System Performance Gain by Interference Cancellation

in WCDMA Dedicated and High-Speed Downlink Channels

Hans D. Schotten

Research Mobile Communications

Ericsson Eurolab Germany

Neumeyerstr. 50, 90411 Nuremberg, Germany

Phone: +49-911-2551633, Fax: +49-911-2551960

Email: Hans.Schotten@eed.ericsson.se

Jurgen F. Roßler

Chair of Information Transmission

University Erlangen-Nuremberg

Cauerstrasse 7/NT, D-91058 Erlangen, Germany

Phone: +49-9131-8527667, Fax: +49-9131-8528919

Email: roessler@LNT.de

Abstract—A large number of the existing and planned 3G net-works rely on the 3GPP WCDMA standard. Since the downlinkof WCDMA systems is usually interference limited, interferencecancellation algorithms that reduce the interference on the desiredsignal can significantly improve the system capacity. In this pa-per, the system-level performance gain that can be achieved by in-terference cancellation on the terminal side is investigated. Twodownlink channels are considered: the dedicated downlink chan-nel, which is already part of the WCDMA standard, and the newhigh-speed downlink channel for packet-data support, which willbe part of Release 5 of the WCDMA standard. In the analysis,four receiver classes are compared: the standard Rake receiver,a Rake receiver with pilot subtraction, a linear MMSE equal-izer, and a non-linear iterative successive cancellation algorithm.The resulting performance gains and the necessary implementa-tion complexity are discussed.

I. INTRODUCTION

This year, a large number of commercial 3G systems will

start operation. Many of them will rely on the WCDMA stan-

dard as specified by the 3rd Generation Partnership Program

3GPP. Since the downlink of these systems is usually interfer-

ence limited, terminal receivers that are able to reduce the effec-

tive interference on the desired signal can significantly enhance

the system performance. In this paper, potential interference

mitigation algorithms are investigated for two downlink chan-

nels: the dedicated downlink channel introduced in WCDMA

Release ’99 and the new high-speed downlink channel that is

introduced into the WCDMA standard as part of Release 5.

II. WCDMA DEDICATED AND HIGH-SPEED DOWNLINK

CHANNELS

The dedicated downlink channels are code-multiplexed us-

ing orthogonal channelization codes of variable spreading fac-

tor (OVSF-codes). A fast SIR based power control operating on

slot level (1500 Hz) is used for link adaptation. In principle, the

power control guarantees that the dedicated channel is always

assigned the transmit power necessary to achieve a certain de-

modulation performance at the terminal receiver. On the other

hand, this link adaptation scheme shall avoid that more trans-

mit power than necessary is allocated to a terminal since the

overall transmit power of the base station is a capacity limiting

quantity.

Interference cancellation in the terminal will reduce the ex-

perienced interference and, via the power control, reduce the

transmit power consumption for this terminal. So, interference

cancellation will finally result in a larger system capacity.

The new WCDMA high-speed downlink channel (High-

Speed Downlink Shared CHannel, HS-DSCH) has been opti-

mized to enhance the support of high-speed packet-data trans-

missions by increasing the supported peak data-rates up to rates

exceeding 8Mbit/s and by significantly reducing the round-trip

delays. So, the HS-DSCH extends the WCDMA packet-data

capabilities far beyond the IMT-2000 requirements. The tech-

nologies on which this new channel relies comprise fast link

adaptation by means of adaptive modulation and coding, fast

hybrid ARQ and fast scheduling.

The HS-DSCH uses adaptive modulation and coding for fast

link adaptation. A terminal experiencing ”good link condi-

tions” will be served with a higher data-rate than a terminal

in a less favorable situation. To support the different data-rates,

a wide range of channel coding rates and different modulation

formats (QPSK and 16QAM) are supported. Fast power control

is not used for the HS-DSCH.

A fast hybrid ARQ scheme is introduced for the HS-DSCH.

It is implemented on Node-B level and will enhance the packet-

data characteristics by reducing the round-trip delay.

