Future wireless networks are envisioned to serve massive Internet of things (mIoT) via some radio access technologies, where the random access channel (RACH) procedure should be exploited for IoT devices to access the networks. However, modelling of the dynamic process of RACH of massive IoT devices is challenging. To address this challenge, we first revisit the frame and minislot structure of the radio access network (RAN). Then, we correlate the RACH request of an IoT device with its queue status and analyze the evolution of the queue status. Based on the analysis result, we derive the closed-form expression of the random access (RA) success probability of the device. Besides, considering the agreement on converging different services onto a shared infrastructure, we investigate the RAN slicing for mIoT and bursty ultra-reliable and low latency communications (URLLC) service multiplexing. Specifically, we formulate the RAN slicing problem as an optimization one to maximize the total RA success probabilities of all IoT devices and provide URLLC services for URLLC devices in an energy-efficient way. A slice resource optimization (SRO) algorithm exploiting relaxation and approximation with provable tightness and error bound is then proposed to mitigate the optimization problem.

中文翻译：

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用于大规模物联网和突发 URLLC 服务复用的 RAN 切片：分析和优化

设想未来的无线网络通过一些无线电接入技术为大规模物联网 (mIoT) 提供服务，其中应利用随机接入信道 (RACH) 程序让物联网设备接入网络。然而，对海量物联网设备的 RACH 动态过程建模具有挑战性。为了应对这一挑战，我们首先重新审视无线接入网络 (RAN) 的帧和最小时隙结构。然后，我们将 IoT 设备的 RACH 请求与其队列状态相关联，并分析队列状态的演变。根据分析结果，我们推导出设备随机接入（RA）成功概率的闭式表达式。此外，考虑到将不同服务融合到共享基础设施上的协议，我们研究了用于 mIoT 和突发超可靠低延迟通信 (URLLC) 服务复用的 RAN 切片。具体来说，我们将 RAN 切片问题制定为优化问题，以最大化所有 IoT 设备的总 RA 成功概率，并以节能的方式为 URLLC 设备提供 URLLC 服务。然后提出了一种利用松弛和近似的切片资源优化 (SRO) 算法，该算法具有可证明的紧密性和误差界限，以缓解优化问题。