  Partially-overlapping tones (POT) are known to help mitigate co-channel
interference in uncoordinated multi-carrier networks by introducing intentional
frequency offsets (FOs) to the transmitted signals. In this paper, we explore
the use of (POT) with reinforcement learning (RL) in dense networks where
multiple links access time-frequency resources simultaneously. We propose a
novel framework based on Q-learning, to obtain the (FO) for the multi-carrier
waveform used for each link. In particular, we consider filtered multi-tone
(FMT) systems that utilize Gaussian, root-raised-cosine (RRC), and isotropic
orthogonal transform algorithm (IOTA) based prototype filters. Our simulation
results show that the proposed scheme enhances the capacity of the links by at
least 30\% in additive white Gaussian noise (AWGN) channel at high
signal-to-noise ratio (SNR), and even more so in the presence of severe
multi-path fading. For a wide range of interfering link densities, we
demonstrate substantial improvements in the outage probability and multi-user
efficiency facilitated by (POT), with the Gaussian filter outperforming the
other two filters.
