A Resource-Efficient Coexistence Scheme for Massive Machine-Type and Human-to-Human Communications

The fifth-generation (5G) and beyond networks are expected to accommodate both the original human-to-human (H2H) communication and the emerging massive machine-type communication (mMTC). To enable a harmonious coexistence between the two different types of services, we propose a resource-efficient mMTC/H2H coexistence scheme by jointly considering the random access (RA) and data transmission, where the entire uplink resources are divided for the proposed RA and data transmission procedures. Based on the proposed scheme, we derive the average achievable throughput of the bursty mMTC service and develop a time-nonhomogeneous Markov chain model to characterize the joint state transition of H2H user equipments (HUEs). To tackle the cumbersome Markov model, we approximately decompose the constructed time-nonhomogeneous Markov model into multiple independent Markov chains, where each decomposed Markov chain characterizes one single HUE's state transition. Then, the decomposed Markov model is transformed into a semi-Markov process and the corresponding steady-state condition is obtained based on the queueing network analysis for H2H service. By approximating the evolution of number of HUEs in different states as M/M/1 queues, we derive the stationary probabilities for the embedded Markov chain of the semi-Markov process and obtain the data transmission success probability of each HUE. Based on the abovementioned analytical framework, we formulate a constrained nonlinear integer programming (NLIP) problem to maximize the mMTC throughput under the constraints of H2H quality-of-service (QoS) stabilization and resource allocation. By adopting the modified particle swarm optimization (PSO) algorithm, we solve the formulated problem and obtain the efficient resource allocation strategy for the mMTC/H2H coexistence. Simulation results demonstrate that the developed analytical framework and modified PSO algorithm achieve close to the optimal mMTC/H2H coexisting performance and can be adapted to various network settings.