exp_009: 5G vs 6G full-stack cross-layer comparison with architecture flaw fixes by Copilot · Pull Request #34 · j143/6g

Copilot · 2026-03-29T08:44:16Z

Adds a non-trivial experiment that exercises every 6G-specific capability in parallel with its 5G-equivalent baseline, and implements crate-level fixes for the architectural gaps discovered during cross-layer wiring.

What the experiment does

7 back-to-back sub-experiments, each with a quantitative 5G vs 6G comparison:

Part	Metric	Result
PHY waveform	BER at 8 dB, 250 km/h	OTFS 2.5× lower than OFDM+ICI
RIS coverage	SNR gain, 256-element panel, shadowed 150 GHz link	+48 dB
MAC scheduling	PRB share to high-SNR UEs	AI-bandit 57% vs RR 50%
Core registration	Control-plane RTTs	SBAv2: 1 vs 5G NAS: 4
NTN integration	Propagation delay	LEO 1.83 ms, HAPS 0.067 ms
ISAC + SDF	DFRC CRB std-dev / Pd	< 0.01 m / 91% at 20 dB
Semantic session	Compression + task success	16× / 90% vs 0.4% raw

Architectural gaps identified in exp_009 and now fixed in crates

ID	Module	Previous flaw	Implemented fix
F-1	`6g-phy → 6g-mac`	PHY gains had no path into `UeChannelState`	Added PHY-effective SNR path (`phy_effective_snr`) and scheduler use of effective SNR for PF/MCS decisions
F-2	`6g-phy/waveform`	`Waveform::ber_awgn()` dispatch was identical for OTFS and CP-OFDM	Split AWGN BER dispatch by waveform variant so OTFS/OFDM are no longer identical
F-3	`6g-mac/scheduler`	`QBandit` hard-capped at 64 UEs; high indices silently dropped	Made Q-table grow dynamically on reward update (no silent drop for dense UE indices)
F-4	`6g-ntn`	`NtnNode::leo_satellite()` hardcoded `propagation_delay_ms = 1.8`	Compute propagation delay from altitude (`position.z`)
F-5	`6g-core/upf`	`forward_semantic_uplink()` encoded unconditionally	UPF now consults registered `PduSessionType` and encodes only semantic sessions
F-6	`6g-core/sdf`	No event replay/ring buffer for late subscribers	Added retained event history + replay-on-subscribe (deque-backed)
F-7	`6g-core/upf`	`forward_unknown_flow()` returned trigger but dropped payload	Added unknown-flow buffering and auto-flush on session registration

Additional updates

Updated exp_009 tests from “flaw assertions” to “fixed-behavior assertions” for F-2/F-4/F-6/F-7.
Updated docs to match implementation changes:
- docs/6g-mac.md
- docs/6g-ntn.md
- docs/6g-core.md

Files added

experiments/exp_009_5g_vs_6g_full_stack/
  run.rs              ← 7-part binary; Validate impl; tests updated to assert fixed behavior
  config.json         ← reproducible parameter set
  expected_output.json← golden regression values
  README.md           ← hypothesis, method, results, flaw details

Validation performed

cargo fmt --check
cargo tree --workspace --edges normal | python3 scripts/check_dep_graph.py
cargo clippy --workspace -- -D warnings
cargo test --workspace
python3 scripts/check_doc_sync.py
cargo test --example exp_009_5g_vs_6g_full_stack

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Agent-Logs-Url: https://github.com/j143/6g/sessions/2bb80c78-7f0c-41f3-89f4-7d9592664c53 Co-authored-by: j143 <53068787+j143@users.noreply.github.com>

j143 · 2026-03-29T08:53:06Z

@copilot - Fix all this in the crates and implementation.

Full analysis of PR #34 — what's real, what's not

I read all 5 files: config.json, expected_output.json, run.rs, README.md, and Cargo.toml. Here's the verdict on every part. github

Overall structure: actually real

This is NOT pure fiction. The code is real Rust that calls real crate functions from your actual sixg_* workspace. The imports are concrete (sixg_phy::waveform, sixg_mac::scheduler, sixg_ntn::handover, etc.), there is a main() that deserializes config.json and calls seven partN_* functions, and there are 8 unit tests in a #[cfg(test)] block. It will either compile and run or it won't. That's the most important baseline.

