1 research outputs found
Decentralized Learning for Channel Allocation in IoT Networks over Unlicensed Bandwidth as a Contextual Multi-player Multi-armed Bandit Game
We study a decentralized channel allocation problem in an ad-hoc Internet of
Things network underlaying on the spectrum licensed to a primary cellular
network. In the considered network, the impoverished channel sensing/probing
capability and computational resource on the IoT devices make them difficult to
acquire the detailed Channel State Information (CSI) for the shared multiple
channels. In practice, the unknown patterns of the primary users' transmission
activities and the time-varying CSI (e.g., due to small-scale fading or device
mobility) also cause stochastic changes in the channel quality. Decentralized
IoT links are thus expected to learn channel conditions online based on partial
observations, while acquiring no information about the channels that they are
not operating on. They also have to reach an efficient, collision-free solution
of channel allocation with limited coordination. Our study maps this problem
into a contextual multi-player, multi-armed bandit game, and proposes a purely
decentralized, three-stage policy learning algorithm through trial-and-error.
Theoretical analyses shows that the proposed scheme guarantees the IoT links to
jointly converge to the social optimal channel allocation with a sub-linear
(i.e., polylogarithmic) regret with respect to the operational time.
Simulations demonstrate that it strikes a good balance between efficiency and
network scalability when compared with the other state-of-the-art decentralized
bandit algorithms.Comment: 32 pages, 10 figures, submitted to IEEE TW