Channel state information reporting with basis expansion for advanced wireless communications systems
A reporting and base station technology, applied in radio transmission systems, diversity/multi-antenna systems, transmission systems, etc., can solve problems such as not being able to fully adapt to channel state information
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Embodiment 1
[0108] For a typical 2D dual-polarization array with sufficiently small inter-element spacing (see Figure 3A ), for each polarization (+45° or -45°), the term A(φ k ,θ l ) can be written as the following (see Figure 3B and Figure 3C ):
[0109] A ( φ k , θ l ) = 1 N r N c 1 ...
Embodiment 2
[0116] Note that (1) and (2) facilitate (or at least encourage) linear discretization in the AoD domain. Alternatively, the MIMO channel can also be expressed as a linear combination of basis functions / vectors in the discrete Fourier transform (discreteFouriertransform, DFT) phase domain. That is:
[0117] H ( q , f ) ≅ Σ k = k 0 k 0 + K - 1 Σ l = l 0 l 0 + L - 1 c ...
Embodiment 3
[0135] Starting from Embodiment 1 or 2, if the channel representation in (1) / (1b) or (5) is applied to the channel feature vector instead of the channel itself, another level of dimensionality reduction can be achieved. Using (1b) to exemplify the method (one skilled in the art will easily extend it to use (1) or (5)), the procedure is as follows:
[0136] • Perform eigendecomposition or singular value decomposition on the DLMIMO channel for each polarization and frequency sub-band. Here, channels associated with different receive antennas are concatenated into a channel matrix.
[0137] Based on the selected RI (eg, N=1 or 2), the UE selects N dominant (strongest) eigenvectors (or right singular vectors), and the corresponding eigenvalues are among the N Reflected / captured in the CQI value.
[0138] • Since the UE is located within one or a few small pyramids, each of the N eigenvectors (for each polarization and frequency sub-band) allows the following approximation (see...
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