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Abstract—Millimeter Wave (mm-w), especially the 60 GHz band,
has been receiving much attention as a key enabler for the 5G
cellular networks. Beamforming (BF) is tremendously used with
mm-w transmissions to enhance the link quality and overcome the
channel impairments. The current mm-w BF mecha nism,
proposed by the IEEE 802.11ad standard, is mainly based on
exhaustive searching the best trans mit (TX) and receive (RX)
antenna beams. This BF mechanism requires a very high setup
time, which makes it difficult to coordinate a multiple number of
mm-w Access Points (APs) in mobile channel conditions as a 5G
requirement. In this paper, we propose a mm-w BF mechanism,
which enables a mm-w AP to estimate the best beam to
communicate with a User Equipment (UE) using statistical
learning. In this scheme, the fingerprints of the UE WiFi signal
and mm-w best beam identification (ID) are collected in an offline
phase on a grid of arbitrary learning points (LPs) in target
environments. Therefore, by just comparing the current UE WiFi
signal with the pre-stored UE WiFi fingerprints, the mm-w AP
can immediately estimate the best beam to communicate with the
UE at its current position. The proposed mm-w BF can estimate
the best beam, using a very small setup time, with a comparable
performance to the exhaustive search BF.
I. INTRODUCTION
Millimeter-wave (mm-w) band communications, particularly
the 60 GHz band, have received a considerable attention as a key
enabler for the 5G cellular networks [1] - [3]. The most attractive
feature of the mm-w communication is its ability to attain
multi-Gbps rate, which can increase the cellular network capacity
to sustain the expected huge increase in mobile data traffic.
However, mm-w communication suffers from high propagation
loss due to its high frequency band. In order to compensate the
tremendous propagation loss and reduce the shadowing effect,
high-gain directional antenna array is favored to improve the
system efficiency and transmission range thanks to the millimeter
range of the antenna size and spacing.
Beamforming (BF) determines the best beam direction formed
by multiple antenna elements to maximize the transmission rate.
Mm-w BF schemes based on estimating the entire channel state
information (CSI) suffer from high calculation load and large
overhead [4]. Instead, various Medium Access Control (MAC)
based BF protocols have been proposed for mm-w transmissions,
in which, switched antenna array with a structured codebook is
used. The main MAC based BF protocols proposed for mm-w
communications are the beam codebook [4], the iterative search [5]
and the multiple sector ID capture (MIDC) [6]. The BF protocol
proposed by the IEEE 802.11ad standard is mainly based on the
MIDC protocol, which is an exhaustive search BF protocol [7].
The MIDC BF protocol mainly consists of the Sector Level
Sweep (SLS) phase, the Multiple Sector Identifier (MID) subphase
and the Beam Combining (BC) subphase [6]. The MIDC BF
protocol for downlink transmission (AP-UE link) is shown in Fig.
1. In the SLS phase, the mm-w TX (AP) beam is scanned, by
changing antenna settings (Antenna Weight Vectors, AWVs),
where the RX (UE) beam on the other link end is kept in
quasi-omni mode, as illustrated in Fig. 1 (a). On the other hand, in
the MID subphase, the mm-w RX (UE) beam is scanned while the
TX (AP) beam is kept in quasi-omni, as illustrated in Fig. 1 (b). At
the end of the SLS and MID, TX and RX beam candidate tables are
constructed. These tables contain the TX and RX antenna sector
IDs (AWVs) corresponding to higher quality TX and RX links [6].
Using these TX and RX candidate beams, a beam combining
subphase (BC) is performed in a round robin trail, Fig. 1 (c). The
BC subphase is used to overcome the problem of imperfect
quasi-omni antenna pattern, which cannot be avoided using the
beam codebook or the iterative search BF protocols [6]. As a result
of the BC subphase, a table of multiple antenna setting pairs
(ASPs) corresponding to higher link qualities is reserved. This
allows for fast beam switching when link blockage occurs between
the TX and the RX. According to [6], using 32 different antenna
sector IDs in the SLS phase and the MID subphase and 7 beams for
the BC subphase, the typical BF setup time consumed by a mm-w
AP using the MIDC BF protocol is about 1.8 msec. The SLS phase
consumes more than 70 % of the total BF time. This is because
control packets are used in the SLS phase, where one preamble
must be transmitted per one antenna setting [6] [7].
