Tom Marzetta of Bell Labs has been called the "Father of Massive MIMO." His 2010 paper, Noncooperative Cellular Wireless with Unlimited Numbers of Base Station Antennas, has been cited over 1700 times. He and Bell Labs colleague Gerry Foschini have been working on MIMO since the 1990's and made many contributions. For those who want to understand in depth, I've included the abstract of that paper and others below.
In 2014, Tom told me he thought it would take several more years for practical systems. Masayoshi Son of Softbank was unwilling to wait and launched the first commercial deployment in September, 2016. Softbank is installing 100 systems across 43 cities in Japan. Softbank's early results, from five cities, show a 5X to 10X improvement in the same spectrum. They use 128 antennas. Some of the antennas are used for "beamforming," which is proving crucial for deployments. Many top engineers expect a 50X improvement from MIMO in the coming years. Remarkably, the increased performance does not require significantly more power.
Marzetta writes, "Massive MIMO is the most promising technology available to address the ever increasing demand for wireless throughput:
• Orders of magnitude spectral efficiency gains over LTE - large numbers of users communicate simultaneously over entire allotted spectrum through elementary multiplexing signal processing
• Uniformly excellent service throughout the cell - regardless of location relative to base station
• Drastically reduced radiated power
• Simple and scalable design - employs measured channel characteristics rather than assumed channel characteristics
• Naturally green technology - superior energy efficiency
Here are some sources to start your research.
Noncooperative Cellular Wireless with Unlimited Numbers of Base Station Antennas Thomas L. Marzetta
Multiple-input multiple-output (MIMO) technology is maturing and is being incorporated into emerging wireless broadband standards like long-term evolution (LTE) [1]. For example, the LTE standard allows for up to eight antenna ports at the base station. Basically, the more antennas the transmitter/receiver is equipped with, and the more degrees of freedom that the propagation channel can provide, the better the performance in terms of data rate or link reliability. More precisely, on a quasi static channel where a code word spans across only one time and frequency coherence interval, the reliability of a point-to-point MIMO link scales according to Prob(link outage) ` SNR-ntnr where nt and nr are the numbers of transmit and receive antennas, respectively, and signal-to-noise ratio is denoted by SNR. On a channel that varies rapidly as a function of time and frequency, and where circumstances permit coding across many channel coherence intervals, the achievable rate scales as min(nt, nr) log(1 + SNR). The gains in multiuser systems are even more impressive, because such systems offer the possibility to transmit simultaneously to several users and the flexibility to select what users to schedule for reception at any given point in time [2].
Energy and Spectral Efficiency of Very Large Multiuser MIMO Systems
Hien Quoc Ngo, Erik G. Larsson, and Thomas L. Marzetta
Abstract A multiplicity of autonomous terminals simultaneously transmits data streams to a compact array of antennas. The array uses imperfect channel-state information derived from transmitted pilots to extract the individual data streams. The power radiated by the terminals can be made inversely proportional to the square-root of the number of base station antennas with no reduction in performance. In contrast if perfect channel-state information were available the power could be made inversely proportional to the number of antennas. Lower capacity bounds for maximum-ratio combining (MRC), zero-forcing (ZF) and minimum mean-square error (MMSE) detection are derived. A MRC receiver normally performs worse than ZF and MMSE. However as power levels are reduced, the cross-talk introduced by the inferior maximum-ratio receiver eventually falls below the noise level and this simple receiver becomes a viable option. The tradeoff between the energy efficiency (as measured in bits/J) and spectral efficiency (as measured in bits/channel use/terminal) is quantified. It is shown that the use of moderately large antenna arrays can improve the spectral and energy efficiency with orders of magnitude compared to a single-antenna system. Index Terms Energy efficiency, spectral efficiency, multiuser MIMO, very large MIMO systems I. INTRODUCTION In multiuser
Hien Quoc Ngo, Erik G. Larsson, and Thomas L. Marzetta Abstract A multiplicity of autonomous terminals simultaneously transmits data streams to a compact array of antennas. The array uses imperfect channel-state information derived from transmitted pilots to extract the individual data streams. The power radiated by the terminals can be made inversely proportional to the square-root of the number of base station antennas with no reduction in performance. In contrast if perfect channel-state information were available the power could be made inversely proportional to the number of antennas. Lower capacity bounds for maximum-ratio combining (MRC), zero-forcing (ZF) and minimum mean-square error (MMSE) detection are derived. A MRC receiver normally performs worse than ZF and MMSE. However as power levels are reduced, the cross-talk introduced by the inferior maximum-ratio receiver eventually falls below the noise level and this simple receiver becomes a viable option. The tradeoff between the energy efficiency (as measured in bits/J) and spectral efficiency (as measured in bits/channel use/terminal) is quantified. It is shown that the use of moderately large antenna arrays can improve the spectral and energy efficiency with orders of magnitude compared to a single-antenna system. Index Terms Energy efficiency, spectral efficiency, multiuser MIMO, very large MIMO systems I. INTRODUCTION In multiuser multiple-input multiple-output (MU-MIMO) systems, a base station (BS) equipped with multiple antennas serves a number of users. Such systems have attracted much attention for some time now [2]. Conventionally, the communication between the BS and the users is performed by orthogonalizing the channel so that the BS communicates with each user in separate time-frequency resources. This is not optimal from an information-theoretic point of view, and higher rates can be achieved if the BS communicates with several users in the same time-frequency resource [3], [4]. However, complex techniques to mitigate inter-user interference must then be used, such as maximum-likelihood multiuser detection on the uplink [5], or “dirty-paper coding” on the downlink [6], [7]. Recently, there has been a great deal of interest in MU-MIMO with very large antenna arrays at the BS. Very large arrays can substantially reduce intracell interference with simple signal processing.
Scaling Up MIMO: Opportunities and Challenges with Very Large Arrays