TARC: Throughput-aware Random Scalable
Clustering for Network MIMO
Makoto Kobayashi∗ , Bryan Ng† , Winston K.G. Seah † , Shunsuke Saruwatari ‡ and Takashi Watanabe∗∗ Graduate School of Information Science and Technology, Osaka University, Japan† School of Engineering and Computer Science, Victoria University of Wellington, New Zealand‡ Graduate School of Informatics, Shizuoka University, Japan

Abstract—Current one-to-one wireless communications isreaching the Shannon limit. Previous works have studied space
multiplexing schemes, e.g., superposition coding and successive interference cancellation, to overcome the limitation. In this paper,
we focus on a network multiple-input multiple-output (MIMO)
architecture, which is one such space multiplexing scheme,
where multiple access points in the network MIMO systemcooperate with each other to improve wireless communications
capacity. However, channel sounding overhead for estimating thechannel state information of every path between multiple accesspoints and multiple clients is a significant problem. Likewise,
the computational overhead for deciding which paths to use
for the network MIMO transmission is also high. We propose
Throughput-Aware Random Clustering (TARC) of access points
to reduce the network MIMO overhead. TARC takes a cross-layer
approach for choosing access points to participate in network
MIMO transmission on the physical layer based on throughput
in the data link layer. From our validation using simulations, weshow that the proposed method is able to achieve approximately
2.5 times higher throughput than using all access points in the
network and 1.4 times better throughput than adopting static
cluster sizes.

I.

I NTRODUCTION

Recently, there has been an explosive growth of mobile
traffic demand. Mobile data traffic increased 65% between thefirst quarter of 2013 and the first quarter of 2014. If this ratecontinues, mobile data traffic will increase by 100 times in thenext decade [1]. In addition, the number and types of nodes thatconnect to the Internet are also increasing, making it necessary
to achieve a higher data rate within prevailing resource andbandwidth limitations.
Current wireless communications schemes assume one
communications channel in a single space. When one node detects the other communications channel, the node will suppress
transmission in order to avoid collision. In this paper, we define
a single space as the place in which a communications channel
affects another communication by radio wave interference. The
current collision avoidance and one-to-one communications
scheme are reaching the Shannon limit in channel capacity.
Modulation and coding schemes can only achieve the Shannon
theoretical limit and cannot increase capacity any further.
Therefore, it is necessary to move beyond the current oneto-one wireless communications paradigm (Figure 1) and use
space multiplexing, in which many access points send signals
simultaneously in one space (Figure 2) to intentionally create
collisions that positively reinforce the signals. Existing space
multiplexing schemes include successive interference cancellation [2], [3] and superposition coding. This paper focuses on

network multiple-input multiple-output (MIMO), which is usedin such space multiplexing schemes, e.g. [4]–[11]. Network
MIMO can potentially increase network capacity significantly
by simply adding access points in the network.
However, in the network MIMO system, there is a channelsounding overhead problem. Channel sounding is necessary for
estimating the channel state information (CSI) of every path
between multiple access points and multiple clients. Network
MIMO maximizes physical layer channel capacity by using
CSI. Additionally, the computational cost for determining a
combination of any paths at that moment also becomes aproblem because traffic and interference vary with time.
In this paper, we propose Throughput-aware Random Clustering (TARC), which combats the problem of communicationand the computation overhead used by channel sounding.
In TARC, this is achieved by dynamically clustering accesspoints. The efficacy of this dynamic clustering approach has
been shown by simulations to achieve higher performancecompared to both the network MIMO scheme using all accesspoints in the network as well as network MIMO using static
clustering.
The rest of this paper is organized as follows: In Section II, we introduce network MIMO and the problem causedby its channel sounding; in Section III, we describe themethod for solving the channel sounding problem by clusteringwith throughput-awareness, as shown in Section II; following
which, we verify the performance of TARC by computer
simulation in Section IV; Section V covers related works; andfinally, the conclusion is drawn in Section VI.
II.

P ROBLEMS IN NETWORK MIMOIn this section, we introduce network MIMO and present
the network MIMO channel sounding overhead problem. To
increase wireless communications capacity, space multiplexing
by using MIMO has been considered [12]. In a MIMO system,a transmitter can send multiple sets of data to multiple users

Access Point

ClientFig. 1. Current one-to-one wire- Fig. 2. Multiple access points areless communication
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2K and it is therefore not practical to successively determine
whether to perform MIMO transmission using any path andaccess point combinations.

