Reference : Completion Time Minimization in NOMA Systems:Learning for Combinatorial Optimization
Scientific journals : Article
Engineering, computing & technology : Electrical & electronics engineering
http://hdl.handle.net/10993/45730
Completion Time Minimization in NOMA Systems:Learning for Combinatorial Optimization
English
Wang, Anyue mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom >]
Lei, Lei mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom >]
Lagunas, Eva mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom >]
Chatzinotas, Symeon mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom >]
Ottersten, Björn mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
19-Jan-2021
IEEE Networking Letters
Yes
International
2576-3156
[en] NOMA ; Deep Learning ; Resource Optimization ; mixed-integer exponential conic programming
[en] In this letter, we study a completion-time minimization problem by jointly optimizing time slots (TSs) and power allocation for time-critical non-orthogonal multiple access (NOMA) systems. The original problem is non-linear/non-convex with discrete variables, leading to high computational complexity in conventional iterative methods. Towards an efficient solution, we train deep neural networks to perform fast and high-accuracy predictions to tackle the difficult combinatorial parts, i.e., determining the minimum consumed TSs and user-TS allocation. Based on the learning-based predictions, we develop a low-complexity post-process procedure to provide feasible power allocation. The numerical results demonstrate promising improvements of the proposed scheme compared to other baseline schemes in terms of computational efficiency, approximating optimum, and feasibility guarantee.
Researchers ; Professionals ; Students ; General public ; Others
http://hdl.handle.net/10993/45730
10.1109/LNET.2021.3052891
https://ieeexplore-ieee-org.proxy.bnl.lu/stamp/stamp.jsp?tp=&arnumber=9328471
FnR ; FNR11632107 > Lei Lei > ROSETTA > Resource Optimization For Integrated Satellite-5g Networks With Non-orthogonal Multiple Access > 01/09/2018 > 31/08/2021 > 2017

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