11.IJAEST-Vol-No-6-Issue-No-2-Transmitted-Reference-UWB-receiver-for-high-capacity-wireless-applicat by ISERP ISERP

Preeti Rani* et al / (IJAEST) INTERNATIONAL JOURNAL OF ADVANCED ENGINEERING SCIENCES AND TECHNOLOGIES Vol No. 6, Issue No. 2, 237 - 241

Transmitted Reference UWB receiver for high capacity wireless application

Keywords- TR-UWB, AWGN Channel, Iterative algo, BMSR.

Introduction

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Ultra wideband systems are gaining popularity due to their high data rate, low power consumption, low probability of interception and detection and high degree of penetration. In UWB system the bandwidth should not be less than 500 MHz. As an alternative to the complex Rake receiver without channel estimation, a simple scheme called transmitted-reference (TR) scheme is proposed in which a reference pulse is transmitted along with the data pulse with a delay Td between them. We have implemented our ideas after reviewing simple signal model and receiver algorithms reported in [1] and derive a modified signal model and corresponding iterative algorithm which provide improved results. The first TR-UWB system that can be considered practical was proposed by Hoctor and Tomlinson [2], [3]. Pulses are transmitted in pairs referred to as “doublets”, where the first is fixed and considered a “carrier” and the second is modulated by the data. The delay between the pulses can be varied, which serves as a user code. In our proposed TR system, a reference pulse is transmitted before each data-modulated pulse for the purpose of determining the Added white Gaussian noise response. The proposed Auto correlation receiver correlates the data signal with the reference to use all the energy of the data signal without requiring additional channel estimation. Only an analog delay line is needed to align the reference and data pulses[4]. In this TRUWB system, we are considering frame duration (T f) and number of frames per symbol (Nf),it is investigated that data rate is inversely proportional to the product of frame duration and number of frames per symbol, for high data rate Tf and Nf should be low but number of users are proportional to Nf. If we

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decrease Nf to achieve high data rate the number of users decreases, second alternative is to decrease Tf which is proposed in this paper. We have designed a TR-UWB system in which a second order Gaussian pulses are generated which consist of reference and data pulses and then transmitted in AWGN environment with small frame duration which results in inter-frame interference (IFI), autocorrelation terms and cross-correlation terms[5], [6]. Recently many data model had been proposed [7], [8] which are based upon time reversal technique. So, by considering modified data model and iterative receiver algorithm the symbol value can be extracted under noisy environment after neglecting IFI.

Abstract— A high capacity multiple-user TR-UWB system is proposed and its performance in AWGN Channel is investigated. Here, we have proposed a modified signal model and corresponding receiver algorithm assuming lower frame length. We have investigated that in low crowded area with finite number of users this system provides improved BER results at a data rate of more than 500 Mbps. Frame duration (Tf) and Delay(Td) less than channel length(Th) is considered and received signal equations corresponding to 1,2 and 3 number of users are derived with autocorrelation and cross-correlation terms.

Parvinder Singh2 2 Senior Lecturer, Electronics and Communication Engineering Department, Rayat Institute of Engineering and Information Technology, SBS Nagar, Punjab- 144533, India. (E-mail: er.parvinder@gmail.com)

Preeti Rani1 * M.Tech Research Scholar, Electronics and Communication Engineering Department, Rayat Institute of Engineering and Information Technology, SBS Nagar, Punjab- 144533, India. (Corrosponding Author, E-mail: preeti.r1987@gmail.com)

II. TR- UWB SYSTEM

Consider multiple (finite) users TR-UWB system in which pulses are transmitted in pairs with in a frame and each pair of pulses are associated with symbol value si . The pulses can be transmitted over AWGN environment i.e. a channel with white Gaussian noise. The transmitted signal at the output of the antenna can be written as ) U (t) =

)……………….(1) + siZ( Where Z(t) is the convolution product of physical channel Zp(t) and UWB pulse shape g(t), consisting of the transmit pulse including the antenna distortion and low pass/band pass filtering. Although most channels are assumed uncorrelated, the distorted pulse shape g(t) can have a duration of several nanoseconds and thus may introduce some correlations in Z(t). However, these correlations are usually small compared to the total channel energy and go down quickly as the correlation lag increases. A. Single frame per symbol. Let s1 is the symbol value associated with the user and expanding equation (1) for single user, we get U (t) = Z (t) +s1 Z (t – Td) Signal at the multiplier output will be, V1 (t) = U (t) * U (t – Td) V1(t) = [Z(t) + s1 Z(t – Td)] * [Z(t – Td) +s1 Z(t –2Td)] = [ Z2(t – Td) + Z(t)Z(t – 2Td)]s1 +[ Z(t)Z(t – Td)

