ieice communications express, vol.7, no.5, 154 performance … · user multiple-input...
TRANSCRIPT
Performance evaluation ofdistributed antenna in uplinkMU-MIMO using amplitudeinformation and many-to-oneswitch
Soichi Ito1a), Sho Yoshida1, Kentaro Nishimori1,Tomoki Murakami2, Koichi Ishihara2, and Yasushi Takatori21 Graduate School of Science and Technology, Niigata University,
8050 2-no-cho Ikarashi, Nishi-ku, Niigata 950–2181, Japan2 Nippon Telegraph and Telephone Corporation, NTT Access Network Service
Systems Laboratories, 239–0847, Japan
Abstract: This paper proposes a hardware configuration for uplink multi-
user multiple-input multiple-output (MU-MIMO) transmissions with dis-
tributed antenna systems (DASs) in real indoor environments. Beam-forming
(BF) technology is used in the DAS with massive MIMO. In massive MIMO
transmission, signal processing becomes complicated because of the massive
antennas. Therefore, in general, a technique known as digital beamforming
(DBF) is adopted, in which the weight values are calculated through digital
signal processing. Massive MIMO systems applying DBF, which requires
receivers for all massive antennas, have problems related to power con-
sumption and cost because the access point becomes large.
We propose an analog–digital hybrid configuration, which selects anten-
nas using a many-to-one switch connected to the antenna. In the proposed
configuration, it is possible to reduce the number of receivers required, by
grouping antennas using many-to-one switches. In this paper, we evaluate
the effect of the proposed configuration through computer simulations using
the propagation channels obtained from experiments conducted in real
indoor environments. The effectiveness of the proposed configuration is
demonstrated by comparing the basic characteristics, when antennas are
selected according to the conventional full-digital configuration and the
proposed analog–digital hybrid configuration.
Keywords: multiuser MIMO, distributed antenna system, antenna selec-
tion, channel capacity, zero-forcing
Classification: Antennas and Propagation
References
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vol. 6, no. 3, pp. 311–335, Mar. 1998. DOI:10.1023/A:1008889222784[2] Q. H. Spencer, C. B. Peel, A. L. Swindlehurst, and M. Haardt, “An introduction
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[7] E. G. Larsson, “Very large MIMO systems,” Proc. in IEEE ICASSP 2012Tutorial Text.
[8] F. Rusek, D. Persson, B. K. Lau, E. G. Larsson, T. L. Marzetta, O. Edfors, andF. Tufvesson, “Scaling up MIMO - Opportunities and challenges with very largeMIMO -,” IEEE Signal Processing Magazine, vol. 30, no. 1, pp. 40–60, Jan.2013. DOI:10.1109/MSP.2011.2178495
[9] J. Hoydis, S. ten Brink, and M. Debbah, “Massive MIMO in the UL/DL ofcellular networks: How many antennas do we need?” IEEE J. Sel. AreasCommun., vol. 31, no. 2, pp. 160–171, Feb. 2013. DOI:10.1109/JSAC.2013.130205
1 Introduction
Multiuser multiple-input multiple-output (MU-MIMO) transmissions have been
attracting much attention, as a technique for improving the channel capacity of the
entire system by generating a large virtual channel between the access point (AP)
and multiple user terminals (UTs) [1, 2, 3].
Downlink MU-MIMO transmissions have been introduced as one of the main
technologies in the LTE and IEEE 802.11ac standards. Uplink MU-MIMO trans-
mission is currently being standardized under LTE-advanced [4]. In IEEE 802.11ax
[5], the standardization of uplink MU-MIMO and orthogonal frequency division
multiple access (OFDMA) has been decided, and it is expected that their impor-
tance will increase in the future.
Massive MIMO transmissions have been attracting much attention in 5th
generation mobile communication systems (5G) and IEEE 802.11ay, in order to
improve the performance of MU-MIMO transmissions [6, 7, 8, 9].
In massive MIMO transmission, the antenna configuration used for communi-
cation is usually selected by the digital part is adopted. The signal processing
becomes complicated because of the presence of many antennas. In this config-
uration, when massive MIMO transmission is assumed, the scale of the access point
becomes large, as receivers are required for all antennas. The increase in the access
point size is problematic in terms of power consumption and cost.
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In this paper, we propose a hardware configuration for uplink MU-MIMO
transmission, to solve this problem. In the proposed configuration, the distributed
antennas are first divided into multiple groups. Then, the antennas with the highest
received powers are selected using amplitude detectors. Finally, the antennas are
selected using many-to-one switches. This configuration is named the analog–
digital hybrid configuration. The proposed configuration is suitable for implemen-
tation in massive MIMO transmission. In order to clarify the effectiveness of the
proposed configuration, we performed computer simulations using the propagation
channels obtained through experiments conducted in real indoor environments.
The remainder of this paper is organized as follows. Section 2 describes the
conventional and proposed configurations and explains the advantages of the
proposed configuration. Section 3 shows the measurement environment and meas-
urement parameters. In Section 4, the effectiveness of the proposed configuration is
demonstrated by comparing the basic characteristics when antennas are selected
according to the conventional full-digital configuration and the proposed analog–
digital hybrid configuration. The paper is concluded in Section 5.
2 Conventional configuration and proposed configuration
Fig. 1(a) shows the conventional access point (AP) configuration of the DAS in
uplink MU-MIMO. In the conventional configuration, the antennas used for
communication are selected through digital signal processing. Down converters
and analog-to-digital (A/D) converters are necessary for the receivers, and re-
ceivers are connected to all antennas. In the case of massive MIMO, the required
number of receivers increases as the number of antennas increases, and the
hardware is scaled up.
Fig. 1(b) shows the proposed analog–digital hybrid configuration. The pro-
posed configuration uses many-to-one switches and amplitude detectors. The N
antennas are divided into NS groups. NL represents the number of antennas in each
group. All antennas in a group are connected to one many-to-one switch. Therefore,
in the proposed configuration, one antenna is selected by each switch. The received
signal of each group is input to the amplitude detector via the directional coupler.
