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pro vyhledávání: '"Abbas, Syed Mohsin"'
GRAND features both soft-input and hard-input variants that are well suited to efficient hardware implementations that can be characterized with achievable average and worst-case decoding latency. This paper introduces step-GRAND, a soft-input varian
Externí odkaz:
http://arxiv.org/abs/2307.07133
Publikováno v:
GLOBECOM 2022 Workshops
Guessing Random Additive Noise Decoding (GRAND) is a code-agnostic decoding technique for short-length and high-rate channel codes. GRAND tries to guess the channel noise by generating test error patterns (TEPs), and the sequence of the TEPs is the m
Externí odkaz:
http://arxiv.org/abs/2205.00030
Autor:
Batool, Rida, Sahrish, Ahad, Syed Abdul, Maqsood, Quratulain, Ali, Shinawar Waseem, Abbas, Syed Mohsin
Publikováno v:
In Cleaner Water June 2024 1
Autor:
Abbas, Syed Mohsin, Tonnellier, Thibaud, Ercan, Furkan, Jalaleddine, Marwan, Gross, Warren J.
Publikováno v:
IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 2022
Ultra-reliable low-latency communication (URLLC), a major 5G New-Radio use case, is the key enabler for applications with strict reliability and latency requirements. These applications necessitate the use of short-length and high-rate codes. Guessin
Externí odkaz:
http://arxiv.org/abs/2110.13776
Publikováno v:
IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 2022
Guessing Random Additive Noise Decoding (GRAND) is a recently proposed universal Maximum Likelihood (ML) decoder for short-length and high-rate linear block-codes. Soft-GRAND (SGRAND) is a prominent soft-input GRAND variant, outperforming the other G
Externí odkaz:
http://arxiv.org/abs/2109.12225
Guessing Random Additive Noise Decoding (GRAND) is a recently proposed Maximum Likelihood (ML) decoding technique. Irrespective of the structure of the error correcting code, GRAND tries to guess the noise that corrupted the codeword in order to deco
Externí odkaz:
http://arxiv.org/abs/2108.12563
Autor:
Abbas, Syed Mohsin, Tonnellier, Thibaud, Ercan, Furkan, Jalaleddine, Marwan, Gross, Warren J.
Guessing Random Additive Noise Decoding (GRAND) is a recently proposed approximate Maximum Likelihood (ML) decoding technique that can decode any linear error-correcting block code. Ordered Reliability Bits GRAND (ORBGRAND) is a powerful variant of G
Externí odkaz:
http://arxiv.org/abs/2105.07115
Autor:
Zubair, Muhammad, Shafqat, Ayesha, Jabben, Nadia, Shafiq, Muhammad, Balal, Rashad Mukhtar, Tahir, Mukkaram Ali, Hashmi, Muhammad Muneeb, Naqvi, Syed Armaghan Abbas, Ali, Numan, Abbas, Syed Mohsin, Shahid, Muhammad Adnan, Korany, Shereen M., Alsherif, Emad A., Alomrani, Sarah Owdah
Publikováno v:
In Scientia Horticulturae 15 January 2024 324
Guessing Random Additive Noise Decoding (GRAND) is a recently proposed universal decoding algorithm for linear error correcting codes. Since GRAND does not depend on the structure of the code, it can be used for any code encountered in contemporary c
Externí odkaz:
http://arxiv.org/abs/2007.07328
Autor:
Khurshid, Nouman, Bukhari, Muhammad Adnan, Ahmad, Tanveer, Ahmad, Zahoor, Jatoi, Wajid Nasim, Abbas, Syed Mohsin, Latif, Amir, Raza, Amber, Aurangzaib, Muhammad, Hashem, Abeer, Avila-Quezada, Graciela Dolores, Abd_Allah, Elsayed Fathi
Publikováno v:
In Ecotoxicology and Environmental Safety 15 September 2023 263