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Preeti Rani* et al / (IJAEST) INTERNATIONAL JOURNAL OF ADVANCED ENGINEERING SCIENCES AND TECHNOLOGIES Vol No. 6, Issue No. 2, 237 - 241

+ Z (t – Td) Z (t – 2Td)] Also, we define autocorrelation function,

After integration and dump process the received samples are: V1(k) = [G(0,k – Td/ Tsam) + G(2Td ,k )]s1 + [G(Td,k) + G(Td,k – Td/ Tsam)]……………………..…(3)

The signal at the multiplier output, V (t) = U (t)U(t − D) will be integrated over a period Tsam = Tf/n, where the integer n is the “oversampling rate”, and subsequently sampled. Thus, we obtain n samples per frame. The autocorrelation receiver scheme is shown in Fig. 1. Now, consider the resulting signal after correlation due to two transmitted frames, multiplier output will be V1 (t) = U (t) * U (t − D)

V2(k) = [G(Td ,k) + G(Tf + Td, k )

+ G(Tf − Td, k − Td/ Tsam)]

+ [G(2Td ,k) + G(0 ,k − Td/ Tsam) + G (Tf ,k − Td /Tsam)

+ G(2Td − Tf ,k − Td/ Tsam)]s1

+ [G(Tf −2Td ,k)+ G(2Td, k − 2Td/Tsam) + G(0 ,k – (Tf + Td) / Tsam)]s2……….(4)

V2 (t) = [Z (t) + s1Z (t Td)] + [Z (t Tf)

Td)]

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+ s2Z (t Tf Td)] * [Z(t Td) + s1Z(t +[Z (t Tf Td)

+ s2Z (t Tf 2Td)]

V2 (t) = Z (t) + s1Z(t Td) * {[Z (t Td) + s1Z(t

Td)]

+ [Z (t Tf Td) +s2Z (t Tf 2Td)]} + Z (t Tf) +s2Z(t Tf Td)] * {[Z(t Td) + s1Z(t

Td)]

+[Z(t Tf Td)+s2Z(t Tf 2Td)]}

After multiplication in the above equation we get autocorrelation and cross correlation terms viz. S1, S2, S12, S22 and S1S2. After integration and dump process, the received samples are:

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Fig.1. Autocorrelation Receiver

B. Two frames per symbol Let s1 and s2 be the symbol values associated with the users and extending equation (1) for two users, we get U (t) = ) + siZ( ) U (t) = [Z(t) + s1Z(t Td)]+[Z(t Tf) +s2Z (t Tf Td)]

In equation (3), the terms G(0,k) representing the channel segments and the autocorrelation terms G(ґ,k) with ґ ∈ (Td,2Td,) can be assumed very small for an uncorrelated channel.

In equation (4), the terms G(0,k) representing the channel segments and the autocorrelation terms G(ґ,k) with ґ ∈ (Td,2Td,Tf −2Td) are assumed very small for an uncorrelated channel. The integrate and dump process virtually divide the total channel into sub-channels with each sub-channel having its own channel energy and channel autocorrelation function. There are cross correlation terms also that can be assumed negligible for longer correlation terms comparable to frame duration (Tf). C. Three frames per symbol. Consider equation (1) again for three users for i =1 to 3. U (t) = ) + siZ( ) U (t) = [Z (t) +s1Z (t Td)] + [Z (t Tf) +s2Z (t Tf Td)]+ [Z(t 2Tf) +s3Z(t 2Tf Td)]……..(5)

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Preeti Rani* et al / (IJAEST) INTERNATIONAL JOURNAL OF ADVANCED ENGINEERING SCIENCES AND TECHNOLOGIES Vol No. 6, Issue No. 2, 237 - 241

After correlation due to three transmitted frames, multiplier output will be V2 (t) = U (t) * U (t − D) V2(t) = { [Z (t) +s1Z (t Td)]+[Z (t Tf) +s2Z(t Tf Td)] + [Z(t 2Tf) + s3Z(t 2Tf Td)] } * { [Z(t Td) + s1Z(t

Td)]+[Z (t Tf Td)

So in the receiver our purpose is to estimate the value of „Z‟ and„s‟. However, the receiver algorithm must ignore IFI terms. We are considering iterative receiver algorithm rather than blind algorithm because it provides better results than blind receiver algorithm under AWGN environment. Iterative algorithm (for three users) Consider simple data model, v = Zs …………….……..….(6) s = [s1, s2, s3] (symbol values for three users) with modified data model the equation will be, v = Sz

+ s2Z(t Tf 2Td)]+[Z(t 2Tf Td)

Initial estimate: set the channel vector to initial estimate i.e z = zo E.

