E-MICE: Energy-Efficient Concurrent Exploitation of Multiple Wi-Fi Radios

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10.1109/TMC.2016.2609427, IEEE Transactions on Mobile Computing
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E-MICE: Energy-Efficient Concurrent
Exploitation of Multiple Wi-Fi Radios
Yuris Mulya Saputra and Ji-Hoon Yun
Abstract—The concurrent use of multiple Wi-Fi radios in individual frequency channels is a solution readily available today to
the increase of a mobile station’s communication capacity, but at the expense of occasional performance deterioration (when the
heterogeneity of capacity between interfaces gets severe) and additional power consumption. This paper proposes a mobile-
side solution for the concurrent use of multiple radios in a performance-aware and energy-efficient manner, with which a mobile
station activates and deactivates radio interfaces dynamically according to traffic demands and a predicted capacity gain. To this
end, the proposed solution is composed of multiple prediction algorithms and a control algorithm. Prediction when activating an
additional radio interface is relatively difficult since no information of the disabled interface’s current status (and the corresponding
frequency channel’s) is available at the time of prediction. Our experiments show that, despite different types and used channels,
different radio interfaces have a strong correlation of received signal strengths and used PHY rates between them. Based on
this observation, the proposed solution learns a correlation pattern between interfaces whenever multiple interfaces are active
and makes prediction of the coverage, expected PHY rate and capacity impact of an inactive interface based on the learned
correlation with a currently active interface. The design of the prediction algorithms are based on a simple or machine-learning
technique (SVM). The control algorithm then keeps monitoring the utilization of active interfaces and, if any of them has utilization
over a threshold, checks if each inactive interface is within coverage and a valid rate range based on an active interface’s received
signal strength. Finally, an action of a configuration change (either activation, deactivation or no change) selected based on the
prediction of the resulting capacity is applied. Testbed experiments using COTS dual-band Wi-Fi interfaces demonstrate that the
solution can enhance throughput by up to 29.6% (in a close distance to AP) and at most halve power consumption compared to
legacy aggregation while the gain varies depending on the location and traffic conditions.
Index Terms—Link aggregation, Wi-Fi aggregation, multiple radio interfaces, multipath TCP
!
1 INTRODUCTION
Demands for high-speed data services are ever increasing
in wireless networks, giving an impetus to the continued
development of advanced solutions to increase the transmis-
sion capacity of a wireless communication link. Expanding
the service bandwidth of a wireless link by using additional
frequency spectra is a straightforward and effective means
to achieve a capacity increase, and can be realized by
two different approaches: (1) increasing the bandwidth
of a channel; or (2) using multiple frequency channels
concurrently. The second approach is called aggregation
or bonding, and has already been adopted by de facto
wireless systems (e.g. channel bonding in IEEE 802.11n/ac
[1], carrier aggregation in LTE-Advanced [2]).
Aggregation of multiple frequency channels can be im-
plemented using either a single radio interface or multiple
ones. Enabling a single radio interface to aggregate multiple
channels (or extending the aggregated channels of already-
aggregation-capable one) necessitates the changes of ex-
isting standards and radio hardware/software, thus being
• Yuris Mulya Saputra is with the Department of Electrical Engineer-
ing and Informatics, Vocational College, Universitas Gadjah Mada,
Yogyakarta, Indonesia.
Ji-Hoon Yun (corresponding author) is with the Department of Electri-
cal and Information Engineering, Seoul National University of Science
and Technology, Seoul, Korea. E-mail: jhyun@seoultech.ac.kr.
considered as a long-term solution. Meanwhile, the concur-
rent use of multiple radio interfaces exploiting individual
frequency channels can realize aggregation immediately
and the resulting performance gain can be extended further
if each of them is aggregation-capable (by aggregating more
channels). In addition, the multiple-radio approach also
improves resilience against a failure of individual interfaces
[3].
A growing number of access network components and
user devices are equipped with multiple radio interfaces.
Dual-band (2.4 and 5GHz) Wi-Fi access points (APs) al-
ready prevail in the market and are deployed increasingly to
exploit cleaner 5GHz band while supporting legacy 2.4GHz
devices. As IEEE 802.11ad (WiGig) [4] and upcoming
802.11ay [5] technologies penetrate a consumer market,
tri-band Wi-Fi APs using 2.4/5/60GHz are expected to
appear as well [6]. Many femtocell base stations (BSs)
being deployed today have co-located Wi-Fi AP and LTE
eNB within the same box. In the user-device side, we can
let a laptop computer have multiple radio interfaces by
purchasing and installing additional commodity interfaces
like mini PCI or USB-type adaptors. Most smartphones of
today are equipped with multiple radio interfaces such as
cellular radio and Wi-Fi. Currently, however, multiple radio
interfaces of a user device are not used concurrently and
access network components do not support this either, thus
missing the potential benefit of a capacity increase.
Two challenges associated with the concurrent use of

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multiple radio interfaces are (1) performance deteriora-
tion and (2) power consumption. The concurrent use of
multiple interfaces is not always beneficial and sometimes
gets worse than the use of a single interface in terms
of TCP throughput when used interfaces have a severe
heterogeneity of capacities between them, thus resulting in
packet reordering and unnecessary congestion control at a
TCP sender [7].1
In addition, a multiplicative increase of
power consumption by the number of active interfaces is
expected due to duplicate components. A worthwhile obser-
vation to address these challenges is that such performance
deterioration is somewhat predictable and interfaces may
get idle frequently under low traffic conditions.
