Towards Scalable Clustered URLLC IoT Network: Resource Allocation and Cooperation Scheduling for Reliability Enhancement

In this paper, towards enabling massive connectivity in the next generation ultra-reliable low latency communication (URLLC) Internet-of-Things (IoT) network, we investigate a scalable clustered network, where the user scheduling at the access point (AP) is completely replaced by the cooperation scheduling among clustered IoT users, in order to alleviate the overload at AP. In particular, while serving the clustered network, the AP simply broadcasts out all data for the whole network. Each clustered user attempts to decode the broadcast signal. Afterwards, cooperation retransmissions will be scheduled among users for compensating the overall transmission reliability. Considering limited energy and blocklength resources, we start with the cooperation case based on a cluster head and aim at fairly minimizing the maximum error probability among all users, while the resource allocation and cooperation scheduling are jointly designed. To deal with the inherent nonconvexity, we construct a tight convex approximation for the problem based on an arbitrary feasible point, which enables an iterative algorithm for constantly improving the solution until a convergence to a suboptimal. Next, to further exploit the high cooperation flexibility in clustered URLLC network, we extend the whole design to the case allowing arbitrary cooperation among users, i.e., the case without cluster head. Finally, simulation results validate the convergence of our proposed algorithms and highlight the reliability benefits over benchmarks. The impact of cluster head selection and the high cooperation flexibility of the case without cluster head are also illustrated.