ious, including 1/2, 3/4 is broadcast to all users in the downlink, the optimal solution would be to use all pilots for interpolation. However, we show this to be impractical with current digi- tal signal processors (DSPs). For example, in order to interpolate the LTE channel in 20-MHz bandwidth, one needs 1,200 channel coefficients for each pair of transmit and receive antennas from, say, each fourth subcarrier being a pilot. For the optimal Wiener filter, this leads to computation of an interpolation matrix of size 1200 X 300. This process employs a 300 X 300 matrix inversion. Next, the interpolation matrix is multiplied with a vector containing received pilots of size of 300 X 1. The complexity of matrix inversion is given as 3003 = 2.7 X 107. Fortunately, these matrices can be precom- puted for certain signal-to-noise ratio (SNR) values (see reference 16). Therefore, the real constraint lies in the complexity of matrix-vector multiplication 1200 x X = 3.6 X 105, which needs to be realized in real time. If a TTI with 0.5-ms duration is considered as a coherence interval and four times this complexity is needed in a 2 X 2 MIMO system, it requires roughly 4 X 109 real-valued instructions per second. © 2009 by Taylor & Francis Group, LLC Early Real-Time Experiments and Field Trial Measurements Transmitter 1 Pilot 1024 IFFT Tx1 generation Insert Data Tx1, mapping precoding MOD 1 encoding 2048 IFFT Tx1 blocks Data Tx2, 2048 IFFT Tx2 blocks Insert Pilot mapping precoding MOD 2 encoding 1024 IFFT Tx1 generation Transmitter 2 Block- OFDM Tx processing mapping QAM MOD Figure 11.1 Functional blocks of the downlink base band chain. Top: transmitter at BS; bottom: receiver at one UE. This exceeds the processing power of our DSPs and, therefore, optimal interpolation cannot be performed. An alternative simplified algorithm is considered. 11.1.2.2 Localized Interpolation Algorithm The localized interpolation method has been discussed recently to simplify the channel interpolation process in a broadband system. It relies on 