  We consider a cellular network where mobile transceiver devices that are
owned by self-interested users are incentivized to cooperate with each other
using tokens, which they exchange electronically to "buy" and "sell" downlink
relay services, thereby increasing the network's capacity compared to a network
that only supports base station-to-device (B2D) communications. We investigate
how an individual device in the network can learn its optimal cooperation
policy online, which it uses to decide whether or not to provide downlink relay
services for other devices in exchange for tokens. We propose a supervised
learning algorithm that devices can deploy to learn their optimal cooperation
strategies online given their experienced network environment. We then
systematically evaluate the learning algorithm in various deployment scenarios.
Our simulation results suggest that devices have the greatest incentive to
cooperate when the network contains (i) many devices with high energy budgets
for relaying, (ii) many highly mobile users (e.g., users in motor vehicles),
and (iii) neither too few nor too many tokens. Additionally, within the token
system, self-interested devices can effectively learn to cooperate online, and
achieve over 20% higher throughput on average than with B2D communications
alone, all while selfishly maximizing their own utilities.
