Design and Implementation of Optimal Pulse Shaping Filters for Digital Radio Systems

Base band transmission of digital data requires the use of a low pass channel with a bandwidth large enough to accommodate the essential frequency content of the data stream. However the channel is dispersive in that its frequency response deviates from that of an ideal low pass filter. The result of data transmission over such channel is that each received pulse is affected somewhat by adjacent pulses, thereby giving rise to a common form of interference called ISI. To control ISI, control has to be exercised over the pulse shape in the overall system, known as pulse shaping. The mostly used pulse Shaping filters to avoid ISI are Raised Cosine and Root Raised Cosine filters. Another source of bit errors in base band data transmission system is channel noise. Designing a matched filter at the receiver end reduces the additive noise introduced in the channel. Again, widely used matched filters at the receivers are Raised Cosine and Root Raised Cosine filters. All the wireless communication standards like GSM, CDMA etc., use base band pulse shaping over the digital data to reduce ISI and additive noise effects in the wireless channels. The objective of this design and implement these Raised Cosine and Root Raised Cosine transmit as well as receive filters, which does reduce ISI and channel noise effects on the pulses transmitted over the band limited channels. The digital binary data will be pulse shaped using these filters. The frequency response of these filters decrease towards zero gradually rather than abruptly, which can be realized easily. Then finally using the designed Raised Cosine and Root Raised Cosine filters, a 16 QAM base band communication system over noisy channel will be implemented. Digital data is first mapped into symbols of 16 QAM and then these symbols are pulse shaped and transmitted over noisy channel. Noisy base band symbols are received and first matched filtered using Raised Cosine and Root Raised Cosine filters. Filtered symbols are then demodulated and symbol to bit demapping will be done. Then finally BER (Bit Error Rate) is computed by comparing the transmitted and demodulated bits. MATLAB environment is used for the simulation of proposed algorithm.