For 5G mobile applications, a large number of mm-w APs
should communicate concurrently with their associated UEs using
coordinated transmissions. Coordinated mm-w concurrent
transmissions should be used to fully cover a typ ical indoor
environment while maximizing total system capacity, supporting
more users, reducing user outage rate and assuring fairness among
users especially for densely populated networks. Seamless
handover should be also coordinated among the short range mm-w
APs especially for large enterprise scenarios. Coordinated mm-w
AP (TX)
AP (TX) UE (RX)
AP (TX) UE (RX)
AWV (Tx,1)AWV (Rx,1)
AWV (Tx,2)
(a) Sector Level Sweep (SLS)
(b) Multiple Sector ID (MID)
(c) Beam Combining (BC)
Tx: beam scan
Rx: Quasi-omni
Tx: Quasi-omniRx: beam scanTx: beam scan (for
the selected beams)
Rx: beam scan (for
the selected beams)
UE (RX)
AWV (Tx, )
AWV (Rx, )
AWV (Rx,2)
Millimeter Wave Beamforming Based on WiFi
Fingerprinting in Indoor Environment
1,2Ehab Mahmoud Mohamed, 1Kei Sakaguchi, and 1Seiichi Sampei
1Graduate School of Engineering, Osaka University, 2Electrical Engineering Dept., Aswan University.
Email: ehab@wireless.comm.eng.osaka.ac-u.ac.jp, {sakaguchi, sampei}@comm.eng.osaka-u.ac.jp
Fig. 1. The IEEE 802.11ad (MIDC) BF protocol.
transmissions make BF even more difficult, because at every
decision of coordinated transmissions, the best beams for all AP–
UE links should be known beforehand. For example, the mutual
interferences (available data rates), provided by all alternative
AP-UE combinations, should be known before making a decision
for joint user scheduling while maximizing the total system rate
[2]. These available data rates are unknown until the best
transmission and reception beams for all AP-UE links are finalized
[2]. The use of conventional BF (SLS, MID and BC) will result in
very high system setup time because all APs will exhaustively
search the best beams with their associated UEs at every time slot
of coordinated concurrent transmissions. The use of conventional
BF even becomes unrealistic if we try to coordinate a high number
of mm-w APs with a high number of antenna sectors in mobile
channel conditions, which is a typical assumption for the future 5G
networks. Using 32 TX and RX antenna sectors and 7-beam for
BC, 10 mm-w APs will consume more than 18 msec for finalizing
the best beams. This BF setup time will be increased when using a
higher number of antenna sectors, and it should be consumed at
every time slot of mm-w coordinated concurrent transmissions.
In this paper, we propose a novel BF protocol that greatly
reduces the BF setup time with a comparable performance to the
exhaustive search MIDC protocol. The proposed BF is based on
statistical learning, in which we make use of the wide coverage
WiFi (5 GHz) fingerprinting to localize mm-w (60 GHz) best
sector IDs in indoor environment. Therefore, by just comparing
current UE WiFi signal with the pre-stored UE WiFi fingerprints, a
mm-w AP can immediately estimate the best beam to communicate
with the UE at its current position using the pre-localized best
sector IDs. In this paper, we assume the use of dual band (5 GHz /
60 GHz) UEs, which is a typical assumption for future 5G
networks. The UE WiFi signal can be received by the mm-w APs
themselves using dual band (5GHz / 60 GHz) mm-w APs, or using
separate 5 GHz (WiFi) APs. By focusing on the main concept
without losing the generality, we use Received-Signal-Strength
(RSS) of the UE WiFi signal as a simple fingerprint of the WiFi
signal. Other WiFi fingerprinting techniques such as Time
Difference of Arrival (TDOA), Direction of Arrival (DOA) and
Channel State Information (CSI) can be easily used by extending
the proposed BF [8].
Simulation analysis confirms the high efficiency of the proposed
BF protocol in estimating the best beam compared to the
exhaustive search BF. In addition, a great reduction in the BF setup
time is obtained using the proposed BF.
