Prerequisites & Notation
Before You Begin
This chapter builds on MU-MIMO precoding (Chapter 6) and channel estimation with spatial correlation (Chapters 2β3). A solid grasp of eigenvalue decomposition and subspace projections (Book Telecom, Chapter 1) is essential.
- Spatially correlated channel models: one-ring, Kronecker, angular domain(Review mimo-ch02)
Self-check: Can you write the channel covariance for a ULA with a given angular spread?
- MMSE channel estimation under spatial correlation(Review mimo-ch03)
Self-check: Can you state how the MMSE channel estimate exploits the covariance eigenspace?
- Linear precoding: MRT, ZF, regularized ZF(Review mimo-ch06)
Self-check: Can you write the ZF precoder and explain when it is near-optimal?
- Eigenvalue decomposition of Hermitian matrices(Review telecom-ch01)
Self-check: Can you compute the eigenvalues and eigenvectors of a Hermitian matrix?
Notation for This Chapter
Symbols introduced or central to this chapter. See also the NGlobal Notation Table master table in the front matter.
| Symbol | Meaning | Introduced |
|---|---|---|
| Spatial covariance matrix of user : | s01 | |
| Eigenvector matrix spanning the dominant eigenspace of group | s01 | |
| Effective rank (dimension) of group 's channel subspace | s01 | |
| Set of user groups: | s01 | |
| Pre-beamforming matrix for group () | s02 | |
| Inner MU-MIMO precoding matrix for group () | s02 | |
| Effective reduced-dimension channel: | s02 | |
| Set of users assigned to group | s01 | |
| Total number of user groups | s01 | |
| Number of transmit antennas at the base station | s01 | |
| Total number of users | s01 | |
| Achievable rate for user (bits/s/Hz) | s04 |