication systems,

working at different frequencies. Figure 1 relates IBMS to other European research projects.

2 Mbit/s 155 Mbit/s

mobile

portable

fixed

M o b i l i t y

Data Rate

20 Mbit/s

MBS

B-ISDN

MEDIAN

UMTSGSM

DECT

ISDN

HIPERLAN / WAND

IBMS

Figure 1: IBMS concept with respect to other European research projects (V. Brankovic).



II. System Concept

The key features of the IBMS concept are discussed in more detail in the following sections.

Hierarchical structure

An integrated communication system for heterogeneous services and terminals with different air

interfaces and variable bit-rates requires a new network infrastructure. A possible approach could be a

hierarchical structure of Network Service Classes (NSC), which has been introduced [1] to support

different Quality of Service (QoS) parameters with respect to mobility. Each NSC comprises a functional

signaling set (depicted as circles in Figure 2) which is used for configuration of the particular NSC and

selection of the appropriate Traffic CHannel (TCH).

Config. Config. Config. Config.

TCH-A TCH-B TCH-C

GSM+

IS-95

ZATM

DECT

WLL

NSC 0 NSC A NSC B NSC C

Config.

NSC 0

NACCH:

- Registration- Authentication- LocationManagement

High Data Rate WLAN/Home Network

Figure 2: Hierarchical Structure of Network Service Classes (NSC).

Starting from NSC 0, which serves as the entry point and therefore contains only the basic functionalities

of network access and location management, a wide range of possible datarates with different degrees of

mobility is provided by additional network functions in higher-layered NSCs (A-C). A particular NSC

passes all functionalities to higher ordered NSCs. In addition, other network structures (e.g. in-house

overlay networks) can attach to NSC-C as another sub-hierarchical network of NSCs, which also enables

the integration of in-house and outdoor environment. The hierarchical network structure in IBMS also

supports Rate Fallback and Rate Upgrade. If a required QoS cannot be maintained, the system falls back

to lower NSCs without loosing connection and automatically switches back to the original NSC if the

necessary channel characteristics are met. Since higher NSCs exploit the frequency/space capacity of the

network more efficiently (see Network Capacity Considerations), connections of lower NSCs shall becarried along in higher NSCs when channel conditions are appropriate. The maximum bitrates of the

different NSCs are defined as follows:

NSC A NSC B NSC C

64 kbit/s 2Mbit/s 34Mbit/s

Table 1: Maximum bitrates of the Network Service Classes (NSC).



Furthermore, IBMS will allow switching to different communication systems (Figure 2), thus enabling

the integration of proprietary system concepts. The transition is either controlled by IBMS within NSC-0

and permits re-entry or a complete inter-system handoff.

Network Access and Connectivity Channel

A Network Access and Conectivity Channel (NACCH) is an integral part of the IBMS approach. This

basic signaling channel covers all signaling tasks that guarantee constant network access and maintain

established connections. Furthermore, the NACCH comprises additional functionalities in order to

manage the hierarchical structure of IBMS, which includes switching between different NSCs for

supporting Rate Fallback and NSC Upgrade. The NACCH comprises the following main functionalities:

• registration and authentication

• call setup and release

• network access configuration

• mobility management (location management, handoff, paging)

• power saving management

• management of Rate Fallback and NSC Upgrade.

While the physical NACCH implementation depends on the respective environment, the functional

specification however, is independent from the platform used. This enables the integration of

heterogeneous and proprietary systems, which is essential for a software telecommunications/radios.

Trade-Off Between Data Rate and Mobility

Since bitrate and mobility cannot simultaneously be maximized, a trade-off between datarate and

mobility is introduced by means of different NSCs as a key functionality of IBMS (Figure 3). SmartAntennas shall be deployed to support the different requirements, starting with omnidirectional antennas

in NSC A. Next, NSC B will utilize Smart Antennas at the basestation and, additionally, NSC C at

mobile stations, as well.

Data rate

Mobility

NSC A

NACCH

highportablemovable

high

medium

low NSC BNSC C

BS

A

B

C

A B C

64 kb/s 2 Mb/s 34 Mb/s

~ 25 ~ 24

15 dB 12 dB

ratio betweendata rates

additionalrequiredantenna gain

(- 20 Mb/s) (- 155 Mb/s)

Figure 3: Trade-off between datarate and mobility for

different NSCs and NACCH.

Figure 4: Smart Antennas supporting the

required data rates for different NSCs.