The fast scheduling algorithm assigns the high rate HS-

DSCH to one or a small group of users per time slot depending

on the link performance they are instantaneously experiencing.

This mechanism guarantees that no transmit resources are allo-

cated for terminals in fading dips, but that the terminals expe-

riencing ‘good’ channel conditions, which can thus be served

with high data-rates, are assigned the HS-DSCH with priority.

The new high-speed channel - similar to the current Down-

link Shared Channel - shares a part of the channelization code

resource primarily in the time-domain among several users.

The spreading factor of the assigned channelization codes is

fixed to 16. Up to 15 out of 16 orthogonal codes can be allo-

cated for the HS-DSCH.

In order to allow these ‘fast’ algorithms to work fast, the

Transmission Time Interval (TTI) - the time interval for which

the modulation, coding and spreading format is fixed - is

reduced to 2 ms. A more detailed description of the new

WCDMA high-speed channel can be found in the 3GPP tech-

nical reports (e.g., TR25.950, TR25.848, and TR25.855) and in

[1],[2].

Applying interference cancellation in terminals receiving the

HS-DSCH will allow them to receive transmissions with higher

data-rates. This will increase the system throughput and, de-

pending on the scheduler strategy, enhance the end-to-end per-

formance. So, the benefit of advanced terminals can be seen

from a system as well as an user point of view.

III. INTERFERENCE CANCELLATION STRATEGIES FOR

WCDMA TERMINALS

Four receiver structures for WCDMA terminals have been

investigated:

• The standard Rake receiver, which will be used as refer-

ence.

• A Rake receiver with a simple subtraction algorithm that

subtracts known channels (pilot channel, synchronization

channel) received from the connected base station and

from a limited number of interfering base stations (e.g.,

the base stations in soft handover).

This approach results in low additional receiver complex-

ity since much information on these channels is already

present at the terminal.

• A linear MMSE equalizer on symbol level with and with-

out subtraction of known channels.

Here, not only the inter-path interference of the signal of

the connected base stations but also the noise covariance

matrix of the signals of interfering base stations is con-

sidered. The used MMSE equalizer on symbol level is de-

rived according to [3], but with a sliding window approach

to cope with inter-symbol interference.

• An iterative soft decision interference cancellation (IS-

DIC) algorithm with a matched filter (MF) front-end. This

receiver implementation represents a high-end solution. A

detailed description of the MF ISDIC can be found in [4]

where it is derived for synchronous CDMA and BPSK

with random spreading sequences. To calculate soft de-

cisions at the MF ISDIC for higher-order modulation a

soft decision scheme corresponding to [5] is used. The

utilized ISDIC operates as follows: All symbols of a cer-

tain block are successively estimated with a matched fil-

ter from the received signal whereby interference is can-

celled using the latest soft estimates of interfering sym-

bols. From a matched filter output a new soft estimate

is calculated which is used to cancel interference when

a new symbol of the block is estimated. Having calcu-

lated new estimates for all symbols the channel decoding

unit is exploited to calculate a posteriori probabilities for

all encoded bits which are then used to calculate new soft

estimates for all symbols. The described iteration of the

algorithm is repeated until the algorithm converges.

In all cases, the receiver structures have been adapted to the

characteristics of the investigated channel. To simplify the anal-

ysis, optimistic assumptions concerning channel estimation and

other necessary measurements had to be used. Thus, the results

presented in this paper shall only be used as an indicator de-

scribing the principal impact of these algorithms. Implemented

solutions will probably suffer from measurement and estima-

tion errors.

IV. DEFINITION OF INTERFERENCE SCENARIOS AND

LINK-LEVEL RESULTS

Since the interference cancellation capability of the above

described algorithms critically depends on the structure of the

experienced interference, i.e., the characteristics of the domi-

nant interference sources, the link-level simulations on which

the system analysis is based have to reflect the same interfer-

ence scenarios as modeled in the system-level simulation. For

this reason, based on the analysis of a cellular layout, represen-

tative interference scenarios have been identified. These sce-

narios are characterized by the set of ratios of the powers of the

strongest received interfering base station signals with respect

to the received power of the signal of the connected base sta-

tion. In order to illustrate this approach and the impact of the

interference scenario, some examples for link-level results are

presented in this section.

