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Interference Management Using
Cognitive Base-Stations for UMTS LTE
Alireza Attar, Vikram Krishnamurthy, and Omid Namvar Gharehshiran, University of British Columbia
ABSTRACT
In this article we demonstrate the benefits of
developing cognitive base-stations in a UMTS
Long Term Evolution (LTE) network. Two types
of cognitive base-stations are considered: the
macro-cell evolved-NodeB (eNB) and the femtocell Home evolved NodeBs (HeNB). In the context of an isolated cell or a multi-cell LTE
network, the insufficiency of traditional interference management schemes is shown. Implementation of cognitive tasks such as radio scene
analysis and dynamic resource access are then
introduced. We argue that such cognitive basestations can exploit their knowledge of the radio
scene to intelligently allocate resources and to
mitigate prohibitive Co-Channel Interference
(CCI). Given the distributed architecture of
LTE networks, we will elaborate on cognitive
interference mitigation solutions and further
propose two different Game Theoretical mechanisms to achieve CCI mitigation in a distributed
manner.
INTRODUCTION
1
The CSMA/CA mechanism deployed in the
WLAN standard provides
a simple distributed coexistence solution. However,
this mechanism is not
robust due to its static
resource allocation nature
(e.g., in most existing
WLAN routers the operating channel remains fixed
even as the rate of collision increases) and lack
of learning capability
based on RF environment
feedbacks (e.g., the same
greedy access strategy is
repeated over time irrespective of changes of
extraneous factors such as
changes in the traffic load
or channel interference
pattern, etc).
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Despite more than a decade of research and
development on Cognitive Radios (CR), a real
life implementation of this promising technology
is yet to materialize. In this work we argue in
favor of cognitive base-stations for femtocells.
More specifically, in order to deliver broadband
connectivity to end users, the UMTS Long Term
Evolution (LTE) proposes a distributed network
architecture. A key issue is to enable femtocell
access points to act autonomously and support a
variety of service requirements. These low power
access points, called Home NodeB (HNB) and
Home eNodeB (HeNB) in 3GPP terminology,
will increase the indoor coverage of LTE networks by supporting considerably higher data
rates of the order of several Mbps in homes,
enterprise offices, and any other indoor application [1]. These femtocell access points will need
to be considerably more sophisticated than similar-scale WLAN access points. More specifically,
many functionalities that were traditionally supported by the macro-cell base-station will be performed at the femtocells, thereby alleviating
signaling-heavy operations.
There are several fundamental questions to
address toward a practically-feasible implemen-
0163-6804/11/$25.00 © 2011 IEEE
tation of LTE networks with a high density of
femtocell access points:
• How to ensure efficient coexistence of
HeNBs from the resource allocation perspective?
• How to mitigate/reduce mutual interference
between a given HeNB and the eNB?
• How to handle user hand-off in such a distributed network?
• How to design a self-organizing architecture
that can adapt with traffic load variations?
We will first demonstrate the insufficiency of
traditional coexistence solutions in the LTE context. The main focus of this article is therefore
to propose the advantages of incorporating CR
capabilities into femtocell base-stations in order
to meet the stringent efficiency targets of the
LTE standard in the presence of wide-scale
HeNB deployment.
We argue that such cognitive base-stations
are crucial ingredients for an efficient and distributed radio resource management of LTE
given its distributed architecture. One motivation for this argument is the lessons learned
from widespread deployment of WLAN access
points. The simple plug-and-play nature of
WLAN routers, along with the unlicensed nature
of WLAN spectrum access, alleviated the need
for time and cost-intensive network planning.
This in turn helped a rapid proliferation of
WLAN hotspots. However, as the number of
coexisting WLAN networks increases, so does
their mutual interfering effect, rendering such
simple, selfish coexistence strategies problematic.1 A more detailed discussion on the shortcomings of traditional coexistence solutions are
presented later.
By incorporating the following three main
cognitive capabilities into LTE base-stations [2],
a successful coexistence strategy can be achieved.
• Radio scene analysis.
• Online learning based on the feedback from
the RF environment.
• Agile/dynamic resource access schemes.
Further, practical methods of implementing such
cognitive tasks in LTE networks are introduced.
Such cognitive capabilities provide a major evolution in resource management of LTE networks
compared with existing fixed-policy scheduling
schemes. While the introduction of cognitive
femtocell HeNBs will be the main focus of this
article, as shown in Fig. 1, the extension of the
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proposed solution to coexistence of macro-cell
cognitive eNBs in a multi-cell LTE scenario will
also be addressed.
The rest of this article is organized as follows.
We discuss major traditional coexistence polices
and their shortcomings in the dynamically-changing context of LTE networks. The main benefits
of adopting CR capability in LTE base-stations
are presented. A brief discussion on the extension of the proposed solution to multiple-cell
LTE networks is the focus of another section.
The implementation issues of cognitive HeNBs
are introduced, followed by two game theoretic
approaches for distributed resource allocation.
Finally, we conclude the article.
