Dynamic Resource Optimization Based on Flexible Numerology and Markov Decision Process for Heterogeneous Services The enhanced Mobile Broadband (eMBB) and ultraReliable Low Latency Communications (URLLC) are two main scenarios of 5G networks. There is an obvious difference in service requirements between the two different scenarios. When multiple heterogeneous services coexist in the network, it is important to explore optimal resource scheduling and allocation strategies. In this paper, we study the Quality of Service (QoS) optimization problem in eMBB and URLLC coexisting scenario. Considering the servicesu0027 characteristics of heterogeneity and dynamics, we first introduce the flexible numerology structure which defines a set of flexible transmission time interval (TTI) to satisfy different QoS requirements of heterogeneous services, and then, we formulate a Markov decision process (MDP)based dynamic resource optimization problem with the flexibilities of time and frequency domains. Next, we prove this optimization problem to be NPhard and propose an innovative joint scheduling strategy DRSA based on flexible numerology and deep reinforcement learning method to allocate dynamic resources. Through experiments, the flexible numerology significantly outperforms the nonflexible ones. Comparison experiments with Sequence, Greedy and Random strategies show that the average throughput of DRSA is 7.1%, 14.8% and 23.9% higher than them, and DRSA can reduce URLLC servicesu0027 loss rate by 43.7%, 28.6% and 53.8%.

Dynamic Multipath Resource Management for Ultra Reliable Low Latency Services One of the core novel features of 5G networks is the support of ultrareliable low latency communications (URLLC). This will pave the way for a plethora of mission critical applications in diverse sectors including health, security, gaming, and manufacturing, among others, through guaranteed premium grade reliability and delay; yet, this requires the development of innovative mechanisms that can address the existing challenges. In this work, we focus on the simultaneous utilization of multiple paths via different wireless interfaces and networks in order to deliver data from source to destination in a timely manner and with high reliability. We formulate the problem as a mixed integer program and generate an optimized strategy to efficiently utilize the resources among the different paths. Simulation results are presented for various scenarios to quantify performance gains with respect to other alternative approaches.