EE 422G - Signals and Systems LaboratoryLab 6 Digital TransmissionKevin D. DonohueDepartment of Electrical and Computer EngineeringUniversity of KentuckyLexington, KY 40506October 24, 2011EE 422G - Signals and Systems Laboratory Lab 6 Digital Transmission Kevin D. Donohue Department of Electrical and Computer Engineering University of Kentucky Lexington, KY 40506 October 24, 2011 Objectives: • Understand how signal information is encoded for transmission through base-band analog channels. • Observe the impact of typical sources of corruption in the communication process, namely channel noise and limited bandwidth. • Perform spectral estimation to analyze the relationship between encoded signal bandwidth requirements, channel noise, and bandwidth. 1. Background: This lab considers how a digital data stream can be encoded into analog signals for transmission through a base-band analog channel. Various line coding methods for digital base-band modulation will be implemented and the spectral properties of the codes will be estimated. Through simulation studies the relationship between bit error rates and signal corruption will be examined. In particular, additive Gaussian noise and limited channel bandwidth will be the 2 primary sources of signal corruption in the communication process as shown in Fig. 1. Figure 1. Block diagram of communication system showing the key components that interfere with the information transfer, such as the band-limited channel and the additive white Gaussian noise. Figure 1 illustrates the main components of a communication transmitter. The information to be transmitted is converted into a binary sequence (i.e. ASCII codes for text or sequences of quantized signal amplitudes from a sampled analog waveform). Thebinary data source emits a series of 1’s and 0’s (bits) and represents the communication system source. The baseband modulation operation codes each bit into a continuous waveform for sending the source sequence over a physical channel. The channel is a medium through which the signal can propagate. Typical signals include changes in voltages and currents over a wire, light energy over an optical fiber, or electromagnetic energy through the atmosphere. No medium is totally free of noise (random perturbations on transmitted signal), therefore white Gaussian noise is added to the transmitted signal to simulate this corruption. Noise is an irreversible corruption (i.e. permanent loss of information), while the signals can be filtered to exploit signal redundancies and reduce the impact of noise, there will always be a level of uncertainty in the received signal. Parameters extracted from signals denoting 1s and 0s must be sufficiently separated (i.e. amplitude, frequency, phase …) to limit the impact of the ambiguities introduced by the system noise. In addition to noise, channels have a limited bandwidth that can potentially distort the signals by reducing the frequency content of the signal non-uniformly over its spectrum. So if the coded waveforms are not bandlimited to a value less than channel bandwidth, the waveform will be distorted. Distortion is a deterministic change in the signal and can be reversed in some cases. If the distortion can be modeled as a linear filter, it can be reversed (the operation of undoing this distortion is often referred to as deconvolution) . In most cases of nonlinear distortion, such as clipping or quantization, the distortion cannot be reversed. For the baseband channel a low-pass filter can be used to simulate its distortion. Baseband and low-pass essentially mean the same thing when describing a signal. If the signals were modulated with a high frequency oscillator (shifted up on the frequency axis), such that they contained no DC energy, then the signal would be considered passband (not baseband). This lab considers only baseband signals and channels. Before the data are sent over the channel, the binary digits produced by the source are serially encoded using a variety of signaling formats called line codes for transmission. To help examine their spectral properties, a Matlab function was written, modulb(), to create various line codes from sequences of bits. The function syntax is: >> [y,t] = modulb(binary_sequence, Fd, Fs, line_code_name); where binary_sequence is a vector of 1’s and 0’s denoting the source binary sequence, Fd corresponds to the binary data rate in bits per second (b/s), line_code_name is a special string indicating the particular line code to be used (see help file), and Fs is the sampling frequency of the line code waveform used by the simulation. Note that sampling rate Fs is used to simulate the analog signal, so this sampling rate needs to be much higher than the bit rate (at least by a factor of 10). The modulb function outputs the waveform as vector y and corresponding time axis t. The command supports the following codes: ‘unipolar_nrz’, ‘bipolar_nrz’, ‘bipolar_rz’, ‘ami’, ‘manchester’, ‘miller’, ‘unipolar_nyquist’, and ‘bipolar_nyquist’.The type of line coding is selected to meet various system criteria such as power requirements, bit timings (additional transitions of the line code signals within the bit interval can help in timing recovery), bandwidth efficiency (excessive transitions may require more bandwidth than necessary), low frequency content (some channels block low frequency), error detection, and complexity. Figure 2 shows analog waveforms for various line coding examples. Figure 2. Line code waveform examples. Original binary sequence listed across the top is at a bit rate of 1, and applies to all codes. 2. Pre-Laboratory Assignment 1. Use the modulb() function to plot waveforms representing the binary sequence { }0,0,1,1,1,0,0,0,1,1,0,0,1,0=seq using the following line codes at a bit rate of 1=dRkb/s (choose a reasonable sampling rate for potting the waveforms): a) unipolar NRZ (on-off signaling, NRZ= non-return to zero) b) bipolar NRZ (binary antipodal signaling) c) bipolar RZ (binary antipodal signaling RZ = return to zero) d) AMI (Alternate Mark Inversion) e) Manchester (split-phase); f) Bipolar Nyquist (bipolar transmission where the pulse shape is a sinc function with main lobe extending to fill bit interval and side lobes extending beyond). Turn in a hard copy of the plots with correctly labeled axes and a title for eachplot