Digital signal processing (DSP) refers to several techniques for improving the accuracy and reliability of digital communications. The theory behind DSP is quite complex. Basically, DSP works by clarifying, or standardizing, the levels or states of a digital signal. The ADSP circuit is capable of differentiating between human signals, which are ordered, and noise, which is inherently chaotic.
All communications circuits contain some noise. This is true if the signals are analog or digital, and regardless of the type of information transmitted. Noise is the eternal loss of communications engineers, who are always striving to find new ways to improve signal-to-noise communications systems. Traditional methods of optimizing the S / N ratio include increasing the power of the transmitted signal and increasing the sensitivity of the receiver. (In wireless systems, specialized antenna systems can also help.) Digital signal processing drastically improves the sensitivity of a receiving unit. The effect is most noticeable when the noise competes with a desired signal. A good DSP circuit can sometimes seem like an electronic miracle worker. But there are limits to what you can do. If the noise is so strong that all traces of the signal are obliterated, a DSP circuit can not find any order in the chaos, and no signal will be received.
If an input signal is analog, for example a standard television station, the signal is first converted to digital form by an analog-to-digital converter (ADC). The resulting digital signal has two or more levels. Ideally, these levels are always predictable, accurate or current voltages. However, because the incoming signal contains noise, the levels are not always in the standard values. The DSP circuit adjusts the levels to the correct values. This virtually eliminates noise. The digital signal is converted back to analog via a digital-to-analog converter (DAC).
If a received signal is digital, eg computer data, then the ADC and the DAC are not needed. The DSP acts directly on the incoming signal, eliminating the irregularities caused by the noise, thus minimizing the number of errors per unit time. The increased use of computers has led to increased use and need for digital signal processing. To digitally analyze and manipulate an analog signal, it must be digitized with an analog-to-digital converter. Sampling is generally carried out in two stages, discretization and quantification. Discretization means that the signal is divided into equal time intervals, and each interval is represented by a single amplitude measurement. Quantization means that each amplitude measurement is approximated by a value of a finite set. Rounding real numbers to whole numbers is an example.
The Nyquist-Shannon sampling theorem states that a signal can be reconstructed exactly from its samples if the sampling frequency is greater than twice the highest signal frequency. In practice, the sampling frequency is often significantly greater than twice that required by the limited bandwidth of the signal.
The theoretical DSP analyzes and derivations are typically performed in discrete time signal models without amplitude inaccuracies (quantization error), "created" by the abstract sampling process. Numerical methods require a quantized signal, such as those produced by an analog-to-digital converter (ADC). The processed result can be a frequency spectrum or a set of statistics. But it is often another quantized signal that is converted back to analog form by a digital-to-analog converter (DAC).