  Supporting ultra-high data rates and flexible reconfigurability, Terahertz
(THz) mesh networks are attractive for next-generation wireless backhaul
systems that empower the integrated access and backhaul (IAB). In THz mesh
backhaul networks, the efficient cross-layer routing and long-term resource
allocation is yet an open problem due to dynamic traffic demands as well as
possible link failures caused by the high directivity and high
non-line-of-sight (NLoS) path loss of THz spectrum. In addition, unpredictable
data traffic and the mixed integer programming property with the NP-hard nature
further challenge the effective routing and long-term resource allocation
design. In this paper, a deep reinforcement learning (DRL) based cross-layer
design in THz mesh backhaul networks (DEFLECT) is proposed, by considering
dynamic traffic demands and possible sudden link failures. In DEFLECT, a
heuristic routing metric is first devised to facilitate resource efficiency
(RE) enhancement regarding energy and sub-array usages. Furthermore, a DRL
based resource allocation algorithm is developed to realize long-term RE
maximization and fast recovery from broken links. Specifically in the DRL
method, the exploited multi-task structure cooperatively benefits joint power
and sub-array allocation. Additionally, the leveraged hierarchical architecture
realizes tailored resource allocation for each base station and learned
knowledge transfer for fast recovery. Simulation results show that DEFLECT
routing consumes less resource, compared to the minimal hop-count metric.
Moreover, unlike conventional DRL methods causing packet loss and second-level
latency, DEFLECT DRL realizes the long-term RE maximization with no packet loss
and millisecond-level latency, and recovers resource-efficient backhaul from
broken links within 1s.

Machine Learning