UE2
UE4
eNode-B
f2
Home eNode-B
FREQUENCY REUSE STRATEGY
A widely used interference avoidance mechanism in 2G/3G cellular networks is frequency
reuse. Simply put, the transmission of different
cells or sectors of a cell are orthogonalized in
the frequency domain by splitting the total available spectrum into non-overlapping partitions.
Recently, partial frequency reuse has been proposed whereby the inner area of cells utilize the
same frequency band, i.e., a universal frequency
reuse policy, while the cell edges follow traditional frequency reuse patterns. To implement
frequency reuse, the service providers need to
perform prior network planning, assuming network characteristics will remain static over a
long period of time (on the order of many
months).
The UMTS LTE, however, is a wideband system, designed to operate over channels of up to
20 MHz bandwidth. Many operators have
already invested billions of dollars to acquire 3G
licensed bands, and the probability of any operator acquiring more than one licensed band for its
4G services is highly unlikely. The peak performance of LTE will be achieved over wider channels, therefore limiting the available spectrum in
each cell (macro or femto size) through frequency reuse schemes considerably limit network performance. Furthermore, the topology of the
LTE network is changing over time via the addition or removal of femtocell HeNBs and in that
respect static network planning schemes are not
efficient. Some recent studies in the literature
consider adaptive channel reuse mechanisms for
femtocell scenarios [3].
IEEE Communications Magazine • August 2011
f1
f2
UE1
UE3
WHY TRADITIONAL COEXISTENCE
SOLUTIONS ARE NOT SUFFICIENT?
Radio resource management protocols are not
specified by standards, such as 3GPP’s UMTS
LTE. Thus, there is considerable flexibility in
their design. There is strong motivation to
introduce cognitive abilities to base-stations so
they can adapt their behavior to a changing
number of users, other base-stations, and other
dynamic changes in the RF environment. For
instance, as the number of coexisting links in a
LTE network is likely to be high (associated
with neighboring-cell eNBs and intra-cell
HeNBs), a fixed-scheduling mechanism will not
achieve the required level of LTE efficiency in
the long term.
f1
Home eNode-B
Figure 1. The coexistence problem among eNB and HeNBs in the LTE context.
SELFISH OR COOPERATIVE
COEXISTENCE STRATEGIES
A simple strategy, exploited for instance in the
unlicensed bands, is to allow the coexistence of
selfish radios over a set of shared resources. The
advantage of this approach is its distributed
medium access achieved, for example via “listenbefore-talk” transmission etiquette. As the LTE
femtocell HeNBs will offer similar simplicity of
plug-and-play as WLAN routers, such distributed management of radio resources seems a plausible choice. However, as witnessed in WLAN
networks, the increasingly higher probability of
collision in a dense networking scenario with
selfish nodes results in minimal feasible throughput for all coexisting nodes. Therefore, the benefits of a selfish coexistence strategy can mostly
be harnessed in spatially/temporally sparse networking scenarios. On the other hand, the target
market for femtocell HeNBs are dense urban
areas to provide higher capacity for indoor users
such as enterprises, hotels, and so on.
An alternative coexistence strategy is cooperative communications whereby instead of competing for resources the coexisting links will
collaborate. Various forms of collaboration,
from relaying messages to transmitter or receiver
diversity schemes such as distributed MIMO
techniques, have been proposed in the literature.
Further, there are cooperative game theoretic
analysis frameworks, such as bargaining, which
assist in modeling scenarios where coexisting
nodes try to achieve a higher payoff than if they
did not cooperate. There exists, however, a
trade-off between the advantages of cooperation
in the network and the cost associated with significantly higher overhead. Signaling overhead is
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have demonstrated the fact that in certain channel conditions, a higher total capacity for coexisting links is sustainable by avoiding mutual
interference and instead using non-overlapping
spectrum partitions. This is due to the fact that,
at times, the channel quality between the interfering links is higher compared with the direct
link between each coexisting transceiver pair,
which results in a higher received interference
power than the desired signal power. Furthermore, from a game theoretic perspective, IWF is
a non-cooperative solution that is known to be
inferior to cooperative game solutions. In this
perspective, adaptive transmission power mechanisms, such as IWF, are not always the optimal
transmission policy for a given network.
PR-UE2
SR-HeNB1
PR-UE3
SR-HeNB5
PRUE4
PR-eNB
PR-UE1
SR-HeNB2
PR-UE7
SR-HeNB4
PR-UE8
PR-UE5
SR-HeNB3
PR-UE6
Figure 2. Decomposition of an isolated LTE cell into primary (denoted by prefix PR) and secondary (denoted by prefix SR) networks.
necessary to synchronize cooperating nodes in
performing their tasks (for instance, transmission/reception, coding/decoding, and processing
the data), to identify the cooperation task, and
to provide feedback mechanisms to notify the
cooperating nodes of the outcome (e.g.,
ACK/NACK type messages). This signaling overhead is, therefore, significantly more than the
case of selfish coexistence whereby each link
makes decisions based on its locally-available
information and does not require collaboration
with other links in making decisions or performing tasks. In the context of LTE networks, such
signaling overhead might also result in delays,
which is not acceptable for delay-sensitive applications such as real-time voice.
