Restructure package: descriptive study documentation and clean layout

README.md

@@ -2,60 +2,67 @@
 
 Simulation code, raw results, and figure sources for the manuscript
 "Relevance-Aware Semantic Multiple Access via Meta-Learned User-Wise
-Cross-Attention" (submitted to IEEE Transactions on Wireless Communications).
+Cross-Attention" (submitted to the IEEE Transactions on Communications).
+
+The package contains everything needed to regenerate every number and
+figure in the manuscript: one script per study, the raw JSON/CSV results
+each script produced, and a single plotting script that rebuilds the figure
+PDFs from the stored results without rerunning any experiment.
+
+**Detailed per-study documentation (question, setup, procedure, metrics,
+key results): [docs/EXPERIMENTS.md](docs/EXPERIMENTS.md).**
 
 ## Requirements
 
-- Python 3.10+ with `torch` (CUDA optional; results generated on an NVIDIA
-  RTX A4500, PyTorch 2.10, CUDA 12.8), `numpy`, `matplotlib`, and
+- Python 3.10+ with `torch` (results generated on an NVIDIA RTX A4500,
+  PyTorch 2.10, CUDA 12.8; CPU fallback works), `numpy`, `matplotlib`, and
   `scikit-learn` (real-data study only).
-- All experiments use fixed seed 42 (auxiliary generators seeded as noted in
-  each script).
+- All experiments use fixed seed 42 (auxiliary generators seeded as noted
+  in each script).
 
 ## Layout
 
-- `rev2/lib.py` — shared library: single-superimposed-signal channel
-  (Rayleigh / Rician / Nakagami fading, complex phase residuals, timing
-  offsets, CSI error), UWCA decoder (active-set masking, top-k masking,
-  I/Q input), closed-form LMMSE receivers, training loops (multi-task
-  meta-training and first-order MAML), evaluation metrics.
-- `rev2/e1_fair_baselines.py` … `rev2/e9_topk_online.py` — one script per
-  experiment (see the table below). `*_v2/_v3` scripts supersede their
-  earlier versions where present.
-- `rev2/data/` — raw JSON results behind every quoted number.
-- `rev2/plot_rev2.py` — regenerates every new figure from `rev2/data/`
-  without rerunning experiments.
-- `legacy/` — scripts for the pre-revision figures (synthetic three-scenario
-  study, attention maps, beta sweep, real-data study).
+- `experiments/lib.py` — shared library: the single-superimposed-signal
+  uplink of the manuscript (Rayleigh / Rician / Nakagami fading, complex
+  phase residuals, timing offsets, CSI error; fixed disjoint transmit block
+  masks), the UWCA decoder (learned soft masks, active-set masking, top-k
+  masking, optional I/Q input), closed-form LMMSE reference receivers,
+  training loops, and evaluation metrics.
+- `experiments/e1_fair_baselines.py` … `experiments/e9_topk_online.py` —
+  one self-contained script per study (see the table below).
+- `experiments/data/` — raw JSON results behind every quoted number.
+- `experiments/make_figures.py` — regenerates the figure PDFs from
+  `experiments/data/` only.
+- `simulation/` — the three-scenario synthetic study, hyperparameter
+  ablations, threshold sweeps, and the real-data study.
 - `fig/` — figure PDFs as included in the manuscript.
 
-**Detailed per-experiment documentation (setup, procedure, metrics, key results, reviewer-concern map): [docs/EXPERIMENTS.md](docs/EXPERIMENTS.md).**
-
-## Figure/number → script → data map
+## Study → script → data map
 
 | Manuscript item | Script | Data |
 |---|---|---|
-| Fig. SER vs SNR (3 scenarios) | `legacy/semantic_correlation_sim.py`, `legacy/maml_semantic.py` | `legacy/results/` |
-| Fig. fairness vs optimal linear receivers | `rev2/e1_fair_baselines.py` | `rev2/data/e1_fair_baselines.json` |
-| Fig. residual orthogonality | `rev2/e6_residual_orth.py` | `rev2/data/e6_residual_orth.json` |
-| Phase-error robustness (complex model, CSI error) | `rev2/e2_phase_iui.py` | `rev2/data/e2_phase_iui.json` |
-| Timing-offset robustness | `rev2/e4_v3_async.py` | `rev2/data/e4_v3_async.json` |
-| Dynamic user population | `rev2/e3_dynamic_users.py` | `rev2/data/e3_dynamic_users.json` |
-| Nonlinear view-network study | `rev2/e5_nonlinear.py` | `rev2/data/e5_nonlinear.json` |
-| Meta-adaptation beyond SNR (OOD) | `rev2/e7_v2_meta.py` | `rev2/data/e7_v2_meta.json` |
-| End-to-end anti-collapse study | `rev2/e8_v2_e2e.py` | `rev2/data/e8_v2_e2e.json` |
-| Online top-k acquisition and timing | `rev2/e9_topk_online.py` | `rev2/data/e9_topk_online.json` |
-| Real-data validation | `legacy/revision_realdata_train.py`, `legacy/revision_realdata_plot.py` | `legacy/results/` |
+| SER vs SNR, three scenarios (Fig. 2) | `simulation/semantic_correlation_sim.py`, `simulation/maml_semantic.py` | `simulation/results/` |
+| Optimal-linear-receiver fairness (Fig. 3) | `experiments/e1_fair_baselines.py` | `experiments/data/e1_fair_baselines.json` |
+| Residual orthogonality (Fig. 4) | `experiments/e6_residual_orth.py` | `experiments/data/e6_residual_orth.json` |
+| Complex phase-error model, CSI error | `experiments/e2_phase_iui.py` | `experiments/data/e2_phase_iui.json` |
+| Timing offsets and realignment | `experiments/e4_async.py` | `experiments/data/e4_async.json` |
+| Dynamic user population | `experiments/e3_dynamic_users.py` | `experiments/data/e3_dynamic_users.json` |
+| Nonlinear view-network study | `experiments/e5_nonlinear.py` | `experiments/data/e5_nonlinear.json` |
+| Adaptation across fading families | `experiments/e7_meta.py` | `experiments/data/e7_meta.json` |
+| End-to-end training with anti-collapse | `experiments/e8_e2e.py` | `experiments/data/e8_e2e.json` |
+| Online top-k acquisition and timing | `experiments/e9_topk_online.py` | `experiments/data/e9_topk_online.json` |
+| beta sweep, attention maps, ablations | `simulation/revision_*.py`, `simulation/plot_figures.py` | `simulation/results/` |
+| Real-data validation (Fig. 5) | `simulation/revision_realdata_train.py`, `simulation/revision_realdata_plot.py` | `simulation/results/` |
 