Fig. 1. Hardware configuration
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The amplitude information is measured by the amplitude detectors, and the antenna
with the maximum amplitude is selected by each many-to-one switch. In this
configuration, one receiver is connected to one many-to-one switch. Therefore, the
required number of receivers is reduced from N to NS, when compared to the
conventional AP configuration, and the hardware scale is reduced. Compared to
the conventional configuration in which the antenna is selected in the digital part, in
the proposed configuration, the antenna is selected in the analog part by the many-
to-one switch, and thus, the antenna selection process becomes simple.
3 Antenna selection methods and measurement environment
Fig. 2(a) show the real indoor measurement environment. In Fig. 2(a), the blue
circles are the UTs, and the AP antennas are represented by the colors A, B, C, and
D. Each color indicates one group of AP antennas. The number of AP antennas is
32 and the number of UTs is 32. From 32 users, four users are selected for each
trial. A total of 35,960 trials are conducted for different UT combinations. At this
time, the numbers 1∼4 are assigned to the UTs. Then, #UT 1∼4 are paired with
groups A∼D. A single antenna with the highest signal-to-noise ratio (SNR) is
selected from each group (A, B, C, and D at the AP in Fig. 2(a)): four AP antennas
are selected in total in Fig. 2(a). The resulting 4 � 4 uplink MU-MIMO trans-
mission is evaluated in Fig. 2(a).
Fig. 2(b) shows our measurement parameters. The carrier frequency and
bandwidth are 2.425GHz and 20MHz, respectively. The transmission signal is
an orthogonal frequency division multiplexed (OFDM) signal. The transmission
power is 0 dBm. The transmitters and receivers are connected with the UTs and AP
antennas, respectively, and the uplink channel state information (CSI) between the
UTs and AP antennas is measured. The heights of the AP and UT antennas are 2.3
and 0.7m, respectively. Patch and sleeve antennas are used for the AP and UT
antennas, respectively. The thermal noise power is determined so that the average
received power versus the thermal noise power, i.e., SNR, is 20 dB when consid-
ering all the antennas and UTs.
We now evaluate the Shannon capacity and achievable bit rate using zero
forcing (ZF) while considering the actual propagation channel. The Shannon
capacity in the uplink channel Cs can be written as:
Fig. 2. Measurement environment and parameter
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Cs ¼Xk4
log2 1 þ P
4�2�k
� �; ð1Þ
where �k is the k-th eigenvalue, and P and �2 are the transmit power and noise
power, respectively. The achievable bit rate using ZF CZF can be written as:
CZF ¼Xk4
log2 1 þ SNRZF;k
4
� �; ð2Þ
where SNRZF;k is the k-th SNR obtained from the interference cancellation due to
the ZF for the k-th user.
In the proposed configuration, the antennas are selected in the analog section.
Using the signal sent to the amplitude detector via the directional coupler, the
antenna with the highest SNR is selected. The selected antennas are switched using
the many-to-one switch. This is named DASH.
In the conventional configuration, the antennas are selected in the digital part.
Therefore, the antennas with the highest SNR are selected from each group (A, B,
C, and D) at each subcarrier of OFDM, similar to DASH. This is named
DASH (sub).
4 Comparison of channel capacity
Fig. 3 shows the transmission rates and channel capacities of DASH and
DASH (sub). The solid line represents the transmission rate when ZF is applied
to DASH (sub) and DASH, and the dashed line represents the channel capacity
when applying the Shannon limit to DASH (sub) and DASH.
In the conventional configuration DASH (sub), antennas are selected in the
digital part; therefore, antennas can be selected for each subcarrier of OFDM. It is
thus possible to select a more optimal antenna, when compared to the proposed
configuration that selects the antenna in the analog part. The transmission rate is
also high, accordingly. This difference can also be confirmed from the graph.
However, since the difference is very small, the performance degradation of DASHis also small, when compared to that of DASH (sub).
Fig. 3. Achievable bit rate (DASH and DASH (sub))
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Antenna selection in the conventional configuration is a very complicated
process, and the scale of the base station increases on the hardware side. On the
other hand, the antenna selection process by the proposed configuration is relatively
simple. Furthermore, in the proposed configuration, the scale of the AP can be
reduced by using many-to-one switches. From this, it can be confirmed that the
proposed configuration is very effective as a realistic hardware configuration in
which the performance hardly deteriorates.
5 Conclusion
In this paper, we proposed an analog–digital hybrid hardware configuration using
amplitude detectors and many-to-one switches for uplink MU-MIMO transmission
with DAS.
In the proposed configuration, the distributed antennas were divided into
multiple groups. Each group of antennas were connected to a many-to-one switch,
and an amplitude detector selected the antenna with the highest received power in
the group. The selected antennas were switched by each many-to-one switch.
Compared to the conventional full-digital hardware configuration, the proposed
configuration could reduce the size of the AP by using many-to-one switches. In the
conventional configuration, the processing becomes complicated as many antennas
are used for communication. On the other hand, in the proposed configuration, the
antennas can be selected through relatively simple processing.
In this study, we experimented in real indoor environments assuming DAS and
acquired the propagation channel. From the obtained propagation channel, the
performance was evaluated by comparing the conventional configuration and the
proposed configuration, in terms of the transmission rate and channel capacity.
From the results, it was confirmed that the performance degradation in the proposed
configuration was small, when compared with the conventional configuration
selecting antennas for each subcarrier. It was shown that the proposed configuration
was effective as it could be realized through simple calculations and the sizes of AP
devices could be reduced.
Acknowledgments
We would like to thank the members of the Nishimori Laboratories and NTT
Access Network Service Systems Laboratories for their assistance in the indoor
measurements. Part of this work was supported by KAKENHI, Grant-in-Aid for
Scientific Research (B) (17H03262).