The rest of this paper is organized as follows; Section II provides
the proposed system model. The proposed BF mechanism is
presented in Sect. III. The performance of the proposed BF
protocol is analyzed in Sect. IV via simulation analysis. Section V
concludes this paper.
II. THE PROPOSED SYSTEM MODEL
Figure 2 shows the details of the proposed system model. For the
purpose of generalization, we assume separate deployments for the
5 GHz (WiFi) APs although dual band (5 / 60 GHz) mm-w APs can
be used. The 60 GHz mm-w APs and the 5 GHz WiFi APs are
connected to a local controller. The local controller can be
implemented as an AP controller for mm-w APs and WiFi APs. In
Fig. 2, the WiFi APs and mm-w APs are connected to the controller
via optical fiber links or gigabit Ethernet. This system will be
installed in a target environment to cover it with the high- capacity
mm-w APs. The WiFi and mm-w best sector ID fingerprints are
collected and stored in the controller in an offline phase.
In addition, the controller performs grouping and clustering on the
collected WiFi fingerprints to find out the best WiFi fingerprint
exemplars that can effectively localize each mm-w best sector ID.
In the online BF phase, after comparing current UE WiFi
readings with the pre-stored WiFi exemplars, group of best sector
IDs (best beams) are estimated for a mm-w AP-UE link. A beam
combining (BC) subphase is conducted using the estimated beams
to find out the best beam with the highest link quality. Beside
overcoming the problem of imperfect quasi-omni antenna pattern,
the BC subphase is mainly used by the proposed BF protocol to
alleviate the real-time beam blocking, e.g., human shadowing,
which may not occur in the offline phase. Also, it is used to
overcome the inaccuracy in WiFi RSS measurements.
In addition to the BF functionality, the controller can be used as a
coordinator to coordinate the transmissions among mm-w APs and
WiFi APs such as performing association, re-association, joint user
scheduling... etc. To facilitate the coordination functionality, the
concept of control plane / user plane (C/U) splitting can be used. In
which, control signals are sent to the UEs using the wide coverage
WiFi APs and a combination of mm-w APs and WiFi APs are used
to deliver the data. Also, switching ON / OFF functionality can be
added to the controller to reduce the total energy consumption.
Thus, the controller can switch ON / OFF the mm-w APs based on
the usage. In addition, it can switch ON / OFF the UE mm-w
interface, using the wide coverage WiFi signaling, based on the
usage of mm-w link. Likewise, the controller works as a Gateway
to connect the proposed mm-w / WiFi system with the global
network. The proposed WiFi / mm-w coordination system can be
considered as an enabler for 5G cellular networks as well as for
future wide coverage multi-gigabit WLAN.
III. THE PROPOSED BEAMFORMING MECHANISM
In this section, we give the details of the proposed mm-w BF
mechanism. The proposed mechanism is used to effectively
estimate the best beam for a mm-w AP-UE link using a statistical
learning approach. The idea behind the proposed BF mechanism is
to eliminate the SLS phase, which consumes the highest setup
time, from the real-time BF. SLS elimination can be effectively
done by performing it in an offline phase and positioning the
mm-w best sector IDs using WiFi fingerprints. Consequently, the
real-time BF can be easily done by just comparing the current UE
WiFi readings with the pre-stored WiFi fingerprints. As a result, a
very small setup time BF protocol is obtained with a comparable
performance to the exhaustive search BF. Figure 3 shows the
general framework of the proposed BF for one mm-w AP-UE link,
which will be explained in more details through the subsequent
sub-sections.
Controller
mm-w AP
mm-w AP
Dual Band UE Dual Band UE
WiFi AP
Optical fiber links or Gigabit Ethernet
WiFi AP
WiFi AP
(5 GHz / 60 GHz)
(5 GHz / 60 GHz)
Fig. 2. The proposed system architecture.
