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

Deep Reinforcement Learning Based Cross-Layer Design in Terahertz Mesh Backhaul Networks