[3-5]. If massive MIMO has rendered many traditional problems in communication theory less relevant, it uncovers new issues that need attention .
First, from a research-perspective, fast and distributed coherent signal processing is gaining more attention given the vast amount of baseband data that needs to be processed in real time. A lot of effort is being invested in the design of optimized algorithms and their implementation, as exemplified by Zhang in his work on hermitian precoding for distributed MIMO systems .
Second, building hundreds of low-cost radio frequency chains and down/up converters will require economies of scale in manufacturing while coping with the associated hardware impairments . If massive MIMO relies on the law of large numbers to average out noise, fading and (to some extent) interference, it must take into account the reality of massive MIMO systems being built with low-cost components. This means that hardware imperfections such as phase noise and I/Q imbalance are non-negligible . With low-cost phase locked-loops or even free-running oscillators at each antenna path, phase noise may become a limiting factor. However, what finally matters is how much the phase will drift between the points in time when pilot and data symbols are received at each antenna. The design of smart transmission physical-layer schemes and receiver algorithms offer great potential to get around the phase noise problem.
Third, prototyping and internal power consumption is a concern. Massive MIMO offers the potential to reduce the radiated power by a factor of a thousand while drastically scaling up data rates. In practice, however, the total consumed power must be considered, and this includes the cost of baseband signal processing. Much research must be invested into highly parallel, perhaps dedicated, hardware for the baseband signal processing . While massive MIMO is in its early stages, basic prototyping work into various aspects of the technology is going on in different parts of the world. The Argos testbed  was developed at Rice university in cooperation with Alcatel-Lucent and shows the basic feasibility of the massive MIMO concept with 64 coherently operating antennas. In particular, the testbed shows that TDD operation relying on channel reciprocity is possible. One of the virtues of the Argos testbed in particular is that it is entirely modular and scalable and that is built around commercially available hardware (the WARP platform). Other test systems around the world have also demonstrated the basic feasibility of scaling up the number of antennas . The Ngara test bed in Australia  uses a 32-element base station array to serve up to 18 users simultaneously with true spatial multiplexing. Continued testbed development is highly desired both to prove the massive MIMO concept with even a larger numbers of antennas and to discover potentially new issues that need urgent research.
Fourth, pilot contamination has long been considered a bottleneck in multi-cell systems. It is likely that pilot contamination imposes even more severe limitations on massive MIMO than it does on traditional MIMO systems. However clever channel estimation algorithms  or even blind techniques that circumvent the use of pilots altogether  may mitigate or eliminate the effects of pilot contamination. It has also been argued that pilot contamination is an artifact of the linear channel estimation technique .
Finally, other issues such as reciprocity calibration and system studies into coexistence with small cells and heterogeneous network solutions are gaining more attention as well.
 Erik G. Larsson, Ove Edfors, Fredrik Tufvesson, Thomas L. Marzetta, “Massive Massive MIMO for Next Generation Wireless Systems,” IEEE Communications Magazine, Vol. 52, No. 2, pp. 186-195, Feb. 2014. J. Zhang, X. Yuan, and L. Ping, “Hermitian precoding for distributed MIMO systems with individual channel state information,” IEEE J. Sel. Areas Commun. , vol. 31, no. 2, pp. 241–250, Feb. 2013. M. Ahmed Ouameur, “Massive MIMO – Part 5: The need for practical prototyping and implementation,” http://www.nutaq.com/blog/massive-mimo-%E2%80%93-part-5-need-practical-prototyping-and-implementation
, 2014 E. Björnson, J. Hoydis, M. Kountouris, M. Debbah, “Massive MIMO Systems with Non-Ideal Hardware: Energy Efficiency, Estimation, and Capacity Limits,” http://arxiv.org/abs/1307.2584
, 2014 M. Ahmed Ouameur, “RF imperfections and compensation and compensation parts 1 to 4,” http://www.nutaq.com/blog/rf-imperfection-and-compensation-part-1-effects-iq-imbalance-and-compensation-receiver
, 2013[part 6] M. Ahmed Ouameur, “Massive MIMO – Part 6: Estimation and capacity limits due to transceiver impairments,” http://www.nutaq.com/blog/massive-mimo-%E2%80%93-part-6-estimation-and-capacity-limits-due-transceiver-impairments
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