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Partially-overlapping tones (POT) are known to help mitigate co-channelinterference in uncoordinated multi-carrier networks by introducing intentionalfrequency offsets (FOs) to the transmitted signals. In this paper, we explorethe use of (POT) with reinforcement learning (RL) in dense networks wheremultiple links access time-frequency resources simultaneously. We propose anovel framework based on Q-learning, to obtain the (FO) for the multi-carrierwaveform used for each link. In particular, we consider filtered multi-tone(FMT) systems that utilize Gaussian, root-raised-cosine (RRC), and isotropicorthogonal transform algorithm (IOTA) based prototype filters. Our simulationresults show that the proposed scheme enhances the capacity of the links by atleast 30\% in additive white Gaussian noise (AWGN) channel at highsignal-to-noise ratio (SNR), and even more so in the presence of severemulti-path fading. For a wide range of interfering link densities, wedemonstrate substantial improvements in the outage probability and multi-userefficiency facilitated by (POT), with the Gaussian filter outperforming theother two filters.

RL-Based Interference Mitigation in Uncoordinated Networks with Partially Overlapping Tones