 Open Access
 Total Downloads : 257
 Authors : Pradeep A.V.S, Prasad S.N.V.S
 Paper ID : IJERTV2IS100728
 Volume & Issue : Volume 02, Issue 10 (October 2013)
 Published (First Online): 23102013
 ISSN (Online) : 22780181
 Publisher Name : IJERT
 License: This work is licensed under a Creative Commons Attribution 4.0 International License
Fading Prediction in Mobile Radio OFDM Systems Using ABICM Method
Pradeep A.V.S 1, Prasad S.N.V.S 2
1 Student (M.Tech), Department of ECE, Chaitanya college of Engineering, Andhra Pradesh, India,
2 Associate Professor, Department of ECE, Chaitanya college of Engineering, Andhra Pradesh, India,
Abstract
Orthogonal Frequency Division Multiplexing is a scheme used in the area of highdatarate mobile wireless communications such as cellular phones, satellite communications and digital audio broadcasting. The expression digital communications in its basic form is the mapping of digital information into a waveform called a carrier signal, which is a transmitted electromagnetic pulse or wave at a steady base frequency of alternation on which information can be imposed by increasing signal strength, varying the base frequency, varying the wave phase, or other means. In this instance, orthogonality is an implication of a definite and fixed relationship between all carriers in the collection. Multiplexing is the process of sending multiple signals or streams of information on a carrier at the same time in the form of a single, complex signal and then recovering the separate signals at the receiving end. Adaptive bitinterleaved coded modulation (ABICM) is attractive for rapidly varying mobile radio channels due to its robustness to imperfect channel state information (CSI). A novel ABICM method that exploits the expurgated bound to maintain the target bit error rate (BER) for diverse CSI conditions is proposed and evaluated for an adaptive mobile radio orthogonal frequency divisionmultiplex (OFDM) system aided by the longrange fading prediction. It is demonstrated that ABICM is much less sensitive to prediction errors than adaptive modulation techniques. Practical channel conditions in spectral efficiency are obtained using ABICM Method.
Index Terms: Fading channel, OFDM, Interleaved coding, and Adaptive modulation

INTRODUCTION
The OFDM technology was first conceived in the 1960s and 1970s during research into minimizing Inter Symbol Interference, or ISI, due to multipath. OFDM is a special form of Multi Carrier Modulation (MCM) with densely spaced subcarriers with overlapping spectra, thus allowing for multipleaccess. MCM) is the principle of transmitting data by dividing the stream into several bit streams, each of which has a much lower bit rate, and by using these substreams to modulate several carriers. This
technique is being investigated as the next generation transmission scheme for mobile wireless communications networks. the advent of the Fourier Transform eliminated the initial complexity of the OFDM scheme where the harmonically related frequencies generated by Fourier and Inverse Fourier transforms are used to implement OFDM systems. The Fourier transform is used in linear systems analysis, antenna studies, optics, random process modeling, probability theory, quantum physics, and boundaryvalue problems and has been very successfully applied to restoration of astronomical data.
The adaptive bitinterleaved coded modulation (ABICM) was proposed in [1] to improve robustness of adaptive coded modulation to unreliable channel state information (CSI). In the original ABICM method [1], the Bhattacharyya bound based on the minimum distance of the constellation and a nominal nonadaptive BICM scheme were employed to determine the constellation size and the transmission power. The actual BER of this method significantly deviates from the specified target BER [1], [2]. Hence, additional experimental energy adaptation is required to maintain the BER.
ABICM was also investigated in [3], [4] under the assumption of perfect CSI at the transmitter, which is reasonable for static fading channels such as indoor wireless systems, but not for outdoor mobile radio channels. Moreover, due to the difficulty of evaluating the exact BER, simulations were used in [3], [4] to obtain the thresholds that determine the transmission constellation as well as power.
The expurgated bound provides accurate BER estimates for nonadaptive BICM in additive white Gaussian noise (AWGN) and fading channels [5]. In this letter we design an ABICM system aided by imperfect CSI by employing the expurgated bound. The ability to maintain the target BER using this method is demonstrated using simulations for an adaptive orthogonal frequency division multiplex (OFDM) system.

Multiple Techniques
Multiple access schemes are used to allow many simultaneous users to use the same fixed bandwidth radio
spectrum. In any radio system, the bandwidth, which is allocated to it, is always limited. For mobile phone systems the total bandwidth is typically 50 MHz, which is split in half to provide the forward and reverse links of the system.
Sharing of the spectrum is required in order increase the user capacity of any wireless network. FDMA, TDMA and CDMA are the three major methods of sharing the available bandwidth to multiple users in wireless system [2]. There are many extensions, and hybrid techniques for these methods, such as OFDM, and hybrid TDMA and FDMA systems. However, an understanding of the three major methods is required for understanding of any extensions to these methods.