Figure 1 presents a typical cellular layout with omni-

directional antennas. Let us assume that the considered termi-

nal is located on the black arrow from Base Station 1 (BS 1) to

Base Station 2 (BS 2). The terminal is always connected to BS

1. In order to characterize the interference experienced by the

terminal, we define the so-called ‘geometry’:

G =Ior

Ioc

where Ior is the power of the signal received from BS 1 and Ioc

is the power of the signals received from all other base stations.

In Figure 1, the geometrical cell border (hexagonal layout)

and the contour of the geometry G is plotted.

In our link-level simulations, in addition to the signal from

BS 1, the signals from BS 2, BS 3, and BS 4 are modeled explic-

itly, i.e., these signals and the corresponding fading channels

are generated ‘chip-true’. The powers of these interfering sig-

nals are characterized with respect to the power of the desired

signal from BS 1. Let IBS2, IBS3, and IBS4 be the received

powers of the signals from BS2, BS3, and BS4, respectively.

IAWGN is the received power of all other base stations that are

modeled by AWGN in our link-level simulations. With these

definitions, we get Ioc = IBS2 + IBS3 + IBS4 + IAWGN and

G =Ior

Ioc

=

1

( Ior

IBS2)−1 + ( Ior

IBS3)−1 + ( Ior

IBS4)−1 + ( Ior

IAW GN)−1

.

For four interference scenarios, the sets of characterizing

power ratios are summarized in Table 1. They are calculated for

a propagation loss exponent of 3.45 and equal transmit power

for all base stations. For the cellular layout depicted in Figure

1, the scenarios in Table 1 describe terminal positions on the

black arrow.

A geometry of 20 dB corresponds to a position of the termi-

nal close to the connected base station. Here, the terminal will

experience ‘good’ channel conditions. Intracell interference

due to multipath propagation will often be the dominant inter-

ference part. A geometry of 9 dB corresponds to a more typical

position where depending on the propagation conditions, the in-

tercell interference can already be dominant. With a geometry

of 3 dB and below we are usually entering the soft handover

region close to the cell border.

As an example for the impact of the interference scenario on

the achievable link performance, Figure 2 and 3 present sim-

ulation results for a simplified HS-DSCH scenario. Here, 10

orthogonal codes of spreading factor 16 are used for the HS-

DSCH. These codes are assigned 85% of the total base station

output power. 15% of the base station output power are as-

signed to other channels including the pilot channel. Turbo-

coding is assumed with rate Rc = 1/2 in Figure 2 and rate

Rc = 1/3 in Figure 3. A ‘Vehicular A’ channel model is used.

-1.5 -1 -0.5 0 0.5 1 1.5

-1.5

-1

-0.5

0

0.5

1

1.5

30

20

10

3 0

-10

6

-3

BS 1

BS 2

BS 3

BS 6

BS 5

BS 7

BS 4

geometrical cell border

A

Fig. 1. Multi–cell scenario

In Figure 2 and 3, ‘Rake’ denotes the standard Rake re-

ceiver, ‘Rake pc’ denotes the Rake receiver with pilot cancel-

location 1 location 2 location 3 location 4

G = Ior

Ioc20 dB 9 dB 3 dB −3 dB

Ior

IBS2

26, 9 dB 13, 9 dB 6, 3 dB −1, 1 dB

Ior

IBS328, 2 dB 17, 2 dB 11, 8 dB 7, 6 dB

Ior

IBS4

28, 2 dB 17, 2 dB 11, 8 dB 7, 6 dB

Ior

IAW GN23, 0 dB 13, 2 dB 8, 5 dB 4, 5 dB

TABLE I

TYPICAL POWER RATIOS AT MULTI–CELL SCENARIO

lation (other known channels are subtracted as well), ‘MMSE’

denotes an MMSE equalizer with additional pilot cancellation,

and ‘MF ISDIC’ denotes the above described ISDIC receiver.

