The author, maxfiner, graduated from Xi'an University of Electronic Science and Technology with a master's degree in signal and information processing. Maxfiner has worked in the wireless communication department of Huawei Communication Technology Co., Ltd., and has many years of experience in engineering project development. It also has algorithm theory research, simulation verification, and corresponding hardware design implementation capabilities. It has practical experience in all aspects of communication physical layer development and design. .
Proficient in signal processing design, small TIps (3): the three cornerstones that must be mastered
Signals and information processing cover a wide range of content, and there is a close relationship with automatic control, computer and other disciplines. From the perspective of application orientation, the most widely used applications are communication applications, navigation and positioning, and radar, image, video, voice, classification and recognition. From the professional field subdivision, there are basic signal processing, spectrum estimation, adaptive signal processing, array signal processing and other general directions, each direction is enough for people to study for a while. So, in order to better meet the needs of applications and practices, what should we focus on and what to focus on. There is no doubt that it is definitely the most basic content. All kinds of new things, new concepts, new methods, and new theories in signal processing are based on a common solid foundation. For example, one of the core processing algorithms of signal processing, Fast Fourier Transform, was proposed in 1965. It has been 48 years since now, and it is the core of the modulation methods of wireless communication systems such as LTE, WIFI, and WiMax. For example, the related calculations and the wide range of applications make me believe that it will not be discarded after another hundred years. (I will talk about seven or eight large applications related to engineering operations in the future. When solving these practical problems, the related operations will start. The core key role). Therefore, the basic knowledge is firmly grasped. When encountering various practical problems, it will be much easier to encounter various new concepts that need to be familiar. The reason is very simple, that is, the so-called sharpening of the knife does not mistake the woodworker.
Let me give you an example. Recently, I am familiar with and understand the high-speed serial interface of XILINX's FPGA. In general, this is partial hardware. The FPGA's high-speed serial interface has a pre-emphasis function at the originating end and an equalization function at the receiving end. Both of these measures are designed to solve the distortion problem in high-speed signal transmission. Whether it is pre-emphasis or balance, the principle and function are very easy to understand from the perspective of signal processing. The pre-emphasis is pre-distortion processing before the actual transmission link, and the equalization is the compensation processing after the actual transmission link. The physical transmission link can be regarded as a system. Pre-emphasis and equalization can be regarded as the inverse system of the system composed of transmission links. Any one is connected in series with the actual physical transmission link, so as to approach an ideal one. There is a certain delay, there is no waveform distortion system, thus eliminating inter-symbol interference.
Let me talk about the three cornerstones of signal processing according to my personal mouse light. The first cornerstone: the concept of signal and system, and the corresponding correlation, convolution and other concepts. The second cornerstone: Fourier transform. The third cornerstone: the sampling theorem. The preconditions of these three concepts are ideal situations, such as linear, equal interval sampling, etc. The actual scene and environment are not ideal, but they are all based on ideal conditions for processing, improvement and optimization.
The first cornerstone - the signal and the system. I have contacted some colleagues and friends, and I still can't understand some concepts of signal processing. I found that a very important reason is that I don't have a good understanding of the concept of signals and systems. For the major directions of electronic information, such as signal processing, communication engineering, automatic control, its theoretical basis and analysis are inseparable from the concept of signal and system. It can be said that these three major professions, the signal system concept is a common core foundation. However, communication engineering on this basis contains modulation, coding and some concepts and content above the physical layer. Automatic control focuses on the analysis and research of theories such as control and feedback. The concept of the signal is relatively straightforward and clear, and it is easier to understand. It is important to understand the understanding of several key signals. The concept of the system is relatively abstract and requires attention to several important features of the system.
The second cornerstone, the Fourier transform, is as previously mentioned, and many applications and application scenarios for signal processing are inseparable from the Fourier transform. The Fourier transform is a large branch, and the content is also very rich, including continuous form and discrete form. Each form has a Fourier transform which is divided into periodic signals and a Fourier transform of aperiodic signals. The discrete form of Fourier transform is divided into discrete time Fourier transform, discrete Fourier transform and other forms. It is easy to get rid of the first contact, and it is necessary to find out the relationship between them. My personal feeling is that an understanding is not enough. I think that although I have been studying for so many years, I still need to constantly understand and learn from it. One concept closely related to the Fourier transform is frequency and frequency. Spectrum analysis is a very important analytical tool and has a wide range of applications in many fields. For radio monitoring, 2G, 3G, 4G communication testing, the understanding of the spectrum and personal experience should be more profound.
The third cornerstone - the sampling theorem. The real world is both analog and continuous. The signals obtained by the sensors (temperature sensors, humidity sensors, pressure sensors, antennas, etc.) are analog continuous forms of signals, each having a certain amount of time. However, when signal processing is used to process them, they are discrete digital forms. Because digital processing is more accurate and easier to operate, this is impossible with analog and analog devices. This is also the charm of digital signal processing, and is also the main factor for the wider application of digital signal processing (device). Lenovo's latest mobile phone coprocessor, Qualcomm's hegemony will feel deeper and more intense. This involves the conversion of signals from analog to digital. The key components are AD converters that directly affect the performance of defense military equipment. As is generally understood, after the signal becomes digital, the signal values ​​between the two discrete time points are discarded, and the signal is lost a lot of information, which is different from the original one. But the sampling theorem tells us that under certain conditions, even if the signal is discretized, the signal value between the sampling times is not retained, the information is not lost, and the discrete signal can still be reconstructed back to the original Analog form of the signal. This is one of the reasons why the sampling theorem has become the core of digital signal processing theory. The sampling theorem is far more than just the time when signals are converted from analog to digital, and can be reflected in many aspects of signal processing applications.
In the follow-up, I hope to expand the description of each cornerstone. Learn and feel the signal processing with everyone.
The next issue is to speak - proficient in signal processing design, small TIps (4): the most commonly used signals, and talk about signals and systems , so stay tuned!
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