Energy Efficient Resource Allocation in Machine-to-Machine Communications with Multiple Access and Energy Harvesting for IoT
- Submitting institution
-
Queen Mary University of London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 490
- Type
- D - Journal article
- DOI
-
10.1109/JIOT.2017.2778766
- Title of journal
- IEEE Internet of Things Journal
- Article number
- -
- First page
- 229
- Volume
- 5
- Issue
- 1
- ISSN
- 2327-4662
- Open access status
- Deposit exception
- Month of publication
- February
- Year of publication
- 2018
- URL
-
-
- 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)
-
-
- Citation count
- 77
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper first studied the energy efficient resource allocation for a machine-to-machine network under non-orthogonal multiple access (NOMA) and time division multiple access (TDMA). This represented the outcome of international collaboration with SEU, China (Wei Xu, wxu.seu@gmail.com) and EPSRC project (EP/N029666, £ 541 K, 2016). Work led to invited talk to Huawei staff members (Stephen Wang: stephen.wang1@huawei.com, 2018). Invited talk at Imperial college workshop on NOMA (Bruno: b.clerckx@imperial.ac.uk, 2019). This work also led to the author being a co-organizer of a workshop in IEEE ICC 2020, one of two flagship conferences in wireless communication area (https://icc2020.ieee-icc.org/workshop/ws-14-workshop-edge-machine-learning-5g-mobile-networks-and-beyond).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -