TY - GEN
T1 - Link Rate Selection using Constrained Thompson Sampling
AU - Gupta, Harsh
AU - Eryilmaz, Atilla
AU - Srikant, R.
N1 - Funding Information:
This research has been supported in part by NSF grants: NeTS 1718203, CMMI 1562276, ECCS 16-09370, CNS-NeTS-1514260, CNS-NeTS-1717045, CMMI-SMOR-1562065, CNS-ICN-WEN-1719371, and CNSSpecEES-1824337, the DTRA grants: HDTRA1-15-1-0003, and HDTRA1-18-1-0050 and ARO Grant W911NF-16-1-0259.
Funding Information:
This research has been supported in part by NSF grants: NeTS 1718203, CMMI 1562276, ECCS 16-09370, CNS-NeTS-1514260, CNS-NeTS-1717045, CMMI-SMOR-1562065, CNS-ICN-WEN-1719371, and CNS-SpecEES-1824337, the DTRA grants: HDTRA1-15-1-0003, and HDTRA1-18-1-0050 and ARO Grant W911NF-16-1-0259.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/4
Y1 - 2019/4
N2 - We consider the optimal link rate selection problem in time-varying wireless channels with unknown channel statistics. The aim of optimal link rate selection is to transmit at the optimal rate at each time slot in order to maximize the expected throughput of the wireless channel/link or equivalently minimize the expected regret. Lack of information about channel state or channel statistics necessitates the use of online/sequential learning algorithms to determine the optimal rate. We present an algorithm called CoTS - Constrained Thompson sampling algorithm which improves upon the current state-of-the-art, is fast and is also general in the sense that it can handle several different constraints in the problem with the same algorithm. We also prove an asymptotic lower bound on the expected regret and a high probability large-horizon upper bound on the regret, which show that the regret grows logarithmically with time in an order sense. We also provide numerical results which establish that CoTS significantly outperforms the current state-of-the-art algorithms.
AB - We consider the optimal link rate selection problem in time-varying wireless channels with unknown channel statistics. The aim of optimal link rate selection is to transmit at the optimal rate at each time slot in order to maximize the expected throughput of the wireless channel/link or equivalently minimize the expected regret. Lack of information about channel state or channel statistics necessitates the use of online/sequential learning algorithms to determine the optimal rate. We present an algorithm called CoTS - Constrained Thompson sampling algorithm which improves upon the current state-of-the-art, is fast and is also general in the sense that it can handle several different constraints in the problem with the same algorithm. We also prove an asymptotic lower bound on the expected regret and a high probability large-horizon upper bound on the regret, which show that the regret grows logarithmically with time in an order sense. We also provide numerical results which establish that CoTS significantly outperforms the current state-of-the-art algorithms.
KW - Constrained Thompson sampling
KW - Optimal link rate selection
KW - Regret minimization
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U2 - 10.1109/INFOCOM.2019.8737610
DO - 10.1109/INFOCOM.2019.8737610
M3 - Conference contribution
AN - SCOPUS:85068205684
T3 - Proceedings - IEEE INFOCOM
SP - 739
EP - 747
BT - INFOCOM 2019 - IEEE Conference on Computer Communications
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE Conference on Computer Communications, INFOCOM 2019
Y2 - 29 April 2019 through 2 May 2019
ER -