In this paper, we propose a client-side solution
for the concurrent use of multiple radio interfaces
in a performance-aware and energy-efficient manner,
called Energy-Efficient Multiple Interface Concurrent
Exploitation (E-MICE), which is designed for, but not
restricted to, multiple Wi-Fi interfaces in AP and mobile
stations. With E-MICE, a mobile station activates and
deactivates radio interfaces dynamically according to traffic
demands and a predicted capacity gain so that a minimal
number of interfaces are used while the rest being put to
sleep or be powered off. To realize this operation, E-MICE
should find answers to two essential questions: (1) when to
activate/deactivate some radio interface(s)? and (2) which
radio interface to activate/deactivate?.
To collect the information required to answer the above
questions, E-MICE is equipped with multiple algorithms
that predict the status of an interface and the performance
gain of an interface configuration. While prediction when
deactivating an active interface can be possible, prediction
when activating an inactive interface is relatively difficult
since no information of the inactive interface’s current
status (and the corresponding frequency channel’s) is avail-
able. The basic idea of E-MICE’s prediction algorithms is
based on our experimental observation that, despite differ-
ent types and frequency channels, distinct radio interfaces
have a strong correlation between their statuses such as
received signal strengths and used PHY rates. Therefore,
once a correlation pattern between interfaces is known,
prediction of an inactive interface’s status can be made
based on an active one’s. E-MICE learns a correlation
pattern between interfaces whenever multiple interfaces are
active and makes prediction of the coverage, expected PHY
rate and capacity impact of an inactive interface based on
the learned correlation with a currently active one. The
prediction algorithms are developed based on a simple
design or a machine-learning technique (support vector
machine).
E-MICE’s control algorithm activates/deactivates radio
interfaces based on the prediction results as follows. E-
MICE keeps monitoring the predicted utilization of active
interfaces and, if any of them suffers high utilization over a
threshold, assumes that the current configuration may not be
able to handle input traffic with satisfactory performance.
1. [7] also proposes an analytical model of link aggregation.
Then, E-MICE identifies the set of inactive interfaces that
are within coverage and each’s minimum rate range based
on the corresponding prediction results and makes an action
of a configuration change (either activation, deactivation
or no change) according to the prediction of the resulting
capacity. If no active interface suffers over-utilization, E-
MICE deactivates the one which is predicted to result in a
minimum capacity decrease.
For evaluation, we implement E-MICE using COTS dual-
band (2.4 and 5GHz) Wi-Fi adaptors, perform real-world
experiments in a tesbed, and measure its throughput per-
formance and power consumption under various conditions
such as traffic patterns and locations with and without user’s
mobility. From experimental results, we show that E-MICE
enhances throughput by up to 29.6% and reduces power
consumption by up to 50% compared to legacy (always-
on) aggregation.
The rest of the paper is organized as follows. In Sec-
tion 2, we review the related work. Section 3 describes
the system model and Section 4 presents important obser-
vation of interface correlation. The prediction and control
algorithms of E-MICE are described in Sections 5 and
6, respectively. Then, experimental setup and performance
results are shown in Section 7. Finally, Section 8 concludes
the paper.
2 RELATED WORK
Interface selection and switching. When a mobile device
is equipped with multiple radio interfaces, selecting and
switching to the most suitable one, which is also called
vertical handoff, has been studied by many researchers.
SALSA [18] selects a radio interface considering an energy-
delay tradeoff and delays transfer of a large amount of
delay-tolerant data (i.e., uploading video). Ma et al. [19]
proposed a seamless and proactive vertical handoff scheme
between WiMAX and Wi-Fi networks with consideration
of user’s quality of service. AWNIS [20] selects the best
interface in terms of energy consumption and data transfer
delay based on a mathematical model. It observes link qual-
ity and adjusts a selection interval according to the current
network condition. Meanwhile, Jin et al. [21] proposed a
seamless handoff scheme between two Wi-Fi APs with the
use of multiple radio interfaces; a station uses an interface
to exchange data with a serving AP while using another
one to scan and connect to a target AP, and switches to
the latter one later, thus reducing packet losses during a
handoff.
Interface aggregation. Concurrent use of multiple radio
interfaces has been studied recently. Glia [22] exercises an
array of Wi-Fi radios to boost the link capacity of a point-
to-point connection; it solves the carrier-sensing and defer-
ring problem between interfaces, which is also observed
and analyzed through extensive experiments in [23], by
synchronizing transmission events of different interfaces.
Wang et al. [24] quantified a gain of integrated networks
(including spatial multiplexing, multinetwork and multiuser
diversity gains) and developed a heuristic algorithm that

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makes the association decision between Wi-Fi, WiMAX
or both networks to maximize the max-min throughput
fairness. In [25], three aggregation strategies were intro-
duced; selective offloading diverts uplink ACK packets via
a cellular connection to avoid contention with downlink
packets in Wi-Fi, proxying informs a TCP sender of black-
out events of a Wi-Fi connection, and mirroring sends lost
segments via a cellular connection to reduce retransmission
traffic in Wi-Fi networks. The above approaches focus on
throughput enhancement by using multiple interfaces, but
without consideration of power consumption. Lee et al. [26]
developed a sub-optimal algorithm to find a set of interfaces
to be activated for minimization of a multi-attribute cost
function considering battery life, data quota and completion
time when downloading a file. They, however, assume that
the status information of all interfaces is known and static,
which may not always be true in the real world.
Subflow configuration in multipath TCP. Paasch et
al. [3] showed the efficiency of MPTCP especially when a
Wi-Fi handover happens, in terms of throughput and energy
consumption via experiments. Deng et al. [27] also studied
MPTCP performance through extensive experiments and
showed that selection of a primary subflow and a congestion
control algorithm are of importance for short and long
flows, respectively. Raiciu et al. [28] proposed a simple
MPTCP scheduler which probes all equipped interfaces for
a short period and then switches to the most efficient one
to get higher energy efficiency. A similar approach was
proposed in [29], which is based on a Markov decision
process derived from the measured throughput. MPTCP-
MA [30] arranges the path usage over Wi-Fi networks
to recover throughput quickly and reduce packet losses
in a path failure resulting from a sudden Wi-Fi discon-
nection (e.g. a station moves away from AP). Lim et al.
