Đang chuẩn bị liên kết để tải về tài liệu:
Digital Communication I: Modulation and Coding Course-Lecture 13

The properties of Convolutional codes. We introduced interleaving as a means to combat bursty errors by making the channel seem uncorrelated. We also studied “Concatenated codes” that simply consist of inner and outer codes. They can provide the required performance at a lower complexity. | Digital Communications I: Modulation and Coding Course Term 3 - 2008 Catharina Logothetis Lecture 13 Lecture 13 Last time, we talked about: The properties of Convolutional codes. We introduced interleaving as a means to combat bursty errors by making the channel seem uncorrelated. We also studied “Concatenated codes” that simply consist of inner and outer codes. They can provide the required performance at a lower complexity. Lecture 13 Today, we are going to talk about: Shannon limit Comparison of different modulation schemes Trade-off between modulation and coding Lecture 13 Goals in designing a DCS Goals: Maximizing the transmission bit rate Minimizing probability of bit error Minimizing the required power Minimizing required system bandwidth Maximizing system utilization Minimize system complexity Lecture 13 Error probability plane (example for coherent MPSK and MFSK) Bit error probability M-PSK M-FSK k=1,2 k=3 k=4 k=5 k=5 k=4 k=2 k=1 bandwidth-efficient power-efficient Lecture . | Digital Communications I: Modulation and Coding Course Term 3 - 2008 Catharina Logothetis Lecture 13 Lecture 13 Last time, we talked about: The properties of Convolutional codes. We introduced interleaving as a means to combat bursty errors by making the channel seem uncorrelated. We also studied “Concatenated codes” that simply consist of inner and outer codes. They can provide the required performance at a lower complexity. Lecture 13 Today, we are going to talk about: Shannon limit Comparison of different modulation schemes Trade-off between modulation and coding Lecture 13 Goals in designing a DCS Goals: Maximizing the transmission bit rate Minimizing probability of bit error Minimizing the required power Minimizing required system bandwidth Maximizing system utilization Minimize system complexity Lecture 13 Error probability plane (example for coherent MPSK and MFSK) Bit error probability M-PSK M-FSK k=1,2 k=3 k=4 k=5 k=5 k=4 k=2 k=1 bandwidth-efficient power-efficient Lecture 13 Limitations in designing a DCS Limitations: The Nyquist theoretical minimum bandwidth requirement The Shannon-Hartley capacity theorem (and the Shannon limit) Government regulations Technological limitations Other system requirements (e.g satellite orbits) Lecture 13 Nyquist minimum bandwidth requirement The theoretical minimum bandwidth needed for baseband transmission of Rs symbols per second is Rs/2 hertz. Lecture 13 Shannon limit Channel capacity: The maximum data rate at which error-free communication over the channel is performed. Channel capacity of AWGV channel (Shannon-Hartley capacity theorem): Lecture 13 Shannon limit The Shannon theorem puts a limit on the transmission data rate, not on the error probability: Theoretically possible to transmit information at any rate , with an arbitrary small error probability by using a sufficiently complicated coding scheme For an information rate , it is not possible to find a code that can achieve an arbitrary small error .