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Clarification ofaccommodatable number offunctional split base stationsin TDM-PON fronthaul
Daisuke Hisanoa), Hiroyuki Uzawa, Yu Nakayama,Kenji Miyamoto, Hirotaka Nakamura, Jun Terada,and Akihiro OtakaNTT Access Network Service Systems Laboratories, NTT Corporation,
1–1 Hikarinooka, Yokosuka-shi, Kanagawa 239–0847, Japan
Abstract: Mobile fronthaul (MFH) is the optical link between a central
unit (CU) and a distributed unit (DU) in centralized radio access network
(C-RAN) architecture. To suppress the cost of MFH, we have studied MFH
networking based on a time division multiplexed passive optical network
(TDM-PON). We have proposed low-latency uplink forwarding with a
mobile dynamic bandwidth allocation (M-DBA) scheme. When the M-
DBA scheme is employed, the uplink latency can be suppressed compared
with fixed bandwidth allocation (FBA). In this paper, we clarify the number
of ONUs that can be accommodated by the M-DBA scheme when also
accommodating a novel functional split based C-RAN.
Keywords: TDM-PON, mobile fronthaul, dynamic bandwidth allocation,
5G
Classification: Fiber-Optic Transmission for Communications
References
[1] 3GPP, TR 38.801, v1.0.0, “Study on new radio access technology; Radioaccess architecture and interfaces (Release 14),” Dec. 2016.
[2] T. Tashiro, S. Kuwano, J. Terada, T. Kawamura, N. Tanaka, S. Shigematsu, andN. Yoshimoto, “A novel DBA scheme for TDM-PON based mobile fronthaul,”Optical Fiber Communication Conference, paper Tu3F.3, Mar. 2014. DOI:10.1364/OFC.2014.Tu3F.3
[3] Y. Nakayama, K. Maruta, T. Shimada, T. Yoshida, J. Terada, and A. Otaka,“Utilization comparison of small-cell accommodation with PON-based mobilefronthaul,” J. Opt. Commun. Netw., vol. 8, no. 12, pp. 919–927, Dec. 2016.DOI:10.1364/JOCN.8.000919
[4] H. Nomura, H. Ou, T. Shimada, T. Kobayashi, D. Hisano, H. Uzawa, J. Terada,and A. Otaka, “First demonstration of optical-mobile cooperation interface formobile fronthaul with TDM-PON,” IEICE Commun. Express, vol. 6, no. 6,pp. 375–380, Jun. 2017. DOI:10.1587/comex.2017XBL0030
[5] H. Uzawa, H. Nomura, T. Shimada, D. Hisano, K. Miyamoto, Y. Nakayama, K.Takahashi, J. Terada, and A. Otaka, “Practical mobile-DBA schemeconsidering data arrival period for 5G mobile fronthaul with TDM-PON,”
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Proc. of IEEE/OSA European Conference on Optical Communication(ECOC), pp. M. 1. B. 2, Sep. 2017.
[6] CPRI, “eCPRI specification V1.0,” http://www.cpri.info/spec.html, AccessedDec. 19, 2017, Aug. 2017.
[7] CPRI, “CPRI specification V7.0,” http://www.cpri.info/spec.html, AccessedFeb. 8, 2018, Oct. 2015.
[8] G. Piro, N. Baldo, and M. Miozzo, “An LTE module for the ns-3 networksimulator,” Proc. of the 4th International ICST Conference on Simulation Toolsand Techniques. ICST (Institute for Computer Sciences, Social-Informatics andTele-communications Engineering), pp. 415–422, Mar. 2011.
[9] ns-3, available: http://www.nsnam.org/.[10] 3GPP, TR 36.814, v9.2.0, “Evolved universal terrestrial radio access (E-
UTRA); Further advancements for E-UTRA physical layer aspects (Release9),” Mar. 2017.
1 Introduction
A centralized RAN (C-RAN) architecture is employed for a mobile base station
(MBS) to reduce costs and make MBSs cooperate each other. With the C-RAN
architecture, a radio frequency (RF) functional block and part of the physical
processing unit are located in a distributed unit (DU) at the antenna site [1]. The
other upper layer processing units are consolidated in a centralized unit (CU). The
transmission link between the CU and the DU is connected by an optical fiber and
called mobile fronthaul (MFH). MFH networking has been widely studied to limit
the MFH link cost, and we have studied MFH networking based on a time division
multiplexed passive optical network (TDM-PON) [2, 3]. Specifically, we have
proposed low-latency uplink forwarding with a mobile dynamic bandwidth allo-
cation (M-DBA) scheme. When the M-DBA scheme is employed, the optical line
terminal (OLT) and the DU cooperate as regards the uplink transmission through a
dedicated interface defined as a cooperative interface (C-IF) [4]. Moreover, we have
demonstrated experimentally that the M-DBA can reduce uplink forwarding
latency compared with the conventional fixed bandwidth allocation (FBA) scheme
[5]. However, the number of DUs that can be accommodated has not been
estimated. In general, the cost reduction becomes greater as we increase the number
of accommodatable optical network units (ONUs) connected to the DUs. In this
paper, we clarify the number of ONUs that can be accommodated based on
statistical information regarding the amount of MFH data, with the M-DBA or
the conventional FBA when accommodating functional split based MBSs [1, 6].
2 Overview of mobile-DBA scheme
The bandwidth allocation scheme is detailed in [2], [4], and [5]. The CU calculates
a wireless schedule for a wireless uplink transmission from user equipment (UE).
The CU then allocates the wireless bandwidth to the UE. After 4 transmission time
intervals (TTIs), the UE transmits a wireless signal to the DU. With a long term
evolution (LTE) system, 1 TTI equals one millisecond. In the M-DBA scheme, the
CU transmits the calculated wireless scheduling information to the OLT through the
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C-IF. The OLT calculates the bandwidth for each ONU based on the wireless
scheduling information and allocates the bandwidth to each ONU. After 4 TTIs,
when the DU forwards the MFH signal to the ONU, the ONU has already been
allocated a suitable bandwidth and can forward its signal to the OLT without any
transmission waiting time. Thus the transmission waiting latency is minimized.
