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Journal articles on the topic 'Bit rate'

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Published: 4 June 2021

Last updated: 1 June 2024

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1

Eroǧul, Osman, Hakki Gökhan İlk, and Özlem İlk. "A flexible bit rate switching method for low bit rate vocoders." Signal Processing 81, no. 8 (August 2001): 1737–42. http://dx.doi.org/10.1016/s0165-1684(01)00072-x.

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2

Reed, E. C., and F. Dufaux. "Constrained bit-rate control for very low bit-rate streaming-video applications." IEEE Transactions on Circuits and Systems for Video Technology 11, no. 7 (July 2001): 882–89. http://dx.doi.org/10.1109/76.931115.

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3

Chen, Qunlin, Derong Chen, Jiulu Gong, and Jie Ruan. "Low-Complexity Rate-Distortion Optimization of Sampling Rate and Bit-Depth for Compressed Sensing of Images." Entropy 22, no. 1 (January 20, 2020): 125. http://dx.doi.org/10.3390/e22010125.

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Compressed sensing (CS) offers a framework for image acquisition, which has excellent potential in image sampling and compression applications due to the sub-Nyquist sampling rate and low complexity. In engineering practices, the resulting CS samples are quantized by finite bits for transmission. In circumstances where the bit budget for image transmission is constrained, knowing how to choose the sampling rate and the number of bits per measurement (bit-depth) is essential for the quality of CS reconstruction. In this paper, we first present a bit-rate model that considers the compression performance of CS, quantification, and entropy coder. The bit-rate model reveals the relationship between bit rate, sampling rate, and bit-depth. Then, we propose a relative peak signal-to-noise ratio (PSNR) model for evaluating distortion, which reveals the relationship between relative PSNR, sampling rate, and bit-depth. Finally, the optimal sampling rate and bit-depth are determined based on the rate-distortion (RD) criteria with the bit-rate model and the relative PSNR model. The experimental results show that the actual bit rate obtained by the optimized sampling rate and bit-depth is very close to the target bit rate. Compared with the traditional CS coding method with a fixed sampling rate, the proposed method provides better rate-distortion performance, and the additional calculation amount amounts to less than 1%.

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4

Barannik, Vladimir, Yuriy Ryabukha, Pavlo Gurzhiy, Vitaliy Tverdokhlib, and Igor Shevchenko. "TRANSFORMANTS BIT REPRESENTATION ENCODING WITHIN VIDEO BIT RATE CONTROL." Information systems and technologies security, no. 1 (1) (2019): 52–56. http://dx.doi.org/10.17721/ists.2019.1.52-56.

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The conceptual basements of constructing an effective encoding method within the bit rate control module of video traffic in the video data processing system at the source level are considered. The essence of using the proposed method in the course of the video stream bit rate controlling disclosed, namely, the principles of constructing the fragment of the frame code representation and approaches for determining the structural units of the individual video frame within which the control is performed. The method focuses on processing the bit representation of the DCT transformants, and at his processing stage transformant was considered as a structural component of the video stream frame at which the encoding is performed. At the same time, to ensure the video traffic bit rate controlling flexibility, decomposition is performed with respect to each of the transformants to the level of the plurality of bit planes. It is argued that the proposed approach is potentially capable to reducing the video stream bit rate in the worst conditions, that is, when component coding is performed. In addition, this principle of video stream fragmen code representation forming allows to control the level of error that can be made in the bit rate control process. However, in conditions where the bit representation of the transformant is encoded, the method is able to provide higher compression rates as a result of the fact that the values of the detection probability of binary series lengths and the values of detected lengths within the bit plane will be greater than in the case of component coding. This is explained by the structural features of the distribution of binary elements within each of the bit planes, which together form the transformer DCT. In particular, high-frequency transformer regions are most often formed by chains of zero elements. The solutions proposed in the development of the encoding method are able to provide sufficient flexibility to control the bit rate of the video stream, as well as the ability to quickly change the bit rate in a wide range of values

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5

Sudaradjat, Djadjat, and Andi Rosano. "Low Bit-Rate Parametric Coding." Paradigma - Jurnal Komputer dan Informatika 24, no. 2 (September 2, 2022): 125–29. http://dx.doi.org/10.31294/paradigma.v24i2.1391.

