Expand description
§comnum-mimo — multiple-antenna processing
Detection, space-time coding, precoding and capacity on the generic flat model
$y = H x + w$, with $H$ the $n_{r} \times n_{t}$ channel matrix. The channel models
themselves (Rayleigh and Rician fading, antenna correlation) are in comnum-channel
(fading::Mimo, spatial); waveform-specific resource mapping (per-subcarrier
detection of MIMO-OFDM) belongs to comnum-waveform.
| Module | Content |
|---|---|
detection | MR (matched filter), ZF, MMSE, ordered SIC (V-BLAST), exhaustive ML, sphere decoder, K-best; hard and soft outputs, the estimates of the symbols (detection::Estimates) |
message_passing | LAMA (complex Bayesian AMP) and OAMP for large systems, their state evolution and the recovery thresholds of LAMA |
receiver | the receiver of a link: a detector of detection by name, applied channel use after channel use (spatial layers or Alamouti blocks), with soft outputs |
stbc | orthogonal space-time block codes: Alamouti, the rate-3/4 codes for 3 and 4 antennas; linear decoding |
noma | downlink NOMA with users dropped at random in a disc: the gain distribution, the SIC thresholds, the exact and high-SNR outage probabilities (Ding, Yang, Fan & Poor 2014) |
precoding | SVD (eigenmode) transmission with water-filling, ZF and regularised ZF precoders |
estimation | training-based channel estimation on $S = H P + V$: LS, scaled LS, LMMSE and relaxed MMSE, their MSEs, the orthogonal and MMSE-optimal training, the BLUE combination of training periods (Biguesh and Gershman 2006) |
massive | the uplink of multi-user massive MIMO: perfect or estimated CSI, the closed-form lower bounds on the rates of MR and ZF receivers (Ngo, Larsson and Marzetta 2013) |
capacity | capacity of a channel realisation with equal power or water-filling; the ergodic and outage capacities of i.i.d. Rayleigh fading, the eigenvalue density of a Wishart matrix |
comnum-mimo uses the constellations of comnum-mapping (ADR 0008).
Modules§
- capacity
- Capacity of a MIMO channel realisation.
- detection
- MIMO detection on the generic model $y = H x + w$.
- estimation
- Training-based estimation of a flat MIMO channel: the least-squares, scaled least-squares, LMMSE and relaxed MMSE estimators, their mean square errors, the optimal training matrices and the combination of several training periods (Biguesh & Gershman 2006).
- massive
- The uplink of multi-user massive MIMO: $K$ single-antenna users to a base station of $M$ antennas, with linear receivers (Ngo, Larsson and Marzetta 2013).
- message_
passing - Message-passing detection of large MIMO systems: LAMA (complex Bayesian AMP) and orthogonal AMP (OAMP), with their state evolution.
- noma
- Downlink non-orthogonal multiple access (NOMA) with users dropped at random in a disc (Ding, Yang, Fan & Poor 2014): superposition coding at the base station, successive interference cancellation (SIC) at the users, outage probabilities in closed form.
- precoding
- Linear precoding: eigenmode transmission with water-filling, and the zero-forcing precoders of the multi-user downlink.
- receiver
- The receiver of a link: one detector applied channel use after channel use, with soft outputs.
- stbc
- Orthogonal space-time block codes (OSTBC).