[31] identified MPTCP’s operating region with high power
efficiency based on an energy model and developed the
energy-aware MPTCP which controls the establishment
time of a subflow over LTE such that MPTCP stays in the
region. This work considers the case of a file download,
thus full-buffer traffic based on bandwidth measurement
at TCP, which may not be accurate under a low traffic
condition. All of the above approaches work based on
the information available at the TCP level only with no
consideration of radio-level information. In [32], Chen et
al. proposed a solution that offloads traffic to a subflow over
a low-energy interface considering the current congestion
window size and the channel state of interfaces which
is assumed always available via their feedback. E-MICE
is a link-layer solution and controls interfaces based on
detailed radio-level information of interfaces. E-MICE also
considers the complete deactivation of an interface, thus
handles the situation that an interface does not provide any
information at the time of a decision.
3 SYSTEM MODEL
We consider a mobile station (MS) which is equipped with
multiple Wi-Fi interfaces and connected to a multi-interface
AP with the use of individual frequency channels. The types
of installed interfaces can be either homogeneous or het-
erogeneous (e.g. 802.11n and 802.11a); different interfaces
may have different PHY rate sets, different coverage areas,
etc. The set of activated interfaces of MS is denoted by
Ion and corresponds to the interface configuration to be
controlled by E-MICE. MS and a remote host exchange
data over multiple interfaces using techniques such as mul-
tipath TCP (MPTCP) [8] or conventional TCP with link-
level aggregation (e.g. link aggregation control protocol [9]
between AP and MS), which plays a role in splitting end-
to-end traffic between active interfaces.
MS runs a client-side standalone solution to activate
and deactivate interfaces dynamically without any signaling
with AP and AP-side control. For decision making, MS col-
lects the status information of active interfaces periodically;
a moderate collection interval is considered (one second in
our experiments) such that an interface is not overloaded
due to frequent information requests. Despite the use of
Wi-Fi’s power-saving mode (PSM), it is known that a Wi-
Fi interface still consumes significant power due to idle
listening (it accounts for more than 80 and 60 percents
of consumption in busy and idle networks, respectively)
[10]. Thus, we assume that, when a decision of deactivation
is made for an interface, MS powers it off completely.2
MS runs individual link adaptation algorithms for different
interfaces so that the PHY rate of each interface adapts to
the condition of the corresponding wireless channel.
4 OBSERVATION OF CORRELATION BE-
TWEEN RADIO INTERFACES
In this section, we investigate correlation between different
interfaces’ statuses—received signal strength (RSS) and
PHY rate—based on the statistical analysis of measurement
samples. The samples are collected from a client laptop
computer which is connected to a dual-band AP using two
COTS Wi-Fi interfaces, one in 802.11n mode at 2.4GHz
and the other in 802.11a mode at 5GHz, and moves along
the path depicted in Fig. 16. A sample is obtained every
second as the four-tuple information of (802.11n interface’s
RSS, 802.11n interface’s PHY rate, 802.11a interface’s
RSS, 802.11a interface’s PHY rate).
4.1 One’s RSS vs. Another’s RSS
Fig. 1 shows the 802.11n interface’s RSS vs. the 802.11a
interface’s. In the figure, the 802.11a interface’s RSS is
always lower than the 802.11n interface’s in the same
location; the 802.11a interface’s RSS ranges from -85
to -50dBm while the 802.11n interface’s ranges from -
70 to -35dBm. Despite such a large gap, RSSs of two
interfaces look highly correlated. In statistics, the p-value
is commonly used as a measure of correlation, which
is defined as the probability of obtaining the observed
correlation under the hypothesis of no correlation, i.e., the
2. To deactivate an interface, E-MICE invokes an external command
ifconfig with option down.

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11n − Signal Strength (dBm)
11a−SignalStrength(dBm)
Fig. 1. Correlation between RSSs of 802.11n and
802.11a interfaces
true correlation is zero (RSSs of two interfaces are not
correlated in our problem). Then, a sufficiently small p-
value (typically less than 0.05 or 0.01) implies that it is
un-likely to observe such correlation under this hypothesis
and the new hypothesis of significant correlation (RSSs
of two interfaces are correlated in our problem) should
be accepted as true. For the samples of the figure, the p-
value is obtained as 3.25×10−79
, thus a strong correlation
between two interfaces’ RSSs is concluded. Since many of
an interface’s statuses are highly correlated with its RSS,
our observation shows the possibility that one interface’s
status other than its RSS can also be predicted based on
another’s RSS, which is investigated next.
4.2 One’s RSS vs. Another’s PHY Rate
A Wi-Fi interface supports multiple PHY rates (modulation
and coding schemes) that have different signal-to-noise ra-
tio (SNR) vs. bit error rate (BER) characteristics. Generally,
as a MS moves far from an AP and its RSS gets lower, the
use of a lower PHY rate is desirable for robust transmission;
if RSS gets higher, a higher PHY rate could be better for
higher capacity. For automatic adaptation of the used PHY
rate as such depending on the current link condition, a
link adaptation algorithm runs on an interface. Thus an
interface’s RSS may show a high correlation with its used
PHY rate.
In order to see if one interface’s RSS also has a correla-
tion with another interface’s used PHY rate, we show the
samples of (802.11n interface’s RSS, 802.11a interface’s
PHY rate) and (802.11a interface’s RSS, 802.11n inter-
face’s PHY rate) in Fig. 2 (the Minstrel algorithm [11] was
used for link adaptation in the experiment). The p-values of
these cases are obtained as 1.04×10−18
and 9.78×10−16
,
which shows a strong correlation between one interface’s
RSS and the other’s PHY rate. This implies that, if we have
a priori information of the correlation pattern between one
interface’s RSS and the other’s PHY rate, we may be able
to predict an inactive interface’s PHY rate to be used if
activated based on the RSS of an active interface.