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Abstrak - Dengan metoda pengkodean parametrik (parametric coding) sinyal suara manusia dapat menghasilkan kecepatan bit (bit-rate) yang lebih rendah dibandingkan dengan metoda pengkodean bentuk gelombang (wave form coding). Contohnya pada metoda pengkodean bentuk gelombang akan dibatasi oleh batas minimal frekuensi cuplik (sampling frequency) yang menurut teorema Shannon-Nyquist menyatakan agar tidak ada informasi yang hilang ketika pencuplikan sinyal, maka kecepatan pencuplikan harus minimal dua kali dari lebar pita sinyal tersebut. Dengan demikian pada pengkodean bentuk gelombang kecepatan bit terendah yang dapat dicapai adalah oleh system Modulasi Delta (Delta Modulation) yaitu sebesar 8 Kbit/detik. Sedangkan pada pengkodean parametrik sinyal dapat dicapai kecepatan bit yang lebih rendah yaitu sebesar 2,4 Kbit/detik oleh system LPC (Linear Predictive Coding). Dengan kecepatan bit yang rendah akan diperoleh penghematan penggunaan lebar pita (bandwidth) saluran transmisi dan memori.

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6

Prasanna, B. N. "Christening the digital bit rate." IEE Review 37, no. 4 (1991): 144. http://dx.doi.org/10.1049/ir:19910065.

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7

Amor, E. W., and Peter Fellgett. "Christening the digital bit rate." IEE Review 37, no. 6 (1991): 214. http://dx.doi.org/10.1049/ir:19910097.

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8

Flood, J. E. "Christening the digital bit rate." IEE Review 37, no. 9 (1991): 296. http://dx.doi.org/10.1049/ir:19910135.

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9

Henk, A. J. "Christening the digital bit rate." IEE Review 37, no. 9 (1991): 297. http://dx.doi.org/10.1049/ir:19910136.

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10

Ming Yang. "Low bit rate speech coding." IEEE Potentials 23, no. 4 (October 2004): 32–36. http://dx.doi.org/10.1109/mp.2004.1343228.

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11

Kozlov, Victor V., and Matthias Freyberger. "High-bit-rate quantum communication." Optics Communications 206, no. 4-6 (June 2002): 287–94. http://dx.doi.org/10.1016/s0030-4018(02)01456-6.

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12

Suthaharan, Shan, and Mrinal Mandal. "Low bit-rate multimedia communication." Real-Time Imaging 10, no. 5 (October 2004): 275–76. http://dx.doi.org/10.1016/j.rti.2004.09.001.

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13

Shan Bian, Weiqi Luo, and Jiwu Huang. "Exposing Fake Bit Rate Videos and Estimating Original Bit Rates." IEEE Transactions on Circuits and Systems for Video Technology 24, no. 12 (December 2014): 2144–54. http://dx.doi.org/10.1109/tcsvt.2014.2334031.

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14

Lawrance, Anthony J., and Gan Ohama. "Bit Error Probability and Bit Outage Rate in Chaos Communication." Circuits, Systems & Signal Processing 24, no. 5 (October 2005): 519–34. http://dx.doi.org/10.1007/s00034-005-2404-9.

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15

Assunção, P. A. A., and M. Ghanbari. "Optimal bit rate conversion of MPEG-2 video bit streams." Electronics Letters 33, no. 8 (1997): 675. http://dx.doi.org/10.1049/el:19970482.

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16

Fette, Bruce A. "Variable frame rate, fixed bit rate vocoding method." Journal of the Acoustical Society of America 89, no. 6 (June 1991): 3025. http://dx.doi.org/10.1121/1.400828.