Frequency Division Multiple Access (FDMA):
In Frequency Division Multiple Access (FDMA), the available bandwidth is subdivided into a number of narrower band channels. Each user is allocated a unique frequency band in which to transmit and receive on. During a call, no other user can use the same frequency band.
Fig1 & Fig2 show the allocation of the available bandwidth into several channels.

Time Division Multiple Access:
Time Division Multiple Access (TDMA) divides the available spectrum into multiple time slots, by giving each user a time slot in which they can transmit or receive. Fig.
1.4 shows how the time slots are provided to users in a round robin fashion, with each user being allotted one time slot per frame. TDMA systems transmit data in a buffer and burst method, thus the transmission of each channel is non continuous.
Fig3 TDMA scheme, where each user is allocated a small time slot
1.1.3 Code Division Multiple Access:
Code Division Multiple Access (CDMA) is a spread spectrum technique that uses neither frequency channels nor time slots. In CDMA, the narrow band message (typically digitized voice data) is multiplied by a large bandwidth signal, which is a pseudo random noise code (PN code) [2]. All users in a CDMA system use the
same frequency band and transmit simultaneously. The transmitted signal is recovered by correlating the received signal with the PN code used by the transmitter.
Fig 4 Code Division Multiple Access (CDMA)



OFDM CARRIERS
OFDM is a special form of MCM and the OFDM time domain waveforms are chosen such that mutual orthogonality is ensured even though subcarrier spectra may overlap. With respect to OFDM, it can be stated that orthogonality is an implication of a definite and fixed relationship between all carriers in the collection. It means that each carrier is positioned such that it occurs at the zero energy frequency point of all other carriers. The sinc function, illustrated in Fig. 5 exhibits this property and it is used as a carrier in an OFDM system.
Fig5 OFDM sub carriers in the frequency domain

FOURIER TRANSFORM
The Fourier transform, in essence, decomposes or separates a waveform or function into sinusoids of different frequencies which sum to the original waveform. It identifies or distinguishes the different frequency sinusoids and their respective amplitudes.
The Fourier transform of f(x) is defined as:
F ( )
f (x) e jx dx 1
the channels ISIs, a set of subcarriers can be used to transmit information symbols in parallel subchannels over the channel, where the systems output will be the sum of all the parallel channels throughputs. This is the basis of
and its inverse is denoted by:
how OFDM works. By transmitting in parallel over a set of
f (x)
1 F ( ) e jx d 2
2
2
subcarriers, the data rate per subchannel is only a fraction of the data rate of a conventional single carrier system having the same output. Hence, a system can be designed to support high data rates while deferring the need for channel
However, the digital age forced a change upon the traditional form of the Fourier transform to encompass the discrete values that exist is all digital systems. The modified series was called the Discrete Fourier Transform (DFT). The DFT of a discretetime system, x(n) is defined as:
equalizations.
In addition, once the incoming signal is split into the respective transmission subcarriers, a guard interval is added between each symbol. Each symbol consists of a useful symbol duration, Ts and a guard interval, t, in
N 1
(k ) x(n) e
n0

j 2 kn N
1 k N 3
which, part of the time, a signal of Ts is cyclically repeated. This is shown in Fig. 2.
and its associated inverse is denoted by:
x(n)
4
1 N 1
(k ) e
N n 0
j 2 kn N
1 n N——
N
N
However, in OFDM, another form of the DFT is used, called the Fast Fourier Transform (FFT), which is a DFT algorithm developed in 1965. This new transform reduced the number of computations from something on the order of
N 2 to
2 log 2 N.
——— 5
Fig.6 Combating ISI using a guard interval
As long as the multi path propagation delays do not exceed the duration of the interval, no intersymbol interference occurs and no channel equalization is required.

2.2 INTER SYMBOL INTERFERENCE

As communication systems evolve, the need for high symbol rates becomes more apparent. However, current multiple access with high symbol rates encounter several multi path problems, which leads to ISI. An echo is a copy of the original signal delayed in time. ISI takes place when echoes on differentlength propagation paths result in overlapping received symbols [7]. Problems can occur when one OFDM symbol overlaps with the next one. There is no correlation between two consecutive OFDM symbols and therefore interference from one symbol with the other will result in a disturbed signal
In addition, the symbol rate of communications systems is practically limited by the channels bandwidth [9]. For the higher symbol rates, the effects of ISI must be dealt with seriously. Several channel equalization techniques can be used to suppress the ISIs caused by the channel. However, to do this, the CIR channel impulse response, must be estimated.
Recently, OFDM has been used to transmit data over a multipath channel. Instead of trying to cancel the effects of



