High-mobility 6G sensing places where strict requirements on waveform robustness are required because the large Doppler shifts can degrade velocity estimation. This paper presents a refined simulation-based comparison of Orthogonal Time Frequency Space (OTFS) and Orthogonal Frequency Division Multiplexing (OFDM) for monostatic velocity sensing at 28 GHz. The evaluation uses a three-way receiver design: the first is OTFS with a single delay–Doppler pilot. The second is a strong full-grid OFDM radar baseline with known time–frequency symbols and range–Doppler matched processing. Third is a communication-oriented sparse-pilot OFDM receiver that reflects practical joint communication and sensing (JCAS) pilot budgets. All branches share identical carrier frequency, subcarrier spacing, grid size, cyclic-prefix length, power normalization, and Monte Carlo settings (250 trials per point). Velocity accuracy is reported through root-mean-square error (RMSE), success rate within ±10 km/h, multipath ablations, a velocity sweep, and quantitative response-concentration metrics. The results show that OTFS with one delay–Doppler pilot achieves near-constant RMSE of about 0.43 m/s and 100% success rate across −10 dB to 25 dB SNR at 120 km/h. A full-grid OFDM radar baseline attains essentially the same accuracy, demonstrating that OFDM is not intrinsically incapable of high-mobility velocity estimation when dense known pilots are available. So sparse-pilot OFDM collapses under the same Doppler conditions (RMSE ≈ 79 m/s and zero success rate) while OTFS preserves compact energy concentration in the delay–Doppler plane. These findings justify OTFS for pilot-efficient high-mobility sensing and clarify that OFDM under-performance in high-mobility sensing can be primarily baseline- and overhead-dependent rather than an absolute waveform verdict.

Antenna selection in reconfigurable intelligent surface (RIS)-assisted multiple-input multiple-output (MIMO) systems presents has significant computational challenges.  The contribution of each transmit antenna is determined by the combined effects of direct and RIS-reflected channels. To address the complexity of combinatorial search, a graph neural network (GNN)-based antenna selection framework is proposed. In this framework, transmit antennas are represented as graph nodes with channel-correlation information forming. The graph edges and magnitude-phase channel statistics serve as node features. A three-layer feedforward GNN is trained using greedy-selection labels generated from 1000 channel realizations and evaluated on 200 independent test realizations. For a 16×8 MIMO system assisted by a 64-element RIS at 28 GHz, the proposed method achieves a spectral efficiency of 63.41 bits/s/Hz and corresponding to 94.5% of the greedy baseline performance of 67.09 bits/s/Hz. While reducing the average selection time from 0.441 ms to 0.025 ms. These results determine that graph-structured learning enables near-greedy antenna selection with considerably lower inference complexity. The current study is limited to simulated settings with fixed system dimensions and idealized channel assumptions; future work will address broader channel models and larger-scale configurations.