Enhanced NOMA System Using Adaptive Coding and Modulation Based on LSTM Neural Network Channel Estimation
- Submitting institution
-
Staffordshire University
- Unit of assessment
- 12 - Engineering
- Output identifier
- 5891
- Type
- D - Journal article
- DOI
-
10.3390/app9153022
- Title of journal
- Applied Sciences
- Article number
- -
- First page
- 3022
- Volume
- 9
- Issue
- 15
- ISSN
- 2076-3417
- Open access status
- Compliant
- Month of publication
- July
- Year of publication
- 2019
- URL
-
https://www.mdpi.com/2076-3417/9/15/3022
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
4
- Research group(s)
-
B - Centre for Smart Systems, AI and Cybersecurity (CSSAIC)
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Non-orthogonal multiple access (NOMA) is required in 5G mobile networks to ensure users receive guaranteed resources and performance. Existing approaches to NOMA suffer challenges that can limit this performance. The significance of this paper is that it proposes algorithms to enhance system performance including improving outage probability, bit error rate. This research was funded by an Erasmus Plus International Credit Mobility (KA1) collaboration with the Arab Academy for Science & Technology, Egypt (AAST, Professor Mohamed El-Mahallawy). As a result of this collaboration AAST are funding one of their academics to undertake a PhD at Staffordshire University to continue the work.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -