Noise immunity for 1:N and M:1 nonlinear mappings for source-channel coding

Summary form only given. We compute the noise components in 1:N dimension expanding (error protection) and M:1 dimension reducing (lossy compression) systems for memoryless Gaussian sources transmitted over AWGN channels. A theory for dimension reducing systems was developed wherein the source- and channel coders are combined into one nonlinear operation and the parametric curves are used to map the source onto the channel. For both systems we have two types of distortion. Results show that the 2:1 system works very well and that the 1:2 system is not as close to optimum but the gain compared to a linear system is approximately the same as in the 2:1 case. The solid graphs illustrate the characteristics of the two distortion contributions. The small noise contribution and approximation noise above the optimum point (increasing CSNR), and the threshold- and channel noise below the optimum point