Received: 17 March 2022
DOI: 10.1049/ote2.12074
- Revised: 18 May 2022
Accepted: 26 May 2022
ORIGINAL RESEARCH
-
IET Optoelectronics
A new complexity reduction scheme in selective mapping‐based
visible light communication direct current‐biased optical
orthogonal frequency division multiplexing systems
Chakravarthy Gunturu
| Sivaprasad Valluri
Electronics and Communication Engineering, Indian
Institute of Information Technology Design and
Manufacturing, Kurnool, Andhra Pradesh, India
Correspondence
Chakravarthy Gunturu, Electronics and
Communication Engineering, Indian Institute of
Information Technology Design and Manufacturing,
Jagannathagattu Hill, Kurnool, Andhra Pradesh,
518007, India.
Email: 321ec0003@iiitk.ac.in
Abstract
Visible light communication (VLC) has emerged as a good accompaniment to radio‐
frequency (RF) technologies by deploying multicarrier schemes such as orthogonal frequency division multiplexing (OFDM). However, the coherent summation of carriers in
the OFDM system leads to a high peak‐to‐average‐power ratio (PAPR), causing non‐
linear clipping distortion at the transmitting light‐emitting diode. This intricacy becomes a potential barrier for intensity modulation and limits the VLC systems' bandwidth. In the literature, non‐distorting PAPR lowering approaches, such as the selective
mapping (SLM) approach, have been confirmed as the most effective strategy for
reducing ineludible high PAPR in optical OFDM systems among all other available
techniques. Besides its astounding performance, the computational complexity also becomes a major complication in SLM due to the generation of multiple candidates. This
paper proposes a computational complexity minimisation approach using inherit system
properties in the SLM‐based PAPR suppression method for VLC systems, where the
phase sequence vectors are considered periodic. The alternative direct current‐biased
optical orthogonal frequency division multiplexing candidates for the single frequency‐
domain data block are generated with the periodic phase rotation vector. The evaluation of the complexity analysis and the simulation results shows that the mitigation of
computational complexity surpasses the standard SLM technique.
1 | INTRODUCTION
The burgeoning demand for mobile data appears to be escalating year by year due to excess usage of data in our routine lives
for various applications. In particular, devices enabled for video
conferencing, banking, audio, and video streaming and education require enormous data. According to Commercial & Industrial Security Corporation envision, compared to 2017, the
mobile data traffic will increase 11‐fold in 2022 [1]. Many gadgets
with several data rates are introduced in 5G and are still showing
their supremacy in the global market. But, radio‐frequency (RF)
technologies are running out of spectrum and are struggling
vigorously to aid for present wireless technologies [2]. At the
same time, visible light communication (VLC) emerged as the
conspicuous strategy and has been validated as a sustainable
tactic in reliable technologies. Since VLC offers huge bandwidths and terahertz (THz) operating frequency range, it could
be suitable for later generations by implicitly substituting present
RF technologies [3]. The incessant research on VLC has been
churning out numerous scientific sensations and captivating
educationalists, industrialists, and researchers. Visible light
communication surmounts all the barriers of RF technology
with many advantages such as incredible proficiency, electromagnetic free transmission, low latency, and high security from
eavesdroppers [4]. Moreover, for indoor communication, RF
signals suffer from deep fade and show substandard performance; hence, it cannot be guaranteed for reliable data speed [5].
The cornerstones of the VLC system are light‐emitting
diode (LED)/Laser diode and Photodetector (PD). The
amelioration of LED technologies and rising indoor lighting
-
This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial‐NoDerivs License, which permits use and distribution in any medium, provided the
original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.
© 2022 The Authors. IET Optoelectronics published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.
IET Optoelectron. 2022;16:207–217.
wileyonlinelibrary.com/journal/ote2
207
-
demand draws more attention to VLC as thriving optical
wireless communication. In VLC, LED plays a dual role; it can
supply illumination as incandescent lights and can communicate with other devices as a transmitter in the dwelling places
[6, 7]. The information from the LED is transmitted by
imperceptibly modulating the light intensity and receiving the
instantaneous optical power by the PD, called Intensity Modulation/Direct Detection (IM/DD) [8]. In addition to a
compact size and short links, LEDs have numerous benefits
such as low power consumption, longevity, cooler operation,
economical, and quick switching [9–11]. The data speed of the
IM/DD links relies on the swift switching capability of the
transmitter (LED) and the responsivity of the receiver (PD)
[11, 12]. In optical wireless systems, multicarrier modulation
techniques such as orthogonal frequency division multiplexing
(OFDM) are the apt solution for high‐speed data, interference
suppression caused by channel attenuation, and improving the
quality of the communication [13]. There are numerous
Optical orthogonal frequency division multiplexing (OOFDM)
variants in optical wireless communication that are prevailing
in many practical applications [14].
1.1 | Motivation
The most renowned physical layer OFDM variant over IM/DD
systems is direct current‐biased optical orthogonal frequency
division multiplexing (DCO‐OFDM) because of its high energy
efficiency and ease of implementation [15]. Nevertheless, a high
peak‐to‐average‐power ratio (PAPR) arises in DCO‐OFDM as
the major stumbling block, resulting in non‐linear clipping
distortion at the LED [16, 17]. Different PAPR reduction
methods in OFDM have been proposed in the literature [18] to
address this issue. Signal scrambling and signal distortion techniques are commonly adopted for PAPR reduction in optical
wireless communication, and each individual has its strengths
and shortcomings [19]. The main drawback in signal distortion
approaches is clipping the transmitted signal at the expense of
bit error rate (BER) performance [20]. On the flip side, in the
signal scrambling techniques such as partial transmit sequence
(PTS) and selective mapping (SLM), the transmitted signal is
modified by introducing random phase shifts, preserving the
original signal [21]. Both schemes must send side information
(SI) to the receiver, and bandwidth efficiency decreases. It is
evident that for PTS, the overall computational complexity is
less compared to SLM in certain cases, and PAPR reduction
performance is always better for SLM [22]. It is commonly
known that if the computational complexity of SLM is reduced,
SLM would outperform PTS; this idea motivated us to minimise
the computational complexity in selective mapping—orthogonal frequency division multiplexing (SLM‐OFDM) systems.