A. The offline Statistical Learning Phase
1) Collecting Fingerprints Databases (DBs)
The first step in the offline statistical learning phase is to
construct the micro-wave and mm-wave radio maps for the target
environment. For the sake of simplicity, we will use the Received
Signal Strength (RSS) as a fingerprint of the WiFi signal; however,
other WiFi fingerprinting methodologies can be easily used
without any modifications to the general concept of proposed BF
protocol. Constructing the radio maps can be effectively done by
collecting the average WiFi RSS readings and mm-w APs best
sectors IDs at arbitrary Learning Points (LPs) in the target
environment. Therefore, two databases are constructed as the
fingerprinting radio maps, named the WiFi RSS DB , and the
best sector ID DB , which can be defined as:
=
, (1)
=
, (2)
where is the average WiFi RSS reading at WiFi AP n from a
dual band UE located at LP l. In order to compensate the changes in
the UE transmitted power, these WiFi RSS measurements are
normalized by the UE transmit power in the 5 GHz band. L is the
total number of LPs, and N is the total number of WiFi APs. is
equal to the best sector ID number corresponding to the maximum
power received by a dual band UE located at LP l from mm-w AP
m, where M is the total number of mm-w APs. can be
calculated as:
=
=arg max
(), 1 , (3)
where indicates the sector ID number of mm-w AP m, is
the total number of sector IDs of mm-w AP m,
is the best
sector ID number corresponding to the maximum received power
at LP l from mm-w AP m, and () indicates the power
received at LP l from mm-w AP m using sector ID . We assume
that all LPs are under the coverage of the N WiFi APs.
A null value in the matrix, i.e., = null, means that mm-w AP
m cannot cover LP l. A null value is decided for if no signal is
detected at LP l after mm-w AP m scans all its antenna sectors, or
the maximum received power is less than the power required for
the communication using the lowest Modulation Coding Scheme
(MSC) index. null best sector IDs can be used to solve WiFi and
mm-w association / re-association problem beforehand using the
statistical learning information, which is left as our future work.
Figure 4 shows an example of a mm-w AP best sector ID radio
map using uniformly distributed LPs in a room area of 200 m2. In
this example, the mm-w AP is located at X= 14 m, Y = 3.5 m and
Z= 3 m. The color bar indicates the best sector ID number. Each
square in Fig. 4 indicates a different LP, and each LP is covered by
a certain mm-w best sector ID, and it has a certain WiFi RSS
readings.
2) Grouping and Clustering the WiFi Fingerprints
In the proposed BF scheme, offline WiFi fingerprints are mainly
used to localize mm-w best sector IDs using LPs. Because many of
the LPs may be covered by the same best sector ID
while their
WiFi RSS readings are different, as it is shown in Fig. 4, the WiFi
RSS readings from the LPs covered by the same best sector ID
are grouped together.
=|{
} , 1
, (4)
where = [ … . ]is the WiFi RSS readings vector
from LP l.
is the k-th WiFi RSS vector in group
.
is
the total number of WiFi RSS vectors in group
, which is equal
to the total number of LPs that can be covered by
. For example,
in Fig. 4, all WiFi RSS vectors from the LPs covered by mm-w
best sector ID 15 (yellow squares) are grouped together.
To reduce the area of interest and the computational complexity
of WiFi RSS matching in the online BF phase, clustering is
conducted on each group of WiFi RSS vectors. Accordingly, the
best WiFi RSS exemplars that can effectively represent each best
sector ID
are obtained. Because mm-w transmissions are highly
affected by shadowing and reflections, the radio map of mm-w best
sector IDs is irregular and highly overlapped, as it is shown in Fig.
4. Therefore, an efficient clustering algorithm should consider this
overlapping nature during forming the clusters. The widely used
K-means clustering algorithm [10] cannot be directly applied to the
proposed BF mechanism. This is because in the traditional
K-means clustering, a cluster exemplar is the centroid of the
nearest K vectors, which may not necessarily be a member of the
data set. Thus, by directly applying the K-means algorithm for
clustering the WiFi RSS fingerprints in a best sector ID group,
some of the calculated WiFi RSS exemplars may belong to other
best sector IDs due to the aforementioned overlapping nature.
