  The 6G network enables a subnetwork-wide evolution, resulting in a "network
of subnetworks". However, due to the dynamic mobility of wireless subnetworks,
the data transmission of intra-subnetwork and inter-subnetwork will inevitably
interfere with each other, which poses a great challenge to radio resource
management. Moreover, most of the existing approaches require the instantaneous
channel gain between subnetworks, which are usually difficult to be collected.
To tackle these issues, in this paper we propose a novel effective intelligent
radio resource management method using multi-agent deep reinforcement learning
(MARL), which only needs the sum of received power, named received signal
strength indicator (RSSI), on each channel instead of channel gains. However,
to directly separate individual interference from RSSI is an almost impossible
thing. To this end, we further propose a novel MARL architecture, named GA-Net,
which integrates a hard attention layer to model the importance distribution of
inter-subnetwork relationships based on RSSI and exclude the impact of
unrelated subnetworks, and employs a graph attention network with a multi-head
attention layer to exact the features and calculate their weights that will
impact individual throughput. Experimental results prove that our proposed
framework significantly outperforms both traditional and MARL-based methods in
various aspects.