 ## Running
 
 ```bash
-python rev2/e1_fair_baselines.py   # writes rev2/data/e1_fair_baselines.json
-python rev2/plot_rev2.py           # regenerates the new figure PDFs
+python experiments/e1_fair_baselines.py   # writes experiments/data/e1_fair_baselines.json
+python experiments/make_figures.py        # rebuilds the figure PDFs from data/
 ```
 
-Each experiment script is self-contained and writes its JSON into
-`rev2/data/`.
+Each study script is self-contained and writes its JSON into
+`experiments/data/`.
 
 ## Citation and license
 

docs/EXPERIMENTS.md

@@ -1,20 +1,21 @@
-# Experiment Documentation
+# Study Documentation
 
-Detailed description of every experiment behind the manuscript
+Detailed description of every study behind the manuscript
 "Relevance-Aware Semantic Multiple Access via Meta-Learned User-Wise
-Cross-Attention." Each entry states the reviewer concern it addresses, the
-exact setup, the procedure, the metrics, the key results, and the artifact
-paths. All experiments run on a single NVIDIA RTX A4500 (PyTorch 2.10, CUDA
-12.8), seed 42, and write raw JSON results consumed only by
-`rev2/plot_rev2.py`.
+Cross-Attention": the question each study answers, the exact setup, the
+procedure, the metrics, the key results, and the artifact paths. All
+studies run on a single NVIDIA RTX A4500 (PyTorch 2.10, CUDA 12.8), seed
+42, and write raw JSON results consumed only by
+`experiments/make_figures.py`.
 
-**Common system model** (all rev2 experiments, matching manuscript Eq. (7)):
-single superimposed uplink y = sum_v |h_v| e^{j dphi_v} (e_v (.) m_v) + n over
-one d=64-dimensional frame; fixed {0,1} disjoint transmit block masks
-(d/U coordinates per user, identical for every compared scheme); per-rail
-noise std = sqrt(mean|y_tx|^2 / SNR); U=4 users, HIGH/LOW/MIX scenarios of
-Table II unless stated. The decoder-side masks are separate learned soft
-masks in [0,1]^d initialized at the block pattern. Training is the
+**Common system model** (all `experiments/` studies, matching the
+manuscript's received-signal model): single superimposed uplink
+y = sum_v |h_v| e^{j dphi_v} (e_v (.) m_v) + n over one d=64-dimensional
+frame; fixed {0,1} disjoint transmit block masks (d/U coordinates per user,
+identical for every compared scheme); per-rail noise
+std = sqrt(mean|y_tx|^2 / SNR); U=4 users and the HIGH/LOW/MIX scenarios of
+the manuscript unless stated. The decoder-side masks are separate learned
+soft masks in [0,1]^d initialized at the block pattern. Training is the
 manuscript's first-order meta-training aggregated over SNR tasks
 {0,4,...,20} dB, Adam 1e-3 (mask logits 0.1), 250-300 epochs, batch 64;
 evaluation uses 150-200 Monte Carlo batches of 64 per point.
@@ -22,225 +23,218 @@ evaluation uses 150-200 Monte Carlo batches of 64 per point.
 ---
 
 ## E1 — Comparison fairness against optimal linear receivers
-*Reviewer concerns: R1-10, R3-7 (d/U comparison "mathematically unfair");
-editor point 4.*
 
-- **Question.** Is the OFDMA/SFDMA subspace ceiling an artifact of weak
-  baselines, and how much of the UWCA gain is mere dimensionality?
+- **Question.** Is the d/U subspace ceiling of orthogonal access an
+  artifact of weak comparison schemes, and how much of the UWCA gain is
+  mere dimensionality rather than relevance exploitation?
 - **Setup.** For each scenario the true relevance matrix B (B_uv =
-  beta_u beta_v for shared scene, else 0) and the per-realization channel
+  beta_u beta_v for a shared scene, else 0) and the per-realization channel
   magnitudes are formed. Receivers evaluated on the *same* received frame:
   `uwca` (trained), `ofdma`, `sfdma`, `noma` (full-band power-domain SIC,
   powers 0.40/0.30/0.20/0.10), `lmmse_blind` (closed-form Wiener with
   cross-covariances zeroed), `lmmse_genie` (closed form with true B and
-  channel gains, manuscript Eq. (23)), `tdma_proj` (random orthonormal
-  16-dim projection per user — an arbitrary orthogonal partition).
+  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
-  (SER 0.509 vs 0.508 @10 dB HIGH), tdma_proj and SFDMA coincide with them;
-  HIGH @20 dB: blind 0.327 -> UWCA 0.110 -> genie 0.044 (UWCA recovers 77%
-  of the blind-to-genie gap); LOW: genie = blind (nothing to exploit),
-  UWCA within 0.02 SER.
-- **Artifacts.** `rev2/e1_fair_baselines.py` ->
-  `rev2/data/e1_fair_baselines.json` -> `fig/fig_fair.pdf` (manuscript
-  Fig. 3). Trained checkpoints `e1_uwca_{HIGH,LOW,MIX}.pt` (reused by E4/E6).
+  (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
+  blind-to-genie gap without side information. LOW: genie = blind (nothing
+  to exploit), UWCA within 0.02 SER.
+- **Artifacts.** `experiments/e1_fair_baselines.py` ->
+  `experiments/data/e1_fair_baselines.json` -> `fig/fig_fair.pdf`
+  (manuscript Fig. 3). Trained checkpoints `e1_uwca_{HIGH,LOW,MIX}.pt`
+  (reused by E4/E6).
 