3 Functional split-based MBS
If the MBS employs a conventional functional split point (e.g. common public
radio interface (CPRI) [7]), the MFH link will have to cope with a huge amount of
data in 5G era. Therefore a novel functional split point has been discussed [1, 6] to
reduce the amount of MFH data. In [1], there are several candidate functional split
points. Fig. 1(a) shows the functional blocks with the novel functional split point.
The option number in Fig. 1(a) refers to [1]. A MAC-PHY split (option 6) and an
Intra-PHY split (option 7–2) are particularly suitable with TDM-PON since the
MFH traffic is packetized in a layer-2 frame. For options 6 and 7–2, the amount of
MFH data is following,
Dmfh ¼NlayStbs ðif option 6Þ (1a)
NlayNiqNqNrbNscNsym ðif option 7{2Þ (1b)
(
where Nlay is the number of multiple-input and multiple-output (MIMO) layers, Stbsis the transport block size, Niq is the number of in- and quadrature-phase parts
(¼ 2), Nq is the number of quantization bits of the in- and quadrature-phase parts,
Nrb is the number of resource blocks in a wireless system bandwidth, Nsc is the
number of subcarriers in a resource block, and Nsym is the number of orthogonal
frequency division multiplexed (OFDM) symbols in a physical uplink shared
channel (PUSCH).
In addition, the MFH link has bursty traffic distribution characteristics. With
option 6 in Fig. 1, the wireless signal is demodulated and decoded with respect to
each TTI. The bursty MFH traffic is periodically generated at every TTI. For option
7–2, two types of bursty traffic distribution are conceivable. First is bursty MFH
traffic with a resource block interval. When a wireless signal is forwarded by every
resource block, the interval of the bursty MFH traffic is 0.5 TTI. The other is the
bursty MFH traffic generated by every OFDM symbol. For example, for an LTE
system, 1 TTI is equivalent to 1 millisecond and 14 OFDM symbols are transmitted
during 1 TTI. As a result, bursty MFH traffic with an interval of approximately
71.4 µs is generated. Fig. 1(b)–(d) summarizes the three types of bursty MFH
traffic distribution.
4 Calculation method and analysis result
We estimate the probability P that all DUs generate MFH signals at the same time
without exceeding the latency requirement Treq. We assume the three types of MFH
traffic distribution shown in Fig. 1(b)–(d). The estimation method is divided into
two steps. First we calculate the probability mass function of the discrete proba-
bility distribution for the amount of MFH data. Then we calculate the number of
accommodatable DUs taking the M-DBA into consideration.
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4.1 Probability mass function for MFH data amount
We use the LTE module [8] of a network simulator 3 (ns-3) [9]. The LTE module
generates the TBS and modulation coding scheme (MCS) information per TTI and
we simulate multiple times with different UE deployment locations and different
mobile traffic generation conditions. This is because the wireless throughput
changes depending on the propagation environment between the DU and the UE.
And, in [10], the parameter for the number of the UEs is described as 2, 5, 8, 10,
and 14. Since the cell size was smaller than that in [10], in this paper we assumed 4
UEs as half of the median described in [10]. We then converted the TBS and MCS
information about the LTE system into MFH data assuming a 5G system in
accordance with Eq. (1). We produced a histogram of the MFH data. The class
of the histogram indicates the MFH data volume vmfh. The frequency of the
histogram is equivalent to the discrete probability pðvmfhÞ. We assume that all the
DUs generate an MFH signal according to its histogram.
4.2 Number of accommodatable DUs based on M-DBA
We define the DU identifier as i, and the number of ONUs (¼ DUs) as Nonu. The
request size ri of the uplink transmission from the CU to the OLT through C-IF is
equivalent to the MFH data volume vmfh. We need to calculate the transmission
waiting time Di for all the DUs. The allocated bandwidth bi [byte/polling cycle]
is as follows,
bi ¼ riXNonu
k¼1rk
Cpon; ð2Þ
where the bi unit is bytes per polling cycle and Cpon is the PON link capacity. The
polling cycle means the uplink transmission interval of the TDM-PON system. The
number of polling cycles Nipoll to forward the request size ri is,
Nipoll ¼ ceil
ribi
� �: ð3Þ
ceilðxÞ represents a ceiling function. Therefore the transmission waiting time Di is,
Di ¼ NipollTpoll � Li þ Tfix; ð4Þ
where Tpoll is the polling cycle and Li is the original frame length. Tfix is the fixed
latency caused by processing delay and frequency/synchronization errors. Next, we
(a)
(d)
(b)
(c)
Fig. 1. Functional split point [1]. (a) Functional blocks, burstyMFH traffic distribution with (b) TTI, (c) resource block, and(d) OFDM symbol intervals.
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calculate the probability P.
P ¼XMs1¼0
XMs2¼0
� � �XM
sNonu¼0ðp1ðrs1Þp2ðrs2Þ � � �pNonuðrsNonu
ÞÞ; ð5Þ
ðs1 ≠ s2 ≠ � � � ≠ sNonuÞwhere the identifier si is the class of the histogram and M is the maximum value of
its class. The number of summations depends on the number of ONUs Nonu. When
the element p1ðrs1Þp2ðrs2Þ � � �pNonuðrsNonuÞ is not under the following condition, the
calculation result is excluded from P in Eq. (6).
XNonu
i¼1Di < Treq: ð6Þ
We calculate the probability P for different Nonu values according to Eq. (3)–(6).
4.3 Calculation result and discussion
The calculation parameters are shown in Table I. The exponential distribution
model is defined in [10]. For the exponential distribution model, the average burst
Table I. (a) Calculation parameters for mobile system.
Item Value
Traffic model Exponential distribution
Number of UEs 2 UEs/DU
Wireless data rate from UE 100Mbps
UE deployment Uniform distribution
Transmission power from UE 23.0 dBm
Noise figure from UE 7.0 dB
Nrb 100
System bandwidth 20MHz/CA
Number of carrier aggregations 5 CAs
Nlay 2
Radius of small cell 10.0m
Minimum distance between UE and DU 1.0m
Simulation time 30.0 s
Number of iterations 1000
Fading model Extended typical urban (ETU) model
Path loss model Urban Micro (UMi)
(b) Calculation parameters for TDM-PON.
