It surveys technologies associated with both supply and demand including: energy sources and systems; low power electronics and design; communications, computers, displays, and sensors; and networks, protocols, and operations. Advanced concepts discussed are predicated on continued development by the Army of soldier systems similar to the Land Warrior system on which the committee bases its projections on energy use.
Finally, the volume proposes twenty research objectives to achieve energy goals in the time frame. Book Summary: This monograph is intended for the designers and would-be designers of secure and efficient wireless communication systems under intentional interference. Along with the widespread of wireless devices, especially reconfigurable software defined radios, jamming has become a serious threat to civilian communications. In this book, going beyond traditional communication system design that mainly focuses on accurate information transmission under benign environments, we aim to enhance the physical layer security of communication systems by integrating modern cryptographic techniques into transceiver design, so as to achieve secure high-speed transmission under hostile interference with high reliability and efficiency.
We revisit existing jamming patterns, and introduce new jamming patterns. We analyze the weaknesses of existing anti-jamming techniques. We present innovative and feasible anti-jamming techniques, which can strengthen the inherent security of the 3G, 4G and the upcoming 5G systems with minimal and inexpensive changes to the existing CDMA, frequency hopping and OFDM schemes.
We also provide benchmarks for system performance evaluation under various jamming scenarios through capacity analysis. This book includes design principles, in-depth theoretical analysis and practical design examples, and will be of interest to academic researchers as well as professionals in industry.
Book Summary: This book introduces the development of self-interference SI -cancellation techniques for full-duplex wireless communication systems. The authors rely on estimation theory and signal processing to develop SI-cancellation algorithms by generating an estimate of the received SI and subtracting it from the received signal.
The ASI approach adds an appropriate cancelling signal to each transmitted signal such that the combined signals from transmit antennas attenuate the SI at the receive antennas. The authors illustrate that the SI-pre-cancelling signal does not affect the data-bearing signal. This book is for researchers and professionals working in wireless communications and engineers willing to understand the challenges of deploying full-duplex and practical solutions to implement a full-duplex system.
Advanced-level students in electrical engineering and computer science studying wireless communications will also find this book useful as a secondary textbook. Book Summary: A comprehensive review to the theory, application and research of machine learning for future wireless communications In one single volume, Machine Learning for Future Wireless Communications provides a comprehensive and highly accessible treatment to the theory, applications and current research developments to the technology aspects related to machine learning for wireless communications and networks.
The technology development of machine learning for wireless communications has grown explosively and is one of the biggest trends in related academic, research and industry communities. Deep neural networks-based machine learning technology is a promising tool to attack the big challenge in wireless communications and networks imposed by the increasing demands in terms of capacity, coverage, latency, efficiency flexibility, compatibility, quality of experience and silicon convergence.
The author — a noted expert on the topic — covers a wide range of topics including system architecture and optimization, physical-layer and cross-layer processing, air interface and protocol design, beamforming and antenna configuration, network coding and slicing, cell acquisition and handover, scheduling and rate adaption, radio access control, smart proactive caching and adaptive resource allocations.
Uniquely organized into three categories: Spectrum Intelligence, Transmission Intelligence and Network Intelligence, this important resource: Offers a comprehensive review of the theory, applications and current developments of machine learning for wireless communications and networks Covers a range of topics from architecture and optimization to adaptive resource allocations Reviews state-of-the-art machine learning based solutions for network coverage Includes an overview of the applications of machine learning algorithms in future wireless networks Explores flexible backhaul and front-haul, cross-layer optimization and coding, full-duplex radio, digital front-end DFE and radio-frequency RF processing Written for professional engineers, researchers, scientists, manufacturers, network operators, software developers and graduate students, Machine Learning for Future Wireless Communications presents in 21 chapters a comprehensive review of the topic authored by an expert in the field.
This complete process of interspersing zeros, lowpass FIR filtering, delaying, summing, Smart Antenna Systems and downsampling is also referred to as time domain interpolation beamforming [4]. In addition, the taps of all the lowpass FIR filters can be updated in real-time operation if we use an adaptive algorithm. In this case, we refer to it as adaptive interpolation beamforming. Furthermore, each of the combining upsamplers and lowpass FIR filters can be efficiently implemented by using polyphase interpolation decomposition or multistage interpolation filters [3, 5].
Another type of beamformer that we would like to mention here was developed for array sensors based on the frequency domain by using short-time Fourier analysis or the discrete Fourier transform DFT , known as the frequency-domain beamformer. With some modifications, it is also possible to use the frequency-domain beamformer for the smart antenna system.