 ## E2 — Full complex-baseband phase errors and CSI error
-*Reviewer concerns: R1-1 (phase model underestimates IUI), R2-3 (CSI).*
 
 - **Question.** Does the multi-user superposition amplify residual phase
-  errors into severe inter-user interference once nothing is absorbed into a
-  noise term?
+  errors into severe inter-user interference once every leakage path is
+  simulated explicitly?
 - **Setup.** Complex channel with per-user residual dphi_u ~ N(0, sigma^2),
-  sigma in {0,5,10,15,20,30} deg; both rails simulated so every leakage path
-  exists. Three decoders: mismatch-trained (never saw phase errors),
-  phase-augmented (trained with sigma ~ U[0,20] deg), and a two-rail variant
-  whose keys/values read [Re;Im] (2d input). CSI sweep: amplitude error
-  h_hat = h(1+eps), eps ~ N(0, sigma_h^2), sigma_h up to 0.2, at 10 deg.
-  Learned-mask overlap (mean pairwise cosine) is also measured.
+  sigma in {0,5,10,15,20,30} deg; both rails simulated. Three decoders:
+  mismatch-trained (never saw phase errors), phase-augmented (trained with
+  sigma ~ U[0,20] deg), and a two-rail variant whose keys/values read
+  [Re;Im]. CSI sweep: amplitude error h_hat = h(1+eps),
+  eps ~ N(0, sigma_h^2), sigma_h up to 0.2, at 10 deg. The learned-mask
+  overlap (mean pairwise cosine) is also measured.
 - **Key results.** Mismatch-trained @10 dB: SER 0.270 -> 0.291 (20 deg) ->
-  0.332 (30 deg); @20 dB 0.110 -> 0.127; augmentation and I/Q reading change
-  curves by less than Monte Carlo spread -> leakage is second order because
-  it scales with sin(dphi) x mask overlap (measured 0.26). CSI: UWCA
-  unchanged within 0.004 (uses no explicit CSI).
-- **Artifacts.** `rev2/e2_phase_iui.py` -> `rev2/data/e2_phase_iui.json`;
-  `fig/fig_phase2.pdf` (repo; numbers narrated in manuscript Sec. VII-E).
+  0.332 (30 deg); @20 dB 0.110 -> 0.127; augmentation and I/Q reading
+  change the curves by less than the Monte Carlo spread. The leakage is
+  second order because it scales with sin(dphi) times the mask overlap
+  (measured 0.26). CSI: UWCA unchanged within 0.004 (it uses no explicit
+  CSI).
+- **Artifacts.** `experiments/e2_phase_iui.py` ->
+  `experiments/data/e2_phase_iui.json`; `fig/fig_phase2.pdf`.
 
 ## E3 — Dynamic user population
-*Reviewer concerns: R1-6 (fixed U, costly retraining), R2-3 (fixed
-identities).*
 
-- **Setup.** U_max = 8 mask slots (d=64, 8 dims/slot), HIGH-type correlation
-  (beta_u = 0.6, one scene). Active set resampled per frame; inactive users
-  transmit nothing and their softmax scores are masked to -inf (the
-  scheduler announces the active set). Compared: one model trained with the
-  full population always active (never retrained), an activity-sampled
-  model, and per-count oracle models retrained from scratch for
+- **Question.** Does a decoder provisioned for U_max users need retraining
+  when users arrive and depart?
+- **Setup.** U_max = 8 mask slots (d=64, 8 dims/slot), high-correlation
+  scenario (beta_u = 0.6, one scene). The active set is resampled per
+  frame; inactive users transmit nothing and their softmax scores are
+  masked out (the scheduler announces the active set). Compared: one model
+  trained with the full population always active (never retrained), an
+  activity-sampled model, and per-count models retrained from scratch for
   |A| in {2,4,6,8}. Metric: mean cosine (the sqrt(1/8)=0.35 subspace
   ceiling saturates the binary SER at this scale).
 - **Key results.** The single full-population model sustains fidelity over
-  every active count (c 0.34 -> 0.39 for |A| = 2 -> 8 @10 dB) and matches or
-  exceeds the per-count retrained models (0.33/0.32/0.36/0.39); per-count
-  retraining is counterproductive (sparse populations train each slot on a
-  fraction of the traffic).
-- **Artifacts.** `rev2/e3_dynamic_users.py` ->
-  `rev2/data/e3_dynamic_users.json`; `fig/fig_dynusers.pdf` (repo).
+  every active count (c 0.34 -> 0.39 for |A| = 2 -> 8 @10 dB) and matches
+  or exceeds the per-count retrained models (0.33/0.32/0.36/0.39);
+  per-count retraining is counterproductive, since sparse populations train
+  each slot on a fraction of the traffic.
+- **Artifacts.** `experiments/e3_dynamic_users.py` ->
+  `experiments/data/e3_dynamic_users.json`; `fig/fig_dynusers.pdf`.
 
-## E4 — Symbol-timing offsets (v3 = block-wise realignment)
-*Reviewer concern: R1-8 (asynchronous reception / ISI).*
+## E4 — Symbol-timing offsets and receiver-side realignment
 
+- **Question.** How does the decoder behave under asynchronous reception,
+  and what does the standard timing-correction chain restore?
 - **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 (UWCA and OFDMA),
-  corrected by block-wise realignment using pilot-estimated offsets
-  (each user's block region shifted back individually), and corrected with
-  a deliberately impaired estimator (+-1 symbol on 20% of users).
-  v1 (`e4_async` results in json) showed offset-augmented *training* cannot
-  repair unknown shifts; v2 showed whole-frame realignment breaks
-  cross-block alignment — both superseded by v3.
+  frame (edge energy lost). Conditions: uncorrected reception (UWCA and
+  OFDMA), 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 ->
-  0.76) because i.i.d. embedding coordinates fully decorrelate under a
-  one-symbol misalignment. 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) at every offset. The impaired estimator
-  costs 0.14 SER — whole-symbol residuals sacrifice the affected block, so
-  timing must be sub-symbol (standard timing advance).
-- **Artifacts.** `rev2/e4_v3_async.py` -> `rev2/data/e4_v3_async.json`;
-  `fig/fig_async.pdf` (repo).
+  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)
+  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).
+- **Artifacts.** `experiments/e4_async.py` ->
+  `experiments/data/e4_async.json`; `fig/fig_async.pdf`.
 
