Multiuser detection for DS-CDMA systems in Java Embed PDF-417 2d barcode in Java Multiuser detection for DS-CDMA systems

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8.4.4 Multiuser detection for DS-CDMA systems using barcode creator for j2se control to generate, create pdf 417 image in j2se applications. Customer Bar Code Multiuser detecti tomcat pdf417 on refers to reception techniques that exploit the structure of the MAI in receiver design, rather than ignoring it as in conventional rake reception. An idealized setting suffices to illustrate the basic concepts, hence we consider a K-user discrete-time, real baseband, synchronous CDMA system with BPSK modulation. The N -dimensional received vector r over a given symbol interval is given by.

A k bk s k + W (8.73). where, for 1 k K, bk 1 +1 is the symbol for user k, Ak its amplitude, and sk its spreading vector, normalized for convenience to unit energy. The vector W N 0 2 I is WGN. The analogy between MAI and ISI is immediate, from a comparison of the MAI model (8.

73) and the ISI model (5.25) developed in 5. However, there are two major differences between the MAI and ISI models:.

Wireless communication (i) In the MAI mo del, the interference vectors can be arbitrary. In the ISI model, they are restricted to being acyclic shifts of the channel impulse response. (ii) In the MAI model, the amplitudes Ak k = 1 for the interferers can scale independently of the amplitude A1 of the desired user, and can in fact be much larger than A1 (e.

g., if an interfering transmitter is closer to the receiver than the desired transmitter in a system without power control). We would therefore like our multiuser detection schemes to be near far resistant, i.

e., to provide good performance even in the presence of such a near far problem..

Example 8.4.2 (ML javabean pdf417 2d barcode reception for a two-user system) We take as our running example a two-user system with received vector given by r = A 1 b1 s 1 + A 2 b2 s 2 + W (8.

74). where b1 , b2 are 1 BPSK symbols, and the signal vectors are normalized to unit energy: s1 2 = s2 2 = 1. We denote the signal correlation as = s1 s2 . The matched filters for the two users produce the outputs z 1 = r s 1 = A 1 b1 + A 2 b2 + N 1 z2 = r s2 = A1 b1 + A2 b2 + N2 (8.

75). where N1 N 0 2 barcode pdf417 for Java , N2 N 0 2 are jointly Gaussian with cov N1 N2 = 2 . Conventional reception simply takes the sign of the matched filter outputs: b1 MF = sign z1 b2 MF = sign z2 (8.76).

We henceforth ter m such a receiver the matched filter (MF) receiver. For = 0, each matched filter output is corrupted by interference, so that the matched filter receiver is suboptimal. Let us now consider joint ML reception for the two users; that is, we wish to decide on b = b1 b2 T .

To this end, rewrite (8.74) as r = sb + W where s b = A 1 b1 s 1 + A 2 b2 s 2 The ML rule must maximize the log likelihood ratio, which is proportional to 1 b = r s b sb 2 (8.78) 2 Since r sb = A1 b1 r s1 + A2 b2 r s2 = A 1 b 1 z1 + A 2 b 2 z 2 (8.

79) (8.77). 8.4 Direct sequence spread spectrum we realize that, in order to compute b for all possible b (four possible values in our case), it is necessary and sufficient to compute the matched filter statistics z1 and z2 . The problem with conventional reception is that these statistics are being used separately as in (8.76), rather than jointly according to the ML rule 1 bML = arg max r sb sb 2 (8.

80) b 2 Let us get the ML rule into a more explicit form. Consider the second term above: sb.
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