Iteration (a) With known channel vector z, we can calculate the value of symbol vector using equation (6) as s = Z†v Where † is the Moore-Penrose pseudo-inverse

After multiplication in the above equation, we get long terms of s1, s2 and s3. Again consider the autocorrelation function mentioned in equation (2), after integration and dump process, the received samples contains terms of s1 and s2 same as in equation (4) plus extra terms of s3 ,so we are only considering extra terms in the following equation. V2 (k) = Z(2Tf + Td, k) + Z(0,k – (2Tf + Td)

Where S is the data symbol matrix. This means that unknown data symbols and channel matrix can be obtained by using iterative algorithm similar as follows:

+ s3Z(t 2Tf Td)]}

……….…….......…. (7)

+ Z (2Tf, k– Td/ Tsam) + …….

after expansion of signal equation for two and three users, we conclude that if we further increase the number of users the equations become more complex. So, the signal equation mentioned in (1) is suitable for finite number of users but still high data rate can be attained at finite number of users with lesser value of frame duration.

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D. Improved model (finite number of users). If we consider more than one frame and expand the signal equation we find that there are autocorrelation, cross correlation and inter frame interference (IFI) terms available in the final equation. Cross correlation occurs due to antenna affect which go down quickly as correlation length increases, so for lesser number of frames these terms can be neglected while auto correlation terms (with in one frame) are included in the improved data model So data model becomes, V = Zs + I + noise Where, V = sample vector (collect k samples)

Z = shifted versions of channel correlation vectors

s = unknown symbol vector

I = shifted versions of offset vector Ii (similar to Z) Where Ii = Z(Td, i) + Z(Td, i − Td/ Tsam)

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(b) Make hard decisions on the source symbols s, using their finite alphabet property. (c) With known data symbol s, the channel vector can be estimated again based on (7), as z = S†v Although iterative algorithm is complex, as in there are efficient techniques to invert such matrices with a very low computation complexity. We can use blind receiver algorithm to estimate Z and consider it as Z0 Therefore, we can use known average PDPs as an initial channel estimate, which will significantly improve the convergence of the iterative algorithms. As a result, we only need two iterations for our case. III. Simulation Results and Discussion The simulation parameters are as follows. The pulse shape is the second derivative of a Gaussian function with width parameter tc = 0.1ns, the delay between two pulses in doublet is Td = 0.4ns and channel is an AWGN channel. The frame time Tf = 4ns set to avoid IFI and is fixed for all users, the oversampling rate is n = 20 samples per frame which means that sampling period Tsam = 0.2 ns.

BER vs SNR Fig.2 shows the performance of proposed system at various number of transmitted reference frames (i.e. Nf =1, 2, 3, 4). We conclude that BER varies with change in number of

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Preeti Rani* et al / (IJAEST) INTERNATIONAL JOURNAL OF ADVANCED ENGINEERING SCIENCES AND TECHNOLOGIES Vol No. 6, Issue No. 2, 237 - 241

users at a given symbol duration, we have reported here that data rate decreases with increase in frame duration as shown in fig.3. Therefore, if we donâ&#x20AC;&#x;t want to compromise with number of users, one of the alternatives to design a high data rate TR-UWB system is by decreasing the value of frame duration which has been used here. Fig.3.clearly shows that this system can provide better data rate performance in low crowded area.

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-2

-3

BER

----* ------*--------Nf =1 ----o------o------ Nf =2 -----^-------^---- Nf =3 -----+-------+------Nf =4

-4

-5

-6

-7

-----+-------+-----

-8

14 Eb/No

Fig.2. BER vs SNR plots for AWGN channel model

frames and the duration of frames, so for high data rate we consider constant value of frame duration (Tf = 4ns) and vary the number of frames per symbol and inter-frame interference can be ignored by considering modified data model and iterative algorithm.

