Import the required city geometry into AEDT. Choose base-station and user device locations. Select antenna type from a parametric antennas dictionary or design your own antennas/arrays in HFSS. In this workflow, we import a part of downtown Los Angeles from open street maps. For a single-sector base-station we use a connected slot antenna array backed by a PEC pate. The 2 users are equipped with a single omnidirectional Hertzian dipole antenna.
Configure all sources as either transmitters or receivers. Create a single frequency set-up and specify the frequency. Assign transmit powers to each transmitter. For this example workflow, the simulation frequency is 5GHz. The system is uplink, so that the two hertzian dipoles are simultaneously transmitting to the slot array base-station.
Specify noise power and non-orthogonal transmission to calculate channel and noise correlation matrices. Apply linear detection/combining for uplink or linear precoding for downlink. Calculate signal-to-Interference and noise ratio (SINR) per User Equipment (UE) terminal and display the results. In this example, we use -90dBm of noise power and 0.5W of transmit power per UE terminal.
MRC detection technique uses the Hermitian (conjugate transpose) of the channel matrix to multiply the received symbol vector. Similar to the matched filter, it leverages the fact that different user channels' have low correlation to decode separate data streams. From the bar graph of the SINR we can see that the interference power from user 2, after MRC, when user 1 is decoded is about 14 dB above the signal power of user 1 . This points to the weakness of MRC as a very simple linear architecture and could be improved by the use of ZF or LMMSE detection.
ZF detection technique uses left pseudo-inverse of the channel matrix to multiply the received symbol vector. From the bar graph of SINR we see that the signal power of user 1, after ZF, is about 7 dB above the interference power from user 2 when user 1 is decoded. ZF detector is therefore able to separate the two UEs in space at sufficiently large SINR to allow for space-division multiple access.
LMMSE detection technique uses a minimum mean squared error estimate of the transmitted symbol vector with processing confined to linear operation (vector matrix multiplication). Since both MRC and ZF are both linear, LMMSE is would have a higher spatial resolution than either of MRC or ZF. Moreover, in the limit of high SNR, LMMSE reduces to ZF which is can be seen from the bar graph.
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