The interested reader may refer to [4]. The optimization constraint methods for the beamformer are used to focus on specific signals while suppressing unwanted others at the same time. In an optimal sense, we want to determine a set of optimal weight coefficients to reinforce any signal propagating across the beamformer with a set of delays.
On the other hand, adaptive beamformer algorithms can vary the shading and increase SNR through the temporal, frequency, and spatial filtering based on the signal and noise characteristics at the time of observation. We focus on developing the derivation of the Wiener-Hopf equation, which provides the optimum linear FIR filter coefficients in the optimal sense of mimimum mean square error MMSE for the beamformer.
The optimum linear FIR filter is used to estimate the MMSE for the beamformer, y[n] for d[n], given two wide-sense stationary processes jointly, x[n] and d[n], which are statistically related to each other. We also define the autocorrelation functions, rx k and rd k , and the crosscorrelation function, rdx k.
Equation 5. If we further assume that the matrix Rx [n] is invertible, then w[n] in 5. In order to determine the beamformer output y[n], we need to determine the values of the elements of the filter coefficient vector w[n] that maximizes the ratio of the FSN R criterion in 5. This is also called the canonical correlation function, the canonical discriminant function see Miao [6] , the optimization array gain see Johnson and Dudgeon [4] , the Rayleigh-Ritz ratio, or the signal-to-interference-and-noise ratio see Reed [2].
The determination of the filter coefficient vector of the beamformer that maximizes the FSN R criterion involves solving the eigenvalue and eigenvector Equation 5. H w [n]Rn [n]w[n] Rn [n]w[n] 5. Thus, 5. This further implies that the pool covariance matrix of the output signal y[n] has a unity variance.
To maximize the FSN R ratio in 5. Solving 5. The interested reader may refer to Miao [6] to explore further the optimization methods based on the generalized optimal declustering analysis for the beamformer. Smart Antenna Systems We have discussed selecting the filter coefficients for the beamformer without regard to the nature of the field into which the beamformer is placed.
The filter coefficients of the beamformer yielding the optimal gain depend on the characteristics of the interference and noise. This means that a good set of filter coefficients for the beamformer should adapt to the beamformer environment and should not be fixed before placing the beamformer. This leads to the idea of adapting signal processing algorithms for the beamformer to the signal environment.
For adaptive beamformer algorithms with an application of a sidelobe canceller based on a linear predictive approach to array signal processing, we refer the interested reader to Johnson and Dudgeon [4]. This led to mitigating fading through diversity reception and adaptive beamforming. We then focused on fundamental beamforming structures and discussed their benefits with respect to different signal environments.
These discussions have led to the necessary step of understanding how a beamforming structure affects performances of the smart antenna system, thereby providing a key guideline of designing the smart antenna system for digital communication systems. Of particular importance in the evaluation of smart antenna system performance are beamforming algorithms that laid mathematical foundations based on optimization constraint methods, which have rapidly developed over the last two decades along with practical applications to the digital communications systems.
The systems that exist today and those currently under development certainly reflect these recent advances in the smart antenna system. With applications of using beamformer structures and algorithms, the smart antenna system can further enhance performance of digital radio frequency RF systems and mitigate fading through Signal Processing in Digital Communications diversity reception and space-time processing while minimizing interference through beamformer spatial filtering.
Advanced development in the smart antenna system will be a key to future digital communication systems with evolving higher data rates and spectral efficiencies. Therefore, by discussing smart antenna system technologies, we have placed technical foundations to integrate those technologies into more advanced development of signal processing technologies for digital communication systems. In any case, those physical channels usually introduce linear and nonlinear distortions, random noise, and deterministic interference.
Therefore, efficient and successful communication of messages via imperfect channels is still one of the major triumphs of information technology. In wireless communication, a physical channel affects propagation of radio signals on both forward and reverse links. A signal propagating through the physical channel usually arrives at the destination along with a number of different paths that are referred to as multipath.
These paths come from scattering, reflection, refraction, or diffraction of radiated energy off of objects that are located in the environment. The received signal is much weaker than the transmitted signal because of phenomena such as mean propagation loss, slow fading, and fast fading. In addition, digital communication through a bandlimited wireless channel with multipath phenomena is subject to intersymbol interference ISI.
This problem can be so Signal Processing in Digital Communications severe that the correct reception of transmitted sequence is not feasible anymore without including a specific device in a receiver.