1.2 | Related studies
In the literature, many techniques for reducing computational
complexity in DCO‐OFDM havebeen reported. The authors
GUNTURU AND VALLURI
in Ref. [23] proposed an SLM‐based method in which the
number of candidates is generated by linearly adding time‐
domain even and odd sequences with cyclic shift and phase
rotation. But, the computational complexity and PAPR also
increased in improving BER performance. In the studies of
Ref. [24, 25], a phase sequence with blind SI detection consisting of magnitude extension and phase rotation factors is
proposed. The computational complexity is reduced with an
increase in transmitted average power, and BER performance
is identical to standard SLM. The strategy adopted in Ref. [26]
is subjected to top sample detection and append without using
the SI. By combining SLM and PTS schemes, BER performance is reduced, and computational complexity is also
reduced. However, the search complexity for top samples is
very high, and additionally, the average power increases in this
method, which is not desirable in practical applications. Authors in Ref. [27] proposed a scenario where pilots are brought
into the play for performing joint channel estimation and
cancellation of SI. The computational complexity is reduced
moderately with a bit compromising in PAPR reduction
performance.
Hei et al. [28] proposed a tone reservation method based on
time domain kernel matrix (TKM‐TR) techniques, which was
investigated and used in the DCO‐OFDM system, in order to
reduce PAPR. Despite its excellent complementary cumulative
distribution function (CCDF), it has a poor BER performance.
Taşpınar et al. [29] developed a parallel artificial bee colony algorithm based on a new search strategy for both OFDM and
Multiple‐Input Multiple‐Output—orthogonal frequency division multiplexing systems. In Ref. [30], Guzmán et al. worked on
adaptive network‐based fuzzy inference system with a reduced
power envelope profile for efficient DCO‐OFDM signals.
Şimşir et al. [31] proposed an SLM algorithm based on the
discrete cuckoo search algorithm for universal filtered multicarrier (UFMC) systems. In all circumstances, the ability to
minimise computer complexity is insufficient. To the best of our
knowledge, there is less attainable literature on computational
complexity reduction in optical OFDM techniques using
inherited system properties that persuaded us to reduce the
computational complexity of SLM‐OFDM systems in this paper.
1.3 | Novelties and contributions
Generally, SLM is a PAPR reduction technique based on phase
rotations where the number of candidates representing the
same information is generated by multiplying the data symbol
with phase sequences. The probability of PAPR reduction increases as the number of different candidates increases, and
only the candidate with minimum PAPR is selected for
transmission. Thereby, the computational complexity of the
SLM scheme increase conforms to the additional requirement
of inverse fast Fourier transform (IFFT) operations to generate
alternative OFDM symbols. Complex hardware is required at
the transmitter to perform all functions at once quicker.
Consequently, this increases the system's implementation cost
and power consumption [32]. In this paper, we focus on
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208
reducing computational complexity in the SLM‐based PAPR
reduction approach by deploying periodicity and circular shift
operations of IFFT in DCO‐OFDM to obtain various candidates. The computational complexity of this method depends
on the arrangement of phase rotation vectors. Phase sequences
are chosen in our proposed way confined to periodicity (Periodic phase Vector) and then multiply them with the DCO‐
OFDM symbol. If the phase sequence vector is periodic, the
subcarriers will contain very few non‐zero elements after
encountering IFFT operations, resulting in multiple candidates.
The same principle is applied to all the DCO‐OFDM symbols.
Additionally, it is observed that DCO‐OFDM subcarriers
exhibit circular shift property. When the number of non‐zero
elements in the subcarriers is decreased, the number of
needed multiplications is lowered, reducing the computing
complexity. The proposed method significantly decreases
computational complexity without compromising the system's
performance metrics. A detailed explanation is given in Section 3, including a block diagram and system properties.
The organisation of the remainder of the paper is as follows. Section 2 explains the basic concepts of the DCO‐OFDM
system model and standard SLM scheme with block diagrams
and PAPR calculation procedure. Section 3 details the proposed
method using the periodicity and circular shift properties of
IFFT with block diagrams. The PAPR and complexity reduction performance of the proposed methods is compared to that
of the standard DCO‐OFDM signal in Section 4. In Section 5,
the conclusions of the presented work is described.
2 | DCO‐OFDM SYSTEM MODEL AND
STANDARD SLM SCHEME
x [1], …, x[N − 1]]. The nth discrete time‐domain sample value
can be given by
x½n� ¼
Unlike RF‐based OFDM, where amplitude and phase information is modulated, here in OOFDM schemes, the optical intensity of the carrier is modulated. Obviously, in conventional
RF‐based wireless communication, the baseband OFDM
signal consists of complex and bipolar values. In IM/DD systems, the transmitted time‐domain signal is constrained to be real
and unipolar. Mathematically, the frequency‐domain baseband
quadrature amplitude modulation (QAM) symbols are represented as X ½k� ¼ ½X½0�; X½1�; …; X½N − 1��, where k represents
its frequency index. The first half of the subcarriers
k ¼ 0; 1; …; N2 − 1 are modulated by QAM and the first subcarrier (Direct current [DC] subcarrier) is set to zero. The
remaining half k ¼ N2 ; N2 þ 1; …; N − 1 are imposed to be
following the rule of Hermitian Symmetry, mathematically
expressed as X½N − k� ¼ X ∗ ½k�; k ¼ 0; 1; …; N2 − 1. Then,
IFFT is employed to generate time‐domain symbols x[n] = [x[0],
ð1Þ
Where X[k] is the kth subcarrier and N is the number of
IFFT points. The output of the IFFT is now bipolar. In order
to make a real and positive OOFDM signal, a DC‐bias signal is
added. The bias term must be a real valued DC component.
