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Throughput improvement for cell-edge users using selective cooperation in cellular networks

TL;DR: This paper studies the downlink capacity of edge users in a cellular network and sees whether base station cooperation improves the spectral efficiency, and proposes Selective Cooperation, where the selection criteria is based on throughput.
Abstract: Cooperative transmission schemes are used in wireless networks to improve the spectral efficiency. In a multi-cell environment, inter-cell interference degrades the performance of wireless systems. In this paper, we study the downlink capacity of edge users in a cellular network and see whether base station cooperation improves the spectral efficiency. The base-stations coordinate their transmission to the two cell-edge users in order to improve their Signal-to-interference-noise ratio (SINR) and throughput. Selective Cooperation, where the selection criteria is based on throughput, is proposed. The capacity achieved through Cooperation is shared equally among the cell-edge users. Results show that, the proposed hybrid scheme, provides a better result compared to full-time cooperation. Finally, an example from UMTS is presented.

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Throughput Improvement for Cell-Edge Users
Using Selective Cooperation in Cellular Networks
M. R. Ramesh Kumar
S. Bhashyam
D. Jalihal
Sasken Communication Technologies,India.
Department of Electrical Engineering, Indian Institute of Technology Madras, India.
Email: {mrrk,skrishna,dj}@tenet.res.in
Abstract—Cooperative transmission schemes are used in wire-
less networks to improve the spectral efficiency. In a multi-cell
environment, inter-cell interference degrades the performance of
wireless systems. In this paper, we study the downlink capacity
of edge users in a cellular network and see whether base station
cooperation improves the spectral efficiency. The base-stations
coordinate their transmission to the two cell-edge users in order
to improve their Signal-to-interference-noise ratio (SINR) and
throughput. Selective Cooperation, where the selection criteria is
based on throughput, is proposed. The capacity achieved through
Cooperation is shared equally among the cell-edge users. Results
show that, the proposed hybrid scheme, provides a better result
compared to full-time cooperation. Finally, an example from
UMTS is presented.
Keywords: Cooperative transmission, MIMO, Capacity
I. INTRODUCTION
Ever increasing demand to support higher data rates for
broadband services like triple play, online gaming etc., over
wireless networks, requires a large capacity. However, with
scarcity of available radio resources, to achieve a good capac-
ity and Quality of Service (QoS) efficient utilization of channel
resources is important. In a conventional cellular network, a
terminal receives signals not only from the base station of that
cell, but also from other cell base stations. Using a proper
frequency reuse, such interference is reduced to a tolerable
limit. However, this method of using different frequency bands
for different cells will decrease the spectral efficiency. In a
full frequency re-use network, this interference degrades the
system performance, and thereby reduces network capacity.
Using Base Station Cooperation, this ability to receive signals
from multiple base stations can be utilized as an opportunity
to improve the spectral efficiency of the cellular network and
achieve higher data rates for cell edge users.
Cooperative transmission utilizes the inherent user diver-
sity available in a multi-user environment to provide higher
spectral efficiency [1–3]. In [1] and [3], cooperation among
active users for the uplink channel in wireless networks is
described. The active users under cooperation have its own
information to transmit, and therefore, do not simply act as a
mobile relay stations. Since the inter-user link is also a noisy
channel, there is a possiblity that the information received
by a user from the other user is corrupted. In [3], coded
cooperation is proposed where each user decodes the signal of
the other user that needs to be relayed, and will relay only if
it is succesfully decoded. In case of unsuccessful decoding,
the users go to non-cooperative mode. In [2], cooperative
strategies like amplify-forward and decode-forward for adhoc
or per-to-peer wireless networks are proposed. In [4], it is
shown that the downlink efficiency can be improved using
Coherent Coordinated transmission (CCT) from multiple base
stations. Two types of coordination transmission are proposed,
namely, Equal Rate using Zero Forcing and Equal Rate Using
Dirty Paper Coding. In Equal Rate using Zero Forcing, the
transmission from all base stations intended for a particular
user do not interfere with other users. In the Dirty Paper
Coding scheme, knowledge of the interference is used at the
transmitter for coding. Comparison of different coordination
schemes like full coordination, partial coordination and no
coordination is presented in [5] for a downlink Multiple Input
Multiple Output (MIMO) system in a slow fading channel. In
the full coordination scheme, the transmit covariance matrix
for all the possible downlink channels between base stations
and the users is computed using Dirty Paper Coding by
a central coordinator to provide maximum sum throughput,
based on the Channel Quality Information (CQI) provided by
the base stations. These covariance matrices are then sent to
corresponding base stations. However, this entire process adds
significant latency. A new partial coordination scheme, where
the base stations transmit in Time Division Multiple Access
(TDMA) mode is proposed in [5]. In the alloted slot, each base
station transmit to its associated users using Space Division
Multiple Access (SDMA).
Cooperative encoding and scheduling in a Networked
MIMO system is discussed in [6], in order to supress Other
Cell Interference (OCI) and thereby achieve maximum ca-
pacity in MIMO downlink channel. In [7], it is shown that
in a multi-cell environment, using cooperation the overall
interference can be reduced only marginally, whereas the
interference within the cooperation region is largely reduced.
This leads to a question whether it is worth doing cooperation
all the time, i.e., whether the performance gains are worth the
cost addition in terms of the extra complexity added in the
signal processing to perform cooperation.
In this paper, we analyse the cooperation scenario in a multi
cell environment where the other cell interference is signif-
icant. The capacity achieved through cooperation is shared
equally among the cell-edge users, i.e., resources are shared
fairly among the cooperating users. The transmission rate to
each user is determined based on the signal to interference
978-1-4244-1980-7/08/$25.00 ©2008 IEEE.

