Radio Resource Allocation Optimization of Heterogeneous Services for New Radio in Downlink

5G networks enable enhanced Mobile Broadband (eMBB), Ultra-Reliable Low-Latency Communication (URLLC), and massive Machine-Type Communication (mMTC). The implementation problem is giving services with the necessary resources without affecting performance or usefulness. EMBB services require high data rates, URLLC low latency but high reliability, and mMTC low energy consumption with many connections. Two or more services on the same network infrastructure need optimal resource allocation. 4G resource allocation algorithms like round-robin and proportional fair can be utilized on 5G. This research uses sum-rate maximization to optimize a resource allocation algorithm for eMBB, URLLC, and mMTC services and presents the best multi-service slicing solution. The algorithm maximizes the system's data sum rate while accommodating constraints from the requirements of different services. This sum-rate maximization with an optimization approach is compared to round-robin and proportional fairness. Problem design, algorithm creation, numerical simulation, and performance analysis, i.e. data sum rate, average data sum rate, and spectrum efficiency, are the research areas. Numerical simulations show that the maximize sum-rate optimization algorithm has a better result in terms of sum-rate, average sum-rate, and spectral efficiency than round-robin and proportional fair.