Spatiotemporal Modeling of Massive MIMO Systems with Mixed-Type IoT Devices: Scheduling Optimization with Delay Constraints

Qi Zhang, Howard H. Yang, Tony Q.S. Quek, Shi Jin

Research output: Contribution to journalArticlepeer-review


In this paper, we develop a framework for the analysis of massive multiple-input multiple-output (MIMO) systems where multiple types of devices with different configurations and requirements co-exist, by taking into account the randomness of spatial locations and temporal traffic. A tight closed-form approximation of the spatial mean packet throughput which denotes the average number of packets that are successfully transmitted at any unit time slot and area is derived, by using tools from stochastic geometry and queuing theory, which captures all the key features of the devices in Internet of Things (IoT). Based on the analysis, we investigate the optimal scheduling number for each type of devices that maximizes the spatial mean packet throughput while meeting devices’ delay constraints. It is found that when the BS has excessive number of antennas (M), the BS should schedule all devices under its coverage, regardless of devices’ variances on spatiotemporal configurations and demands. However, when M is limited, the BS should have a bias on scheduling devices with heavier traffic, lower decoding threshold, or higher transmit power. On this basis, if the delay constraint of one device becomes stricter, it will be scheduled more often to access the radio channel, which acts more significantly when the ratio of M to the deployment density of devices gets smaller.

Original languageEnglish
JournalIEEE Internet of Things Journal
Publication statusAccepted/In press - 2021

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Information Systems
  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications

Fingerprint Dive into the research topics of 'Spatiotemporal Modeling of Massive MIMO Systems with Mixed-Type IoT Devices: Scheduling Optimization with Delay Constraints'. Together they form a unique fingerprint.

Cite this