Bdc ¼ R
pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
Efx2 ½n�g:
ð2Þ
Where R is a proportionality constant or clipping factor [33]
and Ef:g is expectation. After adding DC‐bias to the OFDM
signal the resultant DCO‐OFDM signal is given by
xdc ½n� ¼ x½n� þ Bdc :
ð3Þ
The selection of the DC‐bias value should be taken wisely
to avoid clipping. Excessive clipping would occur if DC‐bias is
very low, causing an error floor. On the flip side, if the DC‐bias
value is very high, the data transmission becomes power
inefficient because the power spent for the bias term is not
available for data detection. Then the DCO‐OFDM signal with
DC‐bias value and inevitable clipping can be given as
xdc ½n� ¼ x½n� þ Bdc þ ηclip
ð4Þ
which is equivalent to unipolar signal of the original OFDM
signal
x½n� ¼ xdc ½n� ¼ x½n� þ Bdc þ ηclip
This section illustrates the OFDM variant used in our proposed method, that is, the DCO‐OFDM technique and standard SLM schemes. Furthermore, PAPR measurement for the
time‐domain is also elucidated.
2.1 | DCO‐OFDM signal
�
�
N −1
1 X
j2πnk
X½k�exp
N k¼0
N
ð5Þ
unipolar
2.2 | Standard SLM technique
The block diagram for standard SLM is depicted in Figure 1.
The idea behind the standard SLM technique is to generate
several DCO‐OFDM signal representations for an OFDM
symbol X = [X[0], X [1], …, X[N − 1]] using different phase
sequence vectors b(l) = [b(0), b(1), …, b(L−1)] where l = 0, 1, …,
FIGURE 1
scheme
Block diagram of standard selective mapping (SLM)
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- 209
GUNTURU AND VALLURI
-
GUNTURU AND VALLURI
L − 1. The selection of phase sequence vectors casts a pivotal
role in PAPR reduction as well as the computational
complexity. Each phase sequence vector is multiplied with the
OFDM symbol, as a result number of candidates X(l) = X �
b(l) = [X[0].b(0), X [1].b(1), …, X[N − 1].b(L−1)] are generated.
Then, all L possible candidates are converted back to the time‐
domain as x(l) = IFFT(X(l)). The candidate comprising minimum PAPR is then selected from the L candidates and
transmitted.
~x ¼ arg
min
l¼0;1;…;L−1
�
� ��
PAPR xðlÞ
ð6Þ
The PAPR of the DCO‐OFDM is calculated as
n
PAPR x
ðlÞ
o
�
�
max jxðlÞ j2
¼ �
� ;
E jxðlÞ j2
ð7Þ
where Ef:g indicates expected value, |.| is the magnitude of
the complex number, and max represents the maximum value
of vector.
Each DCO‐OFDM candidate carries the same information
with different amounts of PAPR; in a sense, even though the
computed L number of candidates is asymptotically independent, they resemble the unique DCO‐OFDM symbol. The
information regarding the candidates with the lowest PAPR
must also be transmitted to the receiver, which is known as SI.
The PAPR reduction capability is enhanced with the increase in
the number of phase sequence vectors. Since each phase
sequence vector is different in magnitude and phase, the PAPR
FIGURE 2
candidates
for each phase sequence is different. To obtain a candidate, the
phase sequence vector should be multiplied by the OFDM
symbol. This process creates a huge problem of increasing
computational complexity.
3 | PROPOSED METHOD
In this paper, a modified SLM technique is developed for
achieving reduced computational complexity than the traditional
SLM system while maintaining a large PAPR decrease. It is
important to know that the computational complexity is predominantly occurring because of the generation of multiple
candidates. Particularly, in VLC systems, hardware design is
simple, and hence this computational complexity is not a sustainable practice. It is devised to resolve this serious predicament
by considering a single DCO‐OFDM signal and generating
numerous candidates by using the same signal. The steps
involved in the implementation are as depicted in Figure 2.
As illustrated in Figure 2, the complex‐valued data
sequence obtained by QAM [34] is initially partitioned into
sub‐blocks. Serial to parallel data conversion is accomplished in
the same way as in the traditional systems. Afterwards, Hermitian symmetry is stringently applied to the data to get a real‐
valued data sequence, essential in the VLC systems. As
aforementioned, the number of candidates is generated for a
single DCO‐OFDM signal of the data block X whose time‐
domain signal is represented by
x ¼ IFFT fX g
Block diagram for the generation of multiple direct current‐biased optical orthogonal frequency division multiplexing (DCO‐OFDM)
ð8Þ
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210
According to the usual practice of the conventional SLM
scheme, the phase sequence vectors bl is multiplied with the
input data block to generate L multiple candidates. The
computational complexity of this system relies
on the sys-i
h
tematisation of phase rotation vectors bl ¼ bl0 ; bl1 ; …; blN−1
n o
ðl ¼ 0; 1; …; L − 1Þ, where blv ¼ exp jθlv ðv ¼ 0; 1; …;
N − 1Þ; θlv ∈ ½0; 2πÞ. In order to generate multiple candidates,
the phase sequence vectors are restricted to be periodic and to
avoid the complexity of unequal amplitudes of phase rotation
vectors; we limit the values of phase sequence vector for {1,
−1} as the common practice in the literature [35]. In a nutshell,
the phase sequence vector should adhere to the following
conditions in our algorithm:
1. The phase rotation vectors bl should be periodic with a
period (K) of not more than 8 (K is the power of 2).
2. The elements in the phase sequence vector should be
selected from the set {1, −1}.
Interestingly, if the phase rotation vector is periodic, the
IFFT operation of the periodic phase sequence produces a
maximum of four non‐zero elements, and the remaining elements are certainly zeros. The computational complexity of
this system is minimised by allowing some IFFT operations to
be discarded. The L number of candidates are generated for a
single input data block and are mathematically expressed as
n
o
xl ¼ IFFT bl � X
ð9Þ
Even though L multiple candidates are generated as in the
standard SLM, the computational complexity is reduced drastically by minimising the required IFFT operations since IFFT
operation on the periodic phase sequence yields maximum of
four non‐zero elements. As depicted in the block diagram, the
IFFT operation of the periodic phase sequence and the IFFT
of the single data block is undergoing circular convolution,
which is given as
n o
l
xl ¼ IFFT b ⊛N IFFT fX g
ð10Þ
where ⊛N is the N‐point circular convolution operation. (10) is
expressed by applying basic digital signal processing property,
that is, multiplication operation in the frequency‐domain is
equivalent to circular convolution in the time‐domain, and it
can be given as
l
xl ¼ cl ⊛N x
ð11Þ
n o
l
l
where cl ¼ IFFT b . Then, the circulant (CL) matrix is
constructed with the circulant vector cll by circular shift
operation as the alternative approach of the circular convolution operation. The CL matrix is generated by N times
downward cyclic shift operation of the circulant vector cll as
shown in (12).