As part of the idea behind IBMS, devices of all NSCs shall transmit at similar power levels. Since the bit

energy of the transmitted signal gets weaker with increasing data rate, the idea is that this loss in bit

energy shall be compensated for by the antenna gain of the Smart Antennas. As apparent from Figure 4,

the ratio between the data rates of two adjacent NSCs is about 24

to 25, which requires antenna gains of

the same order of magnitude, i.e. about 12 to 15 dB. After consideration of possible interference

situations we are currently investigating a scenario where users of each NSC span the whole available

bandwidth of approximately 20 - 25 MHz.

Modem aspects

IBMS uses a combination of multiple access methods for the different NSCs. Utilizing Smart Antennas

enables Space Division Multiple Access (SDMA) in NSC B and C. Additionally, another multiple access

method is necessary. Since SDMA cannot be used for users that are not spatially separable and there are

no Smart Antennas in NSC A, Code Division Multiple Access (CDMA) will be used as second multiple

access method for NSCs A and B. The spreading property will then effectively result in a similar

bandwidth for all NSCs. Additional use of single carrier modulation promises an easier modem design

for all NSCs. Since it is not possible to apply CDMA in NSC C which already occupies the complete

bandwidth of one channel due to the high datarate Time Division Multiple Access (TDMA) will be used

if the two NSC C users cannot be separated. This will constrain the access time for each user. In order to

make efficient use of the system resources, IBMS requires intelligent resource management strategies.

The following Table 2 summarizes the intended multiple access and modulation methods used in IBMS:

NSC Multiple-Access Method Modulation

A CDMA OQPSK

B CDMA and SDMA OQPSK

C SDMA and TDMA 32-QAM

Table 2: Multiple Access and Modulation Techniques used in IBMS

Coherent modulation is applied in both, uplink and downlink. Currently, some interference canceling

detection methods are investigated to be utilized at the basestation. This might also be implemented at the

mobile station for NSC C type terminals.

III. Network Capacity ConsiderationsBitrate Gain

The performance increase of systems utilizing Smart Antennas can be categorized as follows:

• antenna gain due to directivity

• Spatial Filtering for Interference Reduction (SFIR)

• Space Division Multiple Access (SDMA).



The spatial filtering influences not only the number of interferers, but also the number of multipath

components, and thereby parameters of the mobile channel, like coherence bandwidth and coherence

time. Therefore we introduce another category

• Spatial Filtering for Channel Improvement (SFCI).

Higher bitrates can be achieved if a significantly reduced delay spread permits the implementation of

spectrally more efficient modulation techniques. This is indicated in Figure 5 where an increased bitrate

(compared to omnidirectional antennas) can be observed with reduced beamwidth of the adaptive array.

Thereby the maximum bitrate has been derived form the coherence-bandwidth and the requirements on a

non-frequency-selective channel.

0 50 100 150 200 250 300 350 4000

2

4

6

8

10

12

b i t r a t e g a i n [ d

B ]

beamwidth [deg]

Figure 5: Bitrate gain over beamwidth of the adaptive antenna.

Network Capacity Aspects

In a first capacity analysis, the mean Signal-to-Interference Ratio (SIR) for the uplink has been

determined according to a method presented in [3], based on a 7 cell cluster network. Other assumptions

were e.g. ideal antenna array pattern, ideal power control, and a simple path loss channel model. The

number of users was equal in each cell of the cluster considered. As an example, the resulting formula of

the SIR is given for the case of NSC C. In the other NSCs similar results were achieved [4].

W

R E D

gK gW

R E D

gK W

R E D

gK

E ggSIR

C C b

BS

BSC C MS B Bb

BS

BS B B A Ab

BS

BS A A

C b BS MS

C

,,,

,

)61()61()61( β β β +++++

=

BS MS gg , antenna-gain at the mobile (MS) and the base-station (BS),

BS D directivity of the BS

antenna array,i R datarate,

iK number of users,i E energy per bit (NSC i),

iβ interference from

adjacent cells,W system bandwidth



Figure 6 shows the user capacity of the system for the different NSCs. It can be seen that for NSCs A and

B, the same number of users can be served in uplink (βA = βB) despite of the higher datarate in NSC B,

which is a result of using Smart Antennas at the basestation in NSC B. In NSC C, the number of users is

less due to stronger interference through the antenna gain of the MS antenna array (gMS).