Item Value
Cpon 10.0Gbps
Tpoll 31.25 µs
Minimum allocated bandwidth 320 byte/Tpoll
Burst overhead 1024 byte/Tpoll
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duration was 1.0 s and the average burst interval was 5.0 s. The latency requirement
Treq was set at 150µs. To compare the performance, we undertook the calculation
when employing an FBA scheme. For the FBA, the bandwidth bi is,
bi ¼ 1
NonuCpon: ð7Þ
We show the calculation result and the histogram in Fig. 2(a)–(f ). From Fig. 2(a),
(b), the functional split point is assumed to be option 6. For example, the P value is
improved more than 20% when 8 DUs are accommodated. For option 7–2,
Fig. 2(e), (f ) show the bursty MFH traffic distribution with a resource block
interval. Fig. 2(c), (d) show the bursty MFH traffic distribution with an OFDM
symbol interval. Since we expect statistical multiplexing effects, we assumed that it
was not necessary for the result to be 100%. The probability threshold value Pth for
determining the number of accommodatable ONUs is set at 90%. Then, from
Fig. 2(c), (d), the number of ONUs that can be accommodated is improved from 2
to 4. Moreover, for Fig. 2(e), (f ), the number is improved from 4 to 6.
5 Conclusion
We calculated the number of accommodatable ONUs when our proposed M-DBA
is applied. We evaluated the number of ONUs based on the mobile traffic generated
by the LTE module of the network simulator 3. For option 6, the probability is
improved by more than 20% when 8 DUs are accommodated. For option 7–2, the
accommodation efficiency is improved 1.5 and 2.0 times in the cases of a bursty
(c)
(a)
(d)
(b)
(f)(e)
Fig. 2. Calculation result. (a) Probability P and (b) histogram foroption 6, (c) and (d) for option 7–2 with OFDM symbolinterval, and (e) and (f ) for option 7–2 with resource blockinterval.
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MFH traffic distribution with OFDM symbol and resource block intervals, respec-
tively when the probability threshold value Pth is set at 90%.
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Second-order spectrumextraction based onionospheric clutter
Anyun Yina) and Zili Lib)
School of Electronic Engineering, GuangXi Normal University,
Guilin City, the Guangxi Zhuang Autonomous Region, 541004, China
Abstract: The reflection and scattering effects of ionosphere can cause
clutter interference to the receiving signal of high frequency surface wave
radar (HFSWR), which makes it difficult to analyze the spectrum of signal
echo. In order to separate the second-order spectrum with wave information
component from spectrum containing ionospheric clutter interference, we
propose a hybrid echo signal processing algorithm. That is, analyzing the
ionospheric signal characteristics for a specific water’s latitude and on the
basis of the existing elimination algorithm, according to the characteristics
of large scale steady state of the ocean, we introduce parameters such as
coherence time and distance value, making an intensive study of the diving
method of second-order spectrum area, and obtaining a new extraction
algorithm with regional characteristics and strong pertinence. Experimental
results show that this method is effective.
Keywords: ionospheric clutter, mixed echo, signal processing, specific sea
area, second-order spectrum extraction
Classification: Fundamental Theories for Communications
References
[1] S. T. Gille, “An introduction to ocean remote sensing,” Eos Trans. Am.Geophys. Union, vol. 86, no. 12, p. 125, 2005. DOI:10.1029/2005EO120009
[2] R. Shah and J. L. Garrison, “Application of the ICF coherence time method forocean remote sensing using digital communication satellite signals,” IEEE J.Sel. Topics Appl. Earth Observ. in Remote Sens., vol. 7, no. 5, pp. 1584–1591,2014. DOI:10.1109/JSTARS.2014.2314531
[3] B. Zhang and Y. J. He, “Research progress of the ocean remote sensinginformation extraction technology under high sea states,” J. Ocean Technol.,2015.
[4] W. Huang and E. W. Gill, “An alternative algorithm for wave informationextraction from X-band nautical radar images,” IET International RadarConference 2013, pp. 1–5, 2013. DOI:10.1049/cp.2013.0298
[5] T. Wen-Long, L. Gao-Peng, and X. Rong-Qing, “Ionospheric clutter mitigationfor high-frequency surface-wave radar using two-dimensional array and beamspace processing,” IET Radar Sonar Navig., vol. 6, no. 3, pp. 202–211, 2012.DOI:10.1049/iet-rsn.2011.0121
[6] Y. Su, Y. Wei, R. Xu, and Y. Liu, “Ionospheric clutter suppression using
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wavelet oblique projecting filter,” IEEE Conference on Radar 2017, pp. 1552–1556, 2017. DOI:10.1109/radar.2017.7944454
[7] M. Ravan, R. J. Riddolls, and R. S. Adve, “Ionospheric and auroral cluttermodels for HF surface wave and over-the-horizon radar systems,” Radio Sci.,vol. 47, no. 3, pp. 1–12, 2012. DOI:10.1029/2011rs004944
[8] X. L. Chu, J. Zhang, J. I. Yong-Gang, et al., “The second order spectrumextraction from high frequency ground radar sea echoes based on the outline ofthe RD spectrum,” Adv. Marine Sci., 2016.
[9] B. Wang, G. Zhang, L. I. Zhi, et al., “Wavelet threshold denoising algorithmbased on new threshold function,” J. Comput. Appl., vol. 34, no. 5, pp. 1499–1502, 2014.
[10] W. H. Zhu, W. An, L. H. You, et al., “Wavelet threshold denoising algorithmbased on improved wavelet threshold function,” Appl. Comput. Syst., vol. 25,no. 6, pp. 191–195, 2016.
