Fiber length estimation method for beamforming at millimeter … 2019-10-28 · Fiber length estimation method for beamforming at millimeter wave band RoF-FWA system Mizuki Sugaa),
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Fiber length estimationmethod for beamforming atmillimeter wave band RoF-FWA system
Mizuki Sugaa), Kota Ito, Yushi Shirato, Naoki Kita,and Takeshi OnizawaNTT Access Network Service Systems Laboratories, NTT Corporation,
1–1 Hikarinooka, Yokosuka-Shi, Kanagawa 239–0847, Japan
Abstract: To supplant partial optical access networks, we study a large
capacity transmission system with millimeter wave applied radio over fiber
(RoF). In this system, it is desirable from the viewpoints of downsizing and
power saving that the base station (BS) is simplified and the central station
(CS) controls beamforming. However, this demands fiber length estimation
because each wavelength must be given a different phase rotation due to
chromatic dispersion in the optical fiber. This paper proposes a method to
estimate fiber length from CS to BS supporting wireless terminal (WT) by
utilizing time synchronization; its performance is evaluated.
Keywords: RoF, millimeter wave, beamforming
Classification: Wireless Communication Technologies
References
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As shown above, the fiber length can be estimated from measured values and
known parameters.
3 Performance evaluations
Actual total communication durations td and tu include measurement error due to
the time synchronization error. Therefore, this section confirms which wavelength
setting minify the fiber length estimation error and verifies the influence of the time
synchronization error on beamforming performance.
3.1 Beamforming scheme
The beamforming scheme used this evaluation directly connects a unique wave-
length to each BS antenna element. The phase for beamforming and the added
phase rotation created by the chromatic dispersion of i-th (1 � i � n) antenna
element are represented by �i, ’i, respectively; n is the number of antenna elements.
The phase for beamforming and the added phase rotation are given by Eq. (7), (8)
�i ¼ 2�di sin
�RFð7Þ
’i ¼ 2� � fRF � l � �i; ð8Þwhere di is distance from reference antenna element, ψ is signal arrival direction,
�RF is RF wavelength, fRF is RF frequency, �i is the group delay time of �i(wavelength assigned to i-th antenna element). The phase of i-th antenna element
set in the phase control unit (�iCS) is shown in Eq. (9).
�iCS ¼ �i � ’i; ð9ÞThe phase of signal arriving at BS (�iBS) is added ’i in fiber as shown in Eq. (10).
�iBS ¼ �iCS þ ’i; ð10ÞThus, ’i is canceled by fiber transmission and only �i remains, in fact �iBS ¼ �i.
3.2 Simulation
Simulation parameters are shown in Table I. RF frequency is 60GHz band which is
a typical millimeter wave band, and fiber length is 10 km (standard length in optical
access networks). The fiber is single mode fiber (SMF), wavelength used fiber
length estimation ð�d; �uÞ is 1300–1625 nm, these values conform to recommenda-
tion ITU-T [6]. Wavelength of fiber transmission is 1300 nm band which is one
of common wavelength band used in optical network systems. The allocated
wavelengths have equal spacing (��) of 0.2–1 nm. Time synchronization error
(described below as time error) is 0.1–20 ns, it takes account of the accuracy of the
global positioning system (GPS).
Fig. 2(a) shows the fiber length estimation error for the wavelengths used, for
the case that time error is 1 ns. The four lines show the impact of wavelength
difference on fiber length estimation (j�d � �uj). Since the wavelengths are limited
to 1300–1625 nm, the plots become shorter as the wavelength difference increases.
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Classification: Wireless Communication Technologies
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1 Introduction
With the rapid growth in the use of smart devices, the demand for mobile wireless
services has increased exponentially, leading to expectations of higher speed, larger
capacity, and lower latency. In 2022, the amount of wireless traffic is estimated to
reach 71% of all IP traffic [1], and the fifth-generation mobile communication
system (5G) is soon to be commercialized in terms of enhanced mobile broadband
(eMBB), ahead of ultra-reliable and low-latency communications (URLLC) and
massive machine type communications (mMTC). Multi-user multiple-input and
multiple-output (MU-MIMO) is an essential technique for 5G systems because
larger capacity can be realized via a single antenna mounted on a mobile station
(MS) [2].
