The Challenge of Low-Cost Hardware
Building hundreds of RF chains, up/down converters, analog-to-digital (A/D)-digital-to-analog (D/A) converters, and so forth, will require economy of scale in manufacturing comparable to what we have seen for mobile handsets.
Hardware Impairments
Massive MIMO relies on the law of large numbers to average out noise, fading and to some extent, interference. In reality, massive MIMO must be built with low-cost components. This is likely to mean that hardware imperfections are larger: in particular, phase noise and I/Q imbalance. Lowcost and power-efficient A/D converters yield higher levels of quantization noise. Power amplifiers with very relaxed linearity requirements will necessitate the use of per-antenna low peak-to-average signaling, which, as already noted, is feasible with a large excess of transmitter antennas. With lowcost phase locked loops or even free-running oscillators at each antenna, phase noise may become a limiting factor. However, what ultimately matters is how much the phase will drift between the point in time when a pilot symbol is received and the point in time when a data symbol is received at each antenna. There is great potential to get around the phase noise problem by design of smart transmission physical layer schemes and receiver algorithms.
Internal Power Consumption
Massive MIMO offers the potential to reduce the radiated power 1000 times and at the same time drastically scale up data rates. However, in practice, the total power consumed must be considered, including the cost of baseband signal processing. Much research must be invested in highly parallel, perhaps dedicated, hardware for the baseband signal processing.
Channel Characterization
There are additional properties of the channel to consider when using massive MIMO instead of conventional MIMO. To facilitate a realistic performance assessment of massive MIMO systems, it is necessary to have channel models that reflect the true behavior of the radio channel (i.e., the propagation channel including effects of realistic antenna arrangements). It is also important to develop more sophisticated analytical channel models. Such models need not necessarily be correct in every fine detail, but they must capture the essential behavior of the channel. For example, in conventional MIMO the Kronecker model is widely used to model channel correlation. This model is not an exact representation of reality, but provides a useful model for certain types of analysis despite its limitations. A similar way of thinking could probably be adopted for massive MIMO channel modeling.
New Deployment Scenarios
It is considered extraordinarily difficult to introduce a radical new wireless standard. One possibility is to introduce dedicated applications of massive MIMO technology that do not require backward compatibility. For example, as discussed earlier, in rural areas, a billboard-sized array could provide 20 Mb/s service to each of 1000 homes using special equipment that would be used solely for this application. Alternatively, a massive array could provide the backhaul for base stations that serve small cells in a densely populated area. Thus, rather than thinking of massive MIMO as a competitor to LTE, it can be an enabler for something that was just never before considered possible with wireless technology.
System Studies and Relation to Small-Cell and Heterogeneous Network Solutions
The driving motivation of massive MIMO is to simultaneously and drastically increase data rates and overall energy efficiency. Other potential ways of reaching this goal are network densification by the deployment of small cells, resulting in a heterogeneous architecture, or coordination of the transmission of multiple individual base stations. From a purely fundamental perspective, the ultimately limiting factor of the performance of any wireless network appears to be the availability of good enough channel state information (CSI) to facilitate phase-coherent processing at multiple antennas or multiple access points. Considering factors like mobility, Doppler shifts, phase noise, and clock synchronization, acquiring high-quality CSI seems to be easier with a collocated massive
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array than in a system where the antennas are distributed over a large geographical area. But at the same time, a distributed array or small cell solution may offer substantial path loss gains and would also provide some diversity against shadow fading. The deployment costs of a massive MIMO array and a distributed or small cell system are also likely to be very different. Hence, both communication-theoretic and techno-economic studies are needed to conclusively determine which approach is superior. However, it is likely that the winning solution will comprise a combination of all available technologies.
Prototype Development
While massive MIMO is in its infancy, basic prototyping work on 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 using 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 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 testbed 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 to both prove the massive MIMO concept with even larger numbers of antennas and discover potentially new issues that urgently need research.
Conclusions and Outlook
In this article we have highlighted the large potential of massive MIMO systems as a key enabling technology for future beyond fourth generation (4G) cellular systems. The technology offers huge advantages in terms of energy efficiency, spectral efficiency, robustness, and reliability. It allows for the use of low-cost hardware at both the base station and the mobile unit side. At the base station the use of expensive and powerful, but power-inefficient, hardware is replaced by massive use of parallel low-cost low-power units that operate coherently together. There are still challenges ahead to realize the full potential of the technology, for example, computational complexity, realization of distributed processing algorithms, and synchronization of the antenna units. This gives researchers in both academia and industry a gold mine of entirely new research problems to tackle.
There are still challenges ahead to realize the full potential of the technology, for example, when it comes to computational complexity, realization of distributed processing algorithms, and synchronization of the antenna units.
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