# Fundamentals of Wireless Communications

### Synopsis

Over the last decade, we have seen an unprecedented move towards wireless technology adoption. Keyboards, mice, television, computers have all moved towards wireless technologies. This course presents the fundamentals of wireless communications, with a lot of focus on theory and applications. Participants will also get to perform several exercises for better understanding of the theory presented. The exercises use DSP theory to implement wireless signal processing systems, and use tools such as VHDL, Sage Math, National Instruments LabView, Mentor Graphics ModelSim / Synopsys VCS-MX, Xilinx Vivado, and Xilinx ChipScopePro.### Course highlights

Participants will learn the fundamentals of digital wireless communications, and have practical design experience using mathematical modelling tools (e.g. Sage or Matlab), industry-standard logic simulators (e.g. Mentor Graphics QuestaSim/ModelSim, Synopsys VCS-MX), hardware design tools (e.g. National Instruments LabView, Xilinx Vivado), VHDL-2008 mathematics and fixed/floating-point packages, and development/test hardware from National Instruments.### What You Will Learn

This course concentrates on the theoretical and practical knowledge to allow participants to achieve the following learning outcomes. Upon completing the course, participants would be able to:- Learn basics of wireless signal processing.
- Mathematically model wireless communications systems using Sage Math or Matlab.
- Design DSP-based systems for wireless communications using VHDL and NI's LabView.
- Physically prove their designs using NI's wireless test platforms such as USRP.

### Who Should Attend

This course is particularly suited for engineers involved in wireless systems design (RF, microwave, RFIC), interested in using the FPGA/ASIC digital design and verification flow for wireless signal processing.### Prerequisites

Participants should have a diploma/degree in electronics (and related) engineering with an understanding of digital systems. Working knowledge on DSP theory is required for this programme.**Participants are strongly encouraged to attend the following course(s) prior to attending this course:**

### Course Methodology

This course is presented in a workshop style with example-led lectures interlaced with demonstrations and hands-on practical for maximum understanding.### Course Duration

Five (5) days, 0900 to 1700.### Course Structure

**Introduction**- Structure of typical wireless communication blocks.
- Stochastic processes in digital communication.

**Estimation theory**- The likelihood function.
- Maximum-likelihood (ML) estimators.
- Applications: CDR, channel coding, equalisation.

**Pseudorandom bit sequences**- Pseudorandom bit sequence (PRBS) generators.
- PRBS checkers.
**Hands-on Practical 1:**Embedding a PRBS generator using LabView.

**Modulation techniques**- Inphase-quadrature (I-Q) modulation.
- Constellation diagrams.
- Frequency shift keying (FSK).
- Phase shift keying (PSK).
**Hands-on Practical 2:**Designing an*M*-ary PSK modulator using LabViewâ€™s RF Communications library.**Hands-on Practical 3:**Designing an*M*-ary PSK demodulator.- Quadrature amplitude modulation (QAM).
**Hands-on Practical 4:**Designing an*M*-ary QAM modem (modulator + demodulator) using LabView.- Orthogonal frequency division multiplexing (OFDM).
**Hands-on Practical 5:**Modelling an OFDM modulator using Sage Math.

**Carrier and symbol synchronisation**- Analogies to digital systems: clock and data recovery (CDR).
- ML carrier phase estimation.
- ML timing estimation.

**Source coding**- Coding for discrete sources
- Discrete memoryless sources (DMS): Huffman coding.
- Lempel-Ziv algorithm (LZ77).
- Lempel-Ziv-Markov chain algorithm (LZMA).
**Hands-on Practical 6:**Modelling the LZMA algorithm.

**[optional]:**Coding for continuous (analogue) sources- Quantisation techniques and analogue-to-digital conversion (ADC)
- Scalar quantisation.
- Vector quantisation.

- Coding techniques
- Temporal waveform coding: PCM, DPCM, adaptive PCM/DPCM, delta modulation (DM).
- Spectral waveform coding
- Subband coding (SBC).
- Adaptive transform coding (ATC)
- Discrete Fourier transform (DFT): fast Fourier transform (FFT) algorithm for DFT computation.
**Hands-on Practical 7:**Modelling the FFT algorithm.- Discrete cosine transform (DCT).
- Discrete wavelet transform (DWT).

- Model-based source coding
- Linear predictive coding (LPC).

- Quantisation techniques and analogue-to-digital conversion (ADC)

- Coding for discrete sources
**Channel coding**- Stochastic/random channel coding.
- Linear block coding
- Overview of linear block codes, generator and parity-check matrices.
- Examples of linear block codes: Hamming, Hadamard.
- The Hamming code.
**Hands-on Practical 8:**Designing a Hamming linear codec using NI LabView- The Hadamard code.
- Cyclic codes: cyclic Hamming, ML shift-register, Bose-Chaudhuri-Hocquenghem (BCH) codes, Reed-Solomon.

- Convolutional coding.
- Overview of convolutional codes.
- Transfer function of a convolutional code.
- Viterbi algorithm: Optimum decoding of convolutional codes.
**Hands-on Practical 9:**Modelling a Viterbi decoder using Sage Math.- Error rate performance (probability of error) of Viterbi algorithm.
- Trellis-coded modulation for bandwidth-limited channels.

**[optional]: Optimum receivers for AWGN channel**- Maximum-likelihood (ML) sequence estimation - revisit.
- Viterbi algorithm for sequential trellis search.

- Optimum receiver models for infinite-bandwidth AWGN channel (without ISI)
- Demodulators: correlation and matched-filter.
- Optimum detectors.
- Probability of error for QAM.
- Optimum receiver for
*M*-ary orthogonal signals. - Probability of error for envelope detection of
*M*-ary orthogonal signals.

- Optimum receiver designs for band-limited AWGN channel (with ISI)
- Inter-symbol interference in bandwidth-limited channels.
- ML sequence detector.
- Line coding
- Runlength-limiting (RLL): RLL coding for pulse/spectrum shaping, DC balancing, and ISI minimisation.
- Return-to-zero (RZ).
- Non-return-to-zero (NRZ).
- Non-return-to-zero-inverted (NRZI).
- Manchester.
- 8b/10b, 64b/66b, 128b/130b.

- Maximum-likelihood (ML) sequence estimation - revisit.
**Suboptimum receivers: adaptive systems**- Overview of adaptive equalisation and filtering.
- Overview of estimation theory
- Maximum-likelihood (ML) estimators.

**Hands-on Practical 10:**Modelling an ML estimator.- Adaptive linear equalisers
- Zero-forcing equaliser.
- Least-mean-square (LMS) equaliser: Mean-square-error minimisation.

- Adaptive decision-feedback equaliser (DFE)
**Hands-on Practical 11:**Modelling an adaptive DFE.- Adaptive channel estimator using ML sequence detection.
- Recursive least-squares algorithms for adaptive equalisation
- Kalman equaliser / filter (Kalman algorithm).
- Lattice filter (linear prediction algorithm).

**Hands-on Practical 12:**Modelling a Kalman equaliser.- Blind equalisers

**Multiuser communications**- Overview of multiple access techniques.
- Overview of multiple access techniques used for wireless communications.
- Orthogonal frequency division multiple access (OFDMA).

**Application development: transmit and receive**- User interface development.
**Hands-on Practical 13:**Simple transmit and receive wireless application.