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17

Chen, Qunlin, Derong Chen, and Jiulu Gong. "A General Rate-Distortion Optimization Method for Block Compressed Sensing of Images." Entropy 23, no. 10 (October 16, 2021): 1354. http://dx.doi.org/10.3390/e23101354.

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Block compressed sensing (BCS) is a promising technology for image sampling and compression for resource-constrained applications, but it needs to balance the sampling rate and quantization bit-depth for a bit-rate constraint. In this paper, we summarize the commonly used CS quantization frameworks into a unified framework, and a new bit-rate model and a model of the optimal bit-depth are proposed for the unified CS framework. The proposed bit-rate model reveals the relationship between the bit-rate, sampling rate, and bit-depth based on the information entropy of generalized Gaussian distribution. The optimal bit-depth model can predict the optimal bit-depth of CS measurements at a given bit-rate. Then, we propose a general algorithm for choosing sampling rate and bit-depth based on the proposed models. Experimental results show that the proposed algorithm achieves near-optimal rate-distortion performance for the uniform quantization framework and predictive quantization framework in BCS.

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18

KINJO, Shigenori, and Shuichi OHNO. "An Adaptive Bit Allocation for Maximum Bit-Rate Tomlinson-Harashima Precoding." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E102.A, no. 10 (October 1, 2019): 1438–42. http://dx.doi.org/10.1587/transfun.e102.a.1438.

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19

Huang, Fei, Jaeyong Lee, and Seoung-Jun Oh. "Video Content-Based Bit Rate Estimation." Journal of Broadcast Engineering 18, no. 2 (March 30, 2013): 297–310. http://dx.doi.org/10.5909/jbe.2013.18.2.297.

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20

Aldababsa, Mahmoud, Caner Goztepe, Gunes Karabulut Kurt, and Oguz Kucur. "Bit Error Rate for NOMA Network." IEEE Communications Letters 24, no. 6 (June 2020): 1188–91. http://dx.doi.org/10.1109/lcomm.2020.2981024.

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21

Chen, S. "Adaptive minimum bit-error-rate filtering." IEE Proceedings - Vision, Image, and Signal Processing 151, no. 1 (2004): 76. http://dx.doi.org/10.1049/ip-vis:20040301.

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22

Olano, M., D. Baker, W. Griffin, and J. Barczak. "Variable Bit Rate GPU Texture Decompression." Computer Graphics Forum 30, no. 4 (June 2011): 1299–308. http://dx.doi.org/10.1111/j.1467-8659.2011.01989.x.

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23

Dedes, I. S., D. R. Vaman, and C. V. Chakravarthy. "Variable bit rate adaptive predictive coder." IEEE Transactions on Signal Processing 40, no. 3 (March 1992): 511–17. http://dx.doi.org/10.1109/78.120794.

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24

Baker, G. "High-bit-rate digital subscriber lines." Electronics & Communications Engineering Journal 5, no. 5 (1993): 279. http://dx.doi.org/10.1049/ecej:19930053.

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25

Vutukuru, Mythili, Hari Balakrishnan, and Kyle Jamieson. "Cross-layer wireless bit rate adaptation." ACM SIGCOMM Computer Communication Review 39, no. 4 (August 16, 2009): 3–14. http://dx.doi.org/10.1145/1594977.1592571.

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26

Crossman, Antony Henry. "Fixed bit rate speech encoder/decoder." Journal of the Acoustical Society of America 103, no. 3 (March 1998): 1249. http://dx.doi.org/10.1121/1.423210.

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27

Chen, S., N. N. Ahmad, and L. Hanzo. "Adaptive minimum bit-error rate beamforming." IEEE Transactions on Wireless Communications 4, no. 2 (March 2005): 341–48. http://dx.doi.org/10.1109/twc.2004.842981.

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28

Keesman, Gertjan, Imran Shah, and Rene Klein-Gunnewiek. "Bit-rate control for MPEG encoders." Signal Processing: Image Communication 6, no. 6 (February 1995): 545–60. http://dx.doi.org/10.1016/0923-5965(94)00038-k.

