| 21 | | (2) In the second window, the large array of channel estimates is processed to determine the rate that a multi-user AP would be able to achieve if the given channel estimates accurately represented the wireless environment at the time that a MU-MIMO waveform could be sent. Specifically, 4-users are selected using the UI elements on the bottom of the window. Using a [http://en.wikipedia.org/wiki/Moore–Penrose_pseudoinverse Moore-Penrose Pseudoinverse], MU-MIMO beamweights are calculated for the instantaneous snapshot of channel estimates (i.e. the zero-forcing MU-MIMO solution). Using these beamweights, the effective SNR to each user is calculated and the achievable rate for each user is determined using Shannon's classic log(1 + SNR). Depending on the instantaneous channels, this matrix may be near singular, thereby collapsing the MU-MIMO AP's ability to send each user an independent data stream. In the live demo, the users can witness this behavior in the rate calculation by moving their devices very close to one another. Furthermore, standard single-user beamforming achievable rates are also plotted for each user for comparison. With every additional antenna, single-user beamforming only has a logarithmic increase in achievable rate, so there are very diminishing returns with large antenna arrays. The promise of MU-MIMO is that the network rate than can be achieved can scale linearly with the number of users once a sufficient number of antennas is used. |
| | 21 | (2) In the second window, the large array of channel estimates is processed to determine the rate that a multi-user AP would be able to achieve if the given channel estimates accurately represent the wireless environment at the time that a MU-MIMO waveform could be sent. Specifically, 4 users are selected using the UI elements on the bottom of the window. Using a [http://en.wikipedia.org/wiki/Moore–Penrose_pseudoinverse Moore-Penrose Pseudoinverse], MU-MIMO beamweights are calculated for the instantaneous snapshot of the complex-valued channel estimates (i.e. the zero-forcing MU-MIMO solution). Using these beamweights, the effective SNR to each user is calculated and the achievable rate for each user is determined using Shannon's classic log(1 + SNR). Depending on the instantaneous channels, this matrix may be near singular, thereby collapsing the MU-MIMO AP's ability to send each user an independent data stream. In the live demo, the users can witness this behavior in the rate calculation by moving their devices very close to one another. Furthermore, standard single-user beamforming achievable rates are also plotted for each user for comparison. With every additional antenna, single-user beamforming only has a logarithmic increase in achievable rate, so there are very diminishing returns with large antenna arrays. The promise of MU-MIMO is that the network rate than can be achieved can scale linearly with the number of users once a sufficient number of antennas is used. |