Single user throughput simulation results in Mbit/s are pre-

sented for 4QAM (solid lines) and 16QAM (dashed lines).

As can be seen from these figures, significant enhancements

of the throughput can be achieved by advanced receiver tech-

niques.

For the Rake receiver, the use of 16QAM often results in a

lower throughput than the use of 4QAM (even with different

coding rates).

For more advanced receiver algorithms and for good channel

conditions, a benefit from the use of 16QAM can be observed.

In general, the ISDIC offers the best results and in some

cases (under the idealized assumptions used in these simula-

tions) comes close to the optimum. However, a good compro-

mise between complexity and performance is the MMSE equal-

izer that shows only small degradation compared to the ISDIC

for medium and high geometry values. Note that these losses

are larger for channel models with less multipath interference.

V. SYSTEM-LEVEL ANALYSIS METHODOLOGY

For the system-level analysis, a certain number of the above

described ‘interference scenarios’ have been identified to repre-

sent the different interference scenarios a terminal will typically

experience in a WCDMA network. These scenarios are charac-

terized by the relative received powers of the four strongest base

stations as defined in the last section. Based on the above de-

scribed multi-cell simulation set-up, link-level simulations are

performed. For each scenario and each channel model, a param-

eter set (orthogonality factor, cancellation efficiency for each

interfering signal, noise enhancement due to mismatched fil-

tering) is derived that describes the link-level performance and

allows a simple and efficient calculation of an equivalent SNR

at the decoder input in the system-level simulation. Based on

this equivalent SNR, the link performance in terms of the block

error rate can be calculated.

Using this pre-calculated characterization of the link perfor-

mance, the system-level simulation is carried out. Here, for

20 9 3 −30

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

<− G [dB]

Thro

ughput [M

bit/s

]

Rake 4QAM

Rake 16QAM

Rake pc 4QAM

Rake pc 16QAM

MMSE 4QAM

MMSE 16QAM

MF ISDIC 4QAM

MF ISDIC 16QAM

Upper bound for 4QAM

Upper bound for 16 QAM

Fig. 2. Throughput at multi–cell scenario for Rc = 1/2

20 9 3 −30

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

<− G [dB]

Thro

ughput [M

bit/s

]

Rake 4QAM

Rake 16QAM

Rake pc 4QAM

Rake pc 16QAM

MMSE 4QAM

MMSE 16QAM

MF ISDIC 4QAM

MF ISDIC 16QAM

Upper bound for 4QAM

Upper bound for 16 QAM

Fig. 3. Throughput at multi–cell scenario for Rc = 1/3

each terminal the received power levels of the four strongest

received base stations are calculated and the ‘closest’ of the

pre-defined interference scenarios is determined. The param-

eters of this scenario are used to calculate the performance of

the terminal.

VI. RESULTS AND COMPARISON

In this section, system-level results for a simple hexagonal

cell layout with omnidirectional antennas are presented. The

propagation loss factor is 3.45, the channel profiles are the ‘In-

door A’ and the ‘Vehicular A’ channel.

In the system-level analysis typical radio network details are

modeled. In order to avoid that the investigated impact of the

receiver structures is masked by other performance limitations,

only simplified traffic, radio resource control and mobility mod-

els are used.

We assume that all cells are fully loaded and that 15% of

the transmit power is allocated to orthogonal common chan-

nels including the pilot channel. The synchronization channel

is not considered. For the dedicated channels, the system per-

formance is measured in terms of the number of users which

can be simultaneously supported. The number of users is step

by step increased (with a simple admission control algorithm)

until the maximum load is found.

Hereafter, the capacity gains that can be achieved for the ded-

icated channels are compared to the results for the Rake re-

ceiver.

With the subtraction algorithm, capacity gains of 2–5% were

found. They depend on the channel profile (2–3.5% for the

Vehicular A channel, 3–5% for the Indoor A channel) and of

the number of signals subtracted. In our simulations, the known

channels of the connected base stations, i.e., base stations in

soft-handover, are always cancelled. The signals of the other

base stations are cancelled if their received power does not fall

below -6dB of the received power of the strongest connected

base station for a period of 10ms. The cancellation accuracy

was determined in the above described link-level simulations.