Hence, more intelligent coexistence strategies
than pure selfish or cooperative approaches for
LTE networks are required. Cognitive base-stations, on the other hand, can decide the optimal
coexistence policy, given the current RF environment and their ability to learn from the past.
ADAPTIVE TRANSMIT POWER STRATEGY
A widely studied coexistence approach is to utilize adaptive transmit power mechanisms. In
general, the transmit power control mechanism
is based on feedback from the receiving node.
The level of received SINR, especially if CoChannel Interference (CCI) is present, is used as
the decision metric to select a proper transmission power. The celebrated Iterative Water-Filling (IWF) approach is known to achieve the
maximum throughput by adjusting the transmit
power of coexisting nodes optimally. However,
several studies in the literature (e.g. see [4])
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WHAT ROLE COGNITIVE
BASE-STATIONS CAN PLAY IN
LTE STANDARD?
To motivate cognitive base-stations, we first outline three main tasks in cognitive communications and elaborate on the implementation of
these tasks in a LTE network. Cognitive Radios
adapt dynamically to their RF environment, by
observing the radio scene in real-time and making decisions based on reasoning (facilitated by
their learning capability). The framework of CRRF environment interaction is captured in the
cognitive cycle of this intelligent radio [2].
RADIO SCENE ANALYSIS
The major first step toward a cognitive communication strategy is to acquire reliable RF-stimuli
information in real-time. Generally speaking,
two types of RF environment information should
be gathered pertinent to the activities of the primary system and the secondary system, respectively. The former case is needed to identify the
interference temperature as well as available
spectrum holes in the licensed band [2]. The latter case of radio scene knowledge of the secondary system is especially valuable in
distributed cognitive networks. In an isolated cell
of a LTE network, although all access points of
this network are equally eligible to use the
licensed band, the operation of femtocell HeNBs
can be adaptively modified based on the activities of the macro-cell eNB. In this setting, one
can interpret the eNB and its associated UEs as
the primary network and each HeNB and its
users as a secondary network (Fig. 2).
Activities of the Primary eNB — Optimal
coexistence strategy for the femtocell HeNBs can
be realized if reliable radio scene information of
the eNB’s transmissions can be gathered. It is
now well understood that a single sensing node,
such as a HeNB, might not be able to detect the
presence of the primary’s signal (here the eNB’s
signal) in a given band, in the face of channel
uncertainties such as fading [5]. A viable solution
is to introduce cooperation among several cognitive sensing nodes, thereby creating a Cognitive
Radio Network (CRN). In practice, however,
such cooperation requires a signaling channel to
bear the exchange of radio scene information.
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The lack of (universally) allocated spectrum
bands for such signaling channels for cognitive
sensing nodes makes practical implementation of
a CRN more challenging. Though the concept of
Cognitive Pilot Channel (CPC) and its extension
to a distributed setting with smart femtocells is
available in the literature [6], real-life implementation of such solutions is still far away.
These observations motivate our proposed
cognitive base-station design in several aspects.
Each cognitive access point together with its
associated users can readily form a CRN in
order to reliably observe the radio scene. Further, there exists dedicated signaling channels in
LTE networks that can be utilized to coordinate
spectrum sensing as well as adaptive radio
resource allocation (which will be discussed
later) in real-time. Therefore, UMTS LTE can
provide a natural habitat for cognitive base-stations for both indoor and outdoor applications.
Activities of Secondary HeNBs — Similar to
detection of eNB activities, each HeNB should
also be aware of other nearby femtocell HeNBs.
This spectrum sensing task, however, can be
handled as a special case of radio scene analysis
in the previous subsection. Beside detection of
spectral activity of other access points, there is a
need for a feedback channel between the transmitter and receiver side of a CR in order to
accomplish certain cognitive tasks [2]. In a LTE
context, the receiver side of communication, for
instance UEs in DownLink (DL), should inform
the transmitter side of the received signal quality. This latter form of interaction concerns the
channel state information (CSI) estimation and
can be used to build predictive models, for
instance to track channel variations. Such signaling requirement are already addressed in the
LTE context.
LEARNING AND DECISION MAKING
A key concept differentiating CR from legacy
communications system is the capability to learn
and make model-based decisions. In his thesis,
Mitola defines various levels of cognitive capabilities, ranging from pre-programmed radios to
protocol-adapting radios [2]. While to date most
wireless systems still fall under the lowest cognition level of pre-programmed, there has been
extensive research in fields such as machine
learning and artificial intelligence that can pave
the way to implement learning and decision
making capabilities in radios.
In the context of cognitive LTE base-stations,
various forms of learning in order to cover specific application targets are required. As an
example, if a higher transmission power of a
given HeNB in a certain channel imposes a higher interference on a nearby UE associated with
macro-cell eNB, it might trigger the adaptive
transmission power mechanism of the eNB to
serve that UE with a higher power. This choice,
in turn, reduces the SINR at the femtocell,
which is not the desired output of transmitting
with a higher power in the first place. Such environment feedback provides a reinforcement learning mechanism for the femtocells. Other learning
schemes, pertinent to various other problems,
include but are not limited to supervised and
IEEE Communications Magazine • August 2011
unsupervised learning, transduction, and Paretobased multi-objective learning. Game theoretic
learning frameworks, such as regret-based or
non-regret algorithms, are also plausible choices.
Note that the dynamics of cognitive HeNBs are
directly proportional to the dynamics of underlying variables affecting the target utility. For
example, the variation of the instantaneous value
of channel gains in a LTE network, measured
through the coherence time of that channel,
severely limits the application of learning techniques targeting a utility dependent on the
instantaneous channel gain that requires long
training phases.
AGILE/DYNAMIC RESOURCE ACCESS
The interference mitigation capabilities of CR, if
exploited in LTE base-stations, are ideal tools to
manage the CCI problem in LTE networks.
Consider an isolated LTE cell, comprised of a
macro-cell eNB and a number of randomlylocated HeNBs, as shown in Fig. 1. For several
reasons it is very challenging, in practice, to
coordinate resource allocation among the eNB
and HeNBs. First, deployment of HeNBs should
not affect the Core Network (CN) in LTE, as
the CN is also shared with other access technologies. Therefore, in practice it is not possible
to allocat dedicated infrastructure to HeNBs. A
new interface, named Iuh in 3GPP standards,
alleviates this problem by providing an interface
between any given HeNB and the gateway entity
called Home NodeB Gateway (HNB-GW). This
latter entity serves as an aggregation point for
HeNBs, such that the CN can use the existing
interfaces, such as IuCS/IuPS, to serve HeNBs.
In fact, HNB-GW plays the role of a traditional
RNC, from the CN’s point of view. However it
does not offer any control mechanism over
HeNB operation. A simplified schematic diagram, indicating the aforementioned interfaces,
is shown in Fig. 3. Other factors limiting the
prospect of direct coordination among the eNB
and HeNBs include the cost issue (HeNBS are
expected to be relatively inexpensive) and signaling overhead problem.
While to date most
wireless systems still
fall under the lowest
cognition level of
preprogrammed,
there has been
extensive research in
fields such as
machine learning
and artificial
intelligence that can
pave the way to
implement learning
and decision making
capabilities in radios.
MULTI-CELL LTE NETWORKS
While the focus of this article is mainly on cognitive femtocell base-stations for LTE networks,
the majority of presented discussions can readily
be extended to cognitive macro-cell base-stations, as shown in Fig. 4. In multiple LTE cells,
the CCI is a major barrier as the capacity of a
LTE network, similar to most advanced packetbased wireless technologies, is interference limited. Although the possibility of interaction of
several eNBs in a LTE network is supported
through the X2 interface in 3GPP standards, the
excessively high signaling overhead required to
coordinate resource allocations in several cells
on a packet-time scale (each LTE sub-frame has
0.5 msec duration and two sub-frames constitute
a TTI) makes such coordination limited to special cases such as Cooperative MultiPoint Transmission (CoMP). This further motivates
incorporating cognitive base-stations in the LTE
context, such that eNBs as well as HeNBs can
intelligently and autonomously coexist.
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HeNB
Evolved
packet core
Iuh
Uu
IuCS/IuPS
Safety
GW
HNB-GW
UE
Uu
eNB
MME/SAE
GW
S1
X2
MME/SAE
GW
eNB
S1
Uu
GW: Gateway
UE
MME: Mobility
management
entity
SAE: System
architecture
evolution
Figure 3. A simplified diagram of some of the interfaces involved in HeNB and
eNB operation.
In the following section we elaborate on cognitive interference management schemes pertinent to both cognitive HeNBs and cognitive
eNBs.
LTE INTERFERENCE
MANAGEMENT SOLUTIONS
2
For simplicity of discussion, we did not consider
the possibility of beamforming transmission by
the eNB, which limits the
received power to out-ofrange HeNBs considerably. Clearly in this case,
such out-of-range HeNBs
can assume the sub-channel is not allocated and
use the overlay scheme, as
their short-range transmission will not affect the
scheduled UE.
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In this section, two interference management
solutions based on the availability of cognitive
capabilities at the eNB and HeNBs are introduced. To this end, we will exploit two cognitive
spectrum access strategies, namely overlay and
underlay. Here, overlay cognitive transmission is
meant to refer to the exploitation of spectrum
white holes, i.e., identification and usage of
resource units not utilized by the spectrum
license holder. On the other hand, underlay
scheme refers to the usage of grey spectrum
holes where the secondary transmitter is allowed
to share the same resources as the primary user
conditioned on a below threshold received interference at the primary receiver.