In wired communication, for example, a twisted-pair copper telephone line that was originally intended to carry voice signals at about a 4-kHz frequency bandwidth is now used to transmit from several megabits Mb to 52 Mb of data per second by using a digital subscriber loop DSL , such as HDSL, ADSL, VDSL, and so forth.
This has been possible because of the efficient use of the high-frequency propagation of copper wires, which suffer from a great deal of line attenuation and noise.
The twisted-pair copper wire channel usually has different segments with different gauges ranging from 19 American wire gauges AWG to 26 AWG, multiple bridge taps, a wire drop, and lumped elements. In addition, far- and near-end crosstalk between pairs in a multipair cable is the dominant impairment in any DSL system.
Therefore, with the limitation of frequency bandwidth, twisted-pair copper channel has severe distortion and is subject to ISI. Communication systems usually operate with limitations of frequency bandwidth. This causes ISI and out-of-band radiation in adjacent channels. Pulse-shaping techniques can be designed to reduce the ISI effects and the spectral width of a modulated signal simultaneously.
This leads to matched filtering at the receiver that compensates for the ISI caused by multipath within time-dispersive wireless channels or bandlimited wired channels. In this section, a short overview and the background of communication channels are briefly presented. Section 6. Subsequently, pulse-shaping techniques that are designed to reduce and suppress out-of-band radiation while eliminating ISI effects are given in Section 6.
A brief summary of this chapter is given in Section 6. The transmission path between a transmitter and receiver can vary from a simple line-of-sight LOS path to a nonline-of-sight NLOS path, which is severely obstructed by buildings, mountains, and foliage.
Additionally, when a mobile terminal moves in space, the speed of motion impacts how rapidly the signal level fades. For this reason, the received power Pr d uses a close-in distance d0 as a known received power reference point to relate to Pr d at the close-in distance d0. Equation 6. Example 6. If a receiver antenna has a unity gain, determine a a far-field distance df , b the transmitter power in dBm and dBW, and c the receiver power in dBm at the far-field distance df and the receiver power in dBm at the distance of 5 km.
However, transmitted signals in the wireless communications, such as land mobile radio applications, usually do not experience the free-space propagation. In environments of the land mobile radio applications, a main path is often accompanied by a flat surface-reflected path, which may destructively interfere with the primary path.
Using 6. In that case, we note that the propagation path loss or the received power has alternate minima and maxima when the path distance d is relatively small. There are three basic multipath propagation phenomena that impact propagation in a mobile communication system, including reflection, diffraction, and scattering.
The multipaths formed by the reflectors, diffractors, and scatterers add up at a receiver antenna to produce the received signal. Figure 6. Reflection takes place when a propagating electromagnetic wave or radio wave impinges upon an object including a building, a wall, and even the surface of the Earth, and so forth, which has very large dimensions when compared to the wavelength of the propagating wave.
In other words, when an electromagnetic wave propagating in one medium impinges up another medium having different electrical properties, the electromagnetic wave is partially reflected and partially transmitted. This is to say that the diffraction allows radio waves to propagate around the curved surface of the Earth, beyond the horizon, and to propagate behind obstructions.
When a receiver moves deeper into the obstructed or shadowed region, the received field strength decreases rapidly. However, the diffraction field still exists and usually has sufficient strength to produce a useful signal for the receiver. Scattering takes place when the radio wave strokes objects with dimensions that are small compared to the wavelength. The reflected energy is spread out in all directions due to scattering when the radio wave impinges on a rough surface. Objects such as foliage, street signs, lampposts, and trees tend to scatter energy in all directions, thereby providing additional radio energy at the receiver.
The received power or its reciprocal, path loss is the most important parameter predicted by large-scale and small-scale fading and propagation models based on the physics of the reflection, diffraction, and scattering.
The mathematical treatments on the three basic propagation schemes refer to [1, 3, 4]. This phenomenon creates a so-called multipathintensity profile, which describes relationships among parameters of multipath channels.
Time Delay The term of time delay is used to refer to the excess delay, which represents the delay of the signal propagation that exceeds the delay of the first signal arrival at the receiver. For a transmitted signal impulse, the time Tm between the first and last received component represents the maximum excess delay.
The maximum excess delay is defined to be the time delay during which multipath energy falls some threshold level below the strongest component. Generally, the threshold level can be selected at 10 dB or 20 dB below the level of the strongest component. In addition to the maximum excess delay, there are other terms, mean excess delay and rms delay spread, that are also used for the parameters of the multipath channels.
