Multi-user Detection Method For Large-scale Users Random Access

With the rapid advances in the Internet of Things technology,massive machine type communications will be utilised by various industries,including transportation,agriculture and industrial automation.These changes will significantly facilitate the development of society.The uplink grant-free Non-Orthogonal Multiple Access(NOMA)system can be used to solve the problems associated with massive user access of massive machine type communications by increasing the volume of user access and reducing the signalling load.In this paper,large-scale users refer to the fact that the number of users in a single cell exceeds the number of users of the orthogonal system content limit by more than twice.The traditional communication method is limited by the frequency resources in the system,which leads to a decrease in detection performance.A large amount of scheduling signaling reduces the utilization efficiency of communication resources.To reduce the scheduling overhead associated with massive user access in massive machine type communication,the uplink grant-free NOMA system jointly detects active users and their corresponding user information.In this system,the key issue is multi-user detection combined with compressed sensing,which is a hot research issue both in China and abroad.In this paper,we conduct relevant research on the problem of multi-user detection in large-scale users.The specific content is as follows:In this study,we first investigated the problem of multi-user detection in the large-scale user access system,i.e.uplink grant-free NOMA.To realise multi-user detection under the multi-slot structured sparse model,we used a reconstruction algorithm based on sparsely structured conjugate gradient blocks.Compared with existing reconstruction algorithms,the proposed algorithm uses time-domain correlation to improve detection performance.Next,since the channel coefficients in the large-scale user system are unknown,we developed a channel estimation method combined with a conjugate gradient.Compared with independent channel estimation,this method performs joint detection of channel information and user data to improve channel detection performance.Third,given that a sparsity of active users in the grant-free NOMA system is unknown,we studied the selection of adaptive termination conditions and presented anadaptive method based on second-order difference.Compared with other adaptive methods,our proposed method requires no other prior information and fully utilised the inherent structural information,which reduces the use of prior information.Finally,since random access or departure can occur in a large-scale user system,we considered the detection scheme used in the sparsity adaptive hybrid model.Using a public support set,we propose a detection algorithm that dynamically adjusts the confidence parameters,which improves the use of the public support set information in the independent detection by time slots,making it is more applicable for practical communication scenarios.This paper verifies the multi-user detection performance of the proposed algorithm through simulation.The results show that the proposed algorithm has a significant performance improvement and has broad application prospects in large-scale communication systems.