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Conference

CORE-CSI: Minimal-Pilot CSI Prediction via Complex One-Step Residual Evolution for Mobile QPSK Links

Sep 2026 · 2026 IEEE Colombian Conference on Communications and Computing (COLCOM) · pp. 1-6 · 0 citations · 15 references

Abstract

Channel aging degrades coherent demodulation in mobile links and makes frequent pilot-based CSI updates costly. This paper proposes CORE-CSI, a lightweight, task-specific, world-model-inspired predictor for one-step complex CSI evolution. Rather than directly regressing the next channel state, CORE-CSI predicts a sample-adaptive complex residual ratio from recent CSI history and reconstructs $H[t+1]=H[t]\odot\widehat{R}[t+1]$. The residual uses log-amplitude and circular sine/cosine phase components to preserve multiplicative channel structure and avoid phase-wrap discontinuities; user motion and Sionna RT geometry proxies are evaluated as candidate side information through ablation. We evaluate uncoded OFDM/QPSK with onetap equalization under LOS and NLOS fixed-path Doppler conditions initialized in the same Munich scene. Condition-specific models use five training seeds and chronological non-overlapping splits. At 10 dB, CORE-CSI reduces stale-CSI BER from $6.734 \times 10^{-2}$ to $(4.940 \pm 0.034) \times 10^{-3}$ in LOS and from $4.718 \times 10^{-1}$ to $(2.618 \pm 0.079) \times 10^{-3}$ in NLOS, corresponding to relative reductions of 92.66% and 99.45%. At 20 dB, no errors are observed over 147,456 bits per seed; the Wilson upper bound is $2.61 \times 10^{-5}$. The statistically competitive AR(1) baseline indicates that the evaluated fixed-path Doppler regime contains strong first-order temporal structure, while ablation does not resolve incremental gains from motion or geometry. Condition-wise unit-energy normalization isolates CSI dynamics and excludes the NLOS path-loss penalty. CORE-CSI requires no additional pilot subcarriers at the target snapshot, whereas the evaluated CPE configuration reserves 25%; this comparison characterizes a zeroadditional-target-pilot operating point rather than a neural-only throughput gain.

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