Current research has focused on testbeds with colocated radios connected to a single antenna array. However, Massive MIMO systems with noncentralized elements offer significant advantages. By distributing antennas or remote radio heads, greater spatial diversity is achieved, potentially enhancing the benefits of MIMO. There are also geometric benefits. Especially at lower frequencies, fitting over 100 antennas into a single array is difficult. Distributed Massive MIMO enables more convenient antenna placement, such as spreading them among multiple buildings. Additionally, distributed baseband units could be used to increase the number of potential use cases.

Mobility is more difficult to achieve in Massive MIMO setups than in conventional wireless systems. Since Massive MIMO relies on up-to-date channel state information to pinpoint each user via beamforming, the shorter channel coherence time of a mobile user may prevent accurate beamforming. The channel coherence time is dependent on the speed of the user device, and the faster the device movies, the more often the channel state information must be updated. In other words, a user equipment (UE) could move faster than the beam could update or track. This is especially challenging in time division duplex setups. Work is needed to determine the physical layer modifications required to ensure Massive MIMO robustly supports high-mobility applications such as vehicular-based UEs.

Most Massive MIMO work has been conducted using patch antennas or omnidirectional dipoles arranged in either uniform linear arrays or 2D planar arrays with consistent spacing. Though this may be ideal for traditional beamforming applications, it’s not clear which antenna patterns provide the best Massive MIMO performance. Antenna geometry, such as curved and distributed arrays, or the greater use of directional antennas could yield significant improvements in performance.

Digital beamforming enables the full potential of Multi-User MIMO, but separate transceiver chains for each antenna in the system can be expensive and energy intensive. Hybrid beamforming aims to not only preserve the benefits of digital beamforming by using multiple digital transceivers but also improve cost and power consumption by using an analog front-end antenna array with multiple elements to perform beam steering. Further research will help develop algorithms, characterize performance, and optimize energy use of a hybrid beamforming approach and compare those results with existing methods.

Massive MIMO should limit radiated power significantly, but using hundreds of transceivers may present a power problem at a real-world base station. This is an especially complex issue with the faster analog-to-digital converters and digital-to-analog converters required for higher bandwidth deployments. New algorithms are needed to reduce energy by selectively impairing antennas, reducing sample rates, or using other strategies for power reduction. Prototyping will determine the extent to which these algorithms can be used.

Massive MIMO base stations must regularly receive channel state information (CSI) from each UE. The transfer of CSI feedback, however, occupies valuable time that could be used to transmit data. Furthermore, until the CSI is received by the base station, no beamforming can occur, which decreases operational efficiency. New methods, such as beamforming CSI reference signals to the base station or using CSI quantization strategies, could solve these problems.

The potential of Massive MIMO to add more users is clear, but scheduling algorithms that unlock the full capacity benefits of the technology need to be developed. Smart scheduling approaches and user-tracking strategies featuring variable beamwidths, long-term channel statistics based beamforming, or the clustering of UEs based on direction offer the promise of greater efficiency in the MAC layer.

Several fundamental system level research problems arise when looking beyond a single MIMO base station and into multiple base-station setups. Pilot contamination, during which a pilot signal from one cell interferes with a pilot signal in an adjacent cell, remains a problem. Further research is needed to determine the best way to hand off a user from one base station to another. Another option is multipoint MIMO, which connects two or more base stations to the same UE. This requires additional synchronization and communication between base stations. Coordination between base stations could also limit interference between cells, which would enable denser networks.

Most Massive MIMO research has used time division duplex (TDD) operation. TDD is useful because it reduces the overhead of acquiring CSI by exploiting channel reciprocity. With frequency division duplex (FDD), on the other hand, CSI overhead increases as the number of base station antennas increases. However, FDD operation offers several useful benefits. Existing wireless networks in particular are largely implemented with FDD. Efficiently adapting Massive MIMO to use FDD would overcome a large hurdle in getting the technology to market.

As new bands such as the 3.5 GHz band open up for commercial wireless applications, new rules include provisions for primary and secondary users. Additionally, the 5 GHz Wi-Fi bands include rules for carrier sense multiple access. To date, the viability of Massive MIMO systems in shared spectrum scenarios has not been evaluated, but it poses some of the greatest opportunities for near-term adoption. The higher frequencies and thus shorter wavelengths of both 3.5 GHz and 5 GHz decrease the overall size of the antenna array, which simplifies deployment.

These Massive MIMO research questions are barriers to 5G technology adoption, but they are also opportunities for groundbreaking research. To solve these problems, NI’s approach is to overcome the limitations of theory by enabling real-world, real-time, rapid prototyping. The new NI MIMO Prototyping System is designed to deliver this game-changing approach to MU-MIMO and Massive MIMO development.