[11] A. M. Atto, D. Pastor, and G. Mercier, “Wavelet shrinkage: Unification of basicthresholding functions and thresholds,” Signal Image Video Process., vol. 5,no. 1, pp. 11–28, 2011. DOI:10.1007/s11760-009-0139-y
1 Introduction
The use of high frequency surface wave radar (HFSWR) in the technology of
remote sensing [1, 2] for detecting the ocean can be seen in recent years, the sea
state information associated with the waves is mainly included in the two spectral
range of the radar echo spectrum, the existing ocean wave information acquisition
technology mainly aims at the analysis and processing of the second-order
spectrum and the extraction of the characteristic parameters to obtain the related
ocean information, such as the effective wave height and the ocean wave spectrum
[3, 4]. HFSWR is affected by ionospheric clutter with different morphological
characteristics in time, space and frequency bands, it shows obvious non-stationary
signal characteristics in frequency spectrum, and its energy is so strong that it often
completely drowns out useful ocean echo spectrum [5, 6, 7]. So it is difficult
to extract the second-order spectrum of ocean echo on the distance element with
ionospheric signal interference, and then it is a great obstacle to the subsequent sea
state information retrieval. Therefore, how to separate the second-order spectra
from the ionospheric echo spectrum effectively is the key to obtain the sea state
information of the region. To resolve the above problem, this paper proposes a
hybrid echo signal processing algorithm. The method of dividing the second-order
spectral region under ionospheric clutter is studied in-depth, and the validity of the
algorithm is verified by the measured data. The new algorithm solves the problem
of two order spectrum extraction under ionospheric interference, and improves the
extraction accuracy, which lays the foundation for the next research.
2 The present situation of second-order spectrum extraction
In the Doppler spectrum of radar signal echo, the second-order spectrum is usually
distributed on both sides of the first order spectrum. The signal-to-noise ratio (SNR)
is obviously lower than that of the first order spectrum, and has a certain spectrum
range. Because of the noise, clutter and other kinds of interference signals in
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Doppler spectrum, the difficulty of extracting second-order spectra is increased. At
present, the extraction of the second-order spectra is mostly aimed at the Doppler
spectrum without ionospheric disturbances and it is extracted by constructing range
Doppler (RD) spectral contour [8]. However, when dealing with the Doppler
spectrum with ionospheric disturbances, the interference signal has obvious influ-
ence on the outline of the RD spectrum, and cannot reflect the general location of
the second-order spectrum.
3 Second-order spectrum extraction under ionospheric clutter
Since the study of second-order spectrum extraction usually takes place without the
ionosphere interference, in the case of ionospheric disturbances, the previous
extraction methods have great limitations, for example, the peaks of the second-
order spectrum and the second-order spectral boundary cannot be determined, and
the second-order spectrum is completely disturbed by the ionosphere. In order to
solve the second-order spectrum extraction in the background of ionospheric
interference, this paper attempts to improve the probability and accuracy of the
second-order spectrum extraction from the spectrum of ionospheric interference
from three directions as follows.
3.1 The prolongation of coherent time
In order to improve the accuracy of the second-order spectrum extraction, the paper
pretreated the echo data and extended the coherence length of the data to achieve
the purpose of broadening the spectrum. According to the initial coherence time of
the echo data, the cumulative increment of the integer double data acquisition time
is performed.
N ¼ t � k ð1ÞIn the upper formula, N is the coherence length of the extended data, and t is the
minimum cumulative time, and k is the coherent multiple.
3.2 The cancellation of ionospheric clutter
In HFSWR echo data, the first-order peak signal information mainly contained
in the low frequency component of the scale coefficient, the ionospheric echo
information is present in the high frequency component of the wavelet coefficients.
The wavelet threshold contraction algorithm [9] will deal with the interference
signal with strong correlation in the wavelet coefficients, and it will be normalized
to the elimination result.
Similar to [10], the threshold function uses the following expression:
ypðxÞ ¼ sgnðxÞffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiðjxjj � �jÞj
q; jxj � �
0; jxj � �
8<: ð2Þ
Adopted from [11], the threshold selection uses the following expression:
� ¼ �ffiffiffiffiffiffiffiffiffiffiffiffiffiffi2 lnðNÞp
lnðj þ 1Þ ð3Þ
j is the decomposition scale.
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3.3 The choice of threshold for signal-to-noise ratio of second-order
spectrum boundaries
The threshold is used to judge the boundary of the second-order spectrum, and the
selection of the threshold must have some ambiguity after the ionospheric elimi-
nation operation. In order to solve the problem of threshold ambiguity, applying
two order boundary spectrum average value dynamic calibration of adjacent
distance element without ionospheric disturbance, and reference spectra of the first
substrate noise and signal-to-noise ratio, to determine the second-order spectrum
threshold.
SNRave ¼ 1
n
Xni¼1
SNRi ð4Þ
SNRave is the mean value of the second-order spectrum boundary; SNRi is the
signal-to-noise ratio of the ith data for the second-order spectrum boundaries.
4 Second-order spectrum extraction and analysis
The experimental data of this paper are taken from the spectrum of a distance
element at 11 am of September 26, 2013 in the Beibu Gulf (08° 13.30B E, 21°
30.30B N) as shown in Fig. 1(a).
4.1 Coherent accumulation of ionospheric elimination
In the time to eliminate the interference on the ionosphere, considering the iono-
spheric cancellation algorithm that will cause loss of signal-to-noise ratio for the
echo spectrum data, due to less number of points in every game, it is easy to cause
the cancellation after the second-order spectrum is not obvious or cancellation of
the elimination of the ionosphere, which is on the order of two subsequent
extractions caused by the spectrum great influence. In order to deal with this
problem, this paper uses the method of increasing coherent accumulation time, and
uses more coherent data to reduce the influence of the ionospheric cancellation
algorithm on the second-order spectrum.
From the comparison of Fig. 1(b)∼(c), it is observed that the spectrum of the
data at 256 sampling points is significantly less than that of the 1024 sampling
points after coherent integration. In the process of ionospheric elimination, it is
obvious that the possibility of extracting two order spectra before coherent
accumulation is relatively small. However, after the coherent accumulation,
although the ionospheric cancellation algorithm has affected the signal-to-noise
ratio of the two order spectrum, the range of the approximate data points contained
in the two order spectrum can be distinguished from the spectrum, which reflects
the advantages of the more data sampling points.
