[1-3], the gains in multi-user systems are even more significant , as such systems provide the ability to transmit simultaneously to several users. We also discussed in the previous posts the benefits of multi-user MIMO as well . With its aggressive spatial multiplexing, massive MIMO takes these gains even further , increasing the capacity ten times or more while simultaneously improving the radiated energy-efficiency in the order of hundreds of times. Interference between receiving nodes can be suppressed by using zero forcing beamforming (ZFBF) at the expense of more complexity when compared to maximum ratio transmission (MRT) or conjugate beamforming techniques .
When it comes to real-time implementation, many challenges arise. Implementing multi-user beamforming (e.g. conjugare or ZF beamforming) with many antennas introduces critical timing constraints imposed by the coherence time of the physical wireless channel. The beamforming technique must collect channel state information (CSI) for each node for use when calculating the beamforming weights within a small fraction of the coherence time . The authors in  have modelled the performance of multi-user beamforming techniques in real word systems. They captured in a single model the key factors pertaining to (i) channel coherence, (ii) precoder spectral efficient, and (iii) other factors related to design choices such as number of antennas and the hardware capability. Figure 1 in  shows a comparison in zero forcing and conjugate beamforming performance for different hardware configurations with 64 antennas and 15 receiving nodes.
As far as CSI collection is concerned, the most promising practical systems use time division duplexing (TDD) to leverage the channel reciprocity. Unfortunately, after accounting for radio frequency hardware impairments, one cannot only use just the uplink estimated channel information for beamforming weight calculation.  addresses a calibration technique which has been generalized in . With transmission nodes made of inexpensive hardware , synchronization and reciprocity calibration are the main burdens for implementing a truly large-scale MIMO system. The work in  examines mechanisms for RF calibration that can enable high-performing massive MIMO operation while not relying on an explicitly self-calibrating RF design.
 M. Ahmed Ouameur “MU-MIMO part 1
” M. Ahmed Ouameur “MU-MIMO part 2
” M. Ahmed Ouameur “MU-MIMO part 3
” E. G. Larsson, F. Tufvesson, O. Edfors, and T. L. Marzetta, Massive MIMO for Next Generation Wireless Systems, IEEE Commun. Mag., 2013, to appear. C. Shepard, H. Yu, N. Anand, L. E. Li, T. L. Marzetta, R. Yang, and L. Zhong, Argos: Practical Many-Antenna Base Stations, in Proc. ACM Int. Conf. Mobile Computing and Networking (MobiCom), Aug. 2012. Clayton Shepard, Narendra Anand, and Lin Zhong, “Practical Performance of MU-MIMO Precoding
in Many-Antenna Base Stations”. Ryan Rogalin, Ozgun Y. Bursaliogluy, Haralabos C. Papadopoulosy, Giuseppe Caire, and Andreas F. Molisch, “Hardware-Impairment Compensation for Enabling Distributed Large-Scale MIMO” NUTAQ “PICO SDR: MIMO Enabled, (0.3 – 3.8 GHz) Virtex-6 Based SDR Solution Supporting GNU Radio,” https://nutaq.com/products/picosdr