In this article, grant-free uplink communication from a large number of machine-type devices in cell-free massive MIMO networks is explored. A novel approach that leverages coded random access (CRA), on the device side, with combining of signals received at properly selected access points (APs) and cooperative successive interference cancelation (SIC), on the network side, is presented. Initially, an analytical framework based on stochastic geometry is developed to investigate performance of AP cooperation through signal combining under diverse AP cluster compositions. The potential gain from AP signal combining is then assessed by evaluating a genie-aided scheme, guiding the network in cluster selection for each active device. Subsequently, two practical AP selection algorithms that operate in grant-free conditions (i.e., do not require prior information regarding the active users) are proposed. Numerical results show how AP cooperation through signal combining and distributed interference cancelation can bring tangible benefits even without prior information about active users, under different signal-to-noise ratio regimes, closing in some cases the gap to the genie-aided approach. Additionally, the results prove that AP cooperation can be used to reduce the devices' energy consumption and the number of APs that have to be deployed by the service providers to achieve specific performance levels.
Access Point Cooperation Strategies for Coded Random Access in Cell-Free Massive MIMO
Tralli, Velio;
2024
Abstract
In this article, grant-free uplink communication from a large number of machine-type devices in cell-free massive MIMO networks is explored. A novel approach that leverages coded random access (CRA), on the device side, with combining of signals received at properly selected access points (APs) and cooperative successive interference cancelation (SIC), on the network side, is presented. Initially, an analytical framework based on stochastic geometry is developed to investigate performance of AP cooperation through signal combining under diverse AP cluster compositions. The potential gain from AP signal combining is then assessed by evaluating a genie-aided scheme, guiding the network in cluster selection for each active device. Subsequently, two practical AP selection algorithms that operate in grant-free conditions (i.e., do not require prior information regarding the active users) are proposed. Numerical results show how AP cooperation through signal combining and distributed interference cancelation can bring tangible benefits even without prior information about active users, under different signal-to-noise ratio regimes, closing in some cases the gap to the genie-aided approach. Additionally, the results prove that AP cooperation can be used to reduce the devices' energy consumption and the number of APs that have to be deployed by the service providers to achieve specific performance levels.I documenti in SFERA sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.