Instead, in this paper, we use the affinity propagation clustering
algorithm [9] as an appropriate clustering algorithm for the
Dual Band UEMm-w APWiFiAPs
Controller
WiFisignal from each Learning Point (LP)
WiFireadings from
the LPs
The Online BF Phase
The Offline Statistical Learning Phase
Antenna Sector Level Sweep
(SLS)
The mm-w AP best sector ID at
each LP
Current WiFisignalUE current Wi Fi
readings
The IDs of the best beams
Beam training using the best beams
IDs
Construct the fingerprints
databases that contain the WiFi
APs readings and the mm-w AP
best sector ID at the LPs.
The mm-w AP best sector ID at
the LPs
Group and cluster the WiFi
fingerprints to find out the best
WiFifingerprint exemplars for
each best sector ID
Estimate a group of
mm-w best beams
The ID of the highest link
quality beam
2 4 6 8 10 12 14 16 18
1
2
3
4
5
6
7
8
9
X
Y
2
4
6
8
10
12
14
16
18
Best sector ID exemplars
Fig. 3. The proposed beamforming mechanism.
Fig. 4. An example of a typical mm-w AP best sector ID radio map.
proposed BF protocol. Affinity propagation is a fast clustering
algorithm that simultaneously considers all WiFi RSS vectors in
the same group as potential exemplars for clusters formation by
assigning the same preference value for all of them. This property
is highly favored by the proposed BF due to the overlapping nature
of the best sector ID radio map. The similarity indicator (,)
indicates how well the WiFi RSS vector k,
, is suited to be the
exemplar for WiFi RSS vector i,
, where
and
belong
to the same group
. (,) can be calculated as [9]:
(,)=
, , 1,2, … ,
,. (5)
The self-similarity value s(,) indicates the preference value of
to be a cluster exemplar. Because all WiFi RSS vectors have
equal potentials to be cluster exemplars, s(,) is set as [9]:
s(,)=.median s(,),, 1,2, … ,
, , (6)
where is a design parameter, which is experimentally determined
to control the number of generated clusters.
The core operation of the affinity propagation algorithm is the
exchange of two-kind of messages in a recursive manner. These
messages are the responsibility message (,) and the availability
message (,). The responsibility message (,) is sent from
to the candidate exemplar
to know how well-suited
is to serve as the exemplar for
. The availability message
(,) is sent from the candidate exemplar
to
to reflect
the suitability that
becomes the exemplar of
.
(,) and (,) can be defined as follows [9]:
(,)=(,)max
..
,+,, (7)
(,)=min 0, (,)+max0, ,
..
, (8)
(,)=max0, ,. (9)
..
At any iteration during the affinity propagation process,
availabilities and responsibilities can be combined to identify the
clusters exemplars and their associated members. At the end of the
affinity propagation clustering process, we obtain the set of WiFi
RSS exemplars
, 1,2, … ,
, for best sector ID
,
where C
is the total number of WiFi RSS exemplars (clusters)
for
. For example, in Fig. 4, four WiFi RSS exemplars are
calculated using the affinity propagation algorithm to effectively
localize and represent the best sector ID 15, and Fig. 4 shows the
LPs corresponding to these exemplars.
The offline statistical learning phase will not be repeated unless
the transmit power and location of the APs are changed, or the
internal structure of the target environment is changed.
B. The Online BF Phase
The proposed real-time BF protocol mainly consists of 3- steps,
the online WiFi RSS measurements, the estimation of best beams
and the BC subphase. The actual BF protocol takes place in this
real-time phase.
1) Collecting the Current WiFi RSS Readings
During this step, the online WiFi RSS vector is measured by
the WiFi APs and collected by the controller from the dual band
UE located at an arbitrary position r. is defined as:
= [ … . ]. (10)
2) The Best Beams Estimation
The second step in the online BF protocol, after collecting the
online WiFi RSS readings, is to estimate a group of best beams for
each mm-w AP-UE link. This can be done by calculating the
smallest Euclidian distance between and the WiFi RSS
exemplars of each best sector ID
, i.e., the Euclidian distance
corresponding to the nearest WiFi RSS exemplar to from each
best sector ID
. Therefore, a vector of smallest Euclidian
distances is obtained with a length up to the total number of best
sector IDs. The controller sorts the obtained vector of smallest
Euclidian distances in an ascending order, and it selects a group of
best sector IDs (best beams)
(1: ) corresponding to the nearer
WiFi RSS exemplars to from different best sector IDs. These
best beams
(1: ) are used for the BC subphase.
