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Force-System Resultants and Equilibrium
Published in Richard C. Dorf, The Engineering Handbook, 2018
Another method of specifying the performance of an error correction coding scheme focuses on coding gain. Coding gain is the reduction in dB of required signal-to-noise ratio needed to achieve a given error rate relative to an uncoded system. Specifically, if an uncoded system needs an energy per bit of EuPs to achieve a symbol error rate of Ps, and a coded system requires EcPs, the coding gain is γPs=10logEuPs/EcPsdB
Forward Error Correction Coding
Published in Jerry D. Gibson, The Communications Handbook, 2018
Vijay K. Bhargava, Ivan J. Fair
transmitted per encoded symbol. Coding gain: The reduction in signal-to-noise ratio required for specified error performance in a block or convolutional coded system over an uncoded system with the same information rate, channel impairments, and modulation and demodulation techniques. In TCM, the ratio of the squared free distance in the coded system to that of the uncoded system. Column distance: The minimum Hamming distance between convolutionally encoded sequences of a
Digital Modulation
Published in Jerry C. Whitaker, The RF Transmission Systems Handbook, 2017
The previously discussed modulation schemes had dependency between signaling elements only over one signaling element. There are advantages to providing memory over several signaling elements from the standpoint of error correction. Historically, the first way that this was accomplished was to encode the data (usually binary) by adding redundant symbols for error correction and use the encoded symbol stream to modulate the carrier. The ratio of information symbols to total encoded symbols is referred to as the code rate. At the receiver, demodulation was accomplished followed by decoding. The latter operation allowed some channel-induced errors to be corrected. Since redundant symbols are added at the transmitter, it is necessary to use a larger bandwidth by a factor of one over the code rate than if no encoding is employed in order to keep the same information rate through the channel with coding as without coding. Thus, the signal-to-noise ratio is smaller by a factor of the code rate with coding than without coding, and the raw bit error probability is higher. Since the code can correct errors, however, this compensates partially for the higher raw bit error probability to a degree. If the code is powerful enough, the overall bit error probability through the channel is lower with coding than without. Usually, this depends on the signal-to-noise ratio: for small values of the signal-to-noise ratio, the compensation by coding is not enough, and the overall bit error probability is higher with coding than without; for sufficiently large values of the signal-to-noise ratio, the error correction capability of the code more than makes up for the higher raw bit error probability, and a lower overall error probability is obtained. The coding gain is defined as the ratio of the signal-to-noise ratios without and with coding, or if they are expressed in decibels, the difference between the signal-to-noise ratios without and with coding, at a given bit error probability.
Overview of the challenges and solutions for 5G channel coding schemes
Published in Journal of Information and Telecommunication, 2021
Madhavsingh Indoonundon, Tulsi Pawan Fowdur
The next-generation mobile communication network, termed 5G, introduced several channel coding challenges. 5G networks will need to provide ultra-reliable and low latency communication (URLLC) which focuses on delay-sensitive applications and services. The physical layer design of URLLC is very challenging because URLLC should satisfy two conflicting requirements: ultra-low latency and ultra-high reliability (Zhang et al., 2018). One could use short packets to reduce latency which in turn causes a severe loss in coding gain. Currently, for the enhanced Mobile Broadband (eMBB) service category in 5G, LDPC codes and Polar codes have been adopted for the data channel and control channel respectively (Session Chairman, 2020). However, to meet all the requirements of the three service classes, multiple aspects of channel coding need to be thoroughly investigated.
Performance of Soft Viterbi Decoder enhanced with Non-Transmittable Codewords for storage media
Published in Cogent Engineering, 2018
Kilavo Hassan, Kisangiri Michael, Salehe I. Mrutu
There is a big challenge behind the error correction for the storage media due to higher demand of digital data of which most of them are stored on storage media. The demand for storage media devices is increasingly vast (Coughlin & Handy, 2008). Large file sizes requirement for high resolution and multi-camera images are among the reason for increasing demand of storage devices (Coughlin, 2015). Ensuring data reliability and quality of data from the storage media is one of the big challenges (Peters, Rabinowitz, & Jacobs, 2006). The demand for storage media increases every day and it is estimated that over 90% of all information and data produced in the world are stored on hard disk drives (Pinheiro, Weber, & Barroso, 2007). Majority of the people are not aware and interested in improving the Forward Error Correction codes for storage media; rather they are interested in improving backup systems and data recovery software (Hassan, Michael, & Mrutu, 2015). To prevent errors from causing data corruption in storage media, data can be protected with error correction codes. The Viterbi decoder was introduced by Andrew J. Viterbi in 1967 (Mousa, Taman, & Mahmoud, 2015; Sargent, Bimbot, & Vincent, 2011). Since then the researchers are tirelessly working to expand his work by finding better Viterbi decoder (Andruszkiewicz, Davis, & Lleo, 2014; Takano, Ishikawa, & Nakamura, 2015). The Viterbi algorithm allows a random number of the most possible sequences to be enumerated. It can be used to efficiently calculate the most likely path of the hidden state process (Cartea & Jaimungal, 2013; Titsias, Holmes, & Yau, 2016). Channel coding techniques in storage media are used to make the transmitted data robust against any impairment. The data in a storage media get corrupted by noise and can be recovered by using channel coding techniques (Cover & Thomas, 2012). The encoding technique can be either systematic encoding or non-systematic encoding. The comparison between channel codes can be done by looking on different metrics such as coding accuracy, coding efficiency and coding gain. The coding accuracy means a channel is strong and can usually recover corrupted data. The accuracy can be compared on how close the recovered data match with the original data which is measured by Bit Error Rate (BER) probabilities (Jiang, 2010). Coding efficiency means the code has relatively a small number of encoder bits per data symbol and this is defined in terms of code rate which is given by R = K/N, where K is an input symbol to each encoder and N is an output symbol from each encoder. Decreasing the redundant bits decreases the number of error per symbol that can correct/detect errors. Coding gain is the measure of the difference between signal to noise ratio (SNR) level between coded system and uncoded system that require reaching the same bit error rate (BER) levels.