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4.2 The extraction of second-order spectrum extraction parameters
After the coherent accumulation, the signal-to-noise ratio of the spectrum changes
with the ionosphere elimination. In this paper, according to the state parameters of
the ocean echo spectrum of the distance elements which are not affected by the
ionospheric disturbances as the reference, the two order spectra are divided after the
ionospheric disturbances are eliminated. The Table I gives the reference mean
values of the related main parameters of adjacent interference free range elements,
and takes the transformation data before and after the first peak cancellation as the
reference basis. The acquisition of the table data parameters is calculated by the
echo data received within one hour, and two distance elements and two adjacent
distance elements are calculated.
4.3 Extraction results
According to the statistics of the above parameters, echo spectrum correlation
parameters are based on adjacent undisturbed range elements, and combined with
the influence of ionospheric cancellation algorithm on SNR of echo spectrum,
ultimately they determine the ionosphere on consumption, increase the coherent
time spectrum of second-order boundary spectral signal-to-noise ratio, and then the
scope of second-order spectrum is determined, finally, according to the peak
parameters of second-order spectrum, the first-order peak area and two spectral
region is separated. The Fig. 2 is the result of the second-order spectral partition of
the coherent accumulation time spectrum after the above processing.
(a)
(b) (c)
Fig. 1. The spectra of two groups before and after coherence
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5 Conclusion
Ionospheric disturbances are one of the most difficult problems to be solved in the
radar signal analysis, just in a resource limited environment, analysis of echo signal
of the ionospheric disturbance under the conditions of the ionosphere and the
elimination of the echo spectrum in a certain extent. In this paper, the good weather
conditions and the characteristics of the states in a specific area are basically the
same, by increasing the coherent time, and combining with the spectrum parameters
of adjacent distance elements without ionospheric disturbances, we develop the
second-order spectrum feature analysis and extraction using echo spectrum with
ionospheric interference by mathematical statistical analysis and approximate
method. Finally, the calculation results show that the echo spectrum of ionospheric
disturbances can be analyzed effectively and reasonably based on certain ocean
condition and the stability principle.
Fig. 2. The result of two order spectrum extraction
Table I. Reference values of related parameters
DI-LSNR DI-RSNR D-N LSNRave RSNRave LBSNRave RBSNRave
(dB) (dB) (dB) (dB) (dB) (dB) (dB)
6.96 14.22 1.98 33.66 40.09 17.28 12.7
7.42 11.74 4.75 32.57 37.24 10.78 13.73
6.67 10.76 5.34 36.78 17.63 28.79 3.83
8.05 13.97 3.07 31.13 14.73 14.48 2.74
7.14 14.07 3.84 29.60 12.40 12.75 8.64
7.59 11.32 4.91 36.07 17.82 23.63 6.44
5.73 15.48 1.79 35.97 18.52 21.48 11.43
9.03 15.02 2.65 35.64 17.86 28.27 12.13
DI-LSNR: Difference of SNR of left first order peak after clutter suppression.DI-RSNR: Difference of SNR of right first order peak after clutter suppression.D-N: Noise base difference before and after interference suppression.LSNRave: Mean value of SNR of left first order peak without interference.RSNRave: Mean value of SNR of right first order peak without interference.LBSNRave: Mean value of SNR of left boundary without interference.RBSNRave: Mean value of SNR of right boundary without interference.
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Acknowledgment
The authors would like to thank many teachers for useful suggestions and
discussions. This work was partially supported by the National Natural Science
Foundation of China (61661009) and Innovation Project of Guangxi Graduate
Education (YCSW2017056).
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Optimization design methodof arbitrarily shaped elementsfor wideband reflectarrays
Shuki Wai, Hiroyuki Deguchia), and Mikio TsujiDept. of Electronics, Doshisha University,
10–3 Tatara-Miyakotani, Kyotanabe, Kyoto 610–0321, Japan
Abstract: In this paper, the authors have proposed an optimization design
method for arbitrarily shaped resonant elements with wideband property and
have constructed an offset-feed reflectarray by the optimized elements in the
X band. Its radiation pattern has been also investigated numerically and
experimentally. Usefulness of the proposed method has been proved from
comparison between the measured radiation pattern and the calculated ones.
Keywords: wideband reflectarray, genetic algorithm, antenna, method of
moments
Classification: Antennas and Propagation
References
[1] C. R. Dietlein, A. S. Hedden, and D. A. Wikner, “Digital reflectarrayconsiderations for terrestrial millimeter-wave imaging,” IEEE Antennas WirelessPropag. Lett., vol. 11, no. 3, pp. 272–275, March 2012. DOI:10.1109/LAWP.2012.2189545
[2] R. E. Hodges, D. J. Hoppe, M. J. Radway, and N. E. Chahat, “Novel deployablereflectarray antennas for CubeSat communications,” Digest 2015 IEEE MTT-SIntern’l Symp., Phoenix AZ, USA, pp. 1–4, May 2015. DOI:10.1109/MWSYM.2015.7167153
[3] T. Asada, S. Matsumoto, H. Deguchi, and M. Tsuji, “Reflectarray with arbitrarilyshaped elements having four-axial symmetry,” Proc. 2014 Asia PacificMicrowave Conf., Sendai, Japan, pp. 1238–1240, FR1G-27, Dec. 2014.
[4] H. Matsumoto, H. Yamada, H. Deguchi, and M. Tsuji, “Reflectarray witharbitrarily shaped elements for linear-to-circular polarization,” Digest 2016Intern’l Symp. Antennas Propagat., Okinawa, Japan, pp. 650–651, D3-5, Nov.2016.
