Location-Informed Interference Suppression Precoding Methods for Distributed Massive MIMO Systems

The evolution of mobile networks towards user-centric cell-free distributed Massive MIMO configurations requires the development of novel signal processing techniques. More specifically, digital precoding algorithms have to be designed or adopted to enable distributed operation. Future deployments are expected to improve coexistence between cellular generations, and between mobile networks and incumbent services such as radar. In dense cell-free deployments, it might also not be possible to have full channel state information for all users at all antennas. To leverage location information in a dense deployment area, we suggest and investigate several algorithmic alterations on existing precoding methods, aimed at location-informed interference suppression, for usage in existing and emerging systems where user locations are known. The proposed algorithms are derived using a theoretical channel model and validated and numerically evaluated using an empirical dataset containing channel measurements from an indoor distributed Massive MIMO testbed. When dealing with measured CSI, the impact of the hardware, in addition to the location-based channel, needs to be compensated for. We propose a method to calibrate the hardware and achieve measurement-based evaluation of our location-based interference suppression algorithms. The results demonstrate that the proposed methods allow location-based interference suppression without explicit CSI knowledge at the transmitter, under certain realistic network conditions.