OVERLAY-STYLE RADIO SCENE
ANALYSIS AND RESOURCE ALLOCATION
The problem of CCI control in an LTE context
boils down to the question of how should each
eNB/HeNB allocate OFDMA resources to their
associated UEs without causing severe interference to other eNB/HeNBs. Let us first consider
the case of CCI between the eNB and HeNBs in
an isolated cell, as shown in Fig. 1. To this end
we can exploit the overlay transmission strategy
of CRs. The sub-channel allocation by the eNB
in DownLink (DL) will be detectable throughout
the cell in two ways. One approach is to listen to
the signaling channel of that eNB, as this basestation needs to inform its associated UEs regarding the sub-channel allocation for those UEs.
Given HeNBs will belong to the same operator
as the eNB, such an “eavesdropping” mechanism
should be acceptable in practice. Alternatively,
no matter where a scheduled UE associated with
the eNB is located, as the eNB will broadcast in
DL over all allocated sub-channels to that UE
with a relatively high transmission power, any
HeNB in the cell should be able to detect the
occupied sub-channels.2 Therefore, each HeNB
at any instant of time can have an updated list of
occupied sub-channels by the eNB and therefore
avoid mutual interference to the eNB by using
idle sub-channels. We note that other alternative
radio scene analysis mechanisms for cognitive
HeNBs can also be envisioned, for example
through a cooperative sensing of the channel by
all UEs associated with that cognitive HeNB.
The cognitive femtocell access point, then, serves
as a data fusion center and reports back the
radio scene information to its UEs.
A more challenging scenario is the management of the CCI among different eNBs in a multicell environment. As discussed in Section I, LTE
network architecture eliminated any centralized
inter-cell coordination entity and instead requires
eNBs to directly interact. Limited-scale interaction of several eNBs can reliably be supported in
this architecture. As an example, the LTE standard supports the so-called Cooperative MultiPoint (CoMP) transmission diversity schemes in
which a given UE will be served simultaneously
by more than one eNB, reminiscent of a distributed MIMO scheme. However, information
exchange among several eNBs on their resource
allocation, which changes rapidly in time, in the
packet time scale, is not feasible in practice.
We propose to solve this problem by incorporating cognitive capabilities in LTE base-stations, which can utilize radio scene analysis to
make optimal resource allocation decisions in a
distributed manner. More specifically, each eNB
cell will form a CRN where the information of
the RF environment is fed back to the cognitive
eNB by UEs in periodic intervals. Interestingly,
based on intra-cell radio scene analysis, along
with channel-state estimation and predictions,
efficient inter-cell dynamic resource allocation
mechanisms can be developed. In Fig. 4 a twocell LTE system is shown whereby a specific subchannel f 1 is currently occupied by eNB1. The
instantaneous level of received interference at
UEs in cell 2 can be a reliable indicator of occupancy of sub-channel f1 by eNB1. Note that such
interference measurements by UEs can be easily
accommodated in the current channel sounding
mechanisms supported by most cellular communications standards, including LTE.
In the next section, we elaborate on an alternative CR-based mechanism to solve the CCI
problem in the LTE context, facilitating a more
efficient utilization of the licensed bands for this
technology.
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UNDERLAY-STYLE RADIO SCENE
ANALYSIS AND RESOURCE ALLOCATION
Another solution to address the problem of CCI
mitigation among multiple eNBs in a multi-cell
LTE scenario, or one eNB and multiple HeNBs
in an isolated cell, can be to exploit a CR underlay transmission scheme. Let us first focus on the
isolated cell scenario.
To implement an underlay transmission
scheme each access point should be able to measure its interfering effect over all other UEs. As
the macro-cell eNB will have to serve UEs
throughout the cell, it is more logical to enforce
HeNBs to act as secondary users with respect to
eNB transmission and not vice versa. To measure its interfering effect on a given UE, each
HeNB should acquire an estimate of the crosschannel gain to that UE. However, given the
lower transmit power of HeNBs compared to
eNBs, only nearby UEs will be affected by a
given HeNB’s transmission. If the LTE network
operates in a Time Division Duplex (TDD),
HeNB can measure the cross-channel gain to the
surrounding UEs associated with the eNB from
their UpLink (UL) communications. On the
other hand, if Frequency Division Duplex (FDD)
is exploited, estimating cross-channel gain would
be practically more challenging. A conservative
channel measurement can be obtained via estimating the location of nearby UEs and considering only the path loss based on this location
estimation. As mentioned before, the HeNB will
create its CRN and can coordinate triangulation
mechanisms to obtain the location of nearby
macro-cell UEs. An alternative approach can be
to allow HeNB to perform channel sounding
toward all in-range UEs, whether or not all such
UEs are associated with this given HeNB. This
method leads to a certain level of signaling overhead; however, it can guarantee a more accurate
channel-state estimation.
