192 antenna elements antenna elements Figure 10.36 Spectral efficiency with different massive MIMO configurations. Figure 10.37 Grid of beams and user-specific beams. Grid of beams User-specific beams deliver nearly the same performance. We take two example solutions in Figure 10.37: a grid of fixed beams and user-specific beams. Figure 10.38 shows the downlink bench- marking of user-specific beamforming with eigen beamforming (EBF) and the simpler grid of beams (GoBs). EBF gives +20% higher average throughput and + 10% higher cell edge throughput than GoBs with multiuser Massive Multiple Input Multiple Output (MU-MIMO). Figure 10.39 shows the uplink data rates with a full-blown Interference Rejection Combining (IRC) receiver and the combination of IRC and beam selection. The IRC receiver combines the signals to maximize the signal to interference and noise ratio (SINR). The alternative solution brings N best beam signals that are used for IRC combining. The simulations assume 64 receiver branches. The performance is very similar when using 8-16IRC with best beam selection: 8IRC receiver with beam selection gives 93% of the 64IRC performance, and 16IRC receiver with beam selection gives 97% of the 64IRC performance. 10.7 Massive MIMO and Beamforming Algorithms User data rate Average Cell edge Average Cell edge SU-MIMO MU-MIMO Figure 10.38 Downlink throughput with Grid of Beams (GoB) vs. user-specific beamforming (eigen beamforming (EBF)). User data rate Average Cell edge 1-best beam + 2IRC 2-best beam + 4IRC 4-best beam + 8IRC 8-best beam + 16IRC 16-best beam + 32IRC 64IRC Figure 10.39 Uplink throughput with beam selection vs. Interference Rejection Combining (IRC). 10.7.2.2 Zero Forcing The beamforming algorithm can be based on signal level maximization signal-to-interference ratio maximization, which is also known as zero forcing or null-steering. The zero forcing algorithm requires that the interference information be known by the transmitter. The exact interference that affects the UE is known based on UE fee