A Scalable 256-Antenna Distributed MIMO Testbed with Real-Time Fully Digital Beamforming

Distributed massive MIMO (D-MIMO) is a promising technology for future generation wireless systems as it takes advantage of both an increased array aperture and a decentralized processing architecture and topology. In order to truly understand the possibilities and limitations of these approaches in real scenarios, practical realization of testbeds is an essential step in the technology advancement. This work presents the Lund University Large Intelligent Surface testbed -- LuLIS, that can operate up to 256 coherent radio frequency (RF) chains using 16 AMD Zynq UltraScale RFSoC ZCU216 evaluation boards acting as distributed processing nodes. Real-time processing is facilitated by acceleration and distribution of MIMO processing algorithms on the FPGA fabric of the boards. The system is easily scalable, as increasing the number of antennas is done in multiples of 16 by adding more RFSoCs, which also implies addition of another processing node. The design allows up-scaling without hardware redesign, introduction of large latencies or data transfer overhead. The testbed is flexible in terms of deployment, with options of fully distributing the nodes (as in D-MIMO) or co-locating them (as in more traditional Massive MIMO). A detailed description of the implementation of the testbed is presented and initial results are shown for an uplink (UL) transmission from four single-antenna user equipments (UEs) to 64, 128 and 256 base-station antennas.

Accurate Direct Positioning in Distributed MIMO Using Delay-Doppler Channel Measurements

Distributed multiple-input multiple-output (D-MIMO) is a promising technology for simultaneous communication and positioning. However, phase synchronization between multiple access points in D-MIMO is challenging, which requires methods that function without the need for phase synchronization. We therefore present a method for D-MIMO that performs direct positioning of a moving device based on the delay-Doppler characteristics of the channel state information (CSI). Our method relies on particle-filter-based Bayesian inference with a state-space model. We use recent measurements from a sub-6 GHz D-MIMO OFDM system in an industrial environment to demonstrate centimeter accuracy under partial line-of-sight (LoS) conditions and decimeter accuracy under full non-LoS.