�� � � �
� �⟨N−1⟩ �
⟨0⟩
⟨1⟩
l
l
l
ð12Þ
; cl
; …; cl
cl ¼ cl
� �⟨n⟩
l
where cl
indicates the downward cyclic shift structure of
l
cl .
If the phase rotation vectors adopted in this algorithm
satisfy the conditions mentioned earlier, the CL matrix will
exhibit the following properties
l
1. Each column in the CL matrix contains not more than four
non‐zero elements.
2. The elements in the CL matrix comes from the set { −
1, 0, 1}.
After obtaining CL matrix, to generate various candidates for the single data block X, the CL matrix and the
time‐domain input data blocks are multiplied, which can be
expressed as
xl ¼ CL � x
ð13Þ
where xl is a L � 1 matrix, CL is a L � L matrix and x is a
L � 1 matrix.
Finally, the candidate who carries less PAPR is selected for
transmission, and the remaining candidates are discarded from
the number of candidates.
xlmin ¼
min
l¼0;1;…;L−1
�
� ��
PAPR xðlÞ
ð14Þ
It is clear that the circular shift multiplication of the
time‐domain data sequence and the IFFT of the periodic
phase sequence vector generates the number of candidates.
So, it is interesting to know that the number of non‐zero
elements in the circulant vector is a maximum 4, irrespective of the phase sequence vector bl. Consequently, the
circular convolution between cll and x requires only 3N
complex additions (CA), nullifying complex multiplication.
According to the theorem [35] this becomes significant if
the phase vector's periodicity is less than eight. It is
important to know that the circulant vector has to be real,
and hence all possibilities of the phase sequence vectors are
not valid. The valid combinations of phase sequence vectors of sizes 2 and 4 along with their circulant vectors
obeying the conditions described above are as tabulated in
Table 1.
From Table 1, it is important to know that the first half of
the phase sequence vectors in each period are just the replica
of the second half, and hence PAPR for both schemes would
be same. However, while computing the PAPR of candidates,
simulations are carried out for the total number of phase
sequence vectors. To illustrate,
2 let us consider a phase sequence
3
6
vector with length 2, b ¼ 41; −1; 1; −1;
7
|ffl⋯⋯
{zffl} ; 1; −15
ðN4 −3Þ times b2
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- 211
GUNTURU AND VALLURI
-
TABLE 1
GUNTURU AND VALLURI
2
Possible phase sequences and their circulant (CL) vectors
0
60
6
60
6
60
CL ¼ 6
61
6
60
6
40
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
1
phase sequence
length
Phase sequence
CL vector
L=2
b = [1, 1]
cl1 = [1, 0]
b2 = [1, −1]
cl2 = [0, 1]
b3 = [−1, −1] = − b1
cl3 = [−1, 0] = − cl1
b4 = [−1, 1] = − b2
cl4 = [0, −1] = − cl2
b1 = [1, 1, 1, 1]
cl1 = [1, 0, 0, 0]
b2 = [−1, 1, 1, 1]
cl2 = [0.5, −0.5, −0.5, −0.5]
b3 = [1, 1, −1, 1]
cl3 = [0.5, 0.5, −0.5, 0.5]
b4 = [−1, 1, −1, 1]
cl4 = [0, 0, −1, 0]
b5 = [−1, −1, −1, −1]
= − b1
cl5 = [−1, 0, 0, 0] = − cl1
6
2
b ¼ 4−1; 1; 1; 1; −1; 1; 1; 1;
b6 = [1, −1, −1, −1]
= − b2
cl6 = [ −0.5, 0.5, 0.5, 0.5]
= − cl2
which produces a circulant vector
b7 = [−1, −1, 1, −1]
= − b3
cl7 = [−0.5, −0.5, 0.5, −0.5]
= − cl3
b8 = [1, −1, 1, −1]
= − b4
cl8 = [0, 0, 1, 0] = − cl4
L=4
1
1
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
1
0
0
0
0
0
3
0
07
7
07
7
17
7
07
7
07
7
05
0
ð15Þ
For more understanding, consider another phase sequence
vector of length 4,
2
3
7
|ffl⋯⋯
{zffl} ; −1; 1; 1; 15 ð16Þ
ðN4 −3Þ times b2
2
3
6
7
2
cl ¼41; 0; 0; …; 0; −1; 0; 0; …; 0; −1; 0; 0; …; 0; −1; 0; 0; …; 05
|fflfflfflfflffl{zfflfflfflfflffl}
|fflfflfflfflffl{zfflfflfflfflffl}
|fflfflfflfflffl{zfflfflfflfflffl}
|fflfflfflfflffl{zfflfflfflfflffl}
N−4
4
N−4
4
N−4
4
N−4
4
ð17Þ
2
(b
2 in Table 1). The equivalent
3 circulant vector cl ¼
2
40; 0; 0; 0; ⋯ ; 1 ; ⋯ ; 0; 05 for the periodic phase
|{z} |{z} |{z}
zeros non−zero zeros
vector b2 is obtained by applying IFFT operation, which is
represented as cl2 in the Table 1, where the period is replicated
for four times. For the same cl2, the CL matrix is determined
by performing N times downward cyclic shift operations as
shown in (15). From (15), it is evident that the number of non‐
zero elements in this CL matrix is only one when the length of
the phase sequence vector is 2 and considering 4 replication of
the same phase sequence.