The coherence bandwidth F0 can also be defined as a relation derived from the rms delay spread. If the coherence bandwidth uses the bandwidth over which the frequency correlation function is above 0. If the frequency correlation function is above 0. The definition of the coherence bandwidth often differs from one reference to another [7], and tends to depend on the extent of the correlation, determined subjectively, over which the two signals are correlated.
Doppler Spread Doppler spread Ds is a measure of spectral broadening fd , as shown in Figure 6. This indicates that the Doppler components Channel Characterization and Distortion arriving at exactly 0 and degrees have an infinite Doppler power spectrum. Coherence time T0 is a statistical measure of the time duration over which the multipath channel impulse response is invariant and quantifies the similarity of the multipath channel response at different times.
This is to say that the coherence time is the time duration over which two received signals have a strong potential for amplitude correlation and the time domain dual of Doppler spread. The coherence time T0 can also be defined as the geometric mean if the coherence time is over which the time correlation function is above 0. Duality Concept In both Figure 6. Two operations are called a duality function [5, 6] if the behavior of one with reference to a time-related domain such as time or time delay is identical to the behavior of the other with reference to the corresponding frequency-related domain such as frequency or Doppler frequency shift.
Delay spread and coherence bandwidth, as developed earlier, are parameters of multipath channels that describe the time-dispersive nature of the multipath channels in a local area. But both of these parameters do not provide information about the time-varying nature of the multipath channels caused either by relative motion between the transmitter and receiver, or by the movement of objects in the multipath channels.
On the other hand, Doppler spread and coherence time are also parameters of the multipath channels that describe the time varying nature of the multipath channel in a small-scale region. Depending on the parameters of wireless mobile multipath channels, different transmitted signals will experience different types of fading over a travel distance from the transmitter to the receiver.
Thus, a mobile user will usually experience signal variation in time. This phenomenon is referred to as fading. Fading is caused by interference between two or more versions of the Channel Characterization and Distortion transmitted signal or multipath waves that arrive at the receiver at slightly different times.
The receiver antenna combines the multipath waves to provide a resulting signal that can vary widely in amplitude and phase, depending on the distribution of the transmitted energy, the propagation times of the radio waves, and the bandwidth of the transmitted signal. The large-scale fading represents the average signal power attenuation or the propagation path loss because of motion over large areas. The small-scale fading represents the dramatic changes of the amplitude and phase that can be experienced as a result of small changes in the spatial separation between a receiver and transmitter due to the multipath propagation.
If no multipath fading is present, then the propagation path loss is the only major factor that must be considered in the wireless mobile communication environment. However, a mobile radio roaming over a large area must process signals that experience both types of fading with the small-scale fading superimposed on the large-scale fading. The value of the path loss exponent n depends on the carrier frequency, antenna heights, and propagation environment. In free space, n is equal to 2, as seen in 6.
When obstructions are present, the value of the path loss exponent n will be increased accordingly. Choosing an appropriate free-space reference distance d0 is also important for the propagation environment. Generally, the value of the reference distance d0 is taken to be 1, meters for large cells, meters for microcells, and 1 meter for indoor channels.
The reference path loss P L d0 is calculated either using 6. The mean path loss P L d in 6. Cox et al. The log-normal distribution is that the path loss P L d over the large-scale fading approaches a normal distribution when Channel Characterization and Distortion plotted on a logarithmic scale in decibels. Multipath propagation in the wireless communication channel creates the small-scale fading effects. The three most important effects given by Rappaport [1] include: 1 signal strength rapidly changes over a small travel distance or time interval; 2 random frequency modulations happen because of varying Doppler shifts on different multipath signals; and 3 time dispersions are caused due to multipath propagation delays.
Rayleigh Fading The small-scale fading is also known as Rayleigh fading. If the multipath reflective paths are large in number and are all NLOS, the envelope of the received signal can be statistically expressed by using a Rayleigh fading distribution. Ricean Fading The small-scale fading is called the Ricean fading when there is a dominant nonfading signal component present, such as the LOS propagation path.
Note that the Ricean fading distribution in 6. The Ricean fading distribution is usually expressed in terms of a parameter k that is defined as the ratio between the deterministic signal power and the variance of the multipath. Frequency-Selective Fading A multipath delay spread leads to time dispersion of the multipath channel, which causes a transmitted signal to undergo either frequency-selective or flat fading.
