Title
CBMoS: Combinatorial Bandit Learning for Mode Selection and Resource Allocation in D2D Systems
Abstract
The complexity of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mode selection and resource allocation (MS&amp;RA)</italic> problem has hampered the commercialization progress of Device-to-Device (D2D) communication in 5G networks. Furthermore, the combinatorial nature of MS&RA has forced the majority of existing proposals to focus on constrained scenarios or offline solutions to contain the size of the problem. Given the real-time constraints in actual deployments, a reduction in computational complexity is necessary. Adaptability is another key requirement for mobile networks that are exposed to constant changes such as channel quality fluctuations and mobility. In this article, we propose an online learning technique (i.e., CBMoS) which leverages combinatorial multi-armed bandits (CMAB) to tackle the combinatorial nature of MS&RA. Furthermore, our two-stage CMAB design results in a tight model, which eliminates the theoretically feasible but practicality invalid options from the solution space. We prototype the first SDR-based D2D testbed to verify the performance of CBMoS under real-world conditions. The simulations confirm that the fast learning speed of CBMoS leads to outperforming the benchmark schemes by up to 132%. In experiments, CBMoS exhibits even higher performance (up to 142%) than in the simulations. This stems from the adaptability/fast learning speed of CBMoS in presence of high channel dynamics which cannot be captured via statistical channel models used in the simulators.
Year
DOI
Venue
2019
10.1109/JSAC.2019.2933764
IEEE Journal on Selected Areas in Communications
Keywords
Field
DocType
Device-to-device communication,Resource management,Interference,Wireless fidelity,Throughput,Computational complexity
Adaptability,Resource management,Mathematical optimization,Computer science,Testbed,Communication channel,Real-time computing,Resource allocation,Interference (wave propagation),Throughput,Computational complexity theory
Journal
Volume
Issue
ISSN
37
10
0733-8716
Citations 
PageRank 
References 
2
0.36
0
Authors
5
Name
Order
Citations
PageRank
Andrea Ortiz1274.68
Arash Asadi2718.16
Max Engelhardt320.36
Anja Klein4396.91
Matthias Hollick575097.29