Research On Resource Allocation Method For Multi-User Ultra-Reliable And Low-Latency Communication System

The construction of infrastructure in wireless communication networks and the explosive growth of mobile devices has put forward stricter requirements for the delay and reliability of data transmission by users.As one of the three major application scenarios of the fifth generation(5G)mobile communication,the ultra-reliable and low-latency communication(URLLC)aims to ensure high reliability and low latency performance requirements.The essential targets in URLLC communication system have to achieve 99.999% reliability and1 ms latency.URLLC derives the time sensitive applications which require high communication reliability,such as the industrial internet and telemedicine.Therefore,keep the transmission requirements of URLLC,and optimizing resource allocation to improve the performance of the industrial Internet is an interesting direction.In order to solve the problem of reliable multi-user communication based on 5G technology,and meanwhile consider the multiple-input multiple-output(MIMO)technology can greatly improve system performance.This paper studies the performance of linear receivers in multiuser URLLC uplink systems based on MIMO technology which aims to satisfy the requirements of 5G URLLC communication systems.Firstly,the MIMO uplink system supporting short packet transmission is constructed,which is configured on the base station with matched filter(MF)and zero forcing(ZF)receivers.Secondly,the analytical expressions of the decoding error probability of two receivers are derived.Then,the performance gap of two linear receivers are analyzed in the uplink URLLC system.Finally,the Monte-Carlo method simulates and verifies the proposed analytical expression.The results show that the proposed analytical expression is accurate and feasible.In addition,configuring both of receivers in URLLC systems with high signal-to-noise ratios can meet the reliability performance of URLLC service,and the reliability performance of the ZF receiver outperforms the MF receiver.Deep reinforcement learning(DRL)has been introduced into wireless communication systems.In particular,it is able to simulate wireless communication system environments and optimize wireless resource allocation.Due to the dynamic of wireless communication systems,resource allocation for URLLC communication systems is a challenge.This paper investigates a resource allocation problem aiming at allocating the blocklength and power of the subcarrier to maximize the sum rate,which ensures the performance of URLLC.This paper proposes the resource optimization method based on DRL,which aims to jointly optimize the blocklength and subcarrier transmit power to maximize the transmission rate.The non-convex optimization problem is decomposed into a multi-agent reinforcement learning process,and each subcarrier works as the agent to decide its own power and blocklength.Based on the proposed optimization method,intelligent configuration of wireless resources is realized.Finally,build the wireless communication system model,and verify the effectiveness and convergence of the proposed optimization methods.The simulation results demonstrate that the proposed DQN optimization method outperforms the benchmark scheme of Q-learning in terms of the effectiveness and convergence.In addition,the proposed method outperforms the benchmark scheme in terms of sum-rate performance under different settings of system environments,and can meet the transmission requirements of URLLC services in industrial Internet scenarios.