 

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

III.


Fig. 3.











An example of network MIMO

by using multiple transmitter antennas and spatial multiplexing[13]. This wireless communication system is commonly called
multi-user MIMO (MU-MIMO) [4]–[11].
Here, we focus on network MIMO, which is one typeof MU-MIMO. In network MIMO, multiple access points
are coordinated to behave like MIMO nodes. Ordinarily,
when each antenna is more separated, MIMO systems achieve
higher data rates. However, unlike typical MIMO antennas,
current access points are not coordinated and transmit signals autonomously. This non-cooperation causes co-channelinterference and interference. This can be overcome whenaccess points are coordinated to do MIMO transmission; sucha coordinated system not only avoids co-channel interference
and interference but can also achieve MIMO gain.
In Figure 3, we show a representative network MIMO
system in which a controller manages multiple access points
that are connected to it via a backhaul network. In Figure 3,
there are three access points (AP1, AP2 and AP3) that arecoordinated to send data to clients by using network MIMO.
Each access point obtains the CSI of paths between all accesspoints and all clients by doing channel sounding. Each accesspoint can precode its signal to clients by using the obtained
CSI. One client receives the signals, divides duplicated signals
from multiple access points, and obtains the required data.
In network MIMO systems, the communication overheadof channel sounding is the problem. CSI, which is necessaryfor MIMO, is estimated by transmission of pilot signals inchannel sounding. Estimation of the CSI for K × N paths isnecessary when the number of access points and clients is K
and N respectively.
One method that has been considered for transmitting pilot
signals from different access points is for each access point
to send a different pilot series. However, this method causes
pilot contamination [14], in which pilot signals collide andcause performance degradation. In the method described inthis paper, access points can avoid pilot contamination by timedivisional pilot signal transmission because all access points
are linked and coordinated. Still, transmission time for pilot
signals is increasing and throughput is decreasing.
Moreover, even if the CSI of all paths can be obtained
instantaneously, determining which access points to use for optimal signal transmission is still extremely challenging. Accesspoints are separated in the network MIMO system, using all of
the access points for transmission does not necessarily improvethroughput. However, when the number of access points is K,
the number of combinations of antennas to transmit data is

T HROUGHPUT- AWARE R ANDOM C LUSTERINGIn this paper, we propose Throughput-aware Random Clustering (TARC) for the channel sounding problem mentioned
in Section II. TARC pays attention to downlink traffic suchas that which occurs during a video downloads and Webbrowsing, because downlink traffic is larger than uplink trafficin wireless networks. More specifically, TARC takes a crosslayer approach for controlling the number of the access-pointsclusters participating in network MIMO transmission to reducechannel sounding overhead and achieve higher throughput.
A. System overview
In TARC systems the controller receives a packet froman upper layer and selects multiple access points to realise
network MIMO from the set of all access points connectedto the controller. In this paper, we refer to a cluster as agroup of access points selected by the controller. In TARC, thecontroller selects access points to join the cluster each time apacket arrives at the controller.
The TARC system is explained as a series of five algorithm
and Table I shows variables and functions that are used in
Algorithm 1 through Algorithm 5. The variables S and C
denote sets of access points connected to a controller andsets of access points participating in a cluster; M presents thetransmission times of using one cluster; Rk is the throughputof the k-th data transmission; R̄ and R̄ are the M transmission time throughput averages of the current cluster and theprevious cluster, respectively; u represents the number of times
R̄ > Rk in M transmission times; and u is the number oftimes R̄ exceeds Rk in the previous M transmission times.
The operation of the TARC system operating in the controller is outlined in Algorithm 1. First, TARC initializes thesubset C of access points that are in the cluster as the null set,∅. Then, each time the controller receives a packet, actionsin the while loop are repeated. In the access point addition
phase, the controller selects an access point randomly andadds it to the cluster C. Then, the controller repeats thedata transmission phase and the throughput evaluation phase
M times by using the same cluster C, which includes theselected access point. Finally, in the access point removal
phase, the controller removes the access point, which may not
be necessary for transmission, from cluster C based on M
times throughput evaluation.
B. Access point addition phase
Algorithm 2 illustrates the operation of the controller in theaccess point addition phase, in which the controller selects anaccess point APi randomly and adds the selected access point
to the cluster. At this time, access point APi is included in theset of access points S, which are connected to the controller
and thus no longer in cluster C.

