###### Abstract  
With the explosive growth of network traffic and the diversification of service demands, network slicing (NS) and dual connectivity (DC) are considered as promising technologies in wireless networks. In this paper, we propose a novel algorithm that solves the resource allocation problem of NS in heterogeneous networks with the assistance of DC, while satisfying the characteristic requirements of eMBB and URLLC services. Firstly, we model the scenario and formulate the optimization problem, which is proved as an NP-Hard problem. Secondly, due to the nonconvex and combinatorial nature, the dueling double deep Q-network with long short-term memory (LSTM-D3 QN) is proposed to solve this problem, aiming to improve the overall network utility, while ensuring the quality of experience (QoE). Then, we analyze the complexity of the algorithm. Finally, the simulation results show that the proposed algorithm can maximize the total utility of the system, while guaranteeing the user QoE. Compared with LSTM-A2C and DQN, the proposed algorithm improves the long-term network utility by 2.6% and 7.2%, respectively. In addition, compared with the algorithm without DC under the conditions of no priority, eMBB priority and URLLC priority, the proposed algorithm improves the network utility by 4.2%, 2.1% and 4.1%, respectively. Keywords:heterogeneous cellular network; network slicing; dual connectivity; dueling double deep Q-network (D3 QN); long short-term memory (LSTM); network utility +
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of resources, dual connectivity (DC) technology was proposed in Release 12 of the 3 GPP. Specifically, through carrier aggregation technology, UE can connect two different network interfaces to obtain more network resources. It not only improves the utilization of network resources, but also improves the network system throughput [8; 9]. Combining these two technologies can improve the utility of the network system, where the utility usually includes QoE, system throughput, spectral efficiency (SE), energy efficiency (EE), etc. In most of the literature, network utility is usually used as the total optimization objective. In this paper, network utility is the weighted sum of network throughput and QoE, minus the weighting of additional energy consumption caused by dual-connected users. Among these optimization metrics, QoE is always constrained by the delay and rate when users receive data packets. However, in 5G/6G systems, it is not only QoE that is greatly enhanced. Due to the role of the DC, the association handover mechanism, mobility management and power allocation strategy will be greatly changed. There have been many studies published on the application of NS and DC in recent years. However, most studies only consider network slicing or DC, and do not consider combining network slicing and DC to provide users with better services. In terms of algorithms for solving the resource allocation problems, deep reinforcement learning (DRL) has great advantages in solving large-scale complex problems [10]. Taking the above factors into consideration, in this paper, the optimization problem of DC-assisted slice resource allocation in the downlink of heterogeneous networks is studied, and an optimization algorithm based on DRL is proposed to obtain a satisfactory resource allocation strategy. In summary, the main contributions of this paper are as follows:  
1. Firstly, a heterogeneous network scenario, containing two typical network slices, is considered. In this scenario, DC is used to solve the problem of low QoE for users due to less network resources in SBSs. Then, an optimization problem with the total system utility weighted by system throughput, QoE and additional system energy is proposed and we prove that the problem is an NP-Hard problem. 2. Secondly, considering the nonconvexity and combinatorial nature of the optimization problem, we propose a D3 QN-based slicing resource allocation algorithm and design state, action and reward for the algorithm.