(1: )=sort
arg min
:. (11)
3) The Beamforming Combining (BC) Subphase
The BC subphase is mainly used by the proposed BF protocol to
overcome the imperfect quasi-omni antenna pattern, to alleviate
the real-time beam blocking, e.g., human shadowing, and to
overcome the inaccuracy in the WiFi RSS measurements. After
selecting the best X beams for mm-w AP m,
(1: ), to
communicate with the UE at its current position r, the controller
sends the IDs of the selected beams to the mm-w AP. If a mm-w
AP wants to communicate with the UE, it should perform a BC
subphase with the UE in the form of beam training using the
estimated beams before data transmission. After the BC training,
the beam pattern corresponding to the highest link quality will be
selected as the best beam for a mm-w AP-UE link.
The BC training can be formulated as:
()=arg max
((
())) , 1 , (12)
where
() is the best beam ID number for mm-w AP m–UE
link, and (
()) is the power received by the UE at its current
position r from mm-w AP m using sector ID
(). Instead of
only estimating the best beam for a mm-w AP-UE link, it is better
to reserve the higher link quality beams, so that fast beam
switching can be accomplished in the case of instantaneous link
blockage.
C. The Frame Format of the Proposed Dual Band BF Protocol
Figure 5 shows the frame format of the proposed BF protocol,
for one mm-w AP-UE scenario. In this frame format a dual band (5
GHz / 60 GHz) MAC protocol is used. For the sake of simple
explanation, the minor details of the MAC protocol, i.e.,
inter-frame durations, random backoff… etc., are not shown in Fig.
5. A probe request (Prob. Req.) management packet is frequently
announced by the UE using its 5 GHz interface for WiFi RSS
measurements. TRSS is the time required for sending the Prob. Req.
packet, and TPT is the processing time required by the controller to
estimate the group of mm-w best beams based on the current WiFi
RSS readings. TPT includes the time required for collecting the
current WiFi RSS readings from the WiFi APs, the time required
for Euclidian distance calculations and the time required for
sending the estimated best beams IDs to the mm-w AP. Using these
estimated beams, a BC subphase using Beam Refinement Packets
(BRPs) is performed by the mm-w AP to find out the best beam for
the mm-w AP-UE link. At the end of the BC subphase, a Feedback
(FB) packet is sent from the UE to the mm-w AP, using its 60 GHz
interface, to report the ID number of the highest link quality TX
beam.
If the UE has many receive antenna sectors, an MID subphase
should be performed before the BC subphase to find out the best
TX / RX antenna beams.
In order to reduce the latency of the proposed scheme, thanks to
the orthogonality between 5 GHz and 60 GHz bands, we propose
that the UE announces the 5 GHz Prob. Req. packet and the
controller estimates the best beams during mm-w data transmission.
Thus, during the current time slot of mm-w data transmission, the
UE sends the 5 GHz Prob. Req. packet and the controller estimates
the best beams for the BC subphase of the next time slot of mm-w
data transmission. Therefore, the latency of the proposed scheme is
highly reduced. If the time slot period for mm-w data transmission
is properly adjusted, the only setup time required by the proposed
BF protocol will be the BC time. If an MID subphase is used, the
total setup time becomes the MID and BC times. Accordingly, The
BF setup time required by the proposed protocol is less than the
time required by the IEEE 802.11ad protocol by the SLS time,
which consumes more than 70 % of the total BF time.
IV. SIMULATION ANALYSIS
In this section, the efficiency of the proposed BF protocol
compared to the exhaustive search IEEE 802.11ad BF protocol is
verified via computer simulations. In the simulation analysis, a
performance metric of the power received from the best beam
estimated by the proposed BF normalized to the power received
from the best beam estimated by the IEEE 802.11ad BF is used.
A. Simulation Area and Simulation Parameters
Figure 6 shows the ray tracing simulation area of an indoor
environment. For the purpose of generalization, we consider
separate deployments of the WiFi APs from the mm-w APs. Room
materials are from concrete except the desks are made of wood.
Other simulation parameters are given in Table I.