0 5 10 15 20 25 300

10

20

30

40

50

60

70

80

90

100

SIR [dB]

# u s e r s

A, B user, calculatedC user, calculatedA user, simulatedB user simulatedC user simulated

0 5 10 15 20 25 3010

−3

10−2

10−1

100

101

102

SIR [dB]

# u s e r s * b i t / s / H z / c e l l

A

B

C

Figure 6: User capacity Figure 7: Network capacity

However, referring to Figure 7, which shows the network capacity, it can be concluded that NSC C

exploits the network capacity most efficiently because of its high datarate. Now it becomes obvious, why

it is desirable to carry a low-rate user along in a higher NSC, which is contained in the IBMS concept as

a NSC Upgrade. Since this will reduce the interference in the system, more users can be served while

exploiting the network capacity more efficiently.

IV. Smart AntennasSpatial Channel Modeling and Verification

Performance analysis of adaptive antenna array receivers in mobile communications requires detailed

knowledge of the time-varying impulse responses between the transmitter and each of the antenna array

outputs, combined also denoted as vector channel impulse response. Due to the beamforming effect of

Smart Antennas, the conventional channel models for omnidirectional antennas cannot be used.

Measurements have been made in order to find parameters for typical impulse responses for a

communication channel when utilizing adaptive antenna arrays. Our measurements have been carried out

using the RUSK ATM vector channel sounder (Figure 8) which has been jointly developed by MEDAV

and Ilmenau University of Technology [5, 10]. The channel sounder can be used to analyse the time

variant vector impulse response in the 5...6 GHz frequency band based on an uniform linear antenna

array with 8 elements. In order to gain superresolution in the delay angular domain, a 2D unitary

ESPRIT procedure [11] is used for joint delay-DOA estimation.