For the linear MMSE filter approach, capacity gains of 8–

10.5% for indoor channels and of 17–20% for vehicular chan-

nels are found. This difference can be explained by the larger

relative impact of the interpath interference in case of the ve-

hicular channel.

If the subtraction algorithm is applied in addition to the

MMSE filtering, we get a capacity gain of 12–14.5% for the in-

door channel and 20–23% for the vehicular channel. Obviously,

the gains for the MMSE filtering and the subtraction algorithm

can roughly be added.

For the non-linear ISDIC, capacity gains of 30–40% for in-

door channels and of 43–55% for vehicular channels can be

observed. Here, especially the set of intercell channels that are

included in the successive cancellation scheme determines the

gain. Thus, the capacity gain which can be achieved can - to

a large extent - be controlled by the additional computational

efforts spent at the receiver.

Compared to these results, three major differences have been

observed for high-speed channels:

For the HS-DSCH, the gain on system-level has to be de-

scribed in terms of the system throughput since not all users are

served with the same average data-rate.

The achievable throughput gains critically depend on the im-

plemented scheduler strategy. If terminals experiencing good

link conditions are served with priority (e.g., max C/I schedul-

ing), receiver structures which reduce the intracell interference

due to multipath propagation show the largest gain since these

terminals are often located closely to the connected base station

where multipath interference is dominant. If more balanced

scheduler strategies are used or multipath interference is less

critical, the other options show system throughput gains similar

to the capacity gains found for dedicated channels. Note that for

realistic traffic models the gain achieved by ‘max C/I’ schedul-

ing compared to a simple round robin scheduling approach is

often reduced. For this case, a throughput gain of up to 35%

is found for the vehicular channel. The additional gain achiev-

able by pilot subtraction is slightly smaller (1.5-3.5% through-

put gain) for the HS-DSCH scenario.

For HS-DSCH, the ISDIC can work more efficiently since

the HS-DSCH is usually allocated much more power than ded-

icated channels and more information on the structure of the

HS-DSCH is known. This allows a more efficient and reliable

demodulation of channels transmitted in neighbored cells and a

higher accuracy of their cancellation. For this reason, this ap-

proach results in significantly larger gains for HS-DSCH than

found for dedicated channels. System throughput gains of up

to 60% for the indoor channel and up to 90% for the vehicular

channel are found for the above described ISDIC receiver.

VII. CONCLUSION

In this paper, the system performance gain of a WCDMA

network achievable by the introduction of advanced terminal

receivers is investigated. WCDMA dedicated channels and the

new WCDMA high-speed channels are considered.

In order to evaluate the impact of advanced receivers that

take the structure and colorness of interfering signals from other

base stations into account, a new evaluation methodology has to

be introduced. A possible approach is described in this paper.

Both, the dedicated channels and the new high-speed chan-

nels will benefit from a future introduction of advanced termi-

nal receivers. For the dedicated channels we will mainly see a

system capacity gain, whereas for the HS-DSCH, a gain can be

seen from the system as well as the user perspective.

Significant differences in the achievable gain have been

found depending on the channel model. Additional factors af-

fecting the gains are the network topology, the distribution of

the users in the cell and, for HS-DSCH scenarios, the scheduler

policy. It will probably be difficult to decide which receiver

algorithm is superior in general. The optimum choice (perfor-

mance per costs) of a receiver algorithm will depend on the

identified bottleneck scenarios characterized in terms of sup-

ported data-rate, position in the network etc. In principle, the

presented linear MMSE filter approach in combination with a

simple subtraction algorithms seems to be a reasonable com-

promise between complexity and performance.

Based on single-user throughput results in can be concluded

that the support of 16QAM will no necessarily result in a ben-

efit for simple Rake receiver terminals. A clear gain by the

introduction of 16QAM for HS-DSCH can only be found if ad-

vanced receivers are introduced.

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