lab3 - ECE4305 Software-Defined Radio Systems and Analysis...

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ECE4305: Software-Defined Radio Systems and Analysis Laboratory 3: Receiver Structure & Waveform Synthesis of a Transmitter and a Receiver C-Term 2011 Objective This laboratory will cover basic receiver structures and implementations. We will also show how to construct a series of orthonormal basis functions that can be combined to produce a wide range of signal waveforms. Then, we will study a specific digital transceiver implementation based on the multicarrier transmission concept called orthogonal frequency division multiplexing (OFDM). In the experimental part of this laboratory, you will implement two different receiver structures and then observe their performance during over the air transmission. This is followed by a Simulink implementation of an OFDM communication system. Finally, the open-ended design problem will focus on bi-directional communication. Contents 1 Theoretical Preparation 3 1.1 Gram-Schmidt Orthogonalization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.2 Optimal Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.2.1 Maximum Likelihood Detection in an AWGN Channel . . . . . . . . . . . . . 4 1.2.2 Maximum A Posteriori Detection . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.3 Basic Receiver Realizations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.3.1 Matched Filter Realization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.3.2 Correlator Realization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 1.4 Multicarrier Transmission . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 1.4.1 Orthogonal Frequency Division Multiplexing . . . . . . . . . . . . . . . . . . . 9 1.4.2 Dispersive Channel Environment . . . . . . . . . . . . . . . . . . . . . . . . . 11 1.4.3 OFDM with Cyclic Prefix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 1.5 Suggested Readings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 1.6 Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2 Software Implementation 16 2.1 Observation Vector Construction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 2.2 Maximum-likelihood Decoder Implementation . . . . . . . . . . . . . . . . . . . . . . 17 2.3 Correlator Realization of a Receiver in Simulink . . . . . . . . . . . . . . . . . . . . . 18 2.4 Multicarrier Modulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 2.4.1 MATLAB Design of Multicarrier Transmission . . . . . . . . . . . . . . . . . . 20 2.4.2 Simulink Design of Multicarrier Transmission . . . . . . . . . . . . . . . . . . 20 1
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3 USRP2 Hardware Implementation 23 3.1 Eye Diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 3.1.1 Discrete-Time Eye Diagram Scope . . . . . . . . . . . . . . . . . . . . . . . . . 23 3.2 Matched Filter Observation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 4 Open-ended Design Problem: Duplex Communication 26 4.1 Duplex Communication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 4.2 Half-duplex . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 4.3 Time Division Duplexing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 4.4 Hints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 5 Lab Report Preparation & Submission Instructions 29 2
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1 Theoretical Preparation Signals can be represented as either waveforms or vectors. Waveform representations are defined by the Fourier series of a signal, or the sum of sines and cosines that make a particular shape. As a vector, a signal is represented as series of orthonormal vectors. This part of the lab will show how to derive these vectors and their basis functions. It will then show two receiver designs and their implementations. 1.1 Gram-Schmidt Orthogonalization In mathematics, particularly linear algebra and numerical analysis, the Gram-Schmidt process is a method for creating an orthonormal set of vectors in an inner product space such as the Euclidean space R n . The Gram-Schmidt process takes a finite, linearly independent set S = { S 1 (t), S 2 ( t ), ..., S M ( t ) } and generates an orthogonal set S = { φ 1 (t), φ 2 (t), ..., φ i (t) } that spans the same subspace of R n as S .
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