In a multi-cell LTE scenario, similar to the
overlay scheme in Section V-A, each eNB can
exploit its RF environment measurement. Consider a three-cell LTE scenario, where the average cell radius is 2.5 km. A total of 20 users are
uniformly distributed over these cells, where
each UE will join the eNB with the highest
received SINR level. The total transmit power of
each eNB is 16.9 dB, and the carrier frequency is
2120 MHz. The path-loss model is COST231,
where we assume the height of eNBs and UEs
are 50 m and 2 m, respectively. We further
assume 8 dB log-Normal shadowing and
Rayleigh fading. In Fig. 5, the average received
interference power for a given sub-channel versus the distance from eNBs is depicted for two
scenarios. In the first scenario, eNBs simply
transmit a fixed power, i.e., uniform distribution
of power over their sub-channels. In the second
scenario, we assume eNB2 and eNB3 decrease
their transmission power by 25 percent and
eNB1 adaptively changes its transmission power
such that the level of sustained throughput is
equal to that of scenario 1. This simple experiment demonstrates that if instead of a greedy
transmission power policy, which might encourage other access points to further increase their
transmission power (in order to compensate for
IEEE Communications Magazine • August 2011
UE4
UE5
f1
eNode-B 1
f1
eNode-B 2
UE1
UE2
UE3
Figure 4. The problem of Co-Channel Interference in a multi-cell LTE context.
a higher received interference), cognitive eNBs
reduce their mutual interfering effect through
transmit power control schemes, the overall LTE
network can reach a higher level of network
capacity. As Fig. 5 demonstrates, the level of
received interference, especially near cell edges,
can be an indicator of the effect of transmission
policy of one eNB on nearby cells. Therefore, if
all eNBs are intelligent (i.e., cognitive base-stations), they can adjust their transmission strategy
and estimate the effect of their strategy choice,
solely based on information available at their
own cell, thereby alleviating the need for an
excessive amount of signaling overhead.
DISTRIBUTED OPTIMIZATION OF
RESOURCE ALLOCATION
In the previous sections, we outlined a framework for cognitive base-stations that use radio
scene analysis to learn and adapt to their environment. In the proposed solutions, cognitive
eNB/HeNBs will obtain crucial information from
the radio scene so as to assist them in their
resource allocation strategies. The question that
remains to be answered is how resource allocation optimization can be achieved in such a distributed scenario. A powerful tool to investigate
distributed systems — both as an analysis and as
a synthesis tool — is Game Theory. There are
several possible ways to formulate the CCI problem in the LTE context in a game theoretic setting. For instance, if the players of the game are
eNBs in a multi-cell LTE scenario, and they are
willing to cooperate in order to improve their
achieved utility value, such as their respective
cell throughput, we can use cooperative game
theory. On the contrary, if players pursue a selfish strategy, various non-cooperative games can
be used to model the scenario. Further, we can
consider the game as being independent in each
resource allocation period, thereby solving a single-shot game model, or else use a repeated
game model.
In the following subsections, we introduce
two such game theoretic solutions, based on,
respectively, coalition formation games and cor-
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Average received inter-cell interference
(dBm) per sub-channel
30
29.5
29
28.5
28
Cell 1
Cell 2
Cell 3
Cell 1 - adaptive trans. power
Cell 2 - 25% lower trans. power
Cell 3 - 25% lower transmit power
27.5
27
0
0.5
1
1.5
Distance from BS (Km)
2
2.5
Figure 5. The average received interference power where UEs join the cell with
the highest channel quality.
related equilibrium games. In each case we
assume cognitive base-stations, i.e., the eNB or
HeNBs have acquired the interference pattern
information of the radio scene as detailed in the
previous sections.
COALITIONAL GAME THEORY
3
The negative sign is
needed to transform the
utility maximization problem into an interference
minimization setting.
158
Traditional cooperative games seek to reach a
globally-optimum grand coalition which almost
all players will join. As an example, Nash Bargaining Solutions (NBS) maximize the difference
in pay-off with and without cooperation. However, in many applications, including the distributed
resource allocation problem in LTE networks,
there exists local constraints that prohibit existence of such grand coalitions, or alternatively
results in the grand coalition not reaching the
global-optimum pay-off. Such local constraints
can be due to the geographical distribution of
players, spatio-temporal variation of certain variables such as variation of channel gains for each
player in the CCI mitigation problem, and any
other locally-valid constraints. Hence, the goal of
coalition formation games will shift to identifying
optimal coalition structures, i.e., identifying localized coalitions to which only a subset of players
subscribe such that the overall network utility is
maximized. Coalition formation games have
recently gained attention in modeling wireless
communication systems [7]. Moreover, this attention will likely increase considerably as cooperative wireless systems are becoming more popular
in practice. A solution concept for this class of
games is called core, which is an equilibrium such
that the sum of the payoffs for all players should
be equal to the outcome that can be achieved
under the most desirable coalition structure.
For the purpose of CCI mitigation in the
LTE context of an isolated cell with several
HeNBs, we can define the players as eNB-UE or
HeNB-UE pairs. Two options as regards formation of coalitions are to define a coalition around
each OFDM sub-channel or alternatively around
each femtocell. Further, a variety of utility functions can be used for each coalition, examples
being the sum achievable throughput of all coalition members (or a function of the sum throughput), the sum received signal to interference plus
noise ratio (SINR) (or a function of sum SINR)
or even the negative3 of the sum of the received
interference (or a function of this quantity).