Algorithm 1 Throughput-aware random clustering
1: C ← ∅2: while do
3:
u←0
4:
Access point addition phase5:
for k = 1 to M do
6:
Data transmission phase
7:
Throughput evaluation phase8:
end for9:
Access pointM remove phase1R̄ = M10:
k=1 RkR̄ ← R̄
11:
12: end while
TABLE I.
Variables, Functions
S
C
M
Rk
R̄
R̄
u
u
RSSI(APi )APmin

VARIABLES AND F UNCTIONS IN A LGORITHM 1 A LGORITHM 5
Explanation
Set of access points connected to controller
Set of access points participating in cluster
Transmission times of using one cluster
The throughput in K-th transmission of current cluster
The M times transmission throughput average of thecurrent cluster
The M times transmission throughput average of theprevious cluster
Number of times that meet R̄ > Rk in M times
transmission of the current cluster
Number of times that meet R̄ > Rk in M times
transmission of the previous cluter
Function that returns the RSSI of APi
Access point that has the smallest RSSI

C. Data transmission phase
In the data transmission phase, the controller sends data
to the client through access points in the cluster C. Figure 4
shows the frame sequence exchange. Currently, there are nostandards for network MIMO, so we base its frame sequenceon the IEEE 802.11ac MU-MIMO.
First, an access point in the cluster transmits a null data
packet announcement (NDPA) and a null data packet (NDP).
NDPA is the packet for announcing the start of channelsounding while NDP is the frame for channel sounding. Then,following the first access point, another access point sends
an NDP. After all access points in the cluster C send an
NDP, the client builds the channel state information feedback(CSI-FB) frame and sends it back to the controller via accesspoints. The CSI-FB is a frame for feedback on CSI denoted byt
CSI(h) where h = [h1 , . . . , hK ] which clients estimate uponreceipt of the NDP from the controller at time index t. This
information is necessary for the throughput evaluation phaseand access point removal phase of the TARC. The controller
precodes transmitting data (DATA frame) to the client basedon the CSI obtained from CSI-FB [15], [16]. Finally, the client
receiving the data sends a block ACK (BA) to access points
and completes the process of adding an access point into acluster.
It should be noted that the frames NDPA, NDP, CSI-FB,
DATA, and BA are the same as those used in IEEE 802.11ac
standards. However, the difference between the standard IEEE
802.11ac and our proposed network MIMO clustering is thatseveral APs send the NDP to yield a single CSI-FB.

Algorithm 2 Algorithm: Access point addition phase
1: Select access point {APi | APi ∈ S \ C} randomly
2: C ← C + {APi }  

 

 

 

 




 


Fig. 4.

   



Frame sequence

D. Throughput evaluation phase
In throughput evaluation phase, the controller evaluates
throughput at the data link layer as shown in Algorithm 3.
In the first line of Algorithm 3, the current throughput Rk
is calculated by the function throughput(C). The function
throughput(C) computes the throughput for data transmissions using the cluster C.
In the function throughput(C), the communication capacity of the n-th client at the physical layer Rn,capacity [bps] isfirst calculated as
Pt ||hn vn ||2
Rn,capacity = W log 1 +W N0
where Rn,capacity denotes the communication capacity of
the n-th client at the physical layer; W [Hz] denotes the
bandwidth; Pt [mW] is transmission power; N [mW/Hz] isnoise power; hn is channel state information of the n-th client,which includes path loss for each path; and vn is the weightvector for the n-th client. Again, the controller uses the CSI(hn )for the data transmission phase.
Then, the controller computes the evaluated throughput on
the data link layer by the calculated communication capacity
Rn,capacity and overhead of the frame. The throughput on thedata link layer is,
N
Tdata n=1 Rn,capacity
Rk =,Ttotalwhere Tdata is the total time that is necessary for data transmission, which means the time of data frame size and Ttotal
denotes the total time taken for data and header transmission
on both the physical and datalink layers. For K number oftransmitting access points (antennas), Ttotal is
Ttotal= TDIFS + TBO + TNDPA +K(TNDP + TSIFS ) + TCSI +TDIFS + TBO + Theader + Tdata +TSIFS + TBA .

(1)

where TDIFS , TBO , TNDPA , TNDP , TSIFS , TCSI , Theader , and
TBA are the lengths of DCF interframe space (DIFS), backoff(BO) , NDPA, NDP, short interframe space (SIFS), CSIFB, header data, and BA, respectively.

