Grant-Free Random Access for Multicell Massive MIMO: Spatiotemporal Modeling with Collision Area

Grant-free random access (GFRA) becomes attractive in Internet-of-Things (IoT) due to its low signaling overhead. In this paper, we investigate the GFRA in a multicell massive multiple-input multiple-output (MIMO) system after considering both the spatial and temporal traffic of devices. By introducing the backoff mechanism, only devices with non-empty buffer and a successful backoff can request access. Unlike previous works on GFRA that regard all devices selecting the same pilot as undetectable, we set a unique collision area for each device to quantify the boundary that BS can detect the collision. With tools of stochastic geometry and queueing theory, we derive a tight approximation for the number of packets successfully transmitted at unit area and time slot, named as packet throughput (T-p). Based on it, we find that the range of the collision area has a remarkable effect on T-p. The optimal backoff parameter that maximizes T-p is also obtained, and we find that a long backoff time is needed when the pilot is insufficient or the packet traffic is heavy. Compared with the fully-loaded access, our optimal backoff mechanism can significantly improve T-p, especially for the system with crowded devices.