Upon joining a given coalition, each player will
receive the surplus of the utility of coalition, i.e.,
the difference between the utility value before
and after the player joining this coalition.
Whereas traditional resource allocation
strategies, such as opportunistic scheduling, follow a myopic optimization goal, coalition formation results in enhancement of the overall
capacity of the networks. As shown in Fig. 6,
coalition formation strategy significantly outperforms opportunistic scheduling. (For details of
the simulation scenario as well as rigorous technical treatment of the topic, please refer to [8].)
CORRELATED EQUILIBRIUM GAMES
The fundamental solution concept for games is
the Nash equilibrium. However, depending on
the application scenario, the Nash equilibrium
might suffer from limitations, such as not being
unique, not being efficient, even at times the
existence of the Nash equilibrium might not be
guaranteed. In game theory, a correlated equilibrium is a solution concept that is more general
than the Nash equilibrium and is defined as follows. Each player in a game chooses his action
according to his observation of the value of a
signal. A strategy assigns an action to every possible observation a player can make. If no player
would deviate from the recommended strategy,
the strategy distribution is called a correlated
equilibrium. Compared to Nash equilibria, correlated equilibria offer a number of conceptual
and computational advantages, including the
facts that sometimes more “fair” payoffs can be
achieved, correlated equilibria can be computed
efficiently for games in standard normal form,
and correlated equilibria are the convergence
notion for several natural learning algorithms.
Furthermore, it has been argued that the correlated equilibria are the natural equilibrium concept consistent with the Bayesian perspective [9].
To model the CCI problem in the LTE context under a correlated equilibrium game framework, we can proceed as follows. The minimum
unit of scheduling in a LTE system is a resource
block (RB) which is a time-frequency block corresponding to one transmission time interval
(TTI) and 12 sub-carriers. Each eNB is a selfish
player who aims to select certain RBs so as to
maximize its own payoff while ensuring the quality of service (QoS) requirement by the UEs in
the cell. The interaction among eNBs is caused
by two facts: the transmission of a eNB in a certain RB may cause interference to neighboring
eNBs, and the number of available RBs is limited. For a given CCI level, the RB selection of
eNBs in the DL of an LTE system can be formulated as a correlated equilibrium game. Furthermore, the correlated equilibrium solution of
such a game can be obtained by using the nonregret learning algorithm. The non-regret learning algorithm does not require the information
of other players or the expression of the payoff
functions. The only thing a player observes is his
own payoffs and actions. Thus, the non-regret
learning algorithm is completely decentralized,
which can be easily applied in a real LTE sys-
IEEE Communications Magazine • August 2011
ATTAR LAYOUT
7/21/11
6:20 PM
Page 159
tem. Please refer to [10] for further details of
the correlated equilibrium game formulation.
Wireless communications standards are striving to
achieve maximal levels of spectral efficiency in
order to meet the increasing demand for data
traffic. As most next generation Radio Access
Networks (RAN) will shift toward Universal Frequency Reuse (UFR), their capacity will become
interference-limited. In order to mitigate CoChannel Interference (CCI) in such systems, we
proposed to use a Cognitive Radio (CR) mechanism in the base-station design of 4G systems.
More specifically we addressed the case of UMTS
Long Term Evolution (LTE) and discussed the
CCI problem in two scenarios, namely, an isolated cell with one e-NodeB (eNB) and several
Home eNodeBs (HeNB), and the case of multicell LTE. We elaborated on the crucial role of
radio scene analysis and argued the potential benefits of cognitive base-stations in a LTE context.
We further proposed to use Game Theory to analyze such advanced systems due to their distributed nature. We have demonstrated the possibility
of formulating the CCI problem as either a coalition formation game or a game with correlated
equilibrium. The key message here is the practical
value of incorporating CR technology with cellular communications to further improve the performance of such interference-limited systems. In
the next phase of our research we focus on the
cognitive resource allocation strategies in the
presence of emerging wireless gaming applications, such as massively multiplayer online games,
with periodic delay-sensitive traffic patterns.
REFERENCES
[1] 3rd Generation Partnership Project, Architecture Aspects
for Home NodeB and Home eNodeB, TR 23.830 V9.0.0,
Sept. 2009.
[2] J. Mitola, “Cognitive Radio: An Integrated Agent Architecture for Software Defined Radio,” Doctor of Technology Dissertation, Royal Inst. Tech. (KTH), Stockholm,
Sweden, May 2000.
[3] Y.-Y. Li et al., Cognitive Interference Management in 3G
Femtocells,” Proc. IEEE PIMRC’09, Sept. 2009, Tokyo,
Japan.
[4] R. Etkin, A. Parekh, and D. Tse, “Spectrum Sharing for
Unlicensed Bands,” IEEE JSAC, vol. 25, no. 3, Apr.
