Classification of voiceband data signals using the constellation magnitude

We discuss the real time classification of high speed (⩾4800 b/s) voiceband data signals using blind or self-recovering equalization techniques. After equalization, the signals are classified, by means of their constellation magnitude, using a new classification scheme which computes the probability that the unknown test signal belongs to each of several predetermined classes. Heuristic rules are applied to these probability functions to perform the actual signal detection. Even in the presence of added channel impairments, the use of this new approach resulted in an overall classification accuracy of greater than 98%, measured using 5100 test segments collected from 6 classes of signals produced by V.29, V.32, and V.33 modems. Classification times range from median values of 0.090 s and 0.219 s for the V.29 modem at 4800 and 7200 b/s, respectively, up to a worse case time of 3.33 s for the 9600 b/s default class, chosen only if no other signal is detected within a prescribed amount of time