e is no interference among the radios within a microcell. Commercial Applications Figure 3.8. Simulation model (random distribution). In Appendix 3B, we derive bounds on the bit error probability for our spread-spectrum system when interference derives from other similar spread-spectrum signals in a network of microcells. In general, obtaining exact expressions for the bit error probabilities is difficult, and we must resort to using easy-to-evaluate upper bounds on them even though upper bounds give worst case analyses, since the true error probabilities are less than the more easily computed upper bounds. We show in Appendix 3B that in the interference-limited case, the bit error probability bound for direct-sequence BPSK signals is given by (3.9) where SJR is the signal-to-interference ratio SJR = A2/Jo (3.10) (3.11) Here Ck is the cross-correlation of the interfering signal's codeword with the receiver's codeword, and A is the signal amplitude. Note that this expres- sion is exactly the same as the bit error probability for differentially coher- ent detection of DBPSK signals if we replace No by Jo/2. Spread-Spectrum CDMA for PCS and PCN The results in Appendix 3B are for the direct-sequence spread-spectrum coherent BPSK modulation with coherent modulation. The corresponding results for differentially coherent demodulation of DBPSK are expected to be within 2 dB of the bit error bounds given here. The DQPSK system may add an additional decibel of required energy per bit to interference. Using convolutional codes with Viterbi decoding will improve this performance by 4 to 6 dB. 3.6.3.2 Computer Simulations A computer program has been developed to calculate the signal-to-inter- ference ratios for the idealized network of direct-sequence S-CDMA spread- spectrum radios. It allows various parameters to be changed to examine the effects of different codewords, power attenuation constants, voice activation factors, and numbers of randomly placed mobile handsets throughout the network. Naturally, the greater the powe