# RIS-Assisted Network Slicing Resource Optimization Algorithm for Coexistence of eMBB and URLLC
###### Abstract  
Enhanced mobile broadband (eMBB) and Ultra-Reliable and Low-Latency Communications (URLLC) are the two main services in the fifth-generation mobile network. URLLC services have stringent latency and reliability requirements, while eMBB services are designed to provide extremely high data rates for content delivery. The differentiated Quality of Service (QoS) requirements of the two services make their coexistence on the same bandwidth resources a challenging issue. To meet this challenge, we introduce reconfigurable intelligent surface (RIS) technology to assist in solving the resource allocation problem. The problem is formulated as two optimization problems. For the eMBB allocation problem, we jointly optimize the resource block allocation, power allocation, and RIS phase shift matrix and propose a two-stage alternating iterative algorithm to maximize the total traversal capacity of eMBB users. For the URLLC allocation problem, we maximize the URLLC packet reception rate and minimize the amount of eMBB rate loss while ensuring the quality of service for eMBB and URLLC. A heuristic URLLC allocation algorithm based on pre-configured RIS is proposed. Simulation results show that the proposed algorithm can meet the strict delay requirements of URLLC even when the URLLC packet reception rate is nearly 95.5%. Meanwhile, the total loss rate of eMBB service caused by the reuse of URLLC service is less than 6%. Thus, the applicability of the proposed solution to the coexistence problem is demonstrated. URLLC; eMBB; Network Slicing; reconfigurable intelligent surface; 5G and beyond +
Footnote †: journal: _Electronics_  
followed by penalty continuous upper bound minimization algorithms and transmission models. In addition, many scholars have used deep reinforcement learning (DRL) to solve resource allocation and optimization problems in cellular networks [8; 9; 10; 11], such as deep deterministic policy-based gradient algorithms and deep Q-learning algorithms. The channel quality for eMBB and URLLC users in coexistence scenarios directly determines their performance, i.e., data rate, reliability, and latency. The above methods can show good performance when the channel conditions are good. However, when faced with a complex wireless communication environment, they do not accurately derive the terminal rates based on the actual puncture states and channel conditions of the two services. Reconfigurable intelligent surface (RIS) has attracted much academic attention as a possible paradigm in future wireless networks. Its ability to control and configure the wireless propagation environment stands out. RIS can effectively solve the problems of high energy consumption of relay communication and the severe difficulty of building 5G base stations, so the study of this technology is introduced for 6G mobile communication development [12]. In existing research, RIS has converged with several communication frontier technologies [13], including multiple-input multiple-output (MIMO), non-orthogonal multiple access (NOMA), mobile edge computing (MEC), and unmanned aerial vehicle (UAV) technologies. Given the ability of RIS to actively modify the wireless communication environment, numerous studies have investigated RIS-assisted wireless networks for the eMBB and URLLC services. The literature [14; 15; 16; 17] studied only single service class application scenarios, i.e., only eMBB or URLLC services. [PERSON] et al. [14] verified that RIS-assisted MIMO systems could achieve the same performance as large-scale MIMO systems (without RIS assistance) while significantly reducing the active antenna and RF chain. [PERSON] et al. [15] used UAV-assisted short data URLLC as an example. With the passive beam forming function of the RIS unit and direct search method, the UAV's optimal location and block length are determined in the theoretical model, and the total decoding BER is reduced. [PERSON] et al. [16] proposed an authorization-free access scheme assisted by RIS to enhance the reliability of URLLC. [PERSON] et al.