Article (Scientific journals)
Completion Time Minimization in NOMA Systems:Learning for Combinatorial Optimization
Wang, Anyue; Lei, Lei; Lagunas, Eva et al.
2021In IEEE Networking Letters
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Keywords :
NOMA; Deep Learning; Resource Optimization; mixed-integer exponential conic programming
Abstract :
[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.
Disciplines :
Electrical & electronics engineering
Author, co-author :
Wang, Anyue ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom
Lei, Lei ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom
Lagunas, Eva  ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom
Chatzinotas, Symeon  ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom
Ottersten, Björn ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
External co-authors :
no
Language :
English
Title :
Completion Time Minimization in NOMA Systems:Learning for Combinatorial Optimization
Publication date :
19 January 2021
Journal title :
IEEE Networking Letters
ISSN :
2576-3156
Publisher :
Institute of Electrical and Electronics Engineers (IEEE), Piscataway, United States - New Jersey
Peer reviewed :
Peer Reviewed verified by ORBi
FnR Project :
FNR11632107 - Resource Optimization For Integrated Satellite-5g Networks With Non-orthogonal Multiple Access, 2017 (01/09/2018-31/08/2021) - Lei Lei
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