Keywords 
OFDM , PAPR , AWGN , PTS 
INTRODUCTION 
OFDM is a special case of multicarrier transmission, where a single data stream is transmitted over a number of lower rate
subcarriers[1]. The main advantages of OFDM are its increased robustness against frequency selective fading or
narrowband interference as well as the efficient use of available bandwidth. Two major drawbacks of OFDM systems are
the great sensitivity to time and frequency synchronization errors, and the high PAPR. Due to high PAPR, the amplified
signal suffers from distortion and outof band noise when passed through nonlinear devices, such as a Power Amplifier
(PA). A solution to this problem is the use of highly linear Power Amplifiers (PA) with sufficient backoff. However this
solution comes at the penalty of high power consumption and it also leads to one of the major problems of OFDM systems
called PEAK TO AVERAGE POWER RATIO [1,2,3,4] (PAPR).There are several techniques to reduce the PAPR in
OFDM system like Clipping , SLM , PTS etc. 
PAPR IN OFDM : Generally each and every system has a pattern of transmitting and receiving the data[2]. OFDM is no
where different. It works according to the following block architecture. 
The block diagram of OFDM system is shown in figure 1. The transmit signal can be generated by a simple IDFT
operation, which can replace the bank of modulators [2] and at the receiver; a DFT can be performed to recover the
transmitted signal. OFDM signal consists of n data symbols transmitted over N0 subcarriers. Let P = { Pk, k=0, 1,2,,N0
1} be a block of n data symbols and each symbol modulating a set of subcarriers {fk, k=0,1, ,N01}. PAPR of the OFDM
signal x (t) is defined as the ratio between peak power and its average power during the OFDM signal.[4] 
PAPR= x  peak ^{2} / x rms^{2} [7] where rms is the root mean square value of the obtained signal. 
The PAPR of the continuoustime OFDM signal cannot be precisely computed in the Nyquist sampling rate, which
corresponds to N samples per OFDM symbol. In this case, signal peaks may be skipped and PAPR estimates are not
precise. So, oversampling is necessary. 
To evaluate the PAPR reduction performance accurately from the statistical point of view, the complementary cumulative
distribution function (CCDF) of the PAPR of OFDM signals is used. CCDF describes the probability of exceeding a given
threshold PAPR0 and is represented as 

Due to the independence of the N samples, the CCDF of the PAPR of single input single output (SISO) OFDM as a data
block with Nyquist rate sampling is given by 

PTS: Stands for PARTIAL TRANSMIT SEQUENCE. In PTS scheme, an input symbol sequence A is partitioned Into V
‘disjoint’ symbol subsequences 

Here, the word ‘disjoint’ implies that for each given k,0 < k<=N1 , A v,k=0 except for at most a single . In other
words, the support sets of are disjoint. The signal subsequence is generated by applying inverse fast Fourier
transform (IFFT) to each symbol subsequence av, often called a sub block. Each signal subsequence is then
multiplied by an unit magnitude constant chosen from a given alphabet , which is usually Z={+1 }or {+1 ,+ j
} and summed to result in a PTS OFDM signal sequence which can be expressed as 

The known sub block partitioning methods [6] can be classified into three categories. The first and simplest
category is called an adjacent method which allocates successive symbols to the same sub block. The second
category is based on interleaving. In this method, the symbols with distance are allocated to the same sub block
[8]. The last one is called a random partitioning method in which the input symbol sequence is partitioned
randomly. For example, let us partition an input symbol sequence of length 16 into 4 symbol subsequences.
Then, is used as a sub block partitioning sequence for the adjacent method, for the interleaved method, and for
the random method. The PAPR reduction performance and the computational complexity of PTS scheme
depend on the method of sub block partitioning. In other words, there is a tradeoff between PAPR reduction
performance and computational complexity in PTS scheme. The random partitioning is known to have the best performance in PAPR reduction. The interleaving method
[5] can reduce the computational complexity of PTS scheme using CooleyTukey FFT algorithm, but the PAPR
reduction performance is the worst.[9, 10] 
PROPOSED PTS METHOD 
The existing novel PTS Scheme is based on the phase factors. With two phase 1 and 1 results in four phase
factors b1={1,1} , b2={1,1} , b3={1,j} and b4={1,j}.[1] These phase weight vectors helps in finding the
appropriate threshold according to the weight generated at these phase vectors mentioning that for the adjacent
phase weighting vectors in same column , only the sign of one element is different. In our proposed scheme we
have introduced a phase 0 (zero ) in the existing phases as sign of zero can neither be considered as positive nor
negative and it would result in better search threshold values. 
With the introduction of 0 in the phase vector , we would be getting the following phase factors {1,1} ,{1,1}
,{1,j} , {1 j}, {0, j} , {0, j} and hence it would result into a better PAPR reduction value. 
The parameter P introduced in novel PTS method [1] would be re written as follows 

RESULTS AND CONCLUSION 
Our proposed scheme results into an efficient reduction scheme whose results are discussed as follows. 
Figure represents the results of enhanced PTS scheme which indicated that with an increased phase factor the value of
PAPR is reduced. 
By comparing of the PAPR reduction performance with different sub blocks in Figure it is clear that PAPR is decreased
from 8.8 dB to 7.8 dB for V4. For V6 the resulting value has been decreased from 6.9 to 6.65.
Our current approach opens up a lot of future possibilities for the future research workers. Our current system does not
include a lot of modulation techniques like QAM 16 , 32 and 64 which may enhance the result. 
Figures at a glance 



Figure 1 
Figure 2 
Figure 3 

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