Precoding techniques are essential for implementation of MU-MIMO, and are
categorized into two approaches: linear precoding (LP) and non-linear precoding
(NLP). NLP provides better system capacity than LP because it reduces noise
enhancement, and has thus emerged as a candidate technique for 5G systems
[3, 4, 5]. Of the various NLP schemes, Tomlinson-Harashima precoding (THP) is
considered a practical approach because the perturbation vector can be generated by
a simple modulo operation [4, 6, 7].
From a system-level perspective, the combination of MU-MIMO with user
scheduling must be taken into account [8, 9, 10]. This is because the capacity of
The actual impact of the WGN after the modulo operation is represented as the sum
of shifted versions of the PDF pZðzÞ in the fundamental region ½��=2; �=2�. Theshifts are integral multiples of the modulo width τ. Thus, the PDF of the WGN after
the modulo operation pZmod ðzmodÞ (��=2 < zmod < �=2) is given by
In consequence, the sum-rate of MU-MIMO THP is represented as
Csum ¼XNr
i¼12ðlog2 � � HðZmodÞÞ
¼XNr
i¼12 log2 � þ
Z �=2
��=2pZmod ðzmodÞ log2 pZmod ðzmodÞdzmod
� �½bps=Hz�: ð10Þ
2.3 Application of user scheduling to MU-MIMO THP
User scheduling is generally performed before precoding because the number of
MSs in a radio zone is more than the number of BS antennas. In this paper, we
consider PF [8] as user scheduling and analyze the system-level performance using
the above-mentioned mod-Λ channel-based approach.
In application of PF to MU-MIMO THP, the system capacity of all possible
combinations of MSs has to be calculated because it is used as a criterion in PF-
based user selection. The PF metric in the k-th combination Mk is given by
Mk ¼XNr
i¼1
Rk;iðtÞTk;iðtÞ ðk ¼ 1; 2; � � � ; KCNr
Þ; ð11Þ
where K is the number of the existing users, and Rk;iðtÞ is the instantaneous systemcapacity of the i-th MS, in the case that the k-th combination is admitted to the
transmission at time t. Tk;iðtÞ is average system capacity of the i-th MS in the k-th
combination until time t, which is represented as
Tk;iðt þ 1Þ ¼ 1 � 1
tc
� �Tk;iðtÞ þ 1
tcR0k;iðtÞ; ð12Þ
where tc is the average time range of the system capacity and R0k;iðtÞ is the
instantaneous system capacity of the i-th MS at time t. Here, R0k;iðtÞ is zero if the
i-th MS is not scheduled at time t. Eqs. (11) and (12) proves that the PF metric
requires the instantaneous system capacity, which is obtained from Eq. (6) or (10).
3 Numerical results
In this section, we evaluate system-level performance of MU-MIMO THP with PF
user scheduling based on the mod-Λ channel-based approach, and compare its
performance to that of the traditional Shannon-Hartley theorem-based approach
with or without considering modulo loss. Fig. 2 shows the evaluation model and its
system parameters. In our performance evaluation, MSs are randomly distributed
and the ordering process [7, 12] is adopted to enhance the transmission perform-
ance of THP. In addition, the MIMO channel is assumed to follow spatially
uncorrelated Rayleigh fading. Moreover, perfect channel state information (CSI)
feedback is assumed, and its feedback error and delay are negligible.
Fig. 3 shows the sum-rate versus the number of existing users K, where the
MIMO antenna configurations are set to be 4 � 4 and 6 � 6. Fig. 3(a) demonstrates
that the performance of the mod-Λ channel-based analysis is lower than that of the
traditional Shannon-Hartley theorem-based approach, regardless of MIMO antenna