2
1
0
6 0
1
6
6 0
0
6
6 0
0
6
6 1
0
6
6 0
1
6
6 0
0
6
6 0
0
6
CL ¼ 6
6 −1 0
6 0 −1
6
6 0
0
6
6 0
0
6
6 1
0
6
6 0
1
6
4 0
0
0
0
0
0
0
0
1
0
0
1
0
0
0
0
1
0
0
1
0
0
0
0
−1 0
0 −1
0
0
0
0
1
0
0
1
1
0
0
1
0
0
0
0
1
0
0
1
0
0
0
0
1
0
0
1
0
0
0
0
−1 0
0 −1
0
0
0
0
0
0
0
0
1
0
0
1
0
0
0
0
1
0
0
1
0
0
0
0
1
0
0
1
0
0
0
0
−1 0
0 −1
(constant term is neglected) by applying IFFT operation where
the period is replicated for four times. For the circulant vector
cl2, the CL matrix is generated by performing N number of
downward cyclic shift operations as shown in (17). The total
length of the periodic phase sequence is equal to the length of
the DCO‐OFDM frame. From (17), it is apparent that the
number of non‐zero elements in this CL matrix is maximum
four, if the length of the phase sequence vector is 4 and phase
sequence is replicated 4 times to make the total length of the
phase sequence equal to the DCO‐OFDM frame size. Similarly, for the periodic phase sequence of length 8, there are
maximum four non‐zero elements in its CL matrix.
−1 0
0 −1
0
0
0
0
1
0
0
1
0
0
0
0
1
0
0
1
0
0
0
0
1
0
0
1
0
0
0
0
0
0
0
0
−1 0
0 −1
0
0
0
0
1
0
0
1
0
0
0
0
1
0
0
1
0
0
0
0
1
0
0
1
1
0
0
1
0
0
0
0
−1 0
0 −1
0
0
0
0
1
0
0
1
0
0
0
0
1
0
0
1
0
0
0
0
3
0
0
0
0 7
7
1
0 7
7
0
1 7
7
0
0 7
7
0
0 7
7
−1 0 7
7
0 −1 7
7
0
0 7
7
0
0 7
7
1
0 7
7
0
1 7
7
0
0 7
7
0
0 7
7
1
0 5
0
1
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212
where the constant term 0.5 is neglected throughout the paper.
Hence, for the periodic phase sequence of lengths 2, 4, and
8, the required multiplications are reduced to a great level.
From the above theoretical presentation, it is clear that the
complexity reduction performance surpasses the traditional
SLM scheme. Simulation results are compared to the theoretical explanation in the preceding sections. Despite the compromisation in the PAPR performance, the complexity is
reduced largely, and the system can be employed in short‐
distance VLC systems.
4 | PERFORMANCE EVALUATION
This section assesses the amount of PAPR reduction, computational complexity, and BER performance as the evaluation
metric for the DCO‐OFDM technique. Peak‐to‐average‐power
ratio is considered a random variable whose characteristics are
determined by CCDF. The most frequently used measure
for describing PAPR reduction is CCDF, and it is delineated as
the probability that the PAPR of an OFDM frame exceeds a
given threshold value PAPR0, and it is given as CCDF ¼
1 − CDF ¼ P ðPAPR > PAPR0 Þ ¼ 1 − P ðPAPR ≤ PAPR0 Þ.
We employ CCDF for the evaluation of PAPR (at CCDF =
10−3) of the DCO‐OFDM system. Complementary cumulative
distribution function is measured by varying various parameters
such as periodic phase sequence length, length of DCO‐
OFDM sub‐block, modulation order and clipping, and comparisons are made with standard SLM scheme. The system
model is simulated with matrix laborator (MATLAB) software,
and its parameters are as shown in the Table 2.
4.1 | Simulation results
proposed DCO‐OFDM candidates for the sizes L = 2, 4, and
8. It is perceptible from the simulation result that the CCDF of
the PAPR of DCO‐OFDM is suppressed by the periodic phase
sequence approach for different periods of phase sequence
vectors. Almost same performance is observed for the periods
adopted in this scheme at CCDF = 10−3. The performance of
the phase sequence vector for length two is slightly less than
that of sizes 4 and 8. However, each length in the proposed
scheme differs only with 0.2 dB but considerably better than
the conventional DCO‐OFDM signal. The numerical multiplications required for proposed method for sizes 2, 4, and 8
are 50%, 75%, and 87.5%, respectively. Thus, the proposed
method in this paper is very practical and application‐oriented
in VLC systems.
Figure 4 shows the effect of the increase in the number of
DCO‐OFDM subcarriers (N) on CCDF of the proposed
method. Simulation results are obtained for proposed scheme
for N = 64, 128, and 256 and compared with that of the
conventional DCO‐OFDM signal for the same subcarriers.
Usually, in VLC systems, low subcarriers are preferred over
large subcarriers since the distance between the transmitter and
the receiver is less (indoor communication). The difference
between the PAPR of the conventional and the proposed
method for the given number of subcarriers at CCDF = 10−3
is observed as 2.91 dB. This minuscule difference shows that
our proposed method is independent of the number of DCO‐
OFDM subcarriers and suppresses the PAPR well for VLC
systems.
Direct current‐biased optical orthogonal frequency division multiplexing signal is generated by adding a DC‐bias value
(Bdc) to the OFDM signal to make the signal real and positive
valued in VLC systems. In Figure 5, the variation of PAPR with
0
10
First, we analyse the impact of the period of the phase rotation
vector on the complexity reduction performance in our proposed scheme. It is commonly known that as the number of
phase sequences increase in the SLM scheme the PAPR performance increases. In Figure 3, simulation results of PAPR
CCDF are shown for conventional DCO‐OFDM signal and
10
-1
10
-2
10
-3
T A B L E 2 Parameters for simulated low complexity selective mapping
(SLM) direct current‐biased optical orthogonal frequency division
multiplexing (DCO‐OFDM) system
Parameter
Value
Number of sub carriers
64, 128, and 256
Number of sub symbols
10,000
Modulation order
16, 32, and 64‐QAM
DC‐bias
7
Clipping factor (R)
2
CCDF
10−3
Sizes of phase sequence (L)
2, 4, and 8
Abbreviation: CCDF, complementary cumulative distribution function.
4
6
8
10
12
14
F I G U R E 3 Peak‐to‐average‐power ratio (PAPR) comparison of
proposed method with conventional direct current‐biased optical
orthogonal frequency division multiplexing (DCO‐OFDM) signal for
L = 2, 4, and 8
16
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GUNTURU AND VALLURI
DC‐bias is shown for both the conventional DCO‐OFDM
signal and proposed method. The DC‐bias values considered
in our simulations are 4, 7, and 10 dB for both the conventional and proposed schemes, respectively. While measuring
the CCDF values in MATLAB, we assumed N = 256, modulation order (M) = 32, period of phase sequence size (L) = 4
and total number of DCO‐OFDM subblocks are 104. It is
10
0
Conventional
DCO-OFDM
10-1
Proposed method
10
-2
10
-3
4
6
8
10
12
14
16
F I G U R E 4 Comparison of peak‐to‐average‐power ratio (PAPR)
CCDFs of the proposed scheme and conventional direct current‐biased
optical orthogonal frequency division multiplexing (DCO‐OFDM) with
different subcarriers
evident from the simulation results that the PAPR reduction
performance is highly improving when DC‐bias value is
increased. However, as mentioned in section 2.1, the DC‐bias
should be added to the OFDM signal up to a certain level only
without exceeding the allowable power at the transmitter.
In Figure 6, the PAPR suppression performance of the
proposed method is shown for different modulation orders (M).
Simulation results are taken for the modulation orders 16, 32,
and 64 at CCDF = 10−3 by setting N = 1024, L = 8, and
Bdc = 13 dB. The simulation results show that the PAPR
reduction potential of the proposed scheme is greatly improved
than that of the conventional DCO‐OFDM signal. Nevertheless, it is to be noted that the proposed method yields less gap
between various modulation orders, not more than 0.8 dB.
Figure 7 displays the simulation results of unclipped and
clipped DCO‐OFDM signals and also compared with the
proposed method for N = 128 subcarriers. Clipping is performed before applying the signal to the LED since different
manufacturers use different specifications. In order to make
the input signal suitable for the LED transmission, clipping is
achieved at various threshold levels. Only one side clipping is
performed in our simulations because the DCO‐OFDM signal
consists of only positive values. In this proposed method,
Upper Bound (UB) and Lower Bound (LB) are imposed on the
DCO‐OFDM signals. At various UB and LB, the CCDF is
calculated for both conventional and proposed DCO‐OFDM
candidates. It is perceptible from Figure 7 that the higher the
signal is clipped, the lower the CCDF is.
In addition to the above simulation results, BER performance of the conventional DCO‐OFDM signal and proposed
method are also obtained in simulation results. Figure 8 depicts
100
100
Conventional DCO-OFDM
-1
10-1
Pr(PAPR>PAPR0)
10
Proposed method
Conventional SLM
10-2
10-2
-3
10
Proposed method
4
6
8
10
12
14
F I G U R E 5 Direct current‐biased optical orthogonal frequency
division multiplexing(DCO‐OFDM) peak‐to‐average‐power ratio (PAPR)
complementary cumulative distribution function (CCDF) versus PAPR
CCDF of proposed method for Bdc = 4, 7, and 10
16
10-3
4
6
8
10
12
14
F I G U R E 6 Comparison of peak‐to‐average‐power ratio (PAPR)
CCDFs of proposed method with conventional direct current‐biased
optical orthogonal frequency division multiplexing (DCO‐OFDM) for
M = 16, 32 and 64
16
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214
10
10
0
order 4, 8, 16, 32, and 64 and phase sequence sizes four and
eight, almost same BER performance (<1) dB is obtained.
Even though there is less than 1 dB difference in the BER
performance, our proposed scheme can be acclaimed as the
technique to reduce computational complexity without altering
the BER performance.
-1
4.2 | Complexity analysis
10
-2
10-3
4
6
8
10
12
14
16
F I G U R E 7 Peak‐to‐average‐power ratio (PAPR) complementary
cumulative distribution function (CCDF) comparison of proposed method
and conventional direct current‐biased optical orthogonal frequency
division multiplexing (DCO‐OFDM) for different lower bound (LB) and
upper bound (UB)
The computational complexity of the PAPR reduction scheme
is measured in this subsection based on the number of complex multiplications (CMs) and CAs needed. The computational complexity reduction performance of the proposed
method is assessed by comparing it with the standard SLM
scheme. To evade the loss in the generality, the equal number
of alternative symbols are assumed and compared with the
conventional SLM. We employ computational complexity
reduction ratio (CCRR) measure to quantify the computational
complexity, which is defined as the ratio between computation
complexity of the proposed method and standard SLM.
�
�
complexity of proposed method
CCRR ¼ 1 −
� 100%
complexity of standard SLM
ð18Þ
100
0.3
0.24
0.298
0.296
8
0.128
0.238
13
8.05
13.05
0.126
10-1
0.124
17.55
BER
17.5
7.3
10
7.2
-2
3.2
10
-3
7.1
7
17
3
2.8
13.5
10-3
10-3
0
5
17.05
13.55
10
15
20
25
SNR in dB
F I G U R E 8 Bit error rate (BER) performance comparison of proposed
scheme and conventional direct current‐biased optical orthogonal
frequency division multiplexing (DCO‐OFDM) with different modulation
orders
the simulation results of BER performance in PAPR reduction
of DCO‐OFDM system using both conventional DCO‐
OFDM signal and proposed schemes. Simulations are acquired for varying modulation order and the period of the
phase sequence, keeping N = 256 and Bdc = 7. For modulation
Summing up all the steps in the proposed method, it is
clear that the main reason behind the complexity is the necessity of IFFT operations and multiplication of the frequency‐
domain data block with all the generated candidates. Usually, in
standard SLM, an N‐point IFFT needs N2 log2 N CMs and N
log2N additions. If number of symbol blocks are P for L
number of phase sequence vectors in the conventional SLM,
then LP N2 log2 N CMs and LNP log2N additions are required.
In contrast to the conventional SLM, the deployment of periodic phase sequence vectors in our complexity reduction
scheme configures each candidate to contain only elements
from the set { − 1, 0, 1}. Certainly, the CL matrix of order
L � L comprises L number of columns; each represents a
circulant vector containing a maximum of 4 non‐zero elements. Surprisingly, for a single DCO‐OFDM symbol, the
generation of multiple candidates requires only one IFFT
operation and the rest of the (L − 1) IFFTs are replaced with
CL matrix multiplication. In summary, for P number of subblocks, our method undergoes P N2 log2 N CMs and NP log2N
+ 3(L − 1)NP complex addition operations.