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29

Qin, Yuan, Juan Fernandez-de-Castro, and Jim Giusti. "Off-track bit error rate modeling." Journal of Applied Physics 85, no. 8 (April 15, 1999): 4985–87. http://dx.doi.org/10.1063/1.370066.

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30

Barford, L. "Sequential Bayesian Bit Error Rate Measurement." IEEE Transactions on Instrumentation and Measurement 53, no. 4 (August 2004): 947–54. http://dx.doi.org/10.1109/tim.2004.831129.

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31

BERGMAN, LARRY A., J. PATRICK GARY, BURT EDELSEN, NEIL HELM, JUDITH COHEN, PATRICK SHOPBELL, C. ROBERTO MECHOSO, CHUNG-CHUN, M. FARRARA, and JOSEPH SPAHR. "HIGH BIT RATE EXPERIMENTS OVER ACTS." International Journal of Satellite Communications 14, no. 3 (May 1996): 259–66. http://dx.doi.org/10.1002/(sici)1099-1247(199605)14:3<259::aid-sat540>3.0.co;2-y.

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32

Kuroda, Hideo, Makoto Fujimura, Yukihiro Nataochi, and Masaaki Ikehara. "Variable bit rate video coding method." Electronics and Communications in Japan (Part I: Communications) 76, no. 4 (1993): 35–46. http://dx.doi.org/10.1002/ecja.4410760404.

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33

Del Rosario, Juan Miguel, and Geoffrey Fox. "Constant bit rate network transmission of variable bit rate continuous media in Video-On-Demand servers." Multimedia Tools and Applications 2, no. 3 (May 1996): 215–32. http://dx.doi.org/10.1007/bf00455019.

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34

Abdul-Rani, A. M., M. Zamri Ismail, M. Ariff Zaky, M. Hariz M. Noor, Y. Y. Zhun, K. Ganesan, T. V. V. L. N. Rao, Subhash Kamal, and Turnad Lenggo Ginta. "Improving Rate of Penetration for PDC Drill Bit Using Reverse Engineering." Applied Mechanics and Materials 607 (July 2014): 153–60. http://dx.doi.org/10.4028/www.scientific.net/amm.607.153.

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In petroleum industry, drilling is one of the most important aspects due to its economics. Reduction in drilling time is desired to minimize operations cost. This work focus on Polycrystalline Diamond Compact (PDC) drill bit which is categorized as a fixed cutter drilling bit. Problem such as wear and tear of PDC cutter are some of the main factors in drilling process failure affecting the rate of penetration (RoP). Thus, an intensive study in drill bit design could potentially save costs if the drill bit efficiency can be improved. The objective of this research is to improve the PDC cutter design and analyse design improvement in relation to the rate of penetration using reverse engineering (RE) approach. RE method is capable of resolving unavailable drill bit blueprint from the manufacturer due to propriety and confidential. RE non-contact data acquisition device, 3D laser scanner will be used to obtain cloud data of the existing worn drill bit. Computer Aided Design (CAD) software is used to convert cloud data of the PDC drill bit into 3D CAD model. Optimization of PDC Drill bit is focused on feature design such as back rake angle, side rake angle and number of cutters. CAE software is used to analyse the effect of the design feature modification to rate of penetration. Results show rate of penetration increases as the angle of both rake angle and number of cutter decreases.

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35

Adebayo, B., and W. A. Bello. "Property Analysis for Correlation of Specific Energy with Penetration Rate and Bit Wear Rate." Advanced Materials Research 367 (October 2011): 547–53. http://dx.doi.org/10.4028/www.scientific.net/amr.367.547.