5 PREDICTION ALGORITHMS OF E-MICE
In order to make a decision of activation/deactivation of
interfaces, prediction of expected statuses and a perfor-
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Fig. 2. Correlation between one interface’s RSS and
another’s PHY rate
mance gain after a configuration change is of importance.
For this purpose, E-MICE is associated with the following
five prediction algorithms (algorithm name in parentheses).
• Link utilization prediction (E-MICE::PredictU): esti-
mates the utilization of a specific interface to check
if any change of the current interface configuration is
needed.
• PHY rate range prediction (E-MICE::PredictRmin):
estimates the minimum PHY rate of an interface below
which no effective throughput is achieved.
• Coverage prediction (E-MICE::PredictCov): checks if
a candidate interface (which is inactive now) can make
a stable connection with AP in terms of RSS.
• Capacity prediction (E-MICE::PredictCap): estimates
the achievable communication capacity when a new
interface configuration is applied.
• PHY rate prediction (E-MICE::PredictR): predicts the
PHY rate to be used by an inactive interface when it
is activated.
The above prediction provides more information about
wireless links than especially TCP-level solutions do and
thus enables E-MICE better adapt to link conditions.
In what follows, we describe each of the prediction
algorithms in detail. Illustrative samples are obtained from
the use of two Wi-Fi interfaces with the configuration used
in Section 3.
5.1 Link Utilization and Rate Range Prediction
The first question that needs to be answered is when to
activate or deactivate an interface—in other words, when
to make a configuration change. In order to answer this
question, MS has to identify how much room for data
transmission remains in the current configuration. For this
purpose, E-MICE monitors an active interface’s utilization;
high utilization implies that active interfaces are busy to
serve incoming traffic and activation of additional one(s)
may be needed while low utilization implies that interfaces
are somewhat idle and deactivation of some can be consid-
ered.
However, extracting the exact utilization information
from a commodity hardware is not always feasible since
multiple levels of buffers are present within the hardware
and the exact state of each is not accessible outside. There-
fore, we measure the utilization of an interface in an indirect
manner as the ratio of MAC service access point (SAP)
throughput to the current PHY rate. Throughput alone is

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(c) Zero samples
Fig. 3. Utilization for different PHY rates with fixed-rate
traffic input
not directly mapped to utilization since, as a PHY rate gets
higher at the same throughput, interfaces are less busy. Thus
we normalize throughput with the used PHY rate to finally
obtain utilization. Then, the PHY rate can be interpreted as
the capacity of a link while MAC SAP throughput shows
how much the capacity is being consumed. So, as this ratio
gets higher, we can assume that the client has insufficient
room for additional traffic and needs an increase of link
capacity. The ratio is measured for each active interface.
Figs. 3(a) and 3(b) show measured utilization samples
and the average, respectively, for different PHY rates (using
802.11a mode) when a constant rate of traffic input is
generated. In Fig. 3(b), as a lower PHY rate is used, higher
utilization is observed, thus showing the validity of the used
metric for utilization measure. It is noted in Fig. 3(a) that
the PHY rate of each sample point is the rate that appears to
be used at the time of a status request to an interface which
is made every second. Therefore, it may not be the only
rate that has been used for the last one second and other
rates may have also been used. This is the reason why the
utilization close to one is achieved at 12Mbps—higher rates
may have been used during the period of each sample, thus
sometimes achieving relatively high throughput—while it
is widely known that the MAC SAP throughput is lower
than the used PHY rate due to the MAC overhead of IEEE
802.11.
Meanwhile, for the PHY rate of 6Mbps in Fig. 3(a), we
observe many samples of zero utilization, implying that,
if an interface uses this PHY rate, it may not contribute
to any capacity increase. The expected reason is that
when MS resides in a coverage boundary it may have
an unreliable link and link adaptation may not adapt well
to a channel condition. E-MICE::PredictRmin learns the
PHY rate threshold of each interface below which no
sufficient utilization is achieved, from the samples of (PHY
rate, utilization level) using the support vector machine
(SVM) technique [12]. Samples are collected from an active
interface; those of non-zero utilization are classified into
class ‘valid rate’ while the other samples into class ‘invalid
rate’ to form a training set. After learning from the training
set, E-MICE::PredictRmin obtains the minimum PHY rate,
denoted as Rmin(i) for interface i, below which rates are
classified into class ‘invalid rate’.
5.2 Coverage Prediction
Before answering which interface to activate, we should
make it sure if a candidate interface is able to make a stable
connection with AP. If we activate the interface which is out
of AP’s coverage, this will result in high power consump-
tion due to idle listening or scanning while not contributing
to performance enhancement. E-MICE::PredictCov predicts
if a specific interface which is inactive is within coverage
based on the current RSS of an active interface. In order
to do this, learning the relationship between one’s RSS
and other’s coverage is performed whenever these two
interfaces are active at the same time; one’s coverage
outage is assumed when no successful transmission can
be made, i.e., the transmission success probability is zero.
From learning, a RSS threshold of an interface over which
another interface is assumed in coverage is obtained and
used for coverage prediction. Since installed interfaces can
be of heterogeneous types and different frequency channels
resulting in different coverage areas between interfaces,
E-MICE::PredictCov predicts the coverage of individual
interfaces. Finally, E-MICE has a N × N threshold matrix
(only the non-diagonal elements are used) if a client has N
installed interfaces; the element of i-th row and j-th column
is denoted by Si
min(j) which is interface i’s RSS threshold
predicting interface j’s coverage.