Algorithm 3 Throughput measurement phase
1: Rk ← throughput(C)
2: Rk,sum ← sum(Rk )3: if Rk,sum < R̄ sum then
4:u←u+1
5: end if

Algorithm 5 remove access point function
1: APmin ← arg min RSSI(APi )APi ∈C

2: if C = {APmin } then3:
C ← C \ {APmin }4: end if
TABLE II.

Algorithm 4 Access point removal phase
1: if u > 0 then2:
remove access point(C)3: end if
4: if u > M/2 then5:
remove access point(C)6: end if

The controller records the number of times u that Rk is
less than R̄ , which is the M transmission throughput averageof the previous cluster,as shown in the second through fourthlines of Algorithm 3.
E. Access point removal phase
In the access point removal phase, the controller removes
an access point from the cluster to reduce channel sounding
overhead. The listing in Algorithm 4 shows the access point
removal phase algorithm. First, when u is more than 0,i.e., there is at least one occurrence of lower throughput
than in the previous cluster size. Subsequently, the controller
remove an access point from the cluster according to the “remove access point” function. When the cluster size becomeslarge and thus increases overhead (which reduces throughput),the cluster size should be reduced. Thus, when u is larger
than M/2, the controller removes an access point. The actionof the “remove access point” function is shown in Algorithm
5. From the access point cluster APi ∈ C, the controller
selects the access point that transmits the smallest received
signal strength indicator (RSSI) by using the RSSI function.
The CSI (h) acquired by the data transmission phase is utilized
for the computation of the RSSI. When the CSI of APi is hi ,the RSSI of APi is calculated as |hi | in the RSSI function.
The controller removes the selected access point from thecluster when there is not only one access point in the cluster;C = {APi }.
IV.

P ERFORMANCE EVALUATION

We performed computer simulations to validate the performance of the proposed TARC.
A. Evaluation environment
We show the topology of the evaluation environment in
Figure 5, in which K access points are equally spaced on astraight line at intervals of dAP , and the client is placed at
distance dAP−C from the center of the line of access points.
The client and access points are equipped with one antenna.
We set dAP and dAP−C to 10m. Access points are assumed tobe ideally connected to the controller by the wired backhaul.
The Log-distance path loss with hard separation model wasused as the radio propagation model. The path loss exponent
is defined as 4, and the transmission power is set to 200 mW.

T IME L ENGTH

Frame and Space
DIFS
backoff
NDPA(Null Data Packet Announcement)
NDP (Null Data Packet)
SIFS
CSI-FBBA (Block ACK)Header Length of Data Frame
Payload Length of Data Frame

Time
34 µs
67.5 µs
64 µs
64 µs
16 µs
1000 µs
44 µs
44 µs
500 µsWe set the data link layer frame length as in [17], [18].
Table II shows each frame lengths: TDIFS is 34µs; TBO is
67.5µs; TNDPA is 64µs; TNDP is 64µs; TSIFS is 16µs; TCSI is
1000µs; Theader is 44µs; and TBA is 44 µs.
We compare the performance of the following three approaches to benchmark the TARC.
1)

2)

3)

Giant MIMO (giant)
Giant MIMO is the system in which all access points
in the network are cooperating and enable network
MIMO transmission to the client. Giant MIMO has
reduced channel sounding time compared to TARC
because Giant MIMO does not do clustering.
Static Clustering (static)
Static Clustering is the scheme that does not change
clustering dynamically and performs MIMO transmission by using a static cluster. In this paper, thenumber of access points in the static cluster is definedas 10.
TARC (proposed)
TARC is the proposed method that was mentioned in
Section III. Transmission times M in the same cluster
is defined as 10.

B. Evaluation of throughput for increasing number of accesspointsFirst, we evaluate the throughput with increasing number ofaccess points K in the network to show the basic performanceof TARC. Figure 6 shows the throughput when the number ofaccess points changes from 1 to 50. To obtain the throughput,
we measure the average transmission times of the last 50,000of the 60,000 times. The horizontal axis is the number of accesspoints, and the vertical axis is the throughput [bps/Hz].
Figure 6 shows the proposed method achieved the highest
throughput compared to other methods. The proposed TARC
maintains the highest throughput when the number of accesspoints was increased. On the other hand, if the number ofaccess points is three or more, as the number of access points
is increases, the throughput of Giant MIMO is reduced. In
Giant MIMO, the overhead for channel sounding increased
as the number of access points increased thus reducing thethroughput.

dAP × (K-1)/2


dAP dAP



dAP-C

dAP

dAP




Fig. 5.