plus noise ratio (SINR). Cooperative transmission by two base-
stations can improve this SINR by transmitting jointly to one
user at a time. However, the increase in terms of throughput
may not always be enough to increase the throughput of
each of the users. In such a scenario, we propose a selective
cooperation scheme based on user throughput that provides
better capacity than full cooperation. The downlink environ-
ment under consideration will not have any interference from
users in the same cell. They are properly seperated in time,
frequency or code such that orthogonality exists. Inter-cell
interference is allowed by doing a full frequency re-use in
each cell.
The rest of the paper is organised as follows: Section 2
describes the system model, signal to interfernce noise ratio
(SINR) and user throughput with and without cooperation.
Section 3 describes the SINR for different modes of Co-
operation considered in this paper. Section 4 presents the
cooperation selection algorithm and an example for UMTS.
Section 5 presents the simulation results and conclusions are
presented in section 6.
NOKIA
NOKIA
BS1
BS2
MS1
MS2
MS2 MS1 MS2MS1
MS2 MS1 MS2MS1
Frame #1 for BS1 transmission Frame #2 for BS1 transmission
Frame #1 for BS2 transmission Frame #2 for BS2 transmission
h
11
h
21
h
22
h
12
Fig. 1: System Model
II. SYSTEM MODEL
The basic system model and transmission protocol is as
shown in Figure 1. Base stations BS1 and BS2 are the candi-
dates for cooperation, to transmit signals to mobile terminals
MS1 and MS2. For BS1, BS2 is one of the interfering base
stations among the total 12 base stations in a re-use1 network.
More than one base station can be involved in cooperation,
but for simplicity we are considering only two stations to
form a coalition. The observation still holds good even for
three station coalition. The signals from the serving BS and
from the neighbor BS arrives at the terminal at the same
time, i.e., received signal by the terminal from the two base
stations are frame synchronized. The frame duration in which
the BS1 transmits to MS1 is divided into two sub-frames,
where the first sub-frame is used for signal transmission to
MS1 and the second one to MS2. Similarly, BS2, which is
under cooperation with BS1, transmits in the same sequence
of BS1. The received signals at MS1 and MS2 is y
1
and y
2
,
and is given by system equation 1, where h
ij
is the channel
between terminal i and BS j. x
1
is transmit signal of BS1 and
x
2
is that of BS2.z
i
is the total interference received by MS i
due to transmissions from all the base stations other than the
one under cooperation (in this case BS2) and n
i
is the additive
white Gaussian noise.
y
1
y
2
=
h
11
h
12
h
21
h
22
x
1
x
2
+
z
1
z
2
+
n
1
n
2
(1)
A. No Cooperation
Under normal operation that is when there is no cooperative
transmission, the signal to interference noise ratio (SINR) in
the downlink for MS1 is given by
SINR
nc
=
|h
11
|
2
E
X
2
1
σ
2
n
+
P
12
k=2
|h
1k
|
2
E {X
2
i
}
(2)
where h
ij
represents the the channel between the terminal i
and base station j, E
X
2
i
is the average transmit power of
Base Sation i, and σ
2
n
is nosie variance.
The capacity (or throughput) for terminal MS1 in bits/sec/Hz
can derived from the Shannon Capacity as
C
nc
= log
2
(1 + bSINR
nc
) (3)
where, b is determined by the SNR gap between the practical
coding scheme and the theoretical limit.
B. Cooperation
When terminal MS1 is in cooperation with BS1 and BS2,
SINR
coop
, SINR of the downlink channel will depend on the
type of cooperation scheme. The details of different ways
of combining the signal is presented in next section. The
capacity (or throughput) for terminal MS1 under cooperation
in bits/sec/Hz will be
C
coop
= α log
2
(1 + bSINR
coop
) (4)
The factor α in eq. 3 defines the proportion of resource
sharing among the terminals under cooperation. In our system,
considering resource fairness, the value for α is
1
2
.
III. MODES OF COOPERATION
In this section, we describe different modes of combining
the two signal received by MS1 from base stations BS1 and
BS2 for cooperation. The following schemes are considered
and their SINR expression is obtained.
1) Cooperative MIMO
In this scheme, the base stations BS1 and BS2 together
transmit information signal to MS1, thereby forming an
Alamouti trasmit diversity of order 2. This scheme is