In order to learn a RSS threshold for each inter-
face’s coverage from noisy measurement samples, E-
MICE::PredictCov uses the SVM technique. Fig. 4 illus-
trates an example that a RSS threshold of 11n’s interface
for 11a’s coverage is produced. Samples of (11n’s RSS,
11a’s transmission success probability) shown in Fig. 4(a)
form a training set by mapping the samples of non-zero
success probability to class ‘in-coverage’ and the others to
class ‘out-of-coverage’. Then, a wide range of RSS values
are input as a test set to obtain the threshold classifying
the values into the two classes. Fig. 4(b) shows the classi-
fication result from which we obtain the RSS threshold of
-67dBm for 802.11a’s coverage. Coverage prediction of an
802.11n interface is illustrated in Fig. 5.
5.3 PHY Rate Prediction
E-MICE also predicts the PHY rate to be used by an
inactive interface based on the RSS of an active one. For
this, E-MICE collects the samples of the PHY rate of an
interface vs. another’s RSS when both are active at the
same time and learns the correlation. Later when one of
the interfaces is inactive while the other is active, E-MICE
can predict the expected PHY rate of the inactive one based
on the other’s RSS.

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Fig. 4. Coverage prediction for 802.11a interface
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Fig. 5. Coverage prediction for 802.11n interface
In more detail, E-MICE collects RSS samples of inter-
face i for each PHY rate of interface j (and vice versa) as
a training set when i and j are both active. Then, E-MICE
derives interface i’s RSS range within which interface j is
likely to use a specific PHY rate, using two approaches: (1)
basic prediction and (2) SVM-based prediction. The basic
prediction method counts the occurrences of each PHY rate
for a RSS value3
in the training set and selects the PHY
rate having the most occurrences as the predicted rate of
the RSS value. The SVM-based prediction method finds
the RSS range of each PHY rate that represents the largest
separation (or margin) from the RSS samples of the other
PHY rates. The classification result of these approaches is
shown in Fig. 6.
In order to investigate the accuracy of prediction, we
show the gap between the rate chosen by the Minstrel al-
gorithm running on an interface itself and the one predicted
based on another interface’s RSS in Fig. 7. The gap is
obtained as the difference between the indices of the two
rates when the lowest rate is indexed as 0; if the gap is
positive (negative), the predicted rate is higher (lower) than
Minstrel’s. The figure shows that the prediction results of
802.11a interface’s PHY rate match with Minstrel’s choices
for around 60% of the samples and the gap is limited by
±3. For the prediction of 802.11n interface’s PHY rate,
the gap is wider; 35% of the prediction results (29% for
the basic prediction) match with Minstrel’s and the worst
gap reaches +4 and -6 (+3 and -8 for the basic prediction).
This is because a wider range of PHY rates are used by
the 802.11n interface especially in the low RSS range of
the 802.11a interface as observed in Fig. 2.
3. The RSS resolution of the experiments is 1dB.
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Basic prediction
SVM prediction
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Fig. 6. PHY rate prediction results
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0
0.2
0.4
0.6
0.8
1
11a − PHY rate prediction gap
Cumulativeprobability
Basic prediction
SVM prediction
−11 −5 0 5 11
0
0.2
0.4
0.6
0.8
1
11n − PHY rate prediction gap
Cumulativeprobability
Basic prediction
SVM prediction
Fig. 7. PHY rate prediction accuracy against Minstrel
5.4 Capacity Prediction
Another important metric to predict is the maximum achiev-
able throughput, which we call capacity, of a new interface
configuration. For this, E-MICE records the maximum
throughput achieved by the interface configuration in use,
with the information of an active interface’s RSS. Illustra-
tive samples are given in Fig. 8 for different interface con-
figurations. In the figure, we make interesting observations
as follows. First, aggregation of multiple interfaces may not
always be beneficial than using a single interface; using
an 802.11n interface only outperforms legacy aggregation
(both interfaces are always active) when RSS is high while
using a single 802.11a interface gets better than legacy
aggregation with low RSS. This is because the capacity gap
between aggregated interfaces gets high in some conditions
and excessive reordering of packets transmitted over them
arises, resulting in unnecessary congestion control of TCP
[7]. Second, some interface (802.11n interface in this exam-
ple) has near-zero capacity although it is not disconnected,
which is also aligned with our discussion in Section 4.1.
Then, E-MICE::PredictCap uses these records to predict
the expected capacity of a new configuration, denoted
by C(I) for interference configuration I, based on an
active interface’s RSS. The rationale behind the prediction
of capacity only, not average throughput is that average
throughput depends on not only input traffic for a client,
but also the load condition of a network; consideration of
the latter may result in the high complexity of a design
and is not aligned with our design target, i.e., a client-side
standalone solution.
6 CONTROL ALGORITHM OF E-MICE
Finally the prediction algorithms described in the pre-
vious section are used by the control algorithm of

information.
7
−80 −70 −60 −50 −40 −30 −20
0
10
20
30
40
50
60
Signal Strength (dBm)
Throughput(Mbps)
802.11n
802.11a
Legacy aggregation
Fig. 8. Signal strength vs. capacity (maximum through-
put)
E-MICE, named E-MICE::Control, to decide when to
change interface configuration and which interface to ac-
tivate/deactivate. Such dynamic adaptation of the interface
configuration to channel conditions is the core difference
to conventional multi-radio systems. The detail of E-
MICE::Control is given in Algorithm 1 and explained in
the following.