C. Performance comparison: AWGN channel Vs. IEEE CM1 ((LOS) channel model. In this section, we compare our AWGN channel model performance with UWB channel model (CM1). We have compared our system with the one reported in [1] on the basis of bit error rate. We have simulated our system with same value of parameters as mentioned in [1] and investigated that BER performance of our system is much improved. Fig.4. shows the performance comparison of proposed AWGN model and IEEE CM1 (channel) model mentioned in [1-3] using iterative and blind receiver algorithms. It is clear from the figure that our system provides much improved bit error rate at the same level of signal to noise ratio.The improvement in BER is more than 2dB than reported in [1].The performance of our system is also better than matched filter

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We have calculated the improved BER results and considering Nf = 3, a sharp decrease in BER occurs between 18dB to 20dB value of SNR.

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Data Rate Vs frame duration (Tf) In the second case, we considered the effect of variable frame duration on data rate and observed that maximum data rate with single user is 1 Gbps. If we further increase the number 1000 900

----* ------*--------Nf =1 ----o------o-------Nf =2 -----^-------^------Nf =3

800

Data Rate(Mbps)

700 600 500 400 300 200 100 0

6 8 Frame duration(ns)

Fig.3.Data Rate vs. Frame duration plots for AWGN Channel.

of users we investigated that data rate more than 100 Mbps can be achieved which is much higher than what is supported by all the existing wireless technologies. System with three

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BER

-4

-* -proposed TR UWB ---o--iterative algo (CM1) --^---^--- BMSR (CM1)

-5

-6

-7

19 20 Eb/No[dB]

Fig.4.BER vs. SNR plots for proposed AWGN channel and IEEE CM1 channel (LOS) for different receiver algorithm.

And BMSR (blind multi symbol receiver). Matched filter receiver uses a single (matched) complete bank of receiver delays for each received chip, and the iterative receiver uses improved data model. If we increase the number of iterations and use the improved data model, it can help to reduce much of the floor effect at high SNRs. IV. Conclusions and Future Work In this paper, a modified version of Transmitted Reference ultra wideband system has been proposed with improved data model. The performance of the proposed system is investigated under Gaussian white noise environment for varying number of users with their numerical equations. BER

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Preeti Rani* et al / (IJAEST) INTERNATIONAL JOURNAL OF ADVANCED ENGINEERING SCIENCES AND TECHNOLOGIES Vol No. 6, Issue No. 2, 237 - 241

performance of the system for different number of reference pulses are investigated and it is reported that improved results occur at Nf = 3.Further we compare our system with IEEE CM1 channel(LOS ) and concluded that BER performance of our system using AWGN channel is dramatically improved than IEEE CM1 channel (LOS) . There is substantial future optimization work that can be considered on the proposed framework. Most important among them is the narrowband interference, which is important both for commercial and UWB military applications. Synchronization becomes easy with oversampling and this may be considered for future work.

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REFERENCES

[1] Quang Hieu Dang, Antonio Trindade, Alle-Jan van der Veen,and Geert Leus,“Signal model and receiver algorithms for a transmit-reference ultra wideband communication System”. IEEE journal on selected areas in communications, Vol. 24,No. 4,April 2006. [2 R. T. Hoctor and H. W. Tomlinson, “An overview of delay-hopped transmitted-reference RF communicateons”,Technique Information Series:G.E. Research and Development Center, January 2002. [3] N. van Stralen, A. Dentinger, K. Welles, R. Gauss, R. Hoctor, and H. Tomlinson, “Delay hopped transmitted reference experimental results,”in Proc. IEEE Conf. Ultra Wideband Syst. Technol., 2002, pp. 93–98. [4] S. Franz and U. Mitra, “Integration Interval Optimization and Performance Analysis for UWB Transmitred Reference Systems,” Proc.IEEE UWBST‟04, 2004. [5] F.Troesch, F. Althaus, and A. Wittneben, "Modified pulse repetition coding boosting energy detector performance in low data rate systems," in IEEE Int. Conf: Ultra-Wideband (ICU), Zurich, Switzerland, Sept. 5- 8, 2005. [6] Seonkeol Woo, Hoongee Yang, Sunghyun Yang and Bongsoon Kang, “A TR-UWB Receiver using Correlation in Frequency Domain,” The 2nd International Conference on Wireless Broadband and Ultra Wideband Communications (AusWireless 2007)0-7695-2842-2/07. [7] X. Liu, B.-Z. Wang, S. Xiao, and J. Deng, “Performance of impuse radio UWB communications based on time reversal technique,” Progress In Electromagnetics Research, PIER 79, 401–413, 2008. [8] Sang-Dong Kim and Jong-Hun Lee, “A new Transmitted-Reference Automotive UWB Radar using Unequaled Amplitude,” International Journal of Signal Processing, Image Processing and Pattern RecognitionVol. 2, No. 2, June 2009.

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