 ## E5 — Nonlinear inter-user semantic structure
-*Reviewer concerns: R1-7, R2-5, R3-2 (linear scalar model too restrictive).*
 
+- **Question.** Does the mechanism survive when no scalar or linear
+  description of the inter-user dependence exists?
 - **Setup.** Embeddings e_u = normalize(g_u([s; p_u])) with fixed random
-  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 *measured* mean pairwise
-  cosine.
-- **Key results.** Linear correlation is destroyed (mean cosine 0.006), so
-  the genie LMMSE collapses onto the ceiling (0.515 vs OFDMA 0.518 @10 dB)
-  — no scalar/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 share, so the further reduction
-  to 0.363 is pure nonlinear cross-user structure.
-- **Artifacts.** `rev2/e5_nonlinear.py` -> `rev2/data/e5_nonlinear.json`
-  (numbers narrated in manuscript Sec. VII-G).
+  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
+  *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
+  0.518 @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
+  share, so the further reduction to 0.363 is pure nonlinear cross-user
+  structure.
+- **Artifacts.** `experiments/e5_nonlinear.py` ->
+  `experiments/data/e5_nonlinear.json`.
 
 ## E6 — Residual orthogonality vs content preservation
-*Reviewer concerns: R1-4, R2-2, R3-3 (semantic-orthogonality
-self-contradiction).*
 
+- **Question.** What exactly decorrelates at the decoder output: the
+  delivered content, or the errors?
 - **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, decoding residuals
-  r_u = e_hat_u - e_u; OFDMA decoded correlation as reference.
-- **Key results.** Decoded-embedding correlation rises with SNR from 0.31
-  toward the 0.39 input level (shared content preserved, not stripped);
-  residual correlation falls 0.23 -> 0.14 (2.8x below input) — the
-  emergent residual orthogonality. OFDMA's decoded correlation is 0.00 at
-  every SNR: orthogonal access erases the inter-user semantic structure.
-- **Artifacts.** `rev2/e6_residual_orth.py` ->
-  `rev2/data/e6_residual_orth.json` -> `fig/fig_resorth.pdf` (manuscript
-  Fig. 4).
+  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.
+- **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
+  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` ->
+  `experiments/data/e6_residual_orth.json` -> `fig/fig_resorth.pdf`
+  (manuscript Fig. 4).
 
-## E7 — Meta-adaptation beyond the SNR axis (v2)
-*Reviewer concerns: R1-3, R3-4, R3-5 (MAML overkill for a 1-D lookup);
-R1-9 (eta stability).*
+## E7 — Adaptation across fading families
 
+- **Question.** Does the meta-trained initialization cover a
+  multi-dimensional space of operating conditions that a one-dimensional
+  per-SNR model bank cannot, and is the sharpness scalar stable?
 - **Setup.** Task family = 6 SNRs x {Rayleigh, Rician K=5 dB, K=10 dB} x
   phase residual {0,10} deg (36 tasks). Systems: one initialization
   meta-trained over the full family (300 epochs, eta and outer-gradient
-  norm logged every epoch); per-SNR specialist bank trained on
-  Rayleigh/no-phase (the 1-D lookup, nearest-SNR index). Held-out tests:
-  Rician K=20 dB + 15 deg (10/18 dB), Nakagami m=3 + 5 deg (a fading law in
-  no training task), Rayleigh + 20 deg @6 dB, plus an in-distribution
-  check; zero-shot and after S in {1,5,10,20} inner steps (SGD 0.02, one
+  norm logged every epoch); a per-SNR specialist bank trained on
+  Rayleigh/no-phase and indexed by nearest SNR. Held-out tests: Rician
+  K=20 dB + 15 deg (10/18 dB), Nakagami m=3 + 5 deg (a fading law in no
+  training task), Rayleigh + 20 deg @6 dB, plus an in-distribution check;
+  zero-shot and after S in {1,5,10,20} inner steps (SGD 0.02, one
   64-sample support batch).
-- **Key results.** Family initialization transfers zero-shot with 9-46%
-  lower SER than the lookup on every held-out condition (e.g. 0.114 vs
-  0.158 Rician-K20-15deg @10 dB; 0.139 vs 0.190 unseen Nakagami); inner-loop
-  adaptation is stable (within 0.005 over 20 steps). eta converges to 0.94
-  (max 1.00), outer gradient norm <= 0.024 vs clip bound 5 — no softmax
-  saturation or divergence.
-- **Artifacts.** `rev2/e7_v2_meta.py` -> `rev2/data/e7_v2_meta.json`
-  (includes eta/gradient trajectories).
+- **Key results.** The family initialization transfers zero-shot with
+  9-46% lower SER than the specialist bank on every held-out condition
+  (e.g. 0.114 vs 0.158 Rician-K20-15deg @10 dB; 0.139 vs 0.190 under the
+  unseen Nakagami law); inner-loop adaptation is stable (within 0.005 over
+  20 steps). eta converges to 0.94 (max 1.00) and the outer gradient norm
+  stays below 0.024 against a clipping bound of 5 — no softmax saturation
+  or divergence.
+- **Artifacts.** `experiments/e7_meta.py` ->
+  `experiments/data/e7_meta.json` (includes eta/gradient trajectories).
 