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This study evaluates rock properties for correlation of specific energy with penetration rate and bit wear rate. In order to achieve these objectives five rock samples were obtained from the study area. These samples were tested in the laboratory for uniaxial compressive strength and tensile strength using 1100kN compression machine and point load tester respectively. Also, the mineral composition of the samples was determined by thin section examination. Bit deterioration was measured with digital vernier calliper at regular intervals. The specific energy was determined from field data using empirical equations. The results of the uniaxial compressive strength of the five rock samples varied from 165-320 MPa and were classified as having very high compressive strength characteristics. The point load strength index of the samples had values ranging from 5.50 – 10.67 MPa representing the tensile strength. The result of the statistical correlation matrix revealed that penetration rate and bit wear rate are dominant factors affecting the prediction of specific energy having high coefficient of correlation. The regression model had multiple coefficient of correlation of R2 = 0.893 which means that 89.3% of variation in specific energy could be attributed to variation in penetration rate and bit wear rate. Ultimately, computer programme DRILLING PROFESSIONAL 2009 was developed to compute penetration rate, wear rate and specific energy when necessary inputs are supplied. This gives quarry operators advance information on time of drilling and bit consumption.

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36

Rezaei, M., M. M. Hannuksela, and M. Gabbouj. "Semi-Fuzzy Rate Controller for Variable Bit Rate Video." IEEE Transactions on Circuits and Systems for Video Technology 18, no. 5 (May 2008): 633–45. http://dx.doi.org/10.1109/tcsvt.2008.919108.

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37

Kwang-Deok Seo, Sang-Hee Lee, Jae-Kyoon Kim, and Jong-Seog Koh. "Rate control algorithm for fast bit-rate conversion transcoding." IEEE Transactions on Consumer Electronics 46, no. 4 (2000): 1128–36. http://dx.doi.org/10.1109/30.920469.

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38

Liang-Jin Lin and A. Ortega. "Bit-rate control using piecewise approximated rate-distortion characteristics." IEEE Transactions on Circuits and Systems for Video Technology 8, no. 4 (1998): 446–59. http://dx.doi.org/10.1109/76.709411.

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39

Suhir, E. "Burn-in-Testing (BIT) Challenge: to BIT or not to BIT?" International Symposium on Microelectronics 2021, no. 1 (October 1, 2021): 000031–38. http://dx.doi.org/10.4071/1085-8024-2021.1.000031.

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Abstract Burn-in testing (BIT) is a costly undertaking. Predictive modeling enables shading useful light on what and how should be tested, if at all. Three analytical (“mathematical”) predictive models recently suggested by the author are addressed in this mini-review: 1) A model based on the analysis of the infant mortality portion (IMP) of the bathtub curve (BTC) suggests that the non-random time derivative of the expected “statistical” failure rate (SFR) at the beginning of the IMP could be viewed as a suitable criterion (“figure of merit”) to answer the basic question of the BIT undertaking: “to BIT or not to BIT?” 2) A model based on the analysis of the random failure rate (RFR) of the mass-produced components that the manufactured product of interest is comprised of suggests that the above derivative is, in effect, the RFR variance of these components. 3) A model based on the use of the kinetic multi-parametric Boltzmann-Arrhenius-Zhurkov (BAZ) constitutive equation is employed to establish the BIT's adequate duration and level, if this kind of failure-oriented-accelerated-testing (FOAT) is found to be necessary. The theoretical findings are illustrated by calculated data. It is concluded that predictive modeling should always precede the actual BIT, that analytical modeling should always complement computer simulations and that future work should address the experimental validation and possible extension of the obtained results and recommendations.

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40

Wang, Peng, Cui Ni, Zhe Li, and Guangyuan Zhang. "Optimal CTU-level bit allocation in HEVC for low bit-rate applications." Multimedia Tools and Applications 78, no. 16 (May 9, 2019): 23733–47. http://dx.doi.org/10.1007/s11042-019-7680-7.

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41

Kumar, Thota Vinay, Garlapati Charanreddy, and Pulgan Lokanadh Reddy. "Low Bit Rate Image Compression Using Filtering, Bit-Plane Coding And Prefiltering." IOSR Journal of Electronics and Communication Engineering 11, no. 05 (May 2016): 91–96. http://dx.doi.org/10.9790/2834-1105019196.

