  We develop a framework based on deep reinforce-ment learning (DRL) to solve
the spectrum allocation problem inthe emerging integrated access and backhaul
(IAB) architecturewith large scale deployment and dynamic environment. The
avail-able spectrum is divided into several orthogonal sub-channels,and the
donor base station (DBS) and all IAB nodes have thesame spectrum resource for
allocation, where a DBS utilizes thosesub-channels for access links of
associated user equipment (UE)as well as for backhaul links of associated IAB
nodes, and anIAB node can utilize all for its associated UEs. This is one ofkey
features in which 5G differs from traditional settings wherethe backhaul
networks were designed independently from theaccess networks. With the goal of
maximizing the sum log-rateof all UE groups, we formulate the spectrum
allocation probleminto a mix-integer and non-linear programming. However, itis
intractable to find an optimal solution especially when theIAB network is large
and time-varying. To tackle this problem,we propose to use the latest DRL
method by integrating anactor-critic spectrum allocation (ACSA) scheme and deep
neuralnetwork (DNN) to achieve real-time spectrum allocation indifferent
scenarios. The proposed methods are evaluated throughnumerical simulations and
show promising results compared withsome baseline allocation policies.