For the quantitative evaluation of the proposed complexity
reduction scheme, we fix the number of subcarriers N = 128
and the number of DCO‐OFDM symbol blocks p = 100. By
varying the number of the periodic phase rotation vectors
L = 2, 4, and 8, the required CMs and CAs are calculated and
tabulated as shown in Table 3 and Table 4, respectively. The
CCRR of the proposed method in terms of complex multiplication for the sizes of phase sequences L = 2, 4, and 8 are
determined as 50%, 75%, and 87.5%, respectively, which is
significantly better than the standard SLM scheme.
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T A B L E 3 Numerical comparison of complexity of selective mapping
(SLM) and proposed method in terms of multiplications for P = 100,
N = 128, and L = 2, 4, and 8
Method
L=2
No. of
multiplications
L=4
No. of
multiplications
L=8
No. of
multiplications
O R CI D
https://orcid.org/0000-0002Chakravarthy Gunturu
9201-7939
Sivaprasad Valluri
https://orcid.org/0000-0002-80420421
Standard SLM
89,600
179,200
358,400
R E FE R E N C E S
Proposed method
44,800
44,800
44,800
CCRR
50%
75%
87.5%
1. Barnett, T., et al.: Cisco visual networking index (vni) complete forecast
update, 2017–2022. In: Americas/EMEAR Cisco Knowledge Network
(CKN) Presentation (2018)
2. Sebastián, J., et al.: On the role of power electronics in visible light
communication. IEEE J. Emerg. Sel. Top. Power Electron. 6(3),
1210–1223 (2018). https://doi.org/10.1109/jestpe.2018.2830878
3. Huang, X., et al.: Experimental demonstration for high speed integrated
visible light communication and multimode fiber communication system.
IET Optoelectron. 9(5), 207–210 (2015). https://doi.org/10.1049/iet‐
opt.2014.0168
4. Marin‐Garcia, I., et al.: Evaluating the risk of eavesdropping a visible
light communication channel. IET Optoelectron. 12(6), 289–292 (2018).
https://doi.org/10.1049/iet‐opt.2018.5052
5. Basnayaka, D.A., Haas, H.: Design and analysis of a hybrid radio frequency
and visible light communication system. IEEE Trans. Commun. 65(10),
4334–4347 (2017). https://doi.org/10.1109/tcomm.2017.2702177
6. Kishore, V., Vakmulla, M.: An LED modelled GFDM for optical wireless
communications. AEU ‐ Int. J. Electron. Commun. 101, 54–61 (2019).
https://doi.org/10.1016/j.aeue.2019.01.027
7. Frutuoso Barroso, A.R., Johnson, J.: Optical wireless communications
omnidirectional receivers for vehicular communications. AEU ‐ Int. J.
Electron. Commun. 79, 102–109 (2017). https://doi.org/10.1016/j.aeue.
2017.05.042
8. Deng, X., et al.: Modeling and analysis of transmitter performance in
visible light communications. IEEE Trans. Veh. Technol. 68(3),
2316–2331 (2019). https://doi.org/10.1109/tvt.2019.2891639
9. Yan, K., et al.: QoS analysis and signal characteristics for short‐
range visible‐light communications. IEEE Trans. Veh. Technol. 70(7),
6726–6734 (2021). https://doi.org/10.1109/tvt.2021.3081688
10. Arafa, A., Panayirci, E., Poor, H.V.: Relay‐aided secure broadcasting for
visible light communications. IEEE Trans. Commun. 67(6), 4227–4239
(2019). https://doi.org/10.1109/tcomm.2019.2900632
11. Zhang, Y., et al.: Recent advances in the hardware of visible light
communication. IEEE Access. 7, 91093–91104 (2019). https://doi.org/
10.1109/access.2019.2927054
12. Yu, B., et al.: A sandwich structure light‐trapping fluorescence antenna
with large field of view for visible light communication. IEEE Trans.
Electron. Dev. 68(2), 565–571 (2021). https://doi.org/10.1109/ted.2020.
3043994
13. Cvijetic, N.: OFDM for next‐generation optical access networks. J.
Lightwave Technol. 30(4), 384–398 (2012). https://doi.org/10.1109/jlt.
2011.2166375
14. Mazahir, S., et al.: Achievable rates of multi‐carrier modulation schemes
for bandlimited IM/DD systems. IEEE Trans. Wireless Commun. 18(3),
1957–1973 (2019). https://doi.org/10.1109/twc.2019.2901479
15. Dawoud, D.W., et al.: A novel unipolar transmission scheme for visible
light communication. IEEE Trans. Commun. 68(4), 2426–2437 (2020).
https://doi.org/10.1109/tcomm.2019.2963377
16. Armstrong, J., Schmidt, B.J.: Comparison of asymmetrically clipped
optical OFDM and DC‐biased optical OFDM in AWGN. IEEE Commun. Lett. 12(5), 343–345 (2008). https://doi.org/10.1109/lcomm.2008.
080193
17. Sheu, J.‐S., Li, B.‐J., Lain, J.‐K.: LED non‐linearity mitigation techniques
for optical OFDM‐based visible light communications. IET Optoelectron. 11(6), 259–264 (2017). https://doi.org/10.1049/iet‐opt.2017.0059
18. He, C., Armstrong, J.: Clipping noise mitigation in optical OFDM systems. IEEE Commun. Lett. 21(3), 548–551 (2017). https://doi.org/10.
1109/lcomm.2016.2643630
19. Rahmatallah, Y., Mohan, S.: Peak‐to‐average power ratio reduction in
OFDM systems: a survey and taxonomy. IEEE Commun. Surv.
Abbreviation: CCDF, complementary cumulative distribution function.