-## E8 — End-to-end training with anti-collapse (v2)
-*Reviewer concern: R1-5 (representation collapse prevents joint JSCC).*
+## E8 — End-to-end training with anti-collapse regularization
 
+- **Question.** Can the encoder be trained jointly with the decoder
+  without representation collapse, and what causes the collapse when it
+  occurs?
 - **Setup.** Four configurations on HIGH: frozen encoder (reference);
   naive end-to-end with the distortion measured against the encoder's own
-  output (the collapse-prone moving target); source-anchored end-to-end
-  (distortion vs normalize(x)); moving target + VICReg-style
-  variance-covariance regularizer (hinge at 1/sqrt(d) per-dim std, 25x
-  variance + 100/d covariance weights). Collapse-proof metric: batch
-  nearest-neighbor retrieval accuracy over the *encoded* gallery, plus the
-  effective rank of the encoder-output covariance.
-- **Key results.** Naive collapses exactly as the reviewers expect:
-  retrieval falls to chance (0.005 vs 0.536 frozen @10 dB) — the pathology
-  is the moving-target objective, not the cross-attention. Source anchoring
-  keeps effective rank 59.6/64 and lands within 0.04 SER of frozen; VICReg
-  restores retrieval to 0.509 with a learned task-specific code. E2E
-  training is therefore demonstrated, and the frozen encoder in the main
-  experiments is a controlled-isolation choice.
-- **Artifacts.** `rev2/e8_v2_e2e.py` -> `rev2/data/e8_v2_e2e.json`.
+  output (a moving target); source-anchored end-to-end (distortion vs
+  normalize(x)); moving target + a VICReg-style variance-covariance
+  regularizer (hinge at 1/sqrt(d) per-dim std, 25x variance + 100/d
+  covariance weights). Collapse-proof metric: batch nearest-neighbor
+  retrieval accuracy over the *encoded* gallery, plus the effective rank
+  of the encoder-output covariance.
+- **Key results.** The naive configuration collapses (retrieval falls to
+  chance, 0.005 vs 0.536 frozen @10 dB), locating the pathology in the
+  moving-target objective rather than the cross-attention. Source
+  anchoring keeps effective rank 59.6 of 64 and lands within 0.04 SER of
+  the frozen reference; the variance-covariance regularizer restores
+  retrieval to 0.509 with a learned task-specific code. The frozen encoder
+  in the main experiments is therefore a controlled-isolation choice.
+- **Artifacts.** `experiments/e8_e2e.py` ->
+  `experiments/data/e8_e2e.json`.
 
 ## E9 — Online top-k relevance acquisition and measured overhead
-*Reviewer concerns: R1-2, R3-6 (circular dependency), R2-4 (cost at large
-populations).*
 
+- **Question.** How does sparse top-k attention obtain the relevance
+  ranking it needs, starting from no knowledge, and what does the
+  acquisition cost?
 - **Setup.** U=32, eight relevance clusters of four, k=4, briefly trained
-  full-attention model. Protocol: frames 1-3 full attention (needs no
-  relevance) while the BS accumulates beta_hat by EWMA (zeta=0.5) of
-  decoded-embedding cosines; from frame 4, per-row top-k via argpartition
-  on beta_hat. References: oracle top-k (peers selected from the true
-  clusters) and permanent full attention. Timing: decoder forward vs
-  EWMA-update + selection wall-clock for U in {8,16,32,64,128}
-  (256-sample frames, GPU, 20-run averages).
+  full-attention model. Protocol: frames 1-3 run full attention (which
+  needs no relevance knowledge) while the BS accumulates beta_hat by EWMA
+  (zeta=0.5) of decoded-embedding cosines; from frame 4, per-row top-k via
+  argpartition on beta_hat. References: an oracle whose peers are selected
+  from the true clusters, and permanent full attention. Timing: decoder
+  forward vs EWMA-update-plus-selection wall-clock for
+  U in {8,16,32,64,128} (256-sample frames, GPU, 20-run averages).
 - **Key results.** From the first post-warm-up frame the online selection
   matches the oracle exactly and slightly exceeds full attention
   (discarding irrelevant peers discards their noise); ranking needs far
   less accuracy than estimation because intra-cluster (~0.42) and
-  inter-cluster (~0) cosines are well separated. Estimation + selection
-  cost 0.02-0.04 ms vs the 0.3-5.4 ms decoder pass, incurred once per
+  inter-cluster (~0) cosines are well separated. Estimation plus selection
+  cost 0.02-0.04 ms against the 0.3-5.4 ms decoder pass, incurred once per
   relevance coherence interval.
-- **Artifacts.** `rev2/e9_topk_online.py` ->
-  `rev2/data/e9_topk_online.json` (trajectory + timing).
+- **Artifacts.** `experiments/e9_topk_online.py` ->
+  `experiments/data/e9_topk_online.json` (trajectory + timing).
 
 ---
 
-## Legacy experiments (retained from the original study)
+## Simulation studies (`simulation/`)
 
