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The threshold settings will greatly also affect the BER performance. 26 and 54 CHAPTER 4.

5. 20 CHAPTER 3. DIRECT SEQUENCE SPREAD SPECTRUM SYSTEM DESIGN r(t) = s(t)+n(t) ... Delay T Delay T a2 a1 a0 Delay T aN ... 9) where s is the known signal vector s = [s1 s2 s3 · · · sN ] and R is the known autocorrelation matrix for the input noise:    r11   r  21   R= .    .   r12 ... r1N  r22 ... ... . .  r2N           rN 1 rN 2 ... rN N If we assume that the input noise is white Gaussian noise, then rmn =    0, m=n   σ 2, 0 m=n where σ02 is the noise power. 10) CHAPTER 3.

The quantization is realized in simulation by truncating or rounding the value to its closest quantization level. The number of the quantization levels is determined by the bit number of each signal in the Z2000. The biggest quantization effect is thought to be from quantizing the PN matched filter input signal which is rounded to 3 bits in the chip. The same number of data bits and iterations were carried out as in the first series of simulations. 24 shows this simulation result. Interestingly, the resulting BER performance is also close to the theoretical one.

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