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42

Raffy, P., M. Antonini, and M. Barlaud. "Optimal subband bit allocation procedure for very low bit rate image coding." Electronics Letters 34, no. 7 (1998): 647. http://dx.doi.org/10.1049/el:19980419.

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43

Gardikis, George. "Quantitative perceptual comparison of variable bit rate over constant bit rate encoding scheme for MPEG-4 video." Journal of Electronic Imaging 16, no. 3 (July 1, 2007): 033017. http://dx.doi.org/10.1117/1.2775465.

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44

Xiong, Jiang, Qing Ming Yi, and Min Shi. "Research and Design of AVS Video Decoder Bit Rate Control." Applied Mechanics and Materials 291-294 (February 2013): 2913–16. http://dx.doi.org/10.4028/www.scientific.net/amm.291-294.2913.

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In common video compression coding technology, data with changing bit rate is not suitable for transmission in fixed bit rate channel. A bit rate control algorithm and a hardware design suitable for AVS decoder are proposed in this paper. The target bits are calculated and allocated in three levels, including GOP, frame and MB. The proposed hardware design passes the simulation verification. The result of synthesis in DC proves that the design meets timing requirements. The design is easy to realize and suitable for controlling the bit rate in real-time encoding.

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45

SHUKLA, PRATIBHA, and Akhilesh A. Waoo. "Bitter Rate of Performance in Wireless Communications on 5G Technology." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 12 (December 23, 2023): 1–13. http://dx.doi.org/10.55041/ijsrem27705.

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Wireless communications have become an integral part of our modern society, enabling seamless connectivity and data exchange. The bit rate of performance in wireless communication systems plays a crucial role in determining the quality of service and user experience. This abstract provides an overview of key factors influencing bit rate in wireless communications and highlights the challenges and advancements in optimizing performance. The bit rate, often measured in bits per second (bps) or multiples thereof, quantifies the rate at which data can be transmitted over a wireless channel. Several factors impact the bit rate in wireless communications, including the available bandwidth, modulation schemes, signal-to- noise ratio (SNR), and channel conditions. The bit rate is closely related to the achievable data throughput and is a critical metric for assessing the efficiency of wireless networks. Wireless technologies, such as 4G and 5G, have significantly increased the bit rates achievable for mobile communication. These technologies employ advanced modulation techniques and multiple-input, multiple- output (MIMO) systems to enhance spectral efficiency and data rates. Additionally, the deployment of small cells and the use of high-frequency bands have improved data rates and reduced latency in wireless networks. However, challenges persist in optimizing bit rates for wireless communications. Factors like signal interference, fading, and path loss can degrade performance. Moreover, the ever- increasing demand for data-intensive applications and the proliferation of IoT devices put additional pressure on wireless networks to deliver higher bit rates with low latency. To address these challenges, ongoing research focuses on developing advanced signal processing algorithms, beamforming techniques, and error correction mechanisms. Machine learning and artificial intelligence are also being employed to optimize wireless communication systems and enhance bit rates.

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46

Yang, Wei Ming, Jian Zhang, and Jin Xiang Peng. "Binomial Bit-Rate Computation Model Based on Wireless Channel." Applied Mechanics and Materials 241-244 (December 2012): 2482–86. http://dx.doi.org/10.4028/www.scientific.net/amm.241-244.2482.

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For the encoding bit-rate problem in H.264 wireless video communication, the bit-rate computation model and the standard deviation distortion model were analyzed to establish the relation between the quantization parameter of encoding bit-rate and the intra-frame refresh rate of macroblocks, a new proposal of the coding rate thus put forward based on the general binomial computation model theory. Furthermore, this method not only can adaptively adjust the bit allocation and quantization parameters to prevent buffer from overflowing downward or upward under given network bandwidth, but also can apply the rate-distortion to perfect the solution method, control the encoding bits accurately and optimize the allocation between the inter-frame encoding macroblocks.