ADAPTIVE OFDM AIDED BY LONGRANGE PREDICTION

Channel Model and LongRange Prediction
The system diagram of the adaptive OFDM system under investigation is shown in Fig.7 We assume the frequency selective widesense stationary (WSS) Rayleigh fading channel as in [1]. The received signal for the lth subcarrier (l = 1. . . L) Of the nth OFDM symbol is
Y (n, l) = H(n, l)X(n, l) +W(n, l) 6
where H(n, l), X(n, l), andW(n, l) are the complex Gaussian channel response H(n, l) CN(0, 1), the transmitted signal, and the complex additive white Gaussian noise with variance N0, respectively. It is assumed the intersymbol interference
(n,l)(ml). Our objective is to maximize the spectral efficiency under the overall energy constraint ET , i.e.,
max L m L E
m E
l
l 1
l 1
H (n,l ) l
T 6
if EH (n,l ) is known as discrete waterfilling algorithm
Fig7 System diagram of LRPenabled adaptive OFDM system with ABICM.
(ISI) is removed using an appropriate cyclic prefix. Without loss of generality, we set X(n, l) = Ep for pilot symbols, where Ep is the pilot symbol energy.
To facilitate the LRP, pilot symbols are inserted in both frequency and time domains. Past pilot observations within a rectangular area that includes (2Pf + 1) pilot tones and Pt past pilot OFDM symbols are employed to predict the current channel coefficient H(n, l) [2, Fig. 4.2]. In this letter, we assume that channel statistics are known and construct a linear minimum mean square error (MMSE) predictor of order (2Pf +1)Pt [2]. In practice, similar prediction accuracy can be achieved by the autoregressive (AR) model based predictors that track fading channel variations. The performance of this predictor can be improved at low and medium SNR by employing noise reduction [2], [9], [10]. However, we do not utilize noise reduction since we focus on robustness of adaptive coding methods to imperfect predictions. The channel coefficient H(n, l) and its linear MMSE prediction.

Adaptive Bit and Power Loading
At the transmitter, the information bits are encoded using a fixedrate convolutional encoder, followed by bit interleaving (Fig. 7). To facilitate analysis, we assume that this interleaved is ideal as in [1], [5]. Due to frequency diversity in OFDM systems, this assumption is realistic even for short interleaving depth in time.
After interleaving, the adaptive bit and power loading algorithm maps the coded bits into Mquadrature amplitude modulation (MQAM) symbols for all subcarriers. The constellation sizes and energies of these symbols are determined using the CSI fed back from the receiver. Without loss of generality, we consider the allocation for one OFDM symbol X(n, l). Given predicted channel coefficient H (n, l), l [1, L], suppose the minimum average symbol energy required to transmit ml coded bits/symbol while maintaining the target BER is E H
based on the BER calculation for the values of 0.001 and
0.1 using Adaptive Trellis coded modulation (ATCM). The ATCM technique explains about the spectral efficiency with SignaltoNoise ratio (SNR) with different adaptive values. the MMSE values are obtained with Gaussian distribution values using 0.001 and 0.1. The ATCM obtains the highest spectral efficiency for medium to high SNR. The spectral efficiency using Adaptive bitinterleaved coded modulation (ABICM) is obtained using prediction range [11]. The prediction of data transmission for Adaptive trellis coded modulation (ATCM) for long range prediction (LRP) is obtained in terms of spectral efficiency and Normalized prediction range [8]. The different algorithms of ATCM and ABICM are used with different Gaussian distribution values for obtaining spectral efficiency values. the prediction factor of K values are taken for obtaining the spectral efficiency. Comparison of the ATCM and ABICM which obtains the prediction in range for different long prediction range (LRP).


RESULTS
The plot explains about the spectral efficiency of ATCM with Gaussian distribution factor 0.001 and 0.1 which is obtained with SNR variations.
Fig8 Spectral efficiency for ATCM with 0.001 and 0.1
The plot explains about the spectral efficiency of Adaptive bitInterleaved coded modulation (ABICM) in long prediction range (LRP) with normalized prediction range.
Fig9 Spectral efficiency of ABICM with LRP
The plot explains about the spectral efficiency of Adapive trellis coded modulation (ATCM) in long prediction range
(LRP).
Fig10 spectral efficiency of ATCM of LRP
The plot explains the spectral efficiency with Adaptive bit interleaved coded modulation (ABICM) with factor k=2 and 4 with Gaussian distribution values of 0.1 and 0.01.
Fig11 Spectral efficiency of ABICM with different K values
The plot explains about the BER calculation of using different modulation techniques of QAM and QPSK.
Fig12 BER calculation using different techniques

CONCLUSION
This paper explains about the spectral efficiency calculation using different algorithms of Adaptive trellis coded modulation (ATCM) and Adaptive bitInterleaved coded modulation (ABICM) using different constant factor values of Gaussian distribution values. The BER calculation explains about the prediction range for different modulation techniques of QAM and QPSK. We can extend the paper for the soft computing techniques with different modulation implementation in future.
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BIOGRAPHIES
A.V.S pradeep pursuing M.Tech in specialization of Digital Electronics and communication systems at Chaitanya Engineering college in Department of Electronics and Communication Engineering.
S.N.V.S. Prasad completed Ph.D and working as an Associate Professor in Chaitanya Engineering college in Department of Electronics and communication Engineering