E-MICE keeps monitoring the utilization of active in-
terfaces (ones in Ion) predicted by E-MICE::PredictU. If
any of active interfaces has utilization, denoted by ui for
interface i, higher than a threshold ui,min, E-MICE::Control
assumes that the current configuration may not be able
to handle incoming traffic with satisfactory performance
and it is now the time to change the configuration. Then,
E-MICE::Control filters out the inactive interfaces which
don’t meet coverage and rate thresholds and produces a set
of candidate interfaces I∗
off. The coverage threshold of an
inactive interface j based on interface i’s RSS Si, denoted
as Si
min(j), is obtained by E-MICE::PredictCov. The rate
threshold Rmin(j) and the expected PHY rate ˆRj(Si) of
interface j based on interface i’s RSS are obtained by
E-MICE::PredictCov and E-MICE::PredictR, respectively.
The interfaces resulting in the highest capacity increase
when activated and deactivated are chosen in I∗
off and Ion,
respectively. Then, the expected capacity is compared with
the current capacity C(Ion) to make a final decision; if
it is predicted higher than C(Ion), a new configuration is
applied; otherwise, the configuration remains unchanged.
Meanwhile, if no active interface suffers over-utilization,
E-MICE::Control deactivates one in Ion which results in a
minimum capacity decrease.4
In order not to make configuration changes too often, E-
MICE::Control runs a timer with a timeout time tctrl; if the
latest configuration change is not older than tctrl, no change
is made although the criterion of a change is met.
E-MICE is able to adapt to time-varying channel con-
ditions since prediction is performed online including the
latest measurement samples and decision is made based on
this. Especially, the channel load (or the status of inter-
ference) is indirectly considered by the estimation of the
maximum achievable throughput (via capacity prediction)
4. When E-MICE activates (or deactivates) an interface in the link-layer
level, the MPTCP stack of MS establishes a new subflow (or tears down
the existing subflow) over the interface automatically.
Algorithm 1 E-MICE::Control
1: Ion: the set of active interfaces
2: Ioff: the set of inactive interfaces
3: t: current time
4: tctrl: time when the last interface configuration change
was made
5: if t − tctrl > τ and ui ≥ ui,min for ∃i ∈ Ion then
6: I∗
off = {j|Si > Si
min(j), ˆRj(Si) ≥ Rmin(j), j /∈ Ion}
7: /* Find the best interface to activate */
8: j∗
= arg max
j∈I∗
off
C(Ion + {j})
9: /* Find the best interface to deactivate */
10: i∗
= arg max
i∈Ion
C(Ion − {i})
11: if C(Ion + {j∗
}) ≥ C(Ion) and C(Ion + {j∗
}) ≥
C(Ion − {i∗
}) then
12: Activate j∗
13: tctrl ← t
14: else if C(Ion − {i∗
}) ≥ C(Ion) and |Ion| > 1 then
15: Deactivate i∗
16: tctrl ← t
17: end if
18: else if t − tctrl > τ and |Ion| > 1 then
19: /* Find the best interface to deactivate */
20: i∗
= arg max
i∈Ion
C(Ion − {i})
21: Deactivate i∗
22: tctrl ← t
23: end if
ARM
Energy
Probe
Dual-band
Wi-Fi
Adapters
Fig. 9. A client laptop with two dual-band Wi-Fi
adapters and a power measurement device, used for
experiments
and the usage of this information for decision. However,
this information could be sometimes inaccurate especially
when there has been no active interface in a considered
channel for a while and thus the information is outdated.
If so, E-MICE will get the new information right after
activation and can roll back the change shortly.
7 EVALUATION
In this section, we evaluate E-MICE in an experimental
testbed under various test scenarios.

information.
8
B
AP
E
DC
5 m
A
Fig. 10. Test locations for experiment
7.1 Testbed Configuration
Our experimental testbed is composed of a dual-band AP
and a client MS; the AP function is run using Linux’s
built-in software module in a desktop computer on which
a remote host also resides and the client MS is a laptop
computer. Both the AP computer and the laptop have two
Wi-Fi USB adapters (NETGEAR WNDA3100) installed
which use the dual-band Atheros chipset supporting IEEE
802.11a/b/g/n.5
Two pairs of Wi-Fi connections are made
between the AP and the MS; one is a 802.11n connec-
tion in 2.4GHz and the other is an 802.11a connection
in 5GHz. The MS and the remote host running on the
desktop computer are connected using MPTCP based on
an open-source protocol stack [13]. Both computers run
Linux Ubuntu 12.10. Traffic generation and throughput
measurement is conducted using iperf [14] and the power
consumption of each Wi-Fi adapter is measured using
the arm-probe program [15] with the ARM Energy Probe
device at the client laptop as shown in Fig. 9. E-MICE
is implemented as an user-space program which reads the
RSS, PHY rate, and frame success probability of each
interface from the carl9170 device driver [16] every second;
the libSVM library is used for SVM implementation. Both
802.11n and 802.11a interfaces use 20MHz channel width.
The supported PHY rates of the 802.11n interface ranges
from 6.5 to 130Mbps (2×2 MIMO with 800ns guard
interval) while those of the 802.11a interface are from 6
up to 54Mbps. The Minstrel algorithm [11] is used for link
adaptation. The utilization threshold ui,min is set to 0.1 for
both interfaces and the timeout time τ is set to one second.
In the following, we first show the average performance
(throughput and power consumption) of E-MICE in dif-
ferent locations and traffic conditions, and then investigate
the dynamic behavior of E-MICE under user’s mobility for
better understanding of the obtained performance gain and
trends.
5. If Wi-Fi interfaces in close proximity to each other use the same
band (either 2.4 or 5GHz), they interfere with each other significantly due
to spurious emission even though they use distinct channels; thus only a
single interface may transmit at a time within a band while others sense
the busy medium, as also observed in [22] and [23]. If Wi-Fi adapters
with a tight spectral mask appear, the E-MICE client with more than two
interfaces can be implemented.