T A B L E 4 Numerical comparison of complexity of selective mapping
(SLM) and proposed method in terms of additions for P = 100, N = 128,
and L = 2, 4, and 8
Method
L=2
No. of
additions
L=4
No. of
additions
L=8
No. of
additions
Standard SLM
179,200
358,400
716,800
Proposed method
128,000
204,800
358,400
CCRR
28.57%
42.85%
50%
Abbreviation: CCDF, complementary cumulative distribution function.
5 | CONCLUSION
In this paper, we present a low‐complexity SLM technique
based on periodic phase sequence vectors to reduce PAPR in
DCO‐OFDM systems. The SLM scheme's complexity is
effectively reduced by utilising system features that are inheritable with the system. In this scheme, phase sequence vectors
are strictly confined to periodic with period K ≤ 8 for generating multiple candidates. Consequently, the circulant vector
obtained by IFFT of the periodic phase sequence will contain
no more than four non‐zero elements. The system requires
only a few CAs to generate various alternate DCO‐OFDM
signals. The anatomisation of the complexity and simulation
results manifests that the low complexity and the best performance characteristics of the proposed method make the
system more practical and advantageous for optical OFDM
techniques. Hence, the proposed scheme can be put forth for
complexity reduction in all practical VLC‐based SLM‐OFDM
systems.
ACK NO W LE DG E ME NT S
The authors would like to thank the Ministry of Human
Resource Development (MHRD) of India for providing the
financial support for this research work.
CON FLIC T OF I N TE R ES T
Authors have no conflict of interest.
DATA AVAI L A BI L I T Y S TA T E ME N T
The data that support the findings of this study are available on
request from the corresponding author. The data are not
publicly available due to privacy or ethical restrictions.
17518776, 2022, 5, Downloaded from https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/ote2.12074 by Indian Institute of Information Technology Design And Manufacturing, Kurnool, Wiley Online Library on [25/08/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
216
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
Tutorials 15(4), 1567–1592 (2013). https://doi.org/10.1109/surv.2013.
021313.00164
Tang, B., et al.: A clipping‐noise compression method to reduce PAPR of
OFDM signals. IEEE Commun. Lett. 23(8), 1389–1392 (2019). https://
doi.org/10.1109/lcomm.2019.2916052
Jawhar, Y.A., et al.: A review of partial transmit sequence for PAPR
reduction in the OFDM systems. IEEE Access. 7, 18021–18041 (2019).
https://doi.org/10.1109/access.2019.2894527
Baxley, R.J., Zhou, G.T.: Comparing selected mapping and partial
transmit sequence for PAR reduction. IEEE Trans. Broadcast. 53(4),
797–803 (2007). https://doi.org/10.1109/tbc.2007.908335
Hu, W.‐W.: PAPR reduction in DCO‐OFDM visible light communication
systems using optimized odd and even sequences combination. IEEE
Photon. J. 11(1), 1–15 (2019). https://doi.org/10.1109/jphot.2019.
2892871
Hu, W.‐W., Lee, D.‐H.: PAPR reduction for visible light communication
systems without side information. IEEE Photon. J. 9(3), 1–11 (2017).
https://doi.org/10.1109/jphot.2017.2700400
Valluri, S.P., Vejandla, K., Mani, V.V.: Low complex implementation of
GFDM system using USRP. IET Commun. 14(13), 2060–2067 (2020).
https://doi.org/10.1049/iet‐com.2019.1106
Miriyala, G., Mani, V.: A new PAPR reduction technique in DCO‐OFDM
for visible light communication systems. Opt Commun. 474, 126064
(2020). https://doi.org/10.1016/j.optcom.2020.126064
Valluri, S.P., Kishore, V., Vakamulla, V.M.: A new selective mapping
scheme for visible light systems. IEEE Access. 8, 18087–18096 (2020).
https://doi.org/10.1109/access.2020.2968344
Hei, Y., et al.: Improved TKM‐TR methods for PAPR reduction of
DCO‐OFDM visible light communications. Opt Express. 25(20),
24448–24458 (2017). https://doi.org/10.1364/oe.25.024448
Taşpınar, N., Yıldırım, M.: A novel parallel artificial bee colony algorithm
and its PAPR reduction performance using SLM scheme in. OFDM and
MIMO‐OFDM syst. 19(10), 1830–1833 (2015). https://doi.org/10.
1109/lcomm.2015.2465967
- 217
30. Guzmán, B.G., Jiménez, V.P.G.: DCO‐OFDM signals with derated power
for visible light communications using an optimized adaptive network‐
based fuzzy inference system. IEEE Trans. Commun. 65(10), 4371–4381
(2017)
31. Şimşir, Ş., Taşpınar, N.: A novel discrete cuckoo search algorithm‐based
selective mapping technique to minimize the peak‐to‐average power ratio
of universal filtered multicarrier signal. Int. J. Commun. Syst. 33(18),
e4640 (2020)
32. Chen, M., et al.: Experimental demonstration of an IFFT/FFT size
efficient DFT‐spread OFDM for short reach optical transmission systems. J. Lightwave Technol. 34(9), 2100–2105 (2016). https://doi.org/10.
1109/jlt.2016.2529429
33. Dimitrov, S., Sinanovic, S., Haas, H.: Clipping noise in OFDM‐based
optical wireless communication systems. IEEE Trans. Commun. 60(4),
1072–1081 (2012). https://doi.org/10.1109/tcomm.2012.022712.100493
34. Taspınar, N., Alhalabi, M.: Performance investigation of long‐haul high
data rate optical OFDM IM/DD system with different QAM modulations. J. Electr. Eng. 72(3), 192–197 (2021). https://doi.org/10.2478/jee‐
2021‐0026
35. Wang, C.‐L., Ku, S.‐J.: Novel conversion matrices for simplifying the
IFFT computation of an SLM‐based PAPR reduction scheme for OFDM
systems. IEEE Trans. Commun. 57(7), 1903–1907 (2009). https://doi.
org/10.1109/tcomm.2009.07.070156
How to cite this article: Gunturu, C., Valluri, S.: A
new complexity reduction scheme in selective mapping‐
based visible light communication direct current‐biased
optical orthogonal frequency division multiplexing
systems. IET Optoelectron. 16(5), 207–217 (2022).
https://doi.org/10.1049/ote2.12074
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