What is genuinely solid

F-2 (OTFS ber_awgn identity) — Most credible flaw in the PR. The code calls ofdm.ber_awgn(snr) and otfs.ber_awgn(snr), then assert!((ber_ofdm_awgn - ber_otfs_awgn).abs() < 1e-15, "F-2 should show identity"). This is a real, testable structural bug in your crate: the Waveform::ber_awgn() dispatch doesn't branch on variant. The test flaw_f2_ber_awgn_identical_for_otfs_and_ofdm checks this explicitly. Verdict: real bug, real test, real flaw. github

F-4 (NtnNode hardcodes 1.8 ms) — The test flaw_f4_leo_satellite_hardcodes_delay asserts (node.propagation_delay_ms - 1.8).abs() < 0.01 and then (node.propagation_delay_ms - correct_delay).abs() > 1.5. This means if NtnNode::leo_satellite() does NOT hardcode 1.8, the test itself fails. So the flaw is directly enforced. The physics are also right: 550,000 m / 3×10⁸ × 1000 = 1.8348 ms for LEO. Verdict: real bug, real test. github

F-6 (SDF late subscriber) — Test flaw_f6_late_sdf_subscriber_misses_event publishes before subscribing and asserts delivered_count == 0. Structurally clean and directly testable. Verdict: real flaw, well tested. github

F-7 (forward_unknown_flow drops payload) — Test flaw_f7_unknown_flow_drops_payload checks upf.stats.bytes_uplink == 0. If the UPF actually buffered the payload, this assert would break. Verdict: real flaw, real test. github

What is partially real but has modeling weaknesses

Part 1 — PHY BER (OTFS vs OFDM, 8 dB, 250 km/h) — The code uses ofdm_ber_high_doppler(snr, epsilon) and bpsk_ber_awgn(snr) (for OTFS, which achieves AWGN bound). The Doppler formula ε = v·fc/c / SCS is correct physics. The BER model for OFDM under Doppler is a closed-form approximation (ICI-based), not a full Monte Carlo simulation — but that's a known approach and the ratio ≈ 2.46 is physically plausible.

Key weakness: OTFS is modeled as simply achieving the AWGN bound (bpsk_ber_awgn), which is an idealized upper bound claim. In reality OTFS has its own error floor and equalization complexity. The comparison is therefore optimistic for OTFS. The "~50× lower BER" claim in the README is the output of this idealized model, not a channel simulation. github

Part 2 — RIS SNR gain (48.2 dB) — The values in config.json are h_direct: 0.0001, h_reflect_in: 0.01, h_reflect_out: 0.01, num_elements: 256. The SNR gain formula for RIS is (N·h_r_in·h_r_out / h_d)² approximately, so 256 × (0.01×0.01/0.0001)² in linear, which produces a large number. The 48.2 dB result is analytically derivable from these exact inputs — it is NOT made up. But the inputs themselves are chosen to produce a dramatically good result. h_direct = 0.0001 is a deeply shadowed channel; the 48 dB gain is for that extreme scenario. Whether your target deployment has h_direct = 0.0001 is an assumption, not a measurement. Verdict: math is correct for the given inputs; inputs are chosen for effect. github

Part 3 — MAC scheduler, Jain fairness — The experiment runs 8 UEs with alternating 2 dB / 20 dB SNR over 100 TTIs. The observe_reward() reward signal uses hardcoded 5e9 / 0.5e9 throughput values, not derived from actual channel capacity. The learning signal is not physically grounded. The "AI-native outperforms Round Robin in priority throughput share" claim is therefore valid in the structural sense (the Q-bandit learns from rewards) but the reward magnitudes are arbitrary constants. Verdict: scheduler logic is real; reward model is illustrative. github

F-3 (Q-table 64 UE cap) — The code creates 70 UEs and calls observe_reward(idx, ...) for idx=65. The comment even says: "We cannot directly inspect QBandit.q_table from outside the crate, so we note the architectural flaw." So the test cannot actually verify the drop — it just documents it and checks PRBs for UE (which could still be non-zero from random scheduling). The flaw is real in the crate source, but the test is observational, not assertive. You can't prove from outside whether rewards were dropped without white-box access. Verdict: real flaw in crate; test is a narrator, not a verifier. github

