tion for various size-16 code- books, with two UE antennas over a TU6 channel. 5.2.3.5.2 Four Node-B, Four UE Antennas, TU6 Channel Model Figure 5.3 plots the percentage throughput gains versus geometry when each code- book is compared to an unoptimized DFT codebook with no ordering. Gains © 2009 by Taylor & Francis Group, LLC MIMO OFDM Schemes for 3GPP LTE TU6 4 X 4 Corr = (0.10, 0.10) Householder codebook based on Grassmanian Constant-modulus Householder codebook Optimized DFT codebook + ordering Optimized DFT codebook + no ordering Unoptimized DFT codebook + no ordering Geometry (dB) TU6 4 X 4 Corr = (0.50, 0.50) Householder codebook based on Grassmanian Constant-modulus Householder codebook Optimized DFT codebook + ordering Optimized DFT codebook + no ordering Unoptimized DFT codebook + no ordering Geometry (dB) Figure 5.3 Throughput gains over unoptimized, unordered DFT codebook for various size-16 codebooks, with four UE antennas over a TU6 channel. C 2009 by Taylor & Francis Group, LLC Long Term Evolution: 3GPP LTE Radio and Cellular Technology are shown for antenna correlations of 0.1 and 0.5, respectively. As seen from the figure, Ordering results in negligible, very small throughput gain over no ordering. The proposed constant-modulus codebook consistently outperforms DFT- based precoders at all geometries, for both low and high antenna correla- tions. In particular, even when the DFT-based codebook is optimized and ordered for optimum throughput, it still suffers an 8% throughput loss when compared to the proposed codebooks at low geometries. Further, even at high geometries, the DFT-based codebook suffers 2% throughput loss when compared to the proposed constant-modulus codebook. 5.2.3.5.3 Four Node-B, Four UE Antennas, SCME-C and -D Channel Models Figure 5.4 plots the percentage throughput gains versus geometry when each code- book is compared to an unoptimized DFT codebook with no ordering. The first plot uses the SCME-C (Spatial Channel Model Extension) channel model and the second uses the SCME-D 