framework of interference functions can be used for the optimization of a coopera- tive network, as illustrated in Figure 7.8. Consider K single-antenna transmitters and M distributed receiving antennas, which can (partly) cooperate. Vector hk contains the complex channel gains from user k to the M distributed antennas. Independent signals S1, SK are transmit- ted over vector-valued channels h1 hk E CM, , with spatial covariance matrices MMSEcoopSIC MMSEcoop ZFcoop MRCcoop maxSINR Selection diversity Plot (dBm) Figure 7.8 SINR versus transmission power in an interference-saturated multi- cellular system (reuse 1) with base station cooperation and SDMA. © 2009 by Taylor & Francis Group, LLC Long Term Evolution: 3GPP LTE Radio and Cellular Technology Fos: NL Upper bounds 5 MHz system with Optimum MU-MIMO variable backhaul Fos: NL Active IF-management ScaleNet ScaleNet MU MIMO without ScaleNet COOPA Partial Partial Partial: 3 cells 10 MHz 20 MHz backhaul backhaul Cooperation Level Figure 7.9 Potential gains for different degrees of cooperation. Fos: theoreti- cal upper bounds [6]; FE: 5-MHz system with varying inter-NB backhaul traffic [7]; 3GET: active IF management with CAs of size 3 [38]; ScaleNet: optimum MU-MIMO without cooperation, but full CSI knowledge [17]. Rk = E[h h ]. The antenna outputs are combined by linear filters U1,..., UK (the distributed "beam formers"). The output of the kth beam former is (7.14) where n E CM is an additive white Gaussian noise (AWGN) vector, with E[nn"] = O2 The interference power coupling coefficients of the kth user are 1<I<K,l#k uHRuu (7.15) l=K+1 Assuming a normalization = 1, the interference function for the beam- forming case is (7.16) © 2009 by Taylor & Francis Group, LLC Cooperative Transmission Schemes It can be observed that the interference coupling is not constant. For any power allocation p > 0, the beam former uk adapts to the interference in such a way that the SINR is maximized. For deterministic channels h we have R =h,h,H SO the interference resulting from opt