referred in some literature as Network MIMO. The SINR
expression for this scheme will be of form:
SINR
coop
=
(|h
11
|
2
+ |h
12
|
2
)E
X
2
1
σ
2
n
+
P
12
k=3
|h
1k
|
2
E {X
2
i
}
(5)
2) Simple cooperation
The signals transmitted by base stations BS1 and BS2
are added using simple vector addition. The SINR
expression for this scheme will be of form:
SINR
coop
=
|h
11
+ h
12
|
2
E
X
2
1
σ
2
n
+
P
12
k=3
|h
1k
|
2
E {X
2
i
}
(6)
3) Cooperation with 1-bit Phase feedback
In this scheme, the addition of two signals is done
with proper co-phasing the information signal from the
second base station based on the 1-bit feedback of the
phase information [8]. The SINR expression for this
scheme will be of form:
SINR
coop
=
(|h
11
|
2
+ h
12
|
2
+ 2<(|h
11
h
12
|))E
X
2
1
σ
2
n
+
P
12
k=3
|h
1k
|
2
E {X
2
i
}
(7)
In all these schemes, the Channel State Information (CSI) for
the downlink of the serving base station and cooperating base
station is known at the user terminal. This assumption is valid
and is used in schedulers for rate adaptation in 3G systems
[9]. Besides, scheme 3 has an additional overhead of 1 bit
to provide the phase information of the cooperating signal in
order to do co-phasing at the received terminal.
IV. COOPERATION SELECTION
Under the resource fairness constraint, the users in the
serving cell and the neighbour cell who decided to cooperate
for an SINR improvement, will share the available resource
(time, frequency or code) between them equally. Therefore,
the individual user throughput is
1
2
of the actual capacity of
the cooperative transmission as in (4). Considering b = 1 in
the capacity expressions (3) and (4), for a low SINR regime,
as log(1 + x) x, for the user capacity in “Cooperation
mode” to be atleast equal to what the same user could
achieve under “No cooperation”, the SINR in the former must
be twice of the latter, i.e., should be 3 dB. The exact
expression for the capacity (or user throughput) for cooperative
scheme with resource constraint, to perform better than normal
transmission, i.e., C
coop
> C
nc
is shown below:
1
2
log(1 + bSINR
coop
) > log(1 + bSINR
nc
)
1 + bSINR
coop
> (1 + bSINR
nc
)
2
1 + bSINR
coop
> 1 + b
2
SINR
2
nc
+ 2bSINR
nc
SINR
coop
> bSINR
nc
2
+ 2SINR
nc
(8)
From the expression (8), for low SINR regime, our earlier
approximation is valid. However, in the high SINR regime,
the relationship between the two SINR is not linear, rather
it is exponential. Even though, the SINR under coopera-
tion (SINR
coop
) is always better than the normal SINR
(SIN Rnc), the user throughput of former is not always better
than the latter. Hence, it is worthwhile, for the user to decide
whether to perform cooperation in the downlink channel.
A brief description of the selection algorithm is given in
Algorithm 1. This selection algorithm is of low complexity
as it is approximation of the exact expression presented in
(8) with b = 1. The user decides on cooperation with the
measurements of its own channel and the nearest neighbor.
The decision is informed to the base station of the serving
cell. The serving station informs the neighbour station whether
to do cooperation or not with a single bit information based
on the input from the user. As an example, the sequence of
operations required to do this selection algorithm in UMTS is
given here and the message flow diagram is shown in Figure
2.
UE Initial State: UE is allocated dedicated resources and
is connected to Node B1 and RNC1 Called Controlling
RNC (CRNC)
Step 1: UE is given list of neighbouring cells and
measurements to perform
Step 2: UE triggers measurement report of neighbouring
cells to network ( RRC is situated in CRNC), if the pre-set
conditions to add a cell from Node B2 (for cooperation)
to the Active Set.
Step 3: CRNC decides to add a new Radio Link in Node
B 2 to the UE based on the available resources.
Step 4: CRNC sends information to Node B 2 to set up
resources for Transmission
Step 5: Once Node B 2 is ready to Transmit, CRNC sends
ActiveSetUpdate Message to UE. Active Set Update is the
message to indicated addition/deletion of Radio links.
Step 6: UE starts Reception on new Radio Link from
Node B 2 together with that of Node B1.
Step 7: UE sends Active Set Update Complete message.
V. SIMULATION AND RESULTS
A 19 cell full re-use multi-cell environment is simulated
based on Monte Carlo methods to analyse the performance
of user capacity and SINR for three transmission scenarios
namely, i) Without Cooperation, ii) With Cooperation and
iii) Selective Cooperation. Selective Cooperation is a hybrid
scheme, where cooperative transmission is performed only if
the (4) is greater than (3) as described in algorithm 1. A
cellular network of radius 500m, operating at 1800 MHz with
one cell edge user per cell is considered for simulations. The
channel gains for both signal and interference are based on
COST-231 path loss model [10] including fading and log-
normal shadowing. The correction factors for the path loss
model are that of metropolitan/urban areas. The shadowing
component is a gaussian random variable with zero mean
and 10 dB of standard deviation. Fading component is an

