## 1 Introduction  
With the emergence of novel smart applications such as smart homes, online education, HD live streaming, etc., both downlink and uplink traffic demands fluctuate dramatically [1]. Furthermore, the utilization of downlink and uplink is affected by the greater path loss of the NR band with higher frequency [2] and a low uplink to downlink duration ratio as a common used configuration in the time-division-duplex (TDD) system [3]. Especially, the uplink channel quality of cell edge user equipments (UEs) will deteriorate severely due to the inter-cell interference and a low uplink transmission power, resulting in the uplink coverage being shrinked compared to the downlink coverage. To better meet the diverse quality-of-service (QoS) of novel services, 5G NR (5 th Generation New Radio) enables several promising features for more flexible radio resource allocation, such as network slicing [4], dynamic TDD, Supplementary Uplink (SUL) [5], etc. The combination of various features needs to be studied to further improve the QoS satisfaction rate. Firstly, network slicing is a basic service form oriented to vertical industries in the 5G era, enabling the flexible allocation of network resources. For example, a flexible soft spectrum slicing based on a predictive approach was studied under asymmetric traffic conditions [6], and a dynamic radio resource slicing framework was proposed for QoS, guaranteed by the joint optimization of band slicing ratio and UE association for a two-tier heterogeneous wireless network [7]. A LSTM-A2C architecture is proposed to track user mobility and to improve the utility of network slice resource allocation [8]. Another LSTM-DQN architecture is proposed to improve the performance during the initial exploration phase [9]. Secondly, dynamic TDD can provide more flexibility to accommodate the downlink and uplink traffic demands between different cells, and then improve the QoS of downlink and uplink [10]. For instance, a dynamic TDD frame structure adaptation scheme was proposed to control the cross-link interference among cells [11]. Deep reinforcement learning (DRL)-based TDD pattern adaptation schemes were proposed according to the downlink and uplink buffer status, as well as the mobility behaviors [12; 13]. Thirdly, 3 rd Generation Partnership Project (3 GPP) R15 introduced the SUL in the Sub-3G [5; 14], which is a different method of spectrum configuration compared with NR UL. 5G NR introduces downlink and uplink decoupling technology, which can transfer the cell edge UEs' uplink transmission to the SUL band. Moreover, SUL is the term for both the frequency division duplexing (FDD) UL band and an uplink enhancement technology with many advantages: a larger coverage area, more uplink spectrum resources, a lower delay, and lower deployment costs [3; 15; 16], and as a newly defined frequency band, it can also make the existing dual connectivity and carrier aggregation achieve higher performances. Ref. [17] discussed two deployment modes, which are independent SUL deployment and SUL and NR FDD co-deployment. An LTE load-based downlink and uplink decoupling mode are discussed in [18]. Furthermore, [PERSON]. [19] discussed the UEs' correlation probability, and impacts on throughput in two-layer heterogeneous networks were discussed. To sum up, all of the above three methods can meet UEs' QoS and throughput requirements by means of resource allocation, and then inevitably interact with each other. However, the current research rarely jointly considers these three methods' effects. It is necessary to develop intelligent and dynamic resource allocation to maximize the QoS satisfaction rate, especially with network slicing and SUL configuration [20]. For this purpose, we propose a novel QoS guaranteed joint resource allocation framework for NR with SUL (QGJRA-SUL), which enables a joint optimization of the dynamic SUL admission strategy and the dynamic TDD pattern, as well as the band slicing scheme in terms of bandwidth. The main contributions of this paper are listed below.