Update figures and docs: OMA naming, uniform legends, TikZ Fig. 1 source

- Rename the orthogonal comparison scheme OFDMA -> OMA in docs and figure legends (data keys keep ofdma)
- Legend labels normalized to the paper dictionary (OMA, SFDMA, UWCA (proposed))
- New TikZ-generated system overview figure with text-consistent notation (soft decoder masks m-tilde, queries q_u, sharpness eta)
- Regenerated fig_fair, fig_resorth, fig_realdata_c, fig_async, fig_dynusers; fig_ser_all legend patched

docs/EXPERIMENTS.md

@@ -36,7 +36,7 @@ evaluation uses 150-200 Monte Carlo batches of 64 per point.
   channel gains, the manuscript's Proposition on optimal linear receivers),
   and `tdma_proj` (random orthonormal 16-dim projection per user — an
   arbitrary orthogonal partition).
-- **Key results.** lmmse_blind = OFDMA at every SNR in all scenarios
+- **Key results.** lmmse_blind = OMA at every SNR in all scenarios
   (SER 0.509 vs 0.508 @10 dB HIGH), and tdma_proj and SFDMA coincide with
   them — the ceiling binds every correlation-blind receiver. HIGH @20 dB:
   blind 0.327 -> UWCA 0.110 -> genie 0.044, so UWCA recovers 77% of the
@@ -95,16 +95,16 @@ evaluation uses 150-200 Monte Carlo batches of 64 per point.
 - **Setup.** Per-user integer offsets delta_u ~ U{0..Delta} symbols,
   Delta in {0,1,2,4,8}, shift each user's transmitted block within the
   frame (edge energy lost). Conditions: uncorrected reception (UWCA and
-  OFDMA), block-wise realignment using pilot-estimated offsets (each
+  OMA), block-wise realignment using pilot-estimated offsets (each
   user's block region shifted back individually), and realignment with a
   deliberately impaired estimator (+-1 symbol on 20% of users).
 - **Key results.** Uncorrected offsets are catastrophic for *every*
-  embedding-level scheme (one symbol: UWCA 0.26 -> 0.73, OFDMA 0.51 ->
+  embedding-level scheme (one symbol: UWCA 0.26 -> 0.73, OMA 0.51 ->
   0.76), because i.i.d. embedding coordinates fully decorrelate under a
   one-symbol misalignment — synchronization is a shared physical-layer
   prerequisite, not a property of the multiple-access mechanism. With
   realignment the degradation is gradual (0.266 -> 0.304 @Delta=1, 0.455
-  @Delta=8) and realigned UWCA stays below realigned OFDMA (0.536-0.638)
+  @Delta=8) and realigned UWCA stays below realigned OMA (0.536-0.638)
   at every offset. The impaired estimator costs 0.14 SER: whole-symbol
   residuals sacrifice the affected block, so timing must be held to
   sub-symbol accuracy (which the closed-loop timing advance provides).
@@ -119,10 +119,10 @@ evaluation uses 150-200 Monte Carlo batches of 64 per point.
   two-layer tanh view networks g_u per user (seed 7): users share the
   scene s only through independent nonlinear transformations. Cases:
   shared scene vs independent scenes (control). Schemes: trained UWCA,
-  OFDMA, NOMA-SIC, and the scalar-parameterized genie LMMSE fed the
+  OMA, NOMA-SIC, and the scalar-parameterized genie LMMSE fed the
   *measured* mean pairwise cosine.
 - **Key results.** The linear correlation is destroyed (mean cosine
-  0.006), so the genie LMMSE collapses onto the ceiling (0.515 vs OFDMA
+  0.006), so the genie LMMSE collapses onto the ceiling (0.515 vs OMA
   0.518 @10 dB) — no scalar or linear receiver can represent the shared
   structure. UWCA still attains 0.363 @10 dB / 0.220 @20 dB. The
   independent-scene control (UWCA 0.441) isolates the manifold-prior
@@ -138,11 +138,11 @@ evaluation uses 150-200 Monte Carlo batches of 64 per point.
 - **Setup.** E1's trained HIGH decoder; per-sample Pearson correlation
   across the 64 dimensions, averaged over user pairs and 100x64 samples
   per SNR, for: input embeddings, decoded embeddings, and decoding
-  residuals r_u = e_hat_u - e_u; OFDMA decoded correlation as reference.
+  residuals r_u = e_hat_u - e_u; OMA decoded correlation as reference.
 - **Key results.** The decoded-embedding correlation rises with SNR from
   0.31 toward the 0.39 input level (the shared content is delivered, not
   stripped), while the residual correlation falls 0.23 -> 0.14 (2.8x below
-  the input level) — the emergent residual orthogonality. OFDMA's decoded
+  the input level) — the emergent residual orthogonality. OMA's decoded
   correlation is 0.00 at every SNR: orthogonal access erases the
   inter-user semantic structure from the delivered embeddings.
 - **Artifacts.** `experiments/e6_residual_orth.py` ->