A B C D E
5
10
15
20
25
30
35
40
45
Location
Throughput(Mbps)
Legacy
E−MICE with basic prediction
E−MICE with SVM prediction
Fig. 11. Throughput performance in different locations
A B C D E
0
1
2
3
4
5
6
7
8
9
x 10
4
Basic SVM
Basic
SVM
Basic SVM
Basic SVM Basic SVM
LocationPacketssent(packets)
802.11n
802.11a
Fig. 12. E-MICE’s usage of interfaces in different
locations
A B C D E
0
0.5
1
1.5
2
2.5
3
3.5
4
Location
Power(W)
Legacy
E−MICE with basic prediction
E−MICE with SVM prediction
Fig. 13. Power consumption in different locations
7.2 Average Performance
7.2.1 Different Locations
We locate a MS in five different locations as illustrated
in Fig. 10 and perform experiments under a full-buffer
traffic condition. It is shown in Fig. 11 that E-MICE
outperforms legacy aggregation (two interfaces are always
activated) in throughput by 29.6% and 16.5% in locations
A and B, respectively, while similar throughput is achieved
in locations C and D and a small gain (by 8.3%) in
location E. The achieved gains in different locations are
well explained by Fig. 12. In locations A and B, only
802.11n interface is used by E-MICE; when both interfaces
are used as in legacy aggregation, performance deterioration
is experienced due to a large heterogeneity of interfaces’
capacities (as observed in Fig. 8). In locations C and D, both
interfaces are used by E-MICE, thus similar performance
with legacy aggregation is observed. Packet distribution
between interfaces in this case is determined by the MPTCP
sender’s congestion control algorithm depending on the
capacity and latency condition of each interface [17]. In

information.
9
2 8 16 32 64 130 262
0
5
10
15
20
25
Maximum send buffer size (KBytes)
Throughput(Mbps)
Legacy
E−MICE with dynamic utilization
Fig. 14. Throughput in various traffic conditions
location E, only 802.11a interface is used by E-MICE and
a gain is achieved due to the same reason as above.
The usage pattern of two interfaces corresponds to the
trend of power consumption directly as shown in Fig. 13.
While legacy aggregation activates both interfaces all the
time, thus consuming a fixed level of power in all locations,
E-MICE mostly uses a single interface in locations A, B
and E, thus spending around half of the power consumption
of the legacy case. In locations C and D, E-MICE uses both
interfaces or a single interface only depending on varying
conditions. Therefore, although it is relatively small, E-
MICE still has a gain in locations C and D over the legacy
case.
The basic and SVM-based prediction approaches show
similar performance in both throughput and power con-
sumption.
7.2.2 Various Traffic Conditions
In order to control a traffic generation rate, we configure
TCP’s maximum send buffer size; then a TCP sender’s
send rate is limited by this size over round-trip time on
average. The experiment is conducted in location C of
Fig. 10, thus both interfaces are frequently activated under
a high traffic condition as shown in Fig. 12. Fig. 14 shows
that as the buffer size increases throughput also increases.
However, when the buffer size is lower than 64KB, E-MICE
shows a slight degradation (by up to 8.5% compared to
legacy aggregation). This is because E-MICE deactivates
one interface whenever utilization is low, thus may not
be able to handle a sudden increase of traffic. Instead,
as shown in Fig. 15, E-MICE reduces power consumption
significantly up to 45.2% in low traffic intensity. In addi-
tion, E-MICE also benefits in power consumption even in
high traffic intensity by around 28%. This benefit results
from opportunistic configuration changes depending on an
instantaneous traffic condition.
7.3 Dynamic Behavior
In order to investigate the dynamic behavior of E-MICE,
we move the MS along the path shown in Fig. 16 for 200
seconds, with full-buffer traffic generation. Figs. 17 and
18 show the RSS and predicted PHY rate of 802.11n and
802.11a interfaces, respectively, along the mobility path.
As can be seen in the figures, a wide range of RSS and
PHY rate is considered in this experiment. In addition,
2 8 16 32 64 130 262
0
0.5
1
1.5
2
2.5
Maximum sender buffer size (KBytes)
Power(W)
Legacy
E−MICE with dynamic utilization
Fig. 15. Power consumption in various traffic condi-
tions
5 m
AP
Start
End
Fig. 16. Mobility trace path
50 100 150 200
−80
−70
−60
−50
−40
−30
−20
Time (s)
SignalStrength(dBm)
50 100 150 200
18
24
36
48
54
Time (s)
PredictedRate(Mbps)
Legacy (Minstrel)
Basic prediction
SVM prediction
Fig. 17. RSS of 802.11n interface and the predicted
rate of 802.11a interface based on 802.11n’s RSS
along the mobility path
we consider static utilization as another behavior option
of E-MICE with which one of the interfaces is set as an
always-on interface and the other is activated/deactivated
opportunistically; we denote the case that the 802.11a
interface is used as an always-on interface and the 802.11n
interface as an opportunistic one by 11a/n and the opposite
case by 11n/a. To differentiate E-MICE’s default behavior
from this, we call it dynamic utilization.
Fig. 19 shows the change of the client’s interface con-
figuration along the mobility path in terms of the number
of active interfaces. We already know from Fig. 8 that
in a close distance using 802.11n interface only is more
beneficial than using both interfaces in our experimental
environment. In static utilization of 11a/n, however, 802.11a
interface is always active and in a close distance 802.11n
interface is also activated, thus both are used. This results
in degraded throughput as shown in Fig. 20. In far distance,
802.11n interface shows poor capacity and thus using
802.11a interface alone is better. In Fig. 19, static utilization

information.