The steering antenna model, which is defined in the IEEE
802.11ad [7], is used as the transmit antenna directivity for the
mm-w AP, in which the 3D beam gain in dB can be defined as
follows:
(,)[] = []min[(()+()), ], (13)
[]=12 + [], (14)
[]=20log.
, (15)
where , are the azimuth and elevation angles, and (),
() are the beam gains in the horizontal and vertical directions,
which can be defined as:
()=min 12
, , (16)
()=min 12
, , (17)
where and are the half power beamwidths in the
horizontal and vertical directions. and are the angles
corresponding to the beam center.
Parameter
Value
Number of WiFi APs
4
Number of mm-w APs
8
Tx power of WiFi APs / mm-w APs
20dBm /10dBm
, for mm-w AP beam
20°
Antenna model for the 5GHz band
Omni
Number of antenna sectors for mm-w AP /
UE
92 / 1
Antenna model of the UE @ 60 G Hz
Quasi-omni
Probability of 60 GHz Line of Sight (LOS)
path blocking due human shadowing [7]
Uniform distribution
(the clusters generation number)
0.3
In this paper, we consider 3D beamforming in order to fully cover
the indoor area, i.e., each beam has different values of and
. Thus, the total channel gain from mm-w AP m to UE k
becomes:
()= (,)(,,)
, (18)
where (,,) is the complex channel response between
mm-w AP m and UE k without beamforming gain.
B. Simulation Results
Figure 7 shows the average Received Power Ratio (RPR) in dB
between the power received from the best beam estimated by the
proposed BF protocol and the power received from the best beam
estimated by the exhaustive search IEEE 802.11ad BF using
different number of LPs. The average is taken over the used
number of mm-w APs (8 mm-w APs). In this simulation, we use
4-WiFi APs, as it is shown in Fig. 6. Also, 5 best beams are
estimated for the BC subphase. For the purpose of comparison, we
give the performance of only using the nearest neighbor (N. N.)
best beam. In this scheme, based on the current WiFi RSS readings
vector, the controller calculates its N. N. offline WiFi RSS vector
from the entire WiFi RSS fingerprints database. Then, it selects
mm-w best sector ID corresponding to this N. N. WiFi RSS vector
as the best beam for the mm-w AP-UE link. Thus, grouping and
clustering are not performed on the offline WiFi RSS fingerprints.
Also, no real-time beam training is used.
From Fig. 7, as the number of LPs is increased, the performance
of the proposed BF protocol is enhanced. This is because, as the
number of LPs is increased, high resolution micro wave and mm
wave radio maps can be constructed for the indoor environment.
Accordingly, the proposed BF can accurately estimate the best
beam for a mm-w AP-UE link. Also, the performance of estimating
a group of best beams for real-time beam training is better than
only selecting the N. N. offline best beam.
Prob. Req.
mm-w AP
t
t
UE / 5 GHz
t
FB
UE / 60 GHz
BRP
DATA
BRPBRP
DATA
Prob. Req.
Wi-Fi AP 1
Wi-Fi AP 3
Wi-Fi AP 2
Wi-Fi AP 4
4 m
mm-w AP 3
mm-w AP 4
mm-w AP 1
mm-w AP 2
mm-w AP 5
mm-w AP 6
mm-w AP 7
mm-w AP 8
Fig. 5. The frame format of the proposed dual band mm-w BF protocol.
Fig. 6. The ray tracing simulation area.
T
ABLE
.1
T
HE SIMULATION PARAMETERS
This is because the real-time beam training overcomes the online
link blockage, and it compensates the fluctuations in the WiFi RSS
measurements. These problems cannot be handled by only
estimating the N. N. offline best beam; simply because the
estimated offline beam may be blocked in the real-time scenario.
From the trade-off between complexity and performance, 90 LPs
are selected as a sufficient number of LPs.
Figure 8 shows the average RPR performance of the proposed
BF protocol using different number of WiFi APs. The WiFi APs
are distributed one by one in room corners and centers. In this
simulation, we use 90 LPs and 5 best beams. As the number of
WiFi APs is increased, the RPR performance is enhanced. This is
because the accuracy of the WiFi RSS fingerprints is increased.