 - **Three-scenario SER study** (manuscript Fig. 2): analytical simulation
-  `legacy/semantic_correlation_sim.py` + trained decoder-only overlay
-  `legacy/maml_semantic.py --decoder_only`; threshold sweep tau in
-  [0.30,0.50] and mean-cosine table in `legacy/revision_experiments.py`.
-- **beta sweep** (monotone gain law): `legacy/revision_betasweep.py`
-  (figure `fig/fig4_beta_sweep.pdf`, narrated in Sec. VII-C).
-- **Attention heatmaps**: `legacy/plot_figures.py`
-  (figure `fig/fig6_hlm.pdf`, narrated in Sec. VII-C).
-- **Hyperparameter ablations** (S, lambda, d, H, K):
-  `legacy/revision_ablation.py`, `legacy/revision_dsweep.py`,
-  `legacy/revision_e2e.py` (Sec. VII-D).
+  `simulation/semantic_correlation_sim.py` with the trained decoder-only
+  overlay from `simulation/maml_semantic.py --decoder_only`; threshold
+  sweep tau in [0.30,0.50] and the mean-cosine comparison in
+  `simulation/revision_experiments.py`.
+- **beta sweep** (monotone gain law): `simulation/revision_betasweep.py`
+  (figure `fig/fig4_beta_sweep.pdf`).
+- **Attention heatmaps**: `simulation/plot_figures.py`
+  (figure `fig/fig6_hlm.pdf`).
+- **Hyperparameter studies** (S, lambda, d, H, K):
+  `simulation/revision_ablation.py`, `simulation/revision_dsweep.py`,
+  `simulation/revision_e2e.py`.
 - **Real-data study** (manuscript Fig. 5): UCI optical-recognition digits
-  (8x8=64 dims), `legacy/revision_realdata_train.py` +
-  `legacy/revision_realdata_plot.py` (Sec. VII-G).
-
-## Reviewer-concern -> experiment map
-
-| Concern | Experiment(s) | Manuscript |
-|---|---|---|
-| R1-1 phase IUI | E2 | Sec. III-A, VII-E |
-| R1-2 / R3-6 top-k circularity | E9 | Sec. V-E, VII-H |
-| R1-3 / R3-4 / R3-5 MAML vs lookup | E7 | Sec. V-E, VII-F |
-| R1-4 / R2-2 / R3-3 orthogonality contradiction | E6 | Def. 3, Prop. 4, Fig. 4 |
-| R1-5 representation collapse | E8 | Sec. VI-B |
-| R1-6 dynamic U | E3 | Sec. IV-B, VII-E |
-| R1-7 / R2-5 / R3-2 linear scalar model | E5 (+ real data) | Sec. III-B, VII-G |
-| R1-8 asynchrony | E4 | Sec. III-A, VII-E |
-| R1-9 eta stability | E7 logs | Sec. IV-A, VII-F |
-| R1-10 / R3-7 d/U fairness | E1 | Prop. 3, Fig. 3, VII-B |
-| R2-1 idealized proofs | assumption block + E1/E5/real data | Sec. V |
-| R2-3 sync / CSI / identities / encoder | E4 / E2 / E3 / E8+E5 | Sec. III, VI-B, VII-E |
-| R2-4 large-population cost | E9 timing | Sec. VII-H |
-| R3-1 DSC novelty | (positioning) | Sec. II-A |
+  (8x8=64 dimensions), `simulation/revision_realdata_train.py` +
+  `simulation/revision_realdata_plot.py`.

experiments/e1_fair_baselines.py

@@ -1,4 +1,4 @@
-"""E1 — Degrees-of-freedom fairness (R1.10, R3.7).
+"""E1 — Degrees-of-freedom fairness study.
 
 Adds full-dimensional receivers on the SAME received signal:
   - lmmse_blind : optimal linear receiver with cross-user correlation set to 0

experiments/e2_phase_iui.py

@@ -1,5 +1,5 @@
-"""E2 — Full complex-baseband phase-error model with inter-user leakage (R1.1)
-plus CSI amplitude-error robustness (R2.3).
+"""E2 — Full complex-baseband phase-error model with inter-user leakage,
+plus CSI amplitude-error robustness.
 
 Three evaluation models on HIGH:
   scalar    : real channel, per-user cos(dphi) attenuation only (old model)

experiments/e3_dynamic_users.py

@@ -1,4 +1,4 @@
-"""E3 — Dynamic user arrivals/departures (R1.6, R2.3).
+"""E3 — Dynamic user arrivals and departures.
 
 U_max = 8 mask slots, d = 64.  Activity-aware meta-training samples a random
 active subset each batch; at inference the attention softmax is restricted to

experiments/e4_async.py

@@ -1,4 +1,4 @@
-"""E4 v3 — Timing offsets with BLOCK-WISE receiver realignment (R1.8).
+"""E4 — Symbol-timing offsets with block-wise receiver realignment.
 
 The BS knows the per-user timing estimates (pilot-based) and realigns each
 user's block region individually inside the single received frame:
@@ -88,7 +88,7 @@ for label, mode, ep in [("uwca_uncorrected", "uwca_unc", 0.0),
     for snr in out["snr_eval"]:
         cur[str(snr)] = [evaluate(mode, dm, snr, ep) for dm in dgrid]
     out["curves"][label] = cur
-    print(f"[E4v3] {label} 10dB SER: "
+    print(f"[E4] {label} 10dB SER: "
           f"{[round(a[0],3) for a in cur['10.0']]}", flush=True)
 
-save_json("e4_v3_async.json", out)
+save_json("e4_async.json", out)

experiments/e5_nonlinear.py

@@ -1,4 +1,4 @@
-"""E5 — Nonlinear inter-user semantic structure (R1.7, R2.5, R3.2).
+"""E5 — Nonlinear inter-user semantic structure.
 
 Embeddings are produced by fixed random per-user nonlinear view networks
 e_u = normalize(g_u([kappa*s ; p_u])), so the inter-user dependence is

experiments/e6_residual_orth.py

@@ -1,4 +1,4 @@
-"""E6 — Residual (error) orthogonality vs. content preservation (R1.4, R3.3, R2.2).
+"""E6 — Residual (error) orthogonality vs. content preservation.
 
 Resolves the claimed contradiction: the decoded embeddings PRESERVE the shared
 scene correlation (rho(e_hat_u, e_hat_v) tracks beta_uv), while the decoding

experiments/e7_meta.py

@@ -1,5 +1,5 @@
-"""E7 v2 — Meta-training over the multi-dimensional task family, OOD transfer,
-adaptation sweep, and eta/gradient logging.
+"""E7 — Meta-training over a multi-dimensional task family: held-out
+transfer, adaptation sweep, and eta/gradient logging.
 
 meta    : the paper's first-order meta-training aggregated over the FULL
           36-task family (SNR x {Rayleigh, Rician K=5,10 dB} x phase {0,10 deg})
@@ -35,15 +35,15 @@ family = [{"snr_db": s, "phase_sigma_deg": p, **f}
 
 eta_log = []
 m_meta = UWCA(d, U, H).to(DEVICE)
-train_multitask(m_meta, gen, family, epochs=300, tag="E7v2-meta",
+train_multitask(m_meta, gen, family, epochs=300, tag="E7-meta",
                 log_state=eta_log)
-torch.save(m_meta.state_dict(), lib.DATA / "e7v2_meta.pt")
+torch.save(m_meta.state_dict(), lib.DATA / "e7_meta.pt")
 
 specialists = {}
 for s in snrs:
     m = UWCA(d, U, H).to(DEVICE)
     train_multitask(m, gen, [{"snr_db": s}], epochs=150,
-                    tag=f"E7v2-spec{int(s)}")
+                    tag=f"E7-spec{int(s)}")
     specialists[s] = m
 