10
50 100 150 200
−90
−80
−70
−60
−50
−40
−30
Time (s)
SignalStrength(dBm)
50 100 150 200
6.5
19.5
26
39
52
65
78
104
117
130
Time (s)
PredictedRate(Mbps)
Legacy (Minstrel)
Basic prediction
SVM prediction
Fig. 18. RSS of 802.11a interface and the predicted
rate of 802.11n interface based on 802.11a’s RSS
along the mobility path
50 100 150 200
1
2
Time (s)
InterfaceUsed
(a) Basic prediction
Dynamic utilization
50 100 150 200
1
2
Time (s)
InterfaceUsed
(b) SVM prediction
Dynamic utilization
50 100 150 200
1
2
Time (s)
InterfaceUsed
Static utilization 11a/n
50 100 150 200
1
2
Time (s)
InterfaceUsed
50 100 150 200
1
2
Time (s)
InterfaceUsed
Static utilization 11n/a
50 100 150 200
1
2
Time (s)
InterfaceUsed
Fig. 19. Interface configuration changes along the
mobility path
of 11a/n behaves as such, but that of 11n/a fails to do
that because 802.11n interface is always on. Fig. 21 shows
that static utilization of 11n/a is good in close distance by
activating 802.11n interface only, but has poor throughput
in far distance by still using 802.11n interface. The dynamic
utilization option of E-MICE activates and deactivates
interfaces adaptively without any fixed configuration, thus
results in good performance in both close and far distance
cases.
Moreover, dynamic utilization typically uses a smaller
number of interfaces than static utilization and lower power
consumption is achieved as well. Power consumption is
almost proportional to the number of active interfaces as
seen in Fig. 22 where the power consumption evolution of
legacy aggregation and E-MICE with dynamic utilization
(SVM prediction) are compared along the mobility path
from 115 to 155 seconds. From the figure, we can observe
that legacy aggregation consumes almost a static level of
power while the power consumption of E-MICE changes
according to the interface configuration given in Fig. 19.
8 CONCLUSION
We developed E-MICE that optimizes the configuration
of multiple interfaces, aiming to enhance throughput and
reduce power consumption. To achieve the goal, E-MICE is
equipped with the multiple algorithms which predict utiliza-
tion, coverage, expected PHY rate, rate range and capacity
based on the learned correlation pattern between interfaces.
After prediction, E-MICE runs the control algorithm which
activates and deactivates radio interfaces dynamically based
20 40 60 80 100 120 140 160 180 200
0
20
40
60
Time (s)
Throughput(Mbps)
Legacy
Dynamic utilization
20 40 60 80 100 120 140 160 180 200
0
20
40
60
Time (s)
Throughput(Mbps)
(b) SVM prediction
Legacy
Dynamic utilization
Fig. 20. Throughput comparison between static
(11a/n) and dynamic utilization options
20 40 60 80 100 120 140 160 180 200
0
20
40
60
Time (s)
Throughput(Mbps)
Legacy
Dynamic utilization
20 40 60 80 100 120 140 160 180 200
0
20
40
60
Time (s)
Throughput(Mbps)
(b) SVM prediction
Legacy
Dynamic utilization
20 40 60 80 100 120 140 160 180 200
0
20
40
60
Time (s)
Throughput(Mbps)
Legacy
Dynamic utilization
20 40 60 80 100 120 140 160 180 200
0
20
40
60
Time (s)
Throughput(Mbps)
(b) SVM prediction
Legacy
Dynamic utilization
Fig. 21. Throughput comparison between static
(11n/a) and dynamic utilization options
0 50 100 150 200 250 300 350 400
0
1
2
3
Time interval (per 100 ms)
Power(W)
(a) Power evolution of legacy aggregation
0 50 100 150 200 250 300 350 400
0
1
2
3
Time interval (per 100 ms)
Power(W)
(b) Power evolution of E−MICE aggregation
Fig. 22. Power evolution comparison between legacy
aggregation and E-MICE (dynamic utilization)
on prediction results. Our experimental results show that
both throughput and power consumption performance is
improved significantly under various channel, traffic and
mobility conditions.
ACKNOWLEDGEMENT
The work reported in this paper was supported by the
National Research Foundation of Korea under Grant NRF-
2014R1A1A2059515.
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Yuris Mulya Saputra received the BS de-
gree in telecommunication engineering from
Institut Teknologi Bandung (ITB), Bandung,
Indonesia, in 2010, and the MS degree in
electrical and information engineering from
Seoul National University of Science and
Technology (SeoulTech), Seoul, Korea, in
2014. He is currently a lecturer in the Depart-
ment of Electrical Engineering and Informat-
ics, Vocational College, Universitas Gadjah
Mada (UGM), Yogyakarta, Indonesia. Before
joining UGM in February 2016, he was a researcher in the Wireless
Networking Laboratory, Smart Computing and Intelligent Networks
Research Group, SeoulTech, Korea, from 2014 to 2015 and an
application software developer at the Digital Appliance Division,
Samsung Electronics, Cikarang, Indonesia, from 2010 to 2012. His
current research focuses on heterogeneous network and wireless
sensor networks.
Ji-Hoon Yun received the BS degree in elec-
trical engineering from Seoul National Uni-
versity (SNU), Seoul, Korea, in 2000, and
both the MS and PhD degrees in electri-
cal engineering and computer science from
SNU in 2002 and 2007, respectively. He is
currently an associate professor in the De-
partment of Electrical and Information Engi-
neering, Seoul National University of Science
and Technology (SeoulTech), Seoul, Korea.
Before joining SeoulTech in March 2012, he
was with the Department of Computer Software Engineering, Kumoh
National Institute of Technology (KIT) as an assistant professor. He
was a postdoctoral researcher in the Real-Time Computing Labora-
tory (RTCL) at The University of Michigan, Ann Arbor, MI, in 2010
and a senior engineer at the Telecommunication Systems Division,
Samsung Electronics, Suwon, Korea, from 2007 to 2009. His current
research focuses on wireless networks and efficient computing of
mobile devices.

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