Algorithm 1 Cooperation Selection
1: Get the channel measurement of the serving DL and
nearest DL
2: Calculate the SINR under normal operation(SINR
nc
)
3: Calculate the SINR under cooperative transmission
(SINR
coop
)
4: case: Low SINR regime
5: for SINR
nc
0 do
6: if SINR
coop
> 2 SINR
nc
then
7: Base stations goes to Cooperative Transmission State
8: else
9: Normal Transmission
10: end if
11: end for
12: case: High SINR regime
13: for SINR
nc
0 do
14: if SINR
coop
> SINR
2
nc
then
15: Base stations goes to Cooperative Transmission State
16: else
17: Normal Transmission
18: end if
19: end for
Node B1
DCCH: Active Set Update
UE1
UE is allocated dedicated resources
and is connected to Node B1 and CRNC
Conditions match to send measurement
event to add a cell in NodeB2 to active set
If Cooperation OK
from both NodeB1 and NodeB2
UE Ready to receive data
DCCH: Active Set Complete
Node B2
Start Receive
Ready to transmit
Setup response
Setup request
DL Synch
UL Synch
Measurement Report
UE report to CRNC
If Node B2 has
extra resources
and receive from UE1
CRNC
Fig. 2: Message flow for an Use Case in UMTS
TABLE I: Average Throughput for cell Edge user (bits/sec/Hz) for
different Cooperation schemes
Type of Schemes Scheme 1 Scheme 2 Scheme 3
Without Cooperation 1.034 1.034 1.034
With Cooperation 1.235 1.197 1.347
Selective Cooperation 1.596 1.582 1.674
iid random variable with zero mean and unit variance. The
transmission power of each base station (at the antenna) is
2W (33 dBm). The superposition of signals for cooperation is
performed in three different ways as mentioned in section 3.
Our observation from simulation revealed that with prob-
ability 0.45, the user throughput with out cooperation (3) is
better than (4) for α =
1
2
. Since, cooperation in a multi-cellular
environment with full resource fairness is advantageous only
half the time, it is better to do a hybrid transmission of both
normal operation and cooperation that can give a better user
throughput. Average throughput and SINR for cell edge user
for different cooperative schemes is shown in Table I and II.
Averaging is done over 10
5
frames for each combination of
cooperative scheme and selection of cooperation. The observed
values from the simulation given in the table, clearly shows
the advantage of selective cooperation over full cooperation.
Eventhough, the average SINR of Scheme 2 with cooperation
is same as Scheme 1 with Selective cooperation, the capacity
of the latter is better than the former. User throughput captured
over 1000 frames for scheme 1 for full cooperation and
selective cooperation is shown in Fig. 3. Throughput captured
for first hundred frames is captured and shown in Fig.4, which
depicts the fact that there are crossovers in user throughput for
with and with out cooperation. Hence, selective cooperation
is a better option to get maximum throughput.
0 200 400 600 800 1000
0
1
2
3
4
5
6
Frame No.
Thruput (bits/sec/Hz)
without Coop
with Coop
Selective Coop
Mean Thru’put for no Coop
Mean Thru’put for Coop
Mean Thru’put for Selective Coop
Fig. 3: User throughput for Scheme 1

