oscenario with mobile speed of 10 m/s based on higher-order singular-value decomposition (HOSVD), which is basically a tensor-based approach [30,60] (Figure 7.15). As a result, the rele- vant dimension D, is much smaller than the full dimension D and, in this case, for an HOSVD threshold of 10-5 by a factor of 240-that is, (N X N X NK N1) = (2,2,128,100) compared to (r1, V2, r3, r4) = (2,2,9,6), where r1 to V4 are the reduced dimensions for each parameter N to N -. This lower dimension has been partly exploited for LTE channel estimation based on the RS grids as defined in Chapter XXX for one to four antenna ports. The reduced dimen- sion D, in typical radio channels allows interpolation from estimates at REs with RSs for REs without RSs. It has been shown [33] that for lower SINRs and low channel dimensions Dr, several decibels of interpolation gain might be achieved. This gain is important because cooperative JT techniques are more sensitive to interpolation errors than single-link MIMO transmissions. Figure 7.16 depicts the far-field radio channel for maximum ratio combin- ing (MRC) of the intended signal as well as the IF null for JT based on ZF. Compared to MRC of the data signals, the IF nulls are located in an extremely small area SO that even slowly moving users will leave this area © 2009 by Taylor & Francis Group, LLC Cooperative Transmission Schemes Channel Amplitude: Urban Macro with MS Speed V = 10m/s, Path Loss not Considered Figure 7.15 Typical SCMe radio channel. very soon, resulting in significant IF. The same effect is well known from the time domain, where the normalized duration of fades for notches 20 dB below mean power might be smaller by a factor of 100 compared to that of power peaks (Figure 7.17). Multicell channel estimation. For cellular radio systems, there is a further hurdle for high-performance channel estimation because RSs cannot be received interference free but, rather, will overlap with RSs from other cells. In Thiele et al. [31], optimized allocation of RSs, together with spe