From the trade-off between deployment cost and performance 3
WiFi APs are selected as a sufficient number of WiFi APs.
Figure 9 shows the average RPR performance of the proposed
BF protocol when increasing the number of estimated best beams
for real-time beam training. In this simulation, 90 LPs and 3-WiFi
APs are used. As it is clearly shown, as we increase the number of
estimated best beams, the RPR performance is enhanced. By only
using 10 training beams, only 1 dB difference in maximum
received power from the exhaustive search BF is obtained using
the proposed BF.
V. CONCLUSION
In this paper, we focused on the problem of developing a fast
mm-w BF protocol with a comparable performance to the
conventional exhaustive search BF as an enabler for applying the
60 GHz mm-w technology in 5G networks. In this regard, we
proposed a novel mm-w BF mechanism based on a statistical
learning approach. In this mechanism, the best beam for a mm-w
AP-UE link is estimated using WiFi fingerprints. The micro-wave
and mm-wave radio maps are pre-constructed for target
environments in an offline phase. Consequently, the best beam for
a mm-w AP-UE link can be immediately estimated by just
comparing the current UE WiFi readings with the pre-stored WiFi
fingerprints. Using 92 antenna sectors, 10 estimated best beams, 3
WiFi APs and 90 LPs, the proposed BF protocol succeeded to
estimate the best beam for a mm-w AP-UE link with a decrease in
BF setup time of more than 70 % compared to the exhaustive
search BF. This high decrease in BF setup time comes at the
expense of only 1 dB decrease in maximum received power. The
proposed BF protocol can be used as an enabler for coordinated
mm-w concurrent transmissions and its extension for 5G cellular
networks. For future work, dynamic learning will be investigated
in conjunction with the proposed BF protocol for further
enhancements of its complexity and performance.
ACKNOWLE DGMENT
This work is partly supported by “Research and development
project for expansion of radio spectrum resources” of MIC, Japan.
REFERENCES
[1] T. S. Rappaport et al., “Millimeter wave mobile communications for 5G
cellular: it will work!,” IEEE Access, vol. 1, pp. 335-349, may 2013.
[2] Ehab M. Mohamed, K. Sakaguchi and S. Sampei, “Delayed offloading
using cloud cooperated millimetre wave gates,” in Proc. IEEE PIMRC,
Sept. 2014, pp. 1852-1856.
[3] A. Ghosh et al., “Millimeter-wave enhanced local area systems: A
high-data-rate approach for future wireless networks,” IEEE J. Sel. Areas
Commun., vol. 32, no. 6, pp. 1152-1163, Jun. 2014.
[4] J. Wang et al., “Beam codebook based beamforming protocol for
multi-Gbps millimeter-wave WPAN systems,” IEEE J. Sel. Areas
Commun., vol. 27, no. 8, pp. 1390-1399, Oct. 2009.
[5] Wireless Gigabit Alliance, Inc., “WiGig MAC and PHY Specification,
Version 1.2,” Mar. 2013.
[6] K. Hosoya et al., “Multiple sector ID capture (MIDC): A novel
beamforming technique for 60 GHz band multi-Gbps WLAN/PAN
systems,” IEEE Trans. Antennas Propagat., vol .63, no. 1, pp. 81-96, Jan.
2015.
[7] IEEE 802.11ad standard: “Enhancements for Very High Throughput in
the 60 GHz Band,” 2012.
[8] C. Feng, W. S. A. Au, S. Valaee, and Z. Tan
“Received-signal-strength-based indoor positioning using compressive
sensing,” IEEE Trans. Mob. Comput., vol. 11, no. 12, pp. 1983-1993,
Dec. 2012.
[9] B.J. Frey and D. Dueck, “Clustering by passing messages between data
points,” Science, vol. 315, no. 1, pp. 972-976, Feb. 2007.
[10] E. Gokcay and J. Principe, “Information theoretic clustering,” IEEE
Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 2, pp.
158-172, Feb. 2002.
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Fig. 9. The average RPR using different number of estimated beams.
Fig. 7. The average RPR using different number of LPs.
Fig. 8. The average RPR using different number of WiFi APs.