TABLE II: SINR of cell Edge user (dB) for different Cooperation
schemes
Type of Schemes Scheme 1 Scheme 2 Scheme 3
Without Cooperation -7.50 -7.50 -7.50
With Cooperation 3.52 2.79 4.70
Selective Cooperation 2.79 2.58 3.88
0 50 100 150
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
Frame No.
User ThroughPut (bits/sec/Hz)
without Coop
with Coop
Selective Coop
Fig. 4: Snapshot of User throughput for first 150 frames for Scheme
1
VI. CONCLUSIONS
In this paper, we presented simulation analysis of downlink
cooperation in a multi-cell cellular network. In a resource
fairness cooperation, the user capacity of a cell-edge user is
not always better than normal transmission. The simulation
results show that for almost half the time user capacity with
cooperation is poorer than the capacity with normal operation.
By doing a selective cooperation, both capacity and SINR is
improved. The throughput improvement is about 33.3% from
full cooperation to selective cooperation for same SINR. Also,
for the same one-bit feedback overhead, selective cooperation
with out phase feedback provides better throughput (an im-
provement of about 18.5%) for cell-edge users compared to
one-bit phase feedback full cooperation scheme.
ACKNOWLEDGEMENTS
First author wishes to acknowledge Ramakrishna Chikkala
for discussions regarding UMTS measurement reports and
Viswanatha Rao Thumparthy for guidance and support.
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Abstract: Mobile users' data rate and quality of service are limited by the fact that, within the duration of any given call, they experience severe variations in signal attenuation, thereby necessitating the use of some type of diversity. In this two-part paper, we propose a new form of spatial diversity, in which diversity gains are achieved via the cooperation of mobile users. Part I describes the user cooperation strategy, while Part II (see ibid., p.1939-48) focuses on implementation issues and performance analysis. Results show that, even though the interuser channel is noisy, cooperation leads not only to an increase in capacity for both users but also to a more robust system, where users' achievable rates are less susceptible to channel variations.

