Analyzing Uplink Massive MIMO Using Stochastic Geometry
Massive multiple-input multiple-out (MIMO) is a promising technique for 5G cellular networks. Prior work showed that high throughput can be achieved with a large number of base station antennas through simple signal processing in massive MIMO networks. The performance of massive MIMO in a large-scale network with irregular base station locations and random user distributions is not yet fully understood.
To analyze the SINR and rate performance of massive MIMO in a large-scale cellular network, WNCG graduate student Tianyang Bai and WNCG Professor Robert Heath have proposed a stochastic geometry framework that incorporates fractional power control and pilot contamination. Their results show that using massive MIMO, the uplink SINR in certain urban macro-cell scenarios is limited by interference. In the interference-limited regime, the results reveal that for MRC receivers, a super-linear (polynomial) scaling law between the number of base station antennas and scheduled users per cell preserves the uplink SIR distribution, while a linear scaling applies to ZF receivers. ZF receivers are shown to outperform MRC receivers in the SIR coverage, and the performance gap is quantified in terms of the difference in the number of antennas to achieve the same SIR distribution. Numerical results also show that the optimal compensation fraction in fractional power control to optimize rate is generally different for MRC and ZF receivers. Besides, simulations show that the scaling results derived from the proposed framework apply to the networks, where base stations are distributed according to a hexagonal lattice.
The related work was in part presented in Globecom 2015 in San Diego, and is available online at http://arxiv.org/abs/1510.02538
This work is supported by NSF under Grant Nos. 1218338, 1319556, and 1514257.