LDPC Decoding Algorithms for Implant to Implant Wireless Body Area Network | Zendy

Albashir Adel Youssef | Zendy; Bassant Abdelhamid | Zendy; Salwa Hussein El-Ramly | Zendy; Hussein M. Elattar | Zendy; Hazem Hassan Ali | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

LDPC Decoding Algorithms for Implant to Implant Wireless Body Area Network

Author(s) -

Albashir Adel Youssef,

Bassant Abdelhamid,

Salwa Hussein El-Ramly,

Hussein M. Elattar,

Hazem Hassan Ali

Publication year - 2018

Publication title -

ieee access

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.587

H-Index - 127

ISSN - 2169-3536

DOI - 10.1109/access.2018.2810293

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

Wireless body area network (WBAN) is a promising network aiming at enhancing the communication in medical applications. It is adopted by medical organizations due to its flexibility in remotely monitoring patient health status. WBANs suffer from many limitations due to excessive channel impairments. Low density parity check (LDPC) codes are proposed to mitigate WBAN's impairments concerning the bit error rate, complexity, and dissipated energy. In this paper, a comprehensive performance analysis of various LDPC decoding algorithms is used to improve communication and reduce complexity in implant to implant WBAN channel. Moreover, a novel low complex LDPC decoding algorithm, which has a performance close to soft decision and a decoding time close to hard decision algorithms is proposed to minimize the dissipated energy. The proposed algorithm can be classified as a hybrid decision algorithm. The results demonstrate extensive analysis and comparisons between hard, soft, and hybrid decision algorithms.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research