6,621 citations


"Throughput improvement for cell-edg..." refers background in this paper

  • ...Cooperative transmission utilizes the inherent user diversity available in a multi-user environment to provide higher spectral efficiency [1–3]....

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Journal ArticleDOI
TL;DR: An overview of the developments in cooperative communication, a new class of methods called cooperative communication has been proposed that enables single-antenna mobiles in a multi-user environment to share their antennas and generate a virtual multiple-antenn transmitter that allows them to achieve transmit diversity.
Abstract: Transmit diversity generally requires more than one antenna at the transmitter. However, many wireless devices are limited by size or hardware complexity to one antenna. Recently, a new class of methods called cooperative communication has been proposed that enables single-antenna mobiles in a multi-user environment to share their antennas and generate a virtual multiple-antenna transmitter that allows them to achieve transmit diversity. This article presents an overview of the developments in this burgeoning field.

3,130 citations


"Throughput improvement for cell-edg..." refers background in this paper

  • ...Cooperative transmission utilizes the inherent user diversity available in a multi-user environment to provide higher spectral efficiency [1–3]....

    [...]

Proceedings ArticleDOI
29 Jun 2001
TL;DR: Two variants of an energy-efficient cooperative diversity protocol are developed that combats fading induced by multipath propagation in wireless networks and can lead to reduced battery drain, longer network lifetime, and improved network performance in terms of, e.g., capacity.
Abstract: We develop two variants of an energy-efficient cooperative diversity protocol that combats fading induced by multipath propagation in wireless networks, The underlying techniques build upon the classical relay channel and related work and exploit space diversity available at distributed antennas through coordinated transmission and processing by cooperating radios. While applicable to any wireless setting, these protocols are particularly attractive in ad-hoc or peer-to-peer wireless networks, in which radios are typically constrained to employ a single antenna. Substantial energy-savings resulting from these protocols can lead to reduced battery drain, longer network lifetime, and improved network performance in terms of, e.g., capacity.

688 citations


"Throughput improvement for cell-edg..." refers background in this paper

  • ...Cooperative transmission utilizes the inherent user diversity available in a multi-user environment to provide higher spectral efficiency [1–3]....

