# Joint User-Slice Pairing and Association Framework Based on H-NOMA in RAN Slicing
### Related Work  
Wireless networks emphasize resource slicing to increase spectrum utilization from NS problems in HetNet, by including user association and resource allocation. Furthermore, most literature saw NS's constraint-isolated resource allocation as an issue. In [15] Earliest Deadline First (EDF) scheduling, originally used in real-time operating systems, is exploited for radio resource allocation in RAN slicing for the first time. Multiplexing the traffic from URLLC and eMBB users constitutes a significant challenge in 5G, hence it has been tackled several times in the literature very recently. Diverse QoS demands, in addition to the spectrum scarcity problem call for innovative approaches in addressing the resource management problem. In [16], a communication-theoretic model involving non-orthogonal sharing of RAN resources in uplink network slicing is studied with the three types of heterogeneous services such as eMBB, uRLLC, and mMTC, and the authors refer the approach to as H-NOMA. For details, see [17]; NOMA is an excellent choice for 5G services that have great spectrum efficiency (eMBB), comprehensive device connectivity (mMTC), and low transmission latency (uRLLC). The objective [18] is to simplify SIC while improving its value and utility for UE. In [19; 20], a comprehensive survey of different candidate NOMA schemes for 5G is presented. In [21], rate-splitting multiple access (RSMA) is used for uRLLC transmission, where a uRLLC device separates its message into two sub-messages depending on the average signal-to-noise ratio (SNR) without immediate channel status information (CSI). The researchers investigated uRLLC and eMBB coexistence under a puncturing approach in multiple-input multiple-output (MIMO) NOMA systems by dividing the original issue into two sub-problems: user selection and power allocation in [22]. This article investigated the coexistence of eMBB and uRLLC services inside a cellular network with a reconfigurable intelligent surface (RIS) [23]. In [24], an enhanced Pre-emptive Scheduling (EPS) scheduler for joint URLLC, and eMBB traffic is proposed to extract the maximum possible eMBB ergodic capacity. In [25], eMBB and uRLLC communications were encoded in the cloud and cracked at the edge nodes to fulfil latency constraints in a Cloud radio access network (C-RAN) scenario. According to [26], eMBB transmission risk and uRLLC dependability are proposed as risk metrics for eMBB transmission to ensure a fair, proportionate allocation of resources to incoming uRLLC traffic. uRLLC scheduling is based on Deep Reinforcement Learning (DRL)-based learning to allocate traffic and optimize eMBB data rate and dependability [27]. This research uses linear, convex, and threshold eMBB rate loss to increase network resource usage and allocate the uRLLC traffic [28]. In [29], the authors overcome the co-channel problem of eMBB and uRLLC users by puncturing to improve the eMBB UE rate using a one-sided matching game. In [30], using a homogenous NOMA per slice in uplink and downlink with fairness enhances system sum-throughput for all users. The multi-connectivity (MC)-based strategy increases resources and minimizes wait times in [31] by spectrum sharing across various base stations and differential QoS for data and machine-to-machine (M2M) services in a dynamic network request. There are many research efforts for better user association schemes suitable in NOMA for HetNets. In [32] power domain NOMA (PD-NOMA), game theory algorithms are proposed based on the QoS threshold to improve user association and power allocation. This work is presented by considering various case studies to demonstrate the effectiveness of PD-NOMA in ultra-dense networks. In [33], the unified PD-NOMA is utilized for user association in a dense heterogeneous network and improved user association, the overall system capacity, and throughput.