 
@@ -78,7 +78,7 @@ for name, t in test_tasks.items():
             sers.append({"ser": s, "cos": c})
         row[label] = sers
     out["results"][name] = row
-    print(f"[E7v2] {name}: meta={[round(x['ser'],3) for x in row['meta']]} "
+    print(f"[E7] {name}: meta={[round(x['ser'],3) for x in row['meta']]} "
           f"lookup={[round(x['ser'],3) for x in row['lookup']]}", flush=True)
 
 etas = [e["eta"] for e in eta_log]
@@ -87,7 +87,7 @@ out["eta_traj"] = etas[::5]
 out["gnorm_traj"] = gns[::5]
 out["eta_final"], out["eta_max"] = etas[-1], max(etas)
 out["gnorm_max"] = max(gns)
-print(f"[E7v2] eta final={etas[-1]:.3f} max={max(etas):.3f} "
+print(f"[E7] eta final={etas[-1]:.3f} max={max(etas):.3f} "
       f"gnorm max={max(gns):.3f}", flush=True)
 
-save_json("e7_v2_meta.json", out)
+save_json("e7_meta.json", out)

experiments/e8_e2e.py

@@ -1,4 +1,4 @@
-"""E8 v2 — End-to-end joint encoder-decoder training (R1.5), source-anchored.
+"""E8 — End-to-end joint encoder-decoder training, source-anchored metrics.
 
 All fidelity metrics are measured against the SOURCE embedding normalize(x),
 never against the trainable encoder output (a moving target that makes
@@ -69,7 +69,7 @@ def train(mode, epochs=300):
         torch.nn.utils.clip_grad_norm_(model.parameters(), 5.0)
         opt.step()
         if ep % 75 == 0:
-            print(f"  [E8v2-{mode}] ep {ep}/{epochs} "
+            print(f"  [E8-{mode}] ep {ep}/{epochs} "
                   f"loss={float(loss)/len(snrs):.4f}", flush=True)
     return model, enc
 
@@ -119,7 +119,7 @@ for mode in ["frozen", "e2e_moving", "e2e_anchored", "e2e_vicreg"]:
     erank = collapse_metrics(enc)
     ss, cc, rr = curves(model, enc)
     out["configs"][mode] = {"ser": ss, "cos": cc, "retr": rr, "erank": erank}
-    print(f"[E8v2] {mode}: erank={erank:.1f} SER@10={ss[5]:.3f} "
+    print(f"[E8] {mode}: erank={erank:.1f} SER@10={ss[5]:.3f} "
           f"cos@10={cc[5]:.3f} retr@10={rr[5]:.3f}", flush=True)
 
-save_json("e8_v2_e2e.json", out)
+save_json("e8_e2e.json", out)

experiments/e9_topk_online.py

@@ -1,5 +1,5 @@
-"""E9 — Online relevance acquisition for sparse top-k attention (R1.2, R3.6)
-and measured selection/sorting overhead (R2.4).
+"""E9 — Online relevance acquisition for sparse top-k attention and measured
+selection overhead.
 
 Protocol (U=32, 8 clusters of 4, k=4):
   frames 1..3 : full attention; the BS estimates beta_hat from the decoded

experiments/lib.py

@@ -1,5 +1,5 @@
 """
-Shared library for TWC revision-2 experiments (new submission).
+Shared library for the UWCA semantic multiple access studies.
 Single-signal uplink model matching the manuscript:
     y = sum_v g_v (e_v ⊙ m_v) + n,   g_v = |h_v| e^{jΔφ_v}
 All experiments import from here.  Seed fixed = 42.

experiments/make_figures.py

@@ -1,4 +1,4 @@
-"""Generate the five new revision figures from data/*.json into ../Relevance_TWCOM_R2/fig/.
+"""Regenerate the figure PDFs from data/*.json into ../fig/.
 Uniform geometry: 8:6 axes box, shared rcParams, no tight bounding box.
 """
 import json
@@ -11,7 +11,7 @@ import numpy as np
 
 HERE = Path(__file__).resolve().parent
 DATA = HERE / "data"
-FIG = HERE.parent / "Relevance_TWCOM_R2" / "fig"
+FIG = HERE.parent / "fig"
 FIG.mkdir(exist_ok=True)
 
 plt.rcParams.update({
@@ -115,7 +115,7 @@ ax.legend(loc="upper left", framealpha=0.9, fontsize=7)
 save(f, "fig_phase2.pdf", [ax])
 
 # ---------------------------------------------------------------- fig_async --
-d = json.load(open(DATA / "e4_v3_async.json"))
+d = json.load(open(DATA / "e4_async.json"))
 dm = d["dmax"]
 f, ax = newfig()
 cur = d["curves"]