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47

Barannik, Volodymyr, Yuriy Ryabukha, Pavlo Hurzhii, Vitalii Tverdokhlib, and Oleh Kulitsa. "TRANSFORMANTS CODING TECHNOLOGY IN THE CONTROL SYSTEM OF VIDEO STREAMS BIT RATE." Cybersecurity: Education, Science, Technique 3, no. 7 (2020): 63–71. http://dx.doi.org/10.28925/2663-4023.2020.7.6371.

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The conceptual basements of constructing an effective encoding method within the bit rate control module of video traffic in the video data processing system at the source level are considered. The essence of using the proposed method in the course of the video stream bit rate controlling disclosed, namely, the principles of constructing the fragment of the frame code representation and approaches for determining the structural units of the individual video frame within which the control is performed. The method focuses on processing the bit representation of the DCT transformants, and at this processing stage transformant was considered as a structural component of the video stream frame at which the encoding is performed. At the same time, to ensure the video traffic bit rate controlling flexibility, decomposition is performed with respect to each of the transformants to the level of the plurality of bit planes. It is argued that the proposed approach is potentially capable to reducing the video stream bit rate in the worst conditions, that is, when component coding is performed. In addition, this principle of video stream fragmen code representation forming allows to control the level of error that can be made in the bit rate control process. However, in conditions where the bit representation of the transformant is encoded, the method is able to provide higher compression rates as a result of the fact that the values of the detection probability of binary series lengths and the values of detected lengths within the bit plane will be greater than in the case of component coding. This is explained by the structural features of the distribution of binary elements within each of the bit planes, which together form the transformer DCT. In particular, high-frequency transformer regions are most often formed by chains of zero elements. The solutions proposed in the development of the encoding method are able to provide sufficient flexibility to control the bit rate of the video stream, as well as the ability to quickly change the bit rate in a wide range of values.

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48

Maiga, Ali, Jean-Yves Baudais, and Jean-François Hélard. "Bit Rate Optimization with MMSE Detector for Multicast LP-OFDM Systems." Journal of Electrical and Computer Engineering 2012 (2012): 1–12. http://dx.doi.org/10.1155/2012/232797.

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We propose a new resource allocation algorithm with minimum mean square error (MMSE) detector for multicast linear precoded orthogonal frequency division multiplexing (LP-OFDM) systems. To increase the total multicast bit rate, this algorithm jointly uses the LP-OFDM modulation technique and an adaptation of the OFDM-based multicast approaches to exploit the transmission link diversities of users. The LP technique applied to multicast OFDM systems with zero forcing (ZF) detector has already proved its ability to increase the unirate multicast system bit rate in a power line communication (PLC) context. The new MMSE detector and the new related bit-loading algorithm are developed to enhance the ZF detector results. To improve both the bit rate and the fairness among multicast users, the utilization of the LP component in multirate multicast systems is then investigated. Simulations are run over indoor PLC channels, and it is shown that the proposed LP-based methods outperform the OFDM-based methods in terms of total bit rate and fairness index for both unirate and multirate multicast systems. Additionally, it is shown that the proposed bit-loading algorithm with MMSE detector outperforms the ZF detector and the OFDM-based receiver in terms of total multicast bit rate and fairness among users.

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M., Jahangir Alam S., M. Rabiul Alam, Hu Guoqing, and Md Zakirul Mehrab. "Improvement of Bit Error Rate in Fiber Optic Communications." International Journal of Future Computer and Communication 3, no. 4 (2014): 281–86. http://dx.doi.org/10.7763/ijfcc.2014.v3.312.

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TIAN, Yishu, Qiang SHEN, Yanwei LIU, Yu ZHANG, and Zhijun ZHAO. "Average bit-rate algorithm optimization for rate control of X264." Journal of Computer Applications 33, no. 3 (September 25, 2013): 680–83. http://dx.doi.org/10.3724/sp.j.1087.2013.00680.

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