English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Theory and method of Baud spaced equalizer (BSE) and fractionally spaced equalizer (FSE) based on constant modulus algorithm (CMA) are reviewed. For the un-changed characteristics channel, adaptive constant modulus algorithm can achieve good performance in blind equalization. But the tracking ability of adaptive constant modulus algorithm gets worse while the char-acteristics of channel changes more quickly. Fractional-ly spaced equalizer can achieve distortion compensation in the received signal and better performance than baud-spaced equalizer, because aliasing caused by under sampling is avoided. Introduction In wireless communication, the information source is mapped to a character, which is valued in a finite char-acter set. so that it is a non-Gauss signal. Before they are received by one or more sensors, they need to be transmitted through the channel, where they will be affected by intersymbol interference (ISI) and inter channel interference (ICI). The receiver noise is narrow, so it is often modeled as additive Gauss noise. Inter-symbol interference and inter channel interference main-ly due to limited band transmitting and receiving filters, amplifiers, delay and multipath transmission, the rela-tive motion between the transmitter and receiver, cou-pling effect and multiple access interference. In the case of known channel characteristics C (f), it is not difficult to design the optimal demodulation filter for the received signal. The problem is that the channel characteristic C (f) is often unknown in practice. And the response of the mobile radio channel is time variant. For such channels, we cannot design a fixed optimal demodulation filter. The basic method to solve the inter symbol interference problem is to design the receiver which can compensate or reduce the interference between the received signals. That is the equalizer. Since the channel characteristics are unknown and time-varying, the effective equalizer should be adaptive and change with the change of chan-nel characteristics. The traditional equalization tech-nique uses the pre-defined guidance training to adjust the filter at the receiver. However, this method takes up the channel capacity, and the blind equalization does not need to occupy the bandwidth. Adaptive algorithms are mainly Kalman filtering algorithm, least mean square (LMS) algorithm, recursive least squares (RLS) algorithm and constant modulus (CM) algorithm. Linear channel equalization can eliminate the distortion of linear channel. Linear equalizer can eliminate the influence of ISI and improve the correct rate of receiv-ing character. When the equalizer tap interval is the symbol interval T (also called Baud), it is called the Baud spaced equalizer(BSE). However, there are some disadvantages in Baud spaced equalizer, and the per-formance is not good. In contrast, the equalizer with a fractional ratio of Baud interval is superior to Baud spaced equalizer, and it is called the fractionally spaced equalizer(FSE). 484 7th International Conference on Education, Management, Computer and Society (EMCS 2017) Advances in Computer Science Research (ACSR), volume 61

Performance Comparison of Blind Equalization Algorithm