Flat fading does not induce ISI distortion, but performance degradation can still be expected because of a loss in SNR whenever the transmitted signal is fading. Time-Selective Fading A Doppler spread leads to frequency dispersion of the multipath channel, which causes a transmitted signal to undergo either fast or slow fading. The fast fading is also referred to as time-selective fading because amplitude of the transmitted signal varies with time. Depending on how rapidly the transmitted signal changes as compared to the rate of change of the multipath channels, the multipath channels can be classified either as a fast fading or slow fading channel.
This causes frequency dispersion because of the Doppler spread, thereby leading to signal distortion at the receiver. The signal distortion of the fast fading increases if the Doppler spread relative to the bandwidth of the transmitted signal increases.
In a slow fading channel, the impulse response of the multipath channel changes much more slowly than the transmitted signal.
This indicates that the Doppler spread of the multipath channel is much less than the bandwidth of the transmitted signal in the frequency domain. The coaxial cable is traditionally used for digital communication inside a building, and for high capacity long-distance facilities in the telephone network. The pair of wires is used for connection of customer premises equipment CPE at home to a central office CO.
Broadband access approaches have been developed to provide a very high data rate over the pair of wires. These broadband access approaches are commonly known as DSL. During the last decade, DSL technologies have been an attractive broadband access service for residential and small business areas. The need for a longrange extended DSL LDSL with capabilities of transmitting the minimum data rate over Kbps is already evident due to the increasing demands of the customers imposed by the proliferation of long-reach services [12].
However, in any case, to transmit higher data rates over a longer distance from CPE to CO, DSL technologies face challenges due to the wired channel of wire pairs with serious distortions. The two-port network has an input port and output port for which the input and output currents are complementary, as shown in Figure 6.
Using the voltage source Vs and load voltage VL as shown in Figure 6. Each of the loop segments is a transmission line that can be viewed as a cascade combination of two-port networks. Thus, the RLGC is important to designers. Usually, the RLGC parameters are found from field or laboratory tests, depending on various loop types and gauges.
Bridge Taps Bridge taps present their open-circuit admittance in the shunt between two in-line two-port networks as shown in Figure 6. For loops with several bridge taps, the location of the notches can be determined by using superposition heuristically.
Cascade Combination of the Loop For the end-to-end loop as shown in Figure 6. The cause of the crosstalk is capacitive and inductive coupling between the wires due to imbalance in the couplings. A precise knowledge of individual pair-to-pair crosstalk transfer functions will be needed in order to implement crosstalk cancellation.
Thus, the signals, which come back toward the source of the interferer, add up to form crosstalk. This crosstalk is referred to as near-end crosstalk NEXT. The NEXT represents a crosstalk of a local transmitter into a local receiver and experiences attenuation. FEXT If one pair j is the interferer and the voltages and currents induced in the other pair i travel in both directions, the signals that continue in the same direction as the interfering signal add up to form crosstalk.
The crosstalk is known as far-end crosstalk FEXT. The FEXT represents a crosstalk of a local transmitter into a remote receiver, and also experiences attenuation. The simulation loop model provides a functional description of the combined end-to-end loop with impairment noise that must be probed at the CPE receiver input of DSL transceiver. The functional diagram of the simulation loop model in Figure 6. These transfer functions are independent of the simulation loop models, but can change with the electrical length of the simulation loop models.
Thus, the simulation loop models allow designers to test performance of various DSL technologies based on loop topology. Therefore, simulating the specific loop models requires referring to the DSL standards. Consequently, a sequence of source bits that represent data or a digitized analog signal must be converted to a continuous-time waveform at the transmitter. Instead, those transmitted pulses overlap, resulting in ISI.
Corresponding of the impulse response in 6. Thus, 6. However, this is impossible in practical applications because other terms cannot be eliminated without a special device in the receiver. The second term in 6. The third term in 6. The received PAM signal y t in 6. The resulting display is called an eye diagram. For an illustration, Figure 6. The effect of the ISI in the system is to cause the eye diagram to close, thereby reducing the margin for additive noise to cause errors.
The effect of the ISI on reducing the opening of a binary eye diagram is graphically illustrated in Figure 6. There are four types of distortions including zero crossing, noise margin, peak, and sensitivity to timing error.
The vertical opening in Figure 6. In some cases, the vertical opening disappears altogether if the ISI and noise are large enough. In this case, the eye diagram is called eye-closed.