Oscillator

Local

Rubidium frequencyreference

Arbitrarywaveformgenerator

A D

~~~~~~ DSP PC

Display

DAT

Oscillator

Local

Rubidium frequencyreference

Stochastic

time variant

multipath

channel

Mobile

Transmitter

Antenna Array

Receiver

Omnidirectionalantenna

8+2 elementULA patch array

FastMultiplexer

Single channel RF-receiver Digital demodulator / correlator

Position

(GPS, wheel sensors)

Position

(data telemetry)

Figure 8: RUSK ATM Channel Sounder (R. Thomä, TU Ilmenau [5, 10]).

The following pictures show some analysis results of measurements carried out in a suburban

environment. The transmitter was moved along a street with the mean distance to the receive array being

approximately 60 m. Figure 9 shows a sequence of impulse responses of one array channel measured at 2

ms intervals. The resulting delay-Doppler spectrum is shown in Figure 10. It indicates clearly the ability

of the applied measurement principle to track the time-variant behaviour of the impulse responses by

representing a Doppler shift of about 90 Hz, which corresponds to a radial speed of 18.5 km/h.

0

50

100

150

200

250

300

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0

1

2

3

4

x 10−4

observation time [ms]

Impulse responses of channel 1 (Doppler−measurement−file MEDAV 01/29/98)

delay [µs]

m a g n i t u d e

−300−200

−1000

100200

300 0

50

100

150

200

250

300

350

400

0

0.02

0.04

0.06

0.08

0.1

0.12

delay [ns]

v = 18.51 km/h

Demonstration of Doppler − LOS became shorter (RUSK−ATM − MEDAV − 01/29/98)

Doppler frequency [Hz]

m a g n i t u d e s q u a r e d

Figure 9: Sequence of impulse responses during

the movement of the transmitter (R.Thomä).

Figure 10: Estimated Delay-Doppler spectrum of

the impulse responses in Figure 9 (R.Thomä).

Let us introduce a spatial channel model in order to estimate the channel improvement when utilizing

Smart Antennas. Our model is based on a single bounce approach, which has also been used by Liberti

[6]. The time delay between transmitter and receiver can be described using ellipses. Furthermore, a

radial exponential distribution of the scatterers around the mobile is assumed, which takes into account

the finite dimension of scattering objects. The geometrical superposition of these results in the joint PDF

of delay and beamwidth. Assuming a simple path-loss-model the delay spread depending on the

beamwidth can be derived. Changing the distance between transmitter and receiver in time, results in



moving ellipses and thereby the timevariant joint PDF of delay and beamwidth, from which the

dependency of the Doppler spread on the respective beamwidth of the adaptive array can be derived.

Figure 11 (solid line) shows the effect of the antenna beamwidth on the delay spread, which correspond

to our measurements (depicted as stars). The respective Doppler spread is shown in Figure 12.

0 20 40 60 80 100 120 140 160 1800.5

0.55

0.6

0.65

0.7

0.75

0.8

0.85

0.9

0.95

1

r e l . D e l a y S p r e a d

Beamwidth [deg]0 20 40 60 80 100 120 140 160 180

0.7

0.75

0.8

0.85

0.9

0.95

1

r e l . D o p p l e r S p r e a d

Beamwidth [deg]

Figure 11: Delay Spread vs. Beamwidth. Figure 12: Doppler Spread vs. Beamwidth.

Simulations

To investigate the performance of beamforming algorithms in realistic scenarios, array imperfections

such as mutual coupling between elements and non-ideal elements have to be taken into consideration.

Figure 13 shows a typical beamforming pattern of an 8-element antenna array wiht the desired signal

located at 30° and an interferer at –30°. The dotted line shows the pattern for “ideal” (omnidirectional)

antenna elements, while the solid line includes both, element imperfections and mutual coupling effects.

ideal Pattern

real Pattern

−1.5 −1 −0.5 0 0.5 1 1.510

−3

10−2

10−1

100

angle in rad

M a g n i t u d e

real Pattern

Figure 13: Antenna pattern of the adapted array.

Given these antenna characteristics, a simulation of a simple communication situation with one user (30°)

and one interferer (-30°) transmitting on the same carrier frequency was performed. The channel was



modeled using the GBSB-approach with two paths [6]. The SINR plot in Figure 14 starts with an

omnidirectional antenna pattern for both, the transmitter and the receiver. This results in a small Signal-

to-Interference-and-Noise-Ratio (SINR), since all incoming signals are received equally. This situation

can be viewed as a typical scenario for NSC A. Switching to NSC B would then require an adaptive

beamforming at the basestation. This is shown in the second part of the SINR plot, when the adaptive

array is turned on after 40 iterations. The antenna array immediately adapts its directivity towards the

direct path from the transmitter and a dramatically improved SINR (about 20 dB) can be observed. This

will enable the implementation of higher order bandwidth efficient modulation schemes, as required for

the data rates in NSC B.

Interference

Interference reflectedTransmitter reflected

Transmitter

0 50 100 150 200−5

0

5

10

15

20

25

30

35

40

iterations

S I N R i n

d B

Figure 14: Simulated Rate Fallback.

Testbed Design

In order to demonstrate the aforementioned potential benefits (enable higher order modulation techniques

with increased spectral efficiency) and tracking capabilities of adaptive arrays, we have developed a

smart antenna testbed based on the following design considerations:

• scaleable DSP array with arbitrary topology and considerable signal processing power

• advanced bus system for high speed data transmission (> 64Mbytes/s throughput)

• simple array geometry with sufficient number of antenna elements