Fig. 6.

Topology: K access points and one clientFig. 7.

Temporal Change of Throughput

Fig. 8.

Overhead vs. Throughput

Number of Access Points vs. Throughput

C. Evaluation of the convergence time of clustering
In Section IV-B, the average throughput is reported. In
this section, we evaluate the temporal throughput variation for
the three different schemes. It is expected that the throughput of the proposed method exhibits oscillation because itreconfigures the cluster dynamically based on the throughputof the data link layer. Therefore, in order to evaluate theclustering convergence, we evaluated the relationship between
the throughput and the time for the first 200ms. Figure 7 shows
the relationship between the throughput and the time when thenumber of access points K is fixed at 50. The horizontal axis
is time [ms], and the vertical axis is the throughput [bps/Hz].
Figure 7 shows that the convergence time of clusteringin the proposed method is approximately 150 ms while theGiant scheme records the lowest temporal variation across time(thus the best convergence). Most traffic flows over networks
typically span durations greater than 150ms (for example filedownloads and video streaming). Thus, the convergence timeof the TARC should not pose a problem to majority of flows.

the near future. In order to evaluate the reduced overheadof channel sounding, we evaluated the relationship between
overhead and the throughput. We evaluated the throughput withchanging overhead through varying the values of (TNDP +TSIFS ) in equation (1). The relationship between overhead
and the throughput is shown in Figure 8. The horizontal
axis is the overhead [µs] and vertical axis is the throughput[bps/Hz]. Figure 8 shows that the proposed TARC is effectivewith increasing overheads. This is expected because CSI istransmitted more frequently and better reflects the up-to-dateconditions of the channel. For as little as 15µs of overhead,
the TARC outperforms both the Giant and static schemes interms of throughput.

D. Evaluation of throughput for overhead
In Section IV-B, we define the channel sounding overhead
as table II based on IEEE 802.11ac. However, the current
advances improving the performance of pilot signals, e.g.,using vertical pilot series, will probably reduce overhead inV.

R ELATED WORKS

There have been extensive studies of network MIMO inrecent years. Some studies [4], [5] have been made on network
MIMO to perform large MIMO transmission by coordinating

a large number of access points. Previous works [19], [20]show that the capacity of network MIMO can increase with
the number of distributed access points or transmit antennas.
In [15], [16], it is shown that the scheduling algorithm usedin MU-MIMO can work in network MIMO. In particular,[16] shows that the MU-MIMO scheme is also applicable tomultiple network MIMO situations.
In a cellular system, network MIMO has been studied in thename of coordinated multi-point (CoMP). CoMP is expected to
be used in next-generation cellular systems such as long-term
evolution (LTE)-Advanced [5]–[7]. CoMP allows cellular basestations to do network MIMO cooperation to mitigate inter-cellinterference.
In contrast to these, network MIMO in wireless local area
networks (WLAN) has been studied in [8]–[11]. In [8], [9],the authors consider the model in which all access points
in the network are coordinated to create a single cluster andtransmit data. Previous research [10], [11] considers the modelin which there are multiple clusters of access points. In [10],the authors consider the method to avoid inter-cell interferenceby exchanging the allocation of transmit and receive antennas
across the cluster. NEMOx, which is proposed in [11], makes
clusters of access points for downlink transmission based ona backhaul network topology. In these methods, how to create
the access-point clusters remains an open question. With theproposed method, however, the access-point cluster formation
algorithm is considered.
Clustering of clients has been studied for uplink transmission in MU-MIMO [21]. It is one of the node clustering
approaches for improving WLAN capacity. In contrast, TARC
is a method for access-points clustering, and differs fromthese other methods in that access points are connected bya backhaul.
VI.

C ONCLUSION

In this paper, we propose TARC to reduce overhead andimprove the throughput of clients in network MIMO systems.
TARC reduces the overhead by using the throughput of thedata link layer and clustering access points to participate in thetransmission of the network MIMO. Performance evaluationshows that TARC not only reduces overhead but also improves
the throughput of clients compared to previous methods. Our
ongoing and future research includes, among others, experimentally validating TARC using a proof-of-concept prototype.
ACKNOWLEDGMENT
This work was supported by Grant-in-Aid for Scientific
Research (A), (JSPS KAKENHI Grant Number 24240009).
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