Otherwise, the eye diagram is referred to as eye-open. An eye-closed means that the bit decisions are no longer sure and some fraction of these is wrong. In the limit, this leads to a probability of error close to 0. On the other hand, the wide eye-open in the vertical spacing between signal levels implies a large degree of immunity to additive noise. Channel Characterization and Distortion Eye Diagram 1. The width of the horizontal eye opening indicates how much a communication system is sensitive to the timing offset, as illustrated in Figure 6.
A very narrow eye opening means that a small timing offset will result in sampling, while a wide horizontal eye opening means that a large timing offset can be tolerated. The slope of the inner eye opening in Figure 6.
A very steep slope means that the eye diagram closes rapidly when the timing offset increases. As a result, a large amount of timing jitter in the sampling times would significantly increase the probability of error in the receiver. The ISI distorts the position of the zero crossing and causes a reduction in the eye opening.
Ideal constellation point Error vector magnitude Measured symbol 0. Without the ISI and noise, the superimposed signals at the sampling instants would result in four distinct points corresponding to the four transmitted signal phases.
As a result, the larger the ISI and noise in a communication system, the larger the scattering of the received signal relative to the transmitted signal points. For example, the ideal complex I and Q constellation points associated with QPSK modulation shall be used as the reference as shown in Figure 6.
Channel Characterization and Distortion Then, the kth received sample in 6. As a result, the selection of the transmitter filter hT kT and receiver filter hR kT in 6. However, a zeroforcing solution may not be an optimal solution depending on the type of detection scheme used [6].
This is because the probability of error may also depend on the second term of noise intensity in 6. Nyquist [23] stated that the ISI could be completely cancelled if the overall response of the communication system is designed such that the response due to all symbols except the current symbol is equal to zero at every sampling instant at the receiver. That is, if Hef f f and hef f t are the transfer function and impulse response of the overall communication system given by 6.
Thus, the condition in 6. In order to satisfy the Nyquist criterion, the channel bandwidth 1 W must be at least equal to 2T. A filter that satisfies the Nyquist criterion is referred to as the Nyquist filter. Thus, it is highly desirable to reduce modulation bandwidth and suppress Channel Characterization and Distortion the out-of-band radiation while eliminating the ISI simultaneously. Pulse shaping is one of the techniques that is used to reduce the ISI effects and spectral width of a modulated signal.
Assume that the channel is an ideal. The raised-cosine frequency response HRC f in 6. The corresponding time-domain raised-cosine pulse of the raised-cosine frequency response in 6. Use a work methodology appropriate to the task.
Assess one's own level of skill acquisition, and plan their on-going learning goals. Use both general and domain specific IT resources and tools. Course with exercises sessions and coding examples and exercises in Python Jupyter Notebooks. Prandoni and M. Exercice, TP. Their treatment is less focused on the mathematics and more on the conceptual aspects, allowing students to think about the subject at a higher conceptual level, thus building the foundations for more advanced topics and helping students solve real-world problems.
The last chapter pulls together the individual topics into an in-depth look at the development of an end-to-end communication system. Richly illustrated with examples and exercises in each chapter, the book offers a fresh approach to the teaching of signal processing to upper-level undergraduates. Digital Signal Processing for Communication Systems looks at various types of coding and modulation techniques, describing different applications of Turbo-Codes, BCH codes and general block codes, pulse modulations, and combined modulation and coding in order to improve the overall system performance.
The book examines DSP applications in measurements performed for channel characterisation, pursues the use of DSP for design of effective channel simulators, and discusses equalization and detection of various signal formats for different channels. A number of system design issues are presented where digital signal processing is involved, reporting on the successful implementation of the system components using DSP technology, and including the problems involved with implementation of some DSP algorithms.
Digital Signal Processing for Communication Systems serves as an excellent resource for professionals and researchers who deal with digital signal processing for communication systems, and may serve as a text for advanced courses on the subject.
Author : Andreas O. Throughout this journey, we will cover signal processing topics that are applicable not just to the field of communications but to many engineering disciplines.
This text steps outside the often dry mathematical presentation of more traditional DSP books and provides a more intuitive approach to this fascinating topic. Some of this book's uniqueness can be summarized as follows: - An intuitive approach to the topic of digital signal processing. All in all, this book is a must-have for students and practicing engineers who want to build upon the principles of Digital Signal Processing, enrich their understanding with advanced topics, and then apply that knowledge to the design of modern wireless modems.
Since its military beginnings as a means of battlefield surveillance, practical use of this technology has extended to a range of civilian applications including environmental monitoring, natural disaster prediction and relief, health monitoring and fire detection.
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