• digital down conversion to baseband in order to ensure I/Q-orthogonality

• A/D conversion at a suitable IF.



The array antenna band (Figure 15) has been designed as a uniform linear array of 8 printed circuit

elements with vertical polarization and λ /2-spacing to match the required gains. The center frequency of

5.2 GHz falls into the European Hiperlan-II band. The antennas are connected to an 8 channel RF front

end with a maximum signal bandwidth of 2 MHz, which can be handled using off-the-shelf DSPs. In a

single stage down-conversion the signals from each antenna are converted to an IF at 10.7 MHz.

Figure 15: Printed circuit antenna array.

The IF signals are fed to a signal processing unit (Figure 16), which has been developed by VSYSTEMS,

Munich, in accordance with our design specifications. Analog-to-Digital converters (Analog Devices

AD9042) with a maximum sampling rate of 41 MHz at 12 bit resolution sample the IF signals, which are

then transformed into baseband (I/Q demodulated) using Digital Down Converters (Graychip GC1012)

and fed to an array of 18 SHARC DSPs (Analog Devices ADSP-21060) with a total of 96 Mbyte DRAM.

Theoretically, the DSP array can achieve more than 2 GFLOPS. In order not to limit the performance by

the inter-processor-communication bottleneck, the DSPs are connected over a RACEway crossbar

network with a maximum throughput of 160 Mbyte/s per link.

Figure 16: Signal Processing Unit: Sun host, ADC, DDC, and DSP array (VSYSTEMS).



The general structure of our testbed is shown in Figure 17.

Analog-Digital-

Conversion

DDCBaseband-Conversion

I/Q-Generation

DSP-ArrayAdaptive Array-

Algorithms &Baseband-processing

R A C E w a y

( 1 6 0 M B y t e s / s / l i n k )

fc=5.2 GHz

8 elements

RF-Front-

End

fIF=10.7 MHz

8 channels

8xADC41MHz @ 12bit

8xDDC50MHz @ 12bitDec. Rate: 2...64

18xADSP-210602.16GFLOPS (FFT)1.44GFLOPS (oth.)

EmbeddedSUN Host

InitializationControl

Visualization

VMEbus

Figure 17: Smart Antenna Testbed.

VI. Summary

A new system concept for future mobile communications systems has been presented, which utilizes

Smart Antennas in order to maximize the system capacity and meanwhile enables implementation of

adaptive (smart) modems. It has been shown that Smart Antennas are capable of reducing the delay

spread, which enables the implementation of higher order spectrally efficient modulation techniques.

First results have been presented based on spatial channel models which have been experimentally

verified.

VII. Acknowledgments

This work is supported by the German Federal Ministry for Education, Science, Research, and

Technology (BMBF) as part of the ATMmobil project. The channel measurements have been jointly

obtained with Prof. Reiner Thomä and his researchers Gerd Sommerkorn, Dirk Hamicke, Uwe

Trautwein, and Andreas Richter from Technical University of Ilmenau. In particular, the authors would

like to thank Prof. Jeffrey Reed from the Mobile and Portable radio Research Group at Virginia Tech for

many helpful discussion when designing our Smart Antenna testbed.

VIII. References

[1] G. Fettweis, K. Iversen, M. Bronzel, H. Schubert, V. Aue, D. Mämpel, J. Voigt, A. Wolisz, G.

Wolf, and J.-P. Ebert: „A Closed Solution For An Integrated Broadband Mobile System

(IBMS)“. ICUPC 1996, Boston, USA.



[2] M. Bronzel, and K. Iversen: „Integriertes Breitbandiges Mobilkommunikations-System (IBMS)

auf ATM-Basis“. Workshop der ITG-Fachtagung Optische Teilnehmerzugangsnetze im Heinrich-

Hertz-Institut, Berlin, November 1996, published in: telekom praxis, vol. 74, pp. 12-19, June

1997.

[3] Joseph C. Liberti, Jr., and Theodore S. Rappaport, "Analytical Results for Capacity

Improvements in CDMA", IEEE Transactions on Vehicular Technology, Vol. 43, No. 3, pp. 680

- 690, August 1994.

[4] M. Bronzel, D. Hunold, J. Jelitto, N. Lohse, G. Fettweis: "Unterstützung variabler Datenraten

durch den Kapazitätsgewinn adaptiver Antennen", ITG-Diskussionssitzung "Intelligente

Antennen", Kaiserslautern, December 5, 1997.

[5] M. Bronzel, J. Jelitto, N. Lohse, G. Fettweis, R. Thomä, G. Sommerkorn, D. Hampicke, U.

Trautwein, and A. Richter, "Experimental Verification of Vector Channel Models for Simulation

and Design of adaptive Antenna Array Receivers", ACTS Mobile Communication Summit 1998,

(accepted).

[6] J. C. Liberti, T. S. Rappaport: „A Geometrically Based Model For Line-Of-Sight Multipath

Radio Channels,“ IEEE Conf. Proc. VTC’96, pp. 844-848, May 1996.

[7] U. Martin: „A Directional Radio Channel Model for Densely Build-Up Urban Area,“ ITG-

Fachbericht 145, The 2nd European Personal Mobile Communications Conference (EPMCC '97)

together with 3. ITG-Fachtagung Mobile Kommunikation, Bonn, Germany, pp. 237-244, Sep.

1997.

[8] J. Jelitto and M. Bronzel, "Development of a Smart Antenna Testbed", Joint Workshop o

Wireless Broadband In-House Digital Networks (IHDN’98), January 22-23, 1998, Ulm,

Germany.

[9] M. Bronzel, D. Hunold, G. Fettweis, T. Konschak, T. Dölle, V. Brankovic, H. Alikhani, J.-P.

Ebert, A. Festag, F. Fitzek, and A. Wolisz, "Integrated Broadband Mobile System (IBMS)

featuring Wireless ATM", ACTS Mobile Communication Summit ‘97, October 7-10, 1997,

Aalborg, Denmark, pp.641-646.

[10] U. Trautwein, K. Blau, D. Brückner, F. Herrmann, A. Richter, G. Sommerkorn, and R. Thomä,

"Radio Channel Measurement for Realistic Simulation of Adaptive Antenna Arrays, The 2nd

European Personal Mobile Communications Conference (EPMCC’97), September 1997, Bonn,

Germany, pp. 491-498.

[11] M. Haardt, M.D. Zoltowski, C.P. Mathews, and J.A. Nossek, "2D Unitary ESPRIT for Efficient

2D Parameter Estimation", Proc. ICASSP’95, Detroit, MI, USA, May, 1995, pp. 2096-2099.

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