Massive multiple-input multiple-output (MIMO) is one of the main candidates to be included in the fifth generation (5G) cellular systems. For further system development it is desirable to have real-time testbeds showing possibilities and limitations of the technology. In this paper we describe the Lund University Massive MIMO testbed -LuMaMi. It is a flexible testbed where the base station operates with up to 100 coherent radio-frequency transceiver chains based on software radio technology. Orthogonal Frequency Division Multiplex (OFDM) based signaling is used for each of the 10 simultaneous users served in the 20 MHz bandwidth. Real time MIMO precoding and decoding is distributed across 50 Xilinx Kintex-7 FPGAs with PCI-Express interconnects. The unique features of this system are: (i) high throughput processing of 384 Gbps of real time baseband data in both the transmit and receive directions, (ii) low-latency architecture with channel estimate to precoder turnaround of less than 500 micro seconds, and (iii) a flexible extension up to 128 antennas. We detail the design goals of the testbed, discuss the signaling and system architecture, and show initial measured results for a uplink Massive MIMO over-the-air transmission from four single-antenna UEs to 100 BS antennas.

This paper sets up a framework for designing a massive multiple-input multiple-output (MIMO) testbed by investigating hardware (HW) and system-level requirements such as processing complexity, duplexing mode and frame structure. Taking these into account, a generic system and processing partitioning is proposed which allows flexible scaling and processing distribution onto a multitude of physically separated devices. Based on the given HW constraints such as maximum number of links and maximum throughput for peer-to-peer interconnections combined with processing capabilities, the framework allows to evaluate modular HW components. To verify our design approach, we present the LuMaMi (Lund University Massive MIMO) testbed which constitutes the first reconfigurable realtime HW platform for prototyping massive MIMO. Utilizing up to 100 base station antennas and more than 50 Field Programmable Gate Arrays, up to 12 user equipments are served on the same time/frequency resource using an LTElike Orthogonal Frequency Division Multiplexing time-division duplex-based transmission scheme. Proof-of-concept tests with this system show that massive MIMO can simultaneously serve a multitude of users in a static indoor and static outdoor environment utilizing the same time/frequency resource.

This paper provides an initial investigation on the application of convolutional neural networks (CNNs) for fingerprint-based positioning using measured massive MIMO channels. When represented in appropriate domains, massive MIMO channels have a sparse structure which can be efficiently learned by CNNs for positioning purposes. We evaluate the positioning accuracy of state-of-the-art CNNs with channel fingerprints generated from a channel model with a rich clustered structure: the COST 2100 channel model. We find that moderately deep CNNs can achieve fractional-wavelength positioning accuracies, provided that an enough representative data set is available for training.