2007, pp. 517–28.
[5] R. Tandra and A. Sahai, “SNR Walls for Signal Detection,” IEEE JSAC, vol. 2, no. 1, Feb. 2008, pp. 4–17.
[6] M. Mueck et al.,“ Smart Femtocell Controller Based Distributed Cognitive Pilot Channel,” Proc. CrownCom’09,
June 2009, Hannover, Germany.
[7] W. Saad et al., “Coalitional Game Theory for Communication Networks,” IEEE Signal Proc., vol. 26, no. 5,
Sept. 2009, pp. 77–97.
[8] O. N. Gharehshiran and V. Krishnamurthy, “Coalition
Formation for Bearings-Only Localization in Sensor Networks-A Cooperative Game Approach,” IEEE Trans. Signal Process., vol. 58, no. 8, pp. 4322-4338, Aug. 2010.
[9] R. J. Aumann, “Correlated Equilibrium as an Expression
of Bayesian Rationality,” Econometrica, vol. 55, no. 1,
1987, pp. 1–18.
[10] M. Maskery, V. Krishnamurthy, and Q. Zhao, “Decentralized Dynamic Spectrum Access for Cognitive Radios:
Cooperative Design of a Noncooperative Game,” IEEE
Trans. Commun., vol. 57, no. 2, Feb. 2009, pp. 459–69.
BIOGRAPHIES
ALIREZA ATTAR [M’08] ([email protected]) received his Ph.D.
from King’s College London, where he was awarded the
UK’s Virtual Centre of Excellence in Mobile and Personal
IEEE Communications Magazine • August 2011
Average payoff per UE (Nat/s/Hz)
CONCLUSIONS AND FUTURE WORK
10
9
8
7
6
5
Opportunistic scheduling - fixed power
Dynamic coalition formation - fixed power
Opportunistic scheduling - adaptive power
Dynamic coalition formation - adaptive power
4
3
2
1
0
5
10
15
Number of UEs (K)
20
25
Figure 6. Comparison of average user performance using opportunistic
scheduling vs. coalition formation game strategy.
Communications (Mobile VCE) scholarship. He is currently
a Post- Doctoral Fellow at the department of electrical and
computer engineering, University of British Columbia,
UBC, Vancouver, Canada. His main areas of interests are
cognitive radio, adaptive radio resource allocation techniques, Game Theory, optimization techniques, MAC and
scheduling mechanism. Alireza is a leading co-guest editor
for the IEEE Journal on Select. Areas in Commun., special
issue on “Game Theory in Wireless Communications”. Further, he served as a co-guest editor for the IEEE Trans. on
Vehicular Tech., special issue on “Achievements and the
Road Ahead: The First Decade of Cognitive Radio,” which
was published in May 2010. He has served as TPC co-chair
for the International Workshop on Cognitive Wireless
Communications and Networking (CWCN), and as publications chair for IEEE International Conference on UltraWideband (ICUWB), both in 2009. He has been an active
member of TPC for most major IEEE conferences in Communication, Signal Processing and Vehicular Technology
societies for the past couple of years and has reviewed
numerous papers for over a dozen IEEE, IET, IEICE and
Elsevier journals. He is a co-recipient of INTERNET’09 best
paper award.
VIKRAM KRISHNAMURTHY [S’90, M’91, SM’99, F’05]
([email protected]) was born in 1966. He received his
bachelor’s degree from the University of Auckland, New
Zealand in 1988, and Ph.D. from the Australian National
University, Canberra, in 1992. He is currently a professor
and holds the Canada Research Chair at the Department
of Electrical Engineering, University of British Columbia,
Vancouver, Canada. Prior to 2002, he was a chaired professor at the Department of Electrical and Electronic
Engineering, University of Melbourne, Australia, where
he also served as deputy head of department. His current
research interests include computational game theory,
stochastic dynamical systems for modeling of biological
ion channels and stochastic optimization and scheduling.
He has served as associate editor for several journals
including IEEE Transactions Automatic Control, IEEE
Transactions on Signal Processing, IEEE Transactions
Aerospace and Electronic Systems, IEEE Transactions
Nanobioscience, and Systems and Control Letters. In
2009 and 2010 he serves as Distinguished lecturer for
the IEEE signal processing society. From 2010 he serves
as Editor in Chief of IEEE Journal Selected Topics in Signal Processing.
OMID NAMVAR GHAREHSHIRAN ([email protected]) received
his B.Sc. degree from Sharif University of Technology,
Tehran, Iran, in 2007, and the M.A.Sc. degree from the
University of British Columbia (UBC), Vancouver, BC,
Canada, in 2010, both in electrical engineering. He is
currently working towards his Ph.D. degree at UBC,
where he is a member of the Statistical Signal Processing
Laboratory. His research interests span stochastic optimization, game theory, and learning in games with
applications in wireless communication and sensor networks. He has been awarded the Four Year Fellowship at
UBC in 2010.
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