A Novel Access Point Deployment Framework for mmWave Cell-Free Massive MIMO Networks

Abstract

Millimeter-wave network deployment is an essential and ongoing problem due to the limited coverage and expensive network infrastructure. In this work, we solve a joint network deployment and resource allocation optimization problem for a mmWave cell-free massive MIMO network considering indoor environments. The objective is to minimize the number of deployed access points (APs) for a given environment, bandwidth, AP cooperation, and precoding scheme while guaranteeing the rate requirements of the user equipments (UEs). Considering coherent joint transmission (C-JT) and non-coherent joint transmission (NC-JT), we solve the problem of AP placement, UE-AP association, and power allocation among the UEs and resource blocks jointly. For numerical analysis, we model a mid-sized airplane cabin in ray-tracing as an exemplary case for IDS. Results demonstrate that a minimum data rate of 1Gbps can be guaranteed with less than 10 APs with C-JT. From a holistic network design perspective, we analyze the trade-off between the required fronthaul capacity and the processing capacity per AP, under different network functional split options. We observe an above 600Gbps fronthaul rate requirement, once all network operations are centralized, which can be reduced to 200Gbps under physical layer functional splits.EU Horizon 2020-Electronics Components and Systems for European Leadership (ECSEL) Joint Undertaking (JU) project Beyond5; EU Horizon 2020 Research and Innovation Programme; Vinnova, Swedish Innovations Agency [876124]; Swedish funding agency VinnovaThis work was supported in part by the EU Horizon 2020-Electronics Components and Systems for European Leadership (ECSEL) Joint Undertaking (JU) project Beyond5 (Building the fully European supplY chain on RFSOI, enabling New RF Domains for Sensing, Communication, 5G and beyond). The Beyond5 project is funded by EU Horizon 2020 Research and Innovation Programme and Vinnova, Swedish Innovations Agency under Grant 876124. This study was conducted partly under Eureka Celtic project RAI-6Green: Robust and AI Native 6G for Green Networks funded by Swedish funding agency Vinnova