rev2/data/e4_async.json

@@ -1,151 +0,0 @@
-{
- "dmax": [
-  0,
-  1,
-  2,
-  4,
-  8
- ],
- "snr_eval": [
-  10.0,
-  20.0
- ],
- "sync_trained": {
-  "10.0": [
-   [
-    0.2683203125,
-    0.49533248856663703
-   ],
-   [
-    0.7208984375,
-    0.242251892760396
-   ],
-   [
-    0.83734375,
-    0.15587599329650403
-   ],
-   [
-    0.91517578125,
-    0.09062198633328081
-   ],
-   [
-    0.95876953125,
-    0.0465814154734835
-   ]
-  ],
-  "20.0": [
-   [
-    0.10884765625,
-    0.5421956545114517
-   ],
-   [
-    0.65294921875,
-    0.2639770006388426
-   ],
-   [
-    0.79462890625,
-    0.1728138119354844
-   ],
-   [
-    0.8930859375,
-    0.09787379436194897
-   ],
-   [
-    0.94958984375,
-    0.048505370183847846
-   ]
-  ]
- },
- "aug_trained": {
-  "10.0": [
-   [
-    0.61974609375,
-    0.4028245759010315
-   ],
-   [
-    0.80044921875,
-    0.20371038138866424
-   ],
-   [
-    0.8644921875,
-    0.14209178265184164
-   ],
-   [
-    0.91814453125,
-    0.08969146355986596
-   ],
-   [
-    0.95642578125,
-    0.05853485576808452
-   ]
-  ],
-  "20.0": [
-   [
-    0.50931640625,
-    0.4357883331179619
-   ],
-   [
-    0.7403515625,
-    0.22275549590587615
-   ],
-   [
-    0.82765625,
-    0.15037441711872815
-   ],
-   [
-    0.8998046875,
-    0.09680132243782281
-   ],
-   [
-    0.94701171875,
-    0.06260592238977551
-   ]
-  ]
- },
- "ofdma": {
-  "10.0": [
-   [
-    0.508203125,
-    0.4361314806342125
-   ],
-   [
-    0.76318359375,
-    0.21654599383473397
-   ],
-   [
-    0.84802734375,
-    0.14174282837659122
-   ],
-   [
-    0.91091796875,
-    0.08382582331076265
-   ],
-   [
-    0.9557421875,
-    0.043560955775901675
-   ]
-  ],
-  "20.0": [
-   [
-    0.32919921875,
-    0.48276128739118573
-   ],
-   [
-    0.68083984375,
-    0.23667482212185859
-   ],
-   [
-    0.79951171875,
-    0.15432738859206438
-   ],
-   [
-    0.88361328125,
-    0.09289443053305149
-   ],
-   [
-    0.94376953125,
-    0.04812081384472549
-   ]
-  ]
- }
-}

rev2/data/e4_v2_async.json

@@ -1,245 +0,0 @@
-{
- "dmax": [
-  0,
-  1,
-  2,
-  4,
-  8
- ],
- "snr_eval": [
-  10.0,
-  20.0
- ],
- "curves": {
-  "uwca_uncorrected": {
-   "10.0": [
-    [
-     0.26302734375,
-     0.49663727134466173
-    ],
-    [
-     0.72521484375,
-     0.24080994725227356
-    ],
-    [
-     0.83720703125,
-     0.1565597005933523
-    ],
-    [
-     0.9140234375,
-     0.09298811599612236
-    ],
-    [
-     0.95828125,
-     0.045774847799912095
-    ]
-   ],
-   "20.0": [
-    [
-     0.1059765625,
-     0.5424016201496125
-    ],
-    [
-     0.64892578125,
-     0.2658117674291134
-    ],
-    [
-     0.79361328125,
-     0.1722280565276742
-    ],
-    [
-     0.89083984375,
-     0.09743562746793032
-    ],
-    [
-     0.94720703125,
-     0.051459523779340086
-    ]
-   ]
-  },
-  "ofdma_uncorrected": {
-   "10.0": [
-    [
-     0.50865234375,
-     0.43710263311862946
-    ],
-    [
-     0.76029296875,
-     0.2157912875711918
-    ],
-    [
-     0.8459375,
-     0.14081338860094547
-    ],
-    [
-     0.9133984375,
-     0.08355670671910048
-    ],
-    [
-     0.9558984375,
-     0.04483862698078155
-    ]
-   ],
-   "20.0": [
-    [
-     0.3259765625,
-     0.4835157571732998
-    ],
-    [
-     0.68376953125,
-     0.23547276966273784
-    ],
-    [
-     0.7949609375,
-     0.1552288055792451
-    ],
-    [
-     0.886640625,
-     0.09062881361693144
-    ],
-    [
-     0.94580078125,
-     0.04779002937488258
-    ]
-   ]
-  },
-  "uwca_corrected": {
-   "10.0": [
-    [
-     0.2655859375,
-     0.49615009009838107
-    ],
-    [
-     0.44373046875,
-     0.4498268289864063
-    ],
-    [
-     0.5089453125,
-     0.4324074760079384
-    ],
-    [
-     0.578828125,
-     0.4135142582654953
-    ],
-    [
-     0.6497265625,
-     0.38951330006122586
-    ]
-   ],
-   "20.0": [
-    [
-     0.11287109375,
-     0.5400555384159088
-    ],
-    [
-     0.30025390625,
-     0.4892691922187805
-    ],
-    [
-     0.38263671875,
-     0.46662179097533224
-    ],
-    [
-     0.4680078125,
-     0.4436407870054245
-    ],
-    [
-     0.56431640625,
-     0.41600462675094607
-    ]
-   ]
-  },
-  "ofdma_corrected": {
-   "10.0": [
-    [
-     0.50662109375,
-     0.4369038107991219
-    ],
-    [
-     0.53580078125,
-     0.42834869906306267
-    ],
-    [
-     0.55255859375,
-     0.42323953911662104
-    ],
-    [
-     0.58455078125,
-     0.41399665489792825
-    ],
-    [
-     0.638125,
-     0.3948879507184029
-    ]
-   ],
-   "20.0": [
-    [
-     0.32869140625,
-     0.48293048948049544
-    ],
-    [
-     0.371953125,
-     0.4720873585343361
-    ],
-    [
-     0.403359375,
-     0.46447199031710623
-    ],
-    [
-     0.45724609375,
-     0.44948955610394475
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rev2/data/e8_e2e_anticollapse.json

@@ -1,107 +0,0 @@
-{
- "snr": [
-  0.0,
-  2.0,
-  4.0,
-  6.0,
-  8.0,
-  10.0,
-  12.0,
-  14.0,
-  16.0,
-  18.0,
-  20.0
- ],
- "configs": {
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-    0.14369791666666668,
-    0.11671875,
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-    0.44846010764439903,
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-   "erank": 63.314430236816406,
-   "rho_off": 0.998196005821228
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