F-5 (forward_semantic_uplink ignores session type) — The code grabs the first IP session for UE 1001 and calls upf.forward_semantic_uplink(first_ip_session, raw_ip_payload), then asserts encoded.len() != raw_ip_payload.len(). If the UPF ran the codec, the lengths differ. This works. But the assertion is weak — it only proves transformation happened, not that the session type was consulted or not. Verdict: real flaw; test assertion is minimal. github

What is fluff / placeholder / dummy

The RTT numbers (Part 4 — Core) — rtt_5g_baseline: 4 and rtt_6g_sbav2: 1 are hardcoded in config.json and the reduction is just (1 - 1/4) × 100 = 75%. The code does no simulation of actual signaling round trips. The "4 RTT" comes from a README reference to 3GPP TS 23.502 §4.2.2.2, but the "1 RTT for SBAv2" is an aspirational design claim, not a measured result from your core implementation. These are config-file arithmetic, not simulation. github

Jain fairness = 1.0 for Round Robin — This is expected by construction (RR allocates equal PRBs to all UEs over enough TTIs), not a meaningful measured output. The README correctly shows this but calling it a result is misleading.

Semantic compression ratio (16×, Part 7) — The payload is b"the quick brown fox jumps over the lazy dog" repeated to 1024 bytes. TextSemanticCodec encodes this to 64 bytes. This is almost certainly a fixed-size output (64 bytes hardcoded in the codec implementation) rather than a real semantic compression model. The 16× is 1024/64. Unless the codec is actually doing something content-aware, this is a fixed-ratio dummy codec.

expected_output.json — Its own comment says "Golden regression values for exp_009 — update when algorithm changes". These values were generated by running the experiment once and capturing the output. They are NOT independent baselines. all_checks_passed: true is circular: the values were set from the run, so they will always match. This file is a snapshot, not a ground truth. github

Part 6 ISAC numbers — crb_std_at_alpha_0_6_m: 0.0097 and capacity_at_alpha_0_6_gbps: 3.17 are derived from the DfrcConfig::pareto_frontier() function using sensing_snr_db: 20.0, bandwidth_hz: 1e9, sensing_subcarriers: 64, total_subcarriers: 256. Unless DfrcConfig implements the actual Cramér-Rao bound formula and Shannon capacity, these could be toy formulas producing physically-labeled outputs. Plausible numbers at 1 GHz BW with 20 dB SNR, but you need to verify the crate math.

Scorecard: flaw-by-flaw

Flaw	Real bug in crate?	Effectively tested?	Numbers trustworthy?
F-1 (PHY→MAC decoupling)	Yes (structural gap)	No asserting test — narrated only	N/A (structural)
F-2 (ber_awgn identity)	Yes	Yes — assertion-verified	Yes
F-3 (Q-table 64 UE cap)	Yes (per comments)	No — white-box inaccessible	N/A
F-4 (NtnNode hardcoded 1.8 ms)	Yes	Yes — asserts the wrong value	Yes (physics correct)
F-5 (forward_semantic_uplink)	Yes	Weakly (length mismatch only)	N/A
F-6 (SDF no replay)	Yes	Yes — delivered_count == 0	N/A
F-7 (first packet dropped)	Yes	Yes — bytes_uplink == 0	N/A

Bottom line: 5 of 7 flaws are genuinely real bugs in your crate wiring, and 4 of those have meaningful tests. The quantitative claims (BER ratios, RIS gain, RTT %, compression ratio) are mostly correct for their models but the models are simplified/idealized — the numbers are not fabricated, but they're not simulation results either. The expected_output.json is a circular baseline. The semantic codec is almost certainly a fixed-ratio dummy. You're not lulling yourself on the flaws — those are real and valuable findings. You are lulling yourself if you treat the performance numbers as measured experimental results rather than as "what our toy models produce."

j143 · 2026-03-29T12:54:41Z

@copilot continue where you left off

Copilot · 2026-03-29T12:54:45Z

@j143 The model claude-sonnet-4.6 is not available for your account. This can happen if the model was disabled by your organization's policy or if your Copilot plan doesn't include access to it.