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BookDOI
01 Jun 2006
TL;DR: Holma et al. as mentioned in this paper proposed a radio resource management architecture for HSDPA and showed that HSUPA bit rates, capacity and coverage can be improved by using IP header compression.
Abstract: Preface. Acknowledgements. Abbreviations. 1. Introduction (Harri Holma and Antti Toskala). 1.1 WCDMA technology and deployment status. 1.2 HSPA standardization and deployment schedule. 1.3 Radio capability evolution with HSPA. 2. HSPA standardization and background (Antti Toskala and Karri Ranta-Aho) 2.1 3GPP. 2.2 References. 3. HSPA architecture and protocols (Antti Toskala and Juho Pirskanen). 3.1 Radio resource management architecture. 3.2 References. 4. HSDPA principles (Juho Pirskanen and Antti Toskala). 4.1 HSDPA vs Release 99 DCH. 4.2 Key technologies with HSDPA. 4.3 High-speed dedicated physical control channel. 4.4 BTS measurements for HSDPA operation. 4.5 Terminal capabilities. 4.6 HSDPA MAC layer operation. 4.7 References. 5. HSUPA principles (Karri Ranta-Aho and Antti Toskala). 5.1 HSUPA vs Release 99 DCH. 5.2 Key technologies with HSUPA. 5.3 E-DCH transport channel and physical channels. 5.4 Physical layer procedures. 5.5 MAC layer. 5.6 Iub parameters. 5.7 Mobility. 5.8 UE capabilities and data rates. 5.9 References and list of related 3GPP specifications. 6. Radio resource management (Harri Holma, Troels Kolding, Klaus Pedersen, and Jeroen Wigard). 6.1 HSDPA radio resource management. 6.2 HSUPA radio resource management. 6.3 References. 7. HSDPA bit rates, capacity and coverage (Frank Frederiksen, Harri Holma, Troels Kolding, and Klaus Pedersen). 7.1 General performance factors. 7.2 Single-user performance. 7.3 Multiuser system performance. 7.4 Iub transmission efficiency. 7.5 Capacity and cost of data delivery. 7.6 Round trip time. 7.7 HSDPA measurements. 7.8 HSDPA performance evolution. 7.9 Conclusions. 7.10 Bibliography. 8. HSUPA bit rates, capacity and coverage (Jussi Jaatinen, Harri Holma, Claudio Rosa, and Jeroen Wigard). 8.1 General performance factors. 8.2 Single-user performance. 8.3 Cell capacity. 8.4 HSUPA performance enhancements. 8.5 Conclusions. 8.6 Bibliography. 9. Application and end-to-end performance (Chris Johnson, Sandro Grech, Harri Holma, and Martin Kristensson) 9.1 Packet application introduction. 9.2 Always-on connectivity. 9.3 Application performance over HSPA. 9.4 Application performance vs network load. 9.5 References. 10. Voice-over-IP (Harri Holma, Esa Malkama ki, and Klaus Pedersen). 10.1 VoIP motivation. 10.2 IP header compression. 10.3 VoIP over HSPA. 10.4 References. 11. RF requirements of an HSPA terminal (Harri Holma, Jussi Numminen, Markus Pettersson, and Antti Toskala). 11.1 Transmitter requirements. 11.2 Receiver requirements. 11.3 Frequency bands and multiband terminals. 11.4 References. Index.

578 citations

Journal ArticleDOI
TL;DR: It is argued that many of the traditional interference management techniques have limited usefulness when viewed in concert with MIMO, and emerging system-level interference-reducing strategies based on cooperation will be important for overcoming interference in future spatial multiplexing cellular systems.
Abstract: Multi-antenna transmission and reception (known as MIMO) is widely touted as the key technology for enabling wireless broadband services, whose widespread success will require 10 times higher spectral efficiency than current cellular systems, at 10 times lower cost per bit. Spectrally efficient, inexpensive cellular systems are by definition densely populated and interference-limited. But spatial multiplexing MIMO systems- whose principal merit is a supposed dramatic increase in spectral efficiency- lose much of their effectiveness in high levels of interference. This article overviews several approaches to handling interference in multicell MIMO systems. The discussion is applicable to any multi-antenna cellular network, including 802.16e/WiMAX, 3GPP (HSDPA and 3GPP LTE), and 3GPP2 (lxEVDO). We argue that many of the traditional interference management techniques have limited usefulness (or are even counterproductive) when viewed in concert with MIMO. The problem of interference in MIMO systems is too large in scope to be handled with a single technique: in practice a combination of complementary countermeasures will be needed. We overview emerging system-level interference-reducing strategies based on cooperation, which will be important for overcoming interference in future spatial multiplexing cellular systems.

383 citations


"Throughput improvement for cell-edg..." refers methods in this paper

  • ...Cooperative encoding and scheduling in a Networked MIMO system is discussed in [6], in order to supress Other Cell Interference (OCI) and thereby achieve maximum capacity in MIMO downlink channel....

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