Ki-Ho Lee
248e637f55
Semantic Multiplexing Gain in Wireless Systems via Expanded Embeddings: A BERT Case Study. Includes the shared library, all experiment scripts (training with and without SNR-aware MAML, the token-domain comparison, the K sweep, and DistilBERT), the replot script that regenerates every figure from the stored results, the supplementary probe-versus-cosine analysis, and the raw results behind every figure in the letter.
43 lines
1.5 KiB
Python
Executable File
43 lines
1.5 KiB
Python
Executable File
# cl_todma_load.py — ToDMA load sweep (evaluation only, no training).
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#
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# Evaluates the ToDMA token-domain scheme (T=24, L=128, genie-aided
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# association) for U in {1,2,3,5,6} on the held-out test set, matching
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# the U=4 run stored in cl_results.json, so that Fig. 3 can show the
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# ToDMA aggregate fidelity across load.
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import argparse, os, json
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import torch
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from cl_experiments import (
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load_agnews_labeled, Extractor, SplitCache, todma_eval, EVAL_SNRS
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)
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def main():
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ap = argparse.ArgumentParser()
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ap.add_argument("--save-dir", default="fig_cl")
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ap.add_argument("--frames", type=int, default=200)
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args = ap.parse_args()
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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os.makedirs(args.save_dir, exist_ok=True)
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print(f"[Device: {device}]", flush=True)
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train_items, test_items = load_agnews_labeled()
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bert = Extractor("bert-base-uncased", device)
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cache = SplitCache(bert, train_items, test_items)
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R = {}
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for U in [1, 2, 3, 5, 6]:
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print(f"\n=== ToDMA U={U} (T=24, L=128) ===", flush=True)
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res, _ = todma_eval(bert, cache, device, U=U, T=24, L=128,
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n_frames=args.frames)
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key = f"todma_U{U}_T24_L128"
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R[key] = {str(s): res[s] for s in EVAL_SNRS}
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with open(os.path.join(args.save_dir,
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"cl_results_todma_u.json"), "w") as f:
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json.dump(R, f, indent=1)
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print("\nToDMA load sweep complete.", flush=True)
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if __name__ == "__main__":
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main()
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