Digital Signal Processing for Wireless Communication using Matlab (Signals and Communication Technology)

Preface
Acknowledgments
Contents
1 Modulation Techniques and Spectral Density Estimation
	1.1 Autocorrelation and the Spectral Density of the Received Baseband Signal  Generated by the Pulse p(t)
	1.2 Computation of Spectral Density  of the Bandpass Signal
	1.3 Pulse Shaping  for Discrete Communication
	1.4 Bandpass Modulation Techniques
		1.4.1 Phase Shift Keying
		1.4.2 Illustration of the Coherent Correlation Receiver (PSK)
		1.4.3 Frequency Shift Keying
		1.4.4 Illustration of the Coherent Correlation Receiver (FSK)
		1.4.5 Computation of Probability of Error (FSK)
		1.4.6 Computation of the Spectral Density of FSK
		1.4.7 Minimum Shift Keying
		1.4.8 Computation of the Probability of Error of the MSK Modulation
		1.4.9 Computation of Spectral Density of MSK Signal
		1.4.10 Quadrature Phase Shift Keying
		1.4.11 Computation of the Probability of Error of the QPSK Signal
		1.4.12 Computation of Spectral Density of the QPSK Signal
	1.5 Coherent Versus Non-coherent Receiver
		1.5.1 Computation of the Probability of Error for the Non-coherent Detection
		1.5.2 Non-coherent Detection Using Matched Filter and Envelope Detector
	1.6 Relationship Between Baseband and Bandpass W.S.S. Random Process the Using Hilbert Transformation
	1.7 Spectral Estimation
		1.7.1 Discrete Realization
		1.7.2 The Bartlett Method
		1.7.3 The Welch Method
		1.7.4 The Blackman and Tukey Method
2 Mathematical Model of Time Varying Wireless Channel Model
	2.1 Multi-Path Model
	2.2 Coherence Time and Doppler Spread
	2.3 Coherence Frequency and Delay Spread
	2.4 Discrete Complex Baseband Time Varying Channel  Model for Wireless Communication
	2.5 Probabilistic Channel Model
		2.5.1 κ–μ Distribution
		2.5.2 η–μ: Type 1
		2.5.3 η–μ: Type 2
	2.6 Case Study
		2.6.1 Case Study Using Flat-Rayleigh Fading Model
		2.6.2 Computation of the Probability of Error of the Flat-Rayleigh Fading Channel
		2.6.3 Case Study Using Flat-Rician Fading Model
		2.6.4 Computation of Probability of Error for Flat-Rician Fading Channel Model
		2.6.5 Baseband Single Tap Channel with Known Estimated Filter Coefficient  g
3 Detection Theory and Estimation Theory for Wireless Communication
	3.1 Detection Theory  for Binary Signal Transmission
		3.1.1 The Bayes Technique
		3.1.2 The Mini-Max Technique
		3.1.3 The Neyman–Pearson Technique
			3.1.3.1 Computation of PFA and PD for the Randomized Decision Rule of the form (3.6) to (3.8)
			3.1.3.2 Comments on the Neyman–Pearson Solution
		3.1.4 Illustration of the Bayes, Mini-Max, and Neyman–Pearson Detector for Discrete Channel
			3.1.4.1 The Bayes Technique
			3.1.4.2 The Mini-Max Technique
			3.1.4.3 The Neyman–Pearson Technique
		3.1.5 Illustration of the Bayes, Mini-Max, and Neyman–Pearson Detector for the Additive Gaussian Noise Channel
			3.1.5.1 The Bayes Rule
			3.1.5.2 The Mini-Max Rule
			3.1.5.3 The Neyman–Pearson Rule
	3.2 Estimation Theory
		3.2.1 Minimum Mean Square Estimation (MMSE)
		3.2.2 Minimum Mean Absolute Estimation (MMAE)
		3.2.3 Maximum A Posteriori Probability (MAP)
		3.2.4 Log-Likelihood Estimation
		3.2.5 The Wiener Filter
			3.2.5.1 FIR Wiener Filter
			3.2.5.2 IIR Wiener Filter
		3.2.6 The Kalman Filter
4 Multiple Input Multiple Output (MIMO), Orthogonal Frequency Division Multiplexing
	4.1 MIMO
		4.1.1 Zero-Forcing Estimator
			4.1.1.1 Zero-Forcing Estimator  to Estimate the Transmitted Vector x with Known Channel Matrix H
			4.1.1.2 Zero-Forcing Estimator to Estimate the Channel Matrix H Given the Pilot Transmission Matrix P
		4.1.2 Linear Minimum Mean Square Estimation (LMMSE)
	4.2 Diversity Techniques for Receiver
		4.2.1 Spatial Diversity
	4.3 Diversity MISO Model
	4.4 Multi User Massive MIMO
	4.5 Computation of Ergodic Channel Capacity  of the Massive MIMO in the Uplink Scenario
	4.6 Multiuser MIMO with Pilot Contamination
		4.6.1 Summary on Massive MIMO
	4.7 Multiuser Multicell  with Perfect and Estimated CSI
	4.8 Ray Tracing Model
	4.9 Beam Forming
	4.10 Spatial Multiplexing Using Decoupling of MIMO System
	4.11 Water Fill Algorithm
	4.12 OFDM Multicarrier Transmission, IFFT/FFT Processing, Cyclic Prefix of OFDM
	4.13 Code Division Multiple Access
5 5G and Beyond Technologies
	5.1 Non-orthogonal Multiple Access in Downlink Scenario with |h1|<|h2|
	5.2 Non-orthogonal Multiple Access in Downlink Scenario with Arbitrary Channel Coefficient
	5.3 Generalized Spatial Modulation Techniques
	5.4 Multicarrier Transmission
		5.4.1 Multicarrier Transmission Through OFDM
		5.4.2 Multi Carrier Transmission Through Frequency Selective Channel
		5.4.3  OQAM Based Multicarrier Transmission
		5.4.4 Multicarrier Transmission (OQAM) Through Frequency Selective Channel
		5.4.5 Illustration of Staggered Spectrum
	5.5 Millimeter Wave MIMO:Channel Modeling and Estimation
		5.5.1 Toy Experiment to Illustrate mm wave Channel Estimation
	5.6 Co-operative Communication
	5.7 Full Duplex Radio: Self Interference and Hybrid Cancellation
	5.8 Unmanned Aerial Vehicle (UAV)  Based Data Collection with Clustered Wireless Sensors and Power Transmission Through Power Beacons
Index