Approximate bit error probability analysis for DSCDMA systems.Introduction The fundamental problem of wireless channel is how to share the common transmission medium by many mobile users in order to accommodate as many users as possible with least degradation in the performance of the system. Code Division Multiple Access CDMA is a spread spectrum SS technology, in which user is assigned a unique pseudorandom code. Using this code, user spread their data in frequency domain. After spreading, the transmitted signal's bandwidth is much larger than the original signal's bandwidth. There are two approaches for spreading the data namely Direct Sequence DS and Frequency Hopping FH. In FHCDMA communication systems channel frequency is changed rapidly according to the spreading code. In DSCDMA based systems user data bits are multiplied by the spreading code bits called chips. The DS method of spreading the user data is employed in most of the commercial communication systems. In CDMA systems since many users share the same channel, an active user signal interferes with the signals of all other active users. The total interference from all other active users to a desired user signal is called Multiple Access Interference MAI. Any increase in MAI degrades the performance of the system. MAI of a CDMA is controlled by the transmission power control techniques used in the system. These power control techniques adjust the transmitted power of each Mobile Station MS such that signals from each MS arrives approximately with equal strength. In addition to transmission power control, admission control is used to keep the MAI within limits. If MAI is at or near its maximum, no new users are admitted into the system. Performance of a CDMA system is closely related to the accuracy of transmission power control. Among the various parameters used to measure the performance of a CDMA system, BER is the most popular and widely used. Exact calculation of BER is very much complicated and time consuming. Therefore BER calculation can not be used for real time applications, hence approximate probability analysis is generally used [3]. Quality of service is measured in terms of BER. System model is described in section 2. Sections 3 and 4, deal with standard Gaussian approximation and simplified Gaussian approximation. In section 5 computationally simple method of probability analysis is discussed. System Model If there are K active users in the system; kth user received signal at Base Station BS is given by [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (1) where [b.sub.k](t) and [c.sub.k](t) are data and spreading sequence respectively, [P.sub.k] is the received power, and [omega].sub.c] the carrier frequency. [[tau].sub.k] is the delay of user k relative to some reference user 0. [[phi].sub.k] is the carrier phase offset of user k relative to reference user 0. Since [[tau].sub.k]and [[phi].sub.k] are relative terms, we can define [[tau].sub.0] = 0 and [[phi].sub.k] = 0. The data signal [b.sub.k](t) and spreading sequence [c.sub.k](t) are sequences of unit amplitude (positive and negative) rectangular pulses. The data signal [b.sub.k](t) and [c.sub.k](t) are independent random processes with outcomes [+1, 1] with equal probability. For detecting the desired user data (k=0) the decision statistics at receiver is given By [Z.sub.0] = [I.sub.0] + [??] + [eta] (2) where [I.sub.0] is contribution from the desired user (k =0) to decision statistic, [??] is multiple access interference from all cochannel users, and [eta] is the noise contribution. MAI contribution [??] is given by [??] = [K1.summation over(k=1)][I.sub.k] (3) where [I.sub.k] are independent and identically distributed random variables given by [I.sub.k]=[2([S.sup.2.sub.k][S.sub.k])(2B+1)+N][cos.sup.2][phi] (4) where [S.sub.k] is the chip delay of [k.sup.th] interfering user signal from the nearest chip of the desired user signal chip (k =0). The quantity B in the equation (4) is the number of times the desired user spreading code changes it's state either from 1 to +1 or from +1 to 1, within a single data bit duration. If the user spreading code is random with odd number of chips N within one data bit duration, then B is binomially distributed with minimum value zero, average value of (N1)/2 and maximum value of (N1) [2,4,8]. Standard Gaussian Approximation Standard Gaussian approximation can be used to determine the BER probability for DSCDMA based systems. BER probability is given by [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (5) where Q(.) function is given by [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (6) Simplified Improved Gaussian Approximation The above discussion is valid only if the number of active users is large otherwise it leads poor accuracy. Simplified improved Gaussian approximation requires the evaluation of [mu] and variance [[sigma].sup.2] of decision statistics [??]. The probability of BER becomes [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (7) where [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] The mean [mu] and variance [[sigma].sup.2] are given by [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (8) Since [mu] is proportional to K and [sigma] is proportional to [square root of K] the SIGA converges to SGA as K increases [5, 6]. Modified Siga It can be noticed that [square root of 3[sigma]] can be greater than [mu] for smaller values of K. This results in imaginary argument to Q function and hence c can not be determined. Under such conditions c can be neglected without the loss of accuracy [7]. If the quantity B, is set to a value (N1)/2 without significant loss in accuracy, [[sigma].sup.2] can be evaluated as [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (9) Many times, it would be required to find BER when all users' carriers and/or chips are synchronized. BS station of CDMA cellular communication systems modulates several messages on to a single carrier. In this case carriers of all messages will be aligned. Further BS can be configured to synchronize the chips of all messages. Hence four different situations can exist as given below * chips aligned and carrier phase aligned * chips aligned and random carrier phases * random chip delays and carrier phases aligned * random chip delays and random carrier phases. Figure 1 shows the probability of BER for these four different situations, where as figure 2 shows the performance of the system for different values of B [1]. [FIGURE 1 OMITTED] [FIGURE 2 OMITTED] Conclusion Gaussian approximation can be further simplified in section 5. When K increases the modified approximation approaches the Gaussian approximation. This modified approximation method is computationally simple and needs less CPU time; hence it can be used in real time systems. As shown in figure 1, synchronization between the carriers of different messages and/or chip delays affects the BER performance of CDMA systems. References [1] Ilkka Saarinen, Aarne Mammela, Pertii Jarvensivu, and Keijo Ruotsalainen, 2001, "Power Control in Feedback Communications Over a Fading Channel" IEEE Transactions on Vehicular Technology, vol. 50, no.5, pp. 1231 1240,. [2] Jay E. Padgett, Christoph G. Gunther, and Takeshi Hattori, 1995, "Overview of Wireless Personal Communications." IEEE Communication Magazine, vol. 34, no. 01, pp. 2841. [3] Jerry D Gibson, 1997, The Mobile Communications Hand Book,Second Edition, IEEE Press, Chap. 17. [4] J. G. Proakis, 1995, Digital Communications, 3rd Edition McGrawHill. [5] Julian Chang, and Norman C. Beaulieu, 2002, "Accurate DSCDMA BitError probability calculation in Rayleigh fading," IEEE Transactions on Wireless Communications, vol. 01, no. 01, pp. 2481252. [6] Mingxi Fan, Ceilidh Hoffmann, and KaiYeung Siu, 2003, "Error Rate Analysis for Multirate DSCDMA Transmission Schemes" IEEE Transactions on Communications vol. 51, no.11, pp. 18971910. [7] Norman C. Beaulieu, and Julian Cheng, 2004, "Precise ErrorRate Analysis of BandwidthEfficient BPSK in Nakagami Fading and Cochannel Interference", IEEE Transactions on Communications vol. 52, no.1,, pp. 149159. [8] W. C. Y. Lee, 1991, "Overview of cellular CDMA." IEEE Transactions on Vehicular Technology, vol. 40, no. 02, pp. 291302. D.N. Kyatanavar (a), *,Rekha S. Patil (b), M.S. Patil (c) and R.G. Zope (d) (a,c,d) S R E S' College of Engineering, Kopargaon423 603 (M.S.) India Email: kyatanavar@yahoo.co.in, (b) J. J. Mugdum College of Engineering, Jaysingpur, Kolhapur (M.S.) India 

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