[5] D. Higashi, H. Deguchi, and M. Tsuji, “Omega-shaped resonant elements fordual-polarization and wideband reflectarray,” Digest 2014 IEEE AP-S Intern’lSymp., Memphis TN, USA, pp. 809–810, July 2014. DOI:10.1109/APS.2014.6904733
1 Introduction
Recently, many studies of reflectarrays have been carried out for various applica-
tions such as millimeter imaging systems, and deployable antennas for satellite
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systems [1, 2]. The GA (genetic algorithm) optimization for reflectarrays is one of
useful design methods [3]. However, the conventional GA-optimized reflectarray
with dual-polarization or low cross-polarization properties was limited in the
narrow frequency band because of adopting experiential phase functions. In this
paper, we propose a new GA procedure to expand the frequency bandwidth. Its
usefulness is confirmed by evaluating radiation properties of the designed reflec-
tarray in the X band numerically and experimentally. Our design method proposed
here to get a broadband property could be useful for various reflectarrays, for
example, the circular polarization conversion reflectarray [4], the multiband reflec-
tarray, the reflectarray with resonant elements having four axial symmetry [5] and
so on.
2 Optimization design method
In the conventional GA-optimized method [3], we determine the element geometry
by fitting the frequency property of its reflection phase into a straight line with
some slope over the specified frequency range. This procedure is repeated for the
parallel straight lines with the same slope in the range of 360 degrees at the center
frequency. Whereas, in the proposed optimization, we adopt two-step optimization
and also search the element geometry by fitting its reflection-phase property into a
curved line corresponding to the resonant characteristic of an element. Furthermore,
optimization is not performed under the fitting line one by one, but done under all
the fitting lines at a time. In the first step, we sort the GA-generated reflection-phase
curves into the group of the N units at intervals of 360=N degrees, based on the
phase value at the center frequency, continue this sort until the given number M of
reflection-phase curves gathers every unit, and select the N initial geometries giving
the suitable phase curve in each unit. Next, one of their phase curves is chosen as
the basic fitting curved line and the N parallel fitting phase curves based on it are
set. In the second step, the initial geometries are modified slightly to be matched
with the fitting curves, and this procedure is repeated until fitness of the error
function is satisfied. It should be noted here that the phase curve of the modified
geometry is compared with all the N fitting curves, so that this process makes it
possible to obtain the optimized geometry from the initial geometry of the different
unit. Thus, this proposed optimization can shorten computation time and extend the
specified frequency range. Fig. 1 illustrates an algorithm of the proposed design
procedure.
Fig. 1. Algorithm of the proposed optimization design method.
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3 Reflection properties of designed elements
The reflection properties are analyzed as infinite array by the method of moments in
spectral domain imposing the periodic boundary condition. The dimension of the
unit cell is 12.0mm, and the thickness of the substrate with relative permittivity
"r ¼ 1:67 is 3.0mm. The strip width of the element is 0.3mm. We design resonant
elements as M ¼ 20 and N ¼ 12 in the frequency range from 6.5GHz to 13.5GHz.
Fig. 2(a) shows each geometries obtained by the optimization. Fig. 2(b) and (c)
shows the calculated reflection phases and amplitude properties of the cross
polarization for the TE incidence, and also the properties for the TM incidence is
very similar to them for the TE one. The phases vary almost in the range of 360
degrees over the specified frequency. The cross polarization level is less than about
−30 dB except a few elements in the specified range. To evaluate degree of parallel
between the reflection-phase curves of the optimized elements, Fig. 2(d) shows
their deviations by normalizing the phase value of each element to 0 degree at the
center frequency 10GHz. You can see from this figure that the deviations is
suppressed within 45 degrees in the higher frequency region and 90 degrees in
the lower one. Fig. 2(e) shows the calculated reflection phases of “Initial Geo-
metries” obtained in the first step for the TE incidence. Although their curves is
similar to those in Fig. 2(a), they are not parallel comparing with Fig. 2(a) and does
not give the phase difference more than 360° in the lower frequency region. So it is
expected that the designed elements are useful for constructing a reflectarray by
their appropriate arrangement.
4 Radiation patterns of reflectarray
To confirm usefulness of the proposed elements, we designed an offset feed
reflectarray antenna with a dimension 180mm � 180mm (15 � 15 cells). Fig. 3(a)
shows the fabricated reflectarray. The offset angle of the primary radiator is 30
Fig. 2. Properties of the designed elements.
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degrees and the main beam is radiated in the specular direction. The distance from
its phase center to the center of the reflectarray antenna was chosen to be 290mm.
Fig. 3(b) shows comparison of the radiation patterns between the calculated results
and the experimental ones at the center frequency 10GHz. The radiation pattern is
calculated by AFIM (aperture field integration method) and HFSS (Ansys). You
can see from this figure that main-beam pattern agrees well with the calculated
ones. The cross polarization level using AFIM is not shown there, because it is
suppressed less than −50 dB. The measured level is supressed less than −45 dBexcept the specular direction. Fig. 3(c) shows the aperture efficiency from 6GHz
to 16GHz, although the experimental results are given only within the specified
frequency range. The aperture efficiency is kept to the level of more than 50% over
a wide range from 7.5GHz to 15.0GHz. It is clear from this experimental
verification that the proposed optimization design method is useful.
5 Conclusion
We have proposed the GA-optimization method for designing resonant elements of
a broadband reflectarray. By adopting two-step optimization, the desirable resonant
elements with broadband property are obtained efficiently. Then the reflectarray
constructed by using them has been evaluated from radiation property for the dual
polarized wave, numerically and experimentally. As a result, the measured main-
beam pattern agrees well with the calculated one and the cross-polarization is
suppressed in the sufficiently low level. The broadband property of the proposed
reflectarray has been confirmed from the aperture efficiency more than 50% over a
wide range from 7.5GHz to 15.0GHz.
Acknowledgments
This work was supported in part by a Grant-in-aid for Scientific Research (C)
(15K06090) from Japan Society for Promotion of Science.
(a) Photograph of the fabricated reflectarray.
(b) Radiation patterns at 10GHz for the TE incident wave.
(c) Comparison between the calculated and the measured aperture efficiencies for both the incident waves.
Fig. 3. Radiation properties.
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