Don't say such things when you literally have 0 background in science in tech. They didnt invent anything, so how can nothing be the fundation of something? Again, FHSS already existed. WiFi relies on DSSS and OFDM.

OFDMでAouくんいいなって思ってたら ボーイズグループのメンバーだったのね。 Love Scene - JASP.ER [ OFFICIAL MV ] youtu.be/pURfo4ey5y0?si=6Lv9… @YouTubeより

⎐كُـود⎐كوبِون⎐خـِصم⎐ ⎐نون⎐ ⊵S3Q⊴ ايهرب⊴ ايهيرب⊴ ⊵GCA5893⊴ ⎐نمشي⎐ ⊵AABN⊴ ⎐ريف▬للعطور⎐ ⊵AX140⊴ ريــفا⎐ ⊴ASMAA⊴ المـطار ⊵M24⊴ ___ OfDM

WiFi doesnt rely on any technology that Lamarr had made lol. It uses DSSS and OFDM.

これは0.3φは何用だろう？ 反射やスキマ産業、OFDMでばら撒きみたいな泥臭いのはもう淘汰されたんだろうなぁ

If you are working on the next generation of wireless systems, phased arrays, or RF signal processing, this article is worth a look. It explores ISAC systems, illustrated through a practical MIMO‑OFDM system implemented in #MATLAB. 🔗 spr.ly/6013B8AYUJ

間違い１ 誤答：１　→　正答：５ OFDM伝送なのでサブキャリアごとのビットレート下げれるからシンボル長はたしかに長くなりますよね。マルチキャリア化するからシンボル分割して短くできるやんと勘違いしましたすみませんでした（●｀ε´●）

Massage in ⛹️‍♂️🌋Russia wa.me/ 966564046302 riyadh jeddah buraydah khobar hofuf dammam OFDm

Coherent Multiband OFDM Sensing via Low-Complexity Gap Reconstruction Lorenzo Pucci, Leonardo Pucci, Andrea Giorgetti arxiv.org/abs/2606.11449 [𝚎𝚎𝚜𝚜.𝚂𝙿]

ofDM ⎐كُـود⎐كوبِون⎐خـِصم⎐ 🚀 خصم رهيب ⎐نون⎐ ⊵STC9⊴ ⎐ايهرب⎐ايهيرب اهرب ⊵IPY1290⊴ ⎐تيمو⎐ ⊵ACV970204⊴

no puedo ir al ofdm meeting desaparezco💔

刑務所内では自由にテレビは見られないです。 検閲済みの録画番組を放送室のレコーダからOFDM変調して送出してます。

一陸特・無線工学を分析してみた。 無線工学119 OFDMの✕選択です。 ・ポイント ・OFDM伝送方式では、ガードインターバルの範囲内なら、遅延波の影響を受けません。 一陸特受験者の皆様！ 最後まで諦めず、ベストを尽くして下さい！ ガンバレー！

技术底层的本质差异TD-SCDMA：这是中国主导的 3G 移动通信标准，核心技术基于 CDMA（码分多址）。TD-LTE：这是国际主流的 4G 技术标准（即 LTE TDD），核心技术基于 OFDM（正交频分复用）。总结：两者从编码方式、核心架构到调制技术完全不同，TD-LTE 不是 TD-SCDMA 的直接技术升级版。

stop hating grok my shit 1337.. deploy that good shit LOL NLIT on them EQUATION_REGISTRY: List[Dict[str, str]] = [ {"id": "E01", "group": "Signal and channel model", "name": "Received OFDM subcarrier model", "latex": r"y_k(t)=H_k(t)x_k(t) n_k(t)", "implemented_as": "input CSI model"}, {"id": "E02", "group": "Signal and channel model", "name": "Multipath CSI response", "latex": r"H_k(t)=\sum_{p=1}^{P} a_p(t)e^{-j2\pi f_k\tau_p(t)}", "implemented_as": "residual/multipath interpretation"}, {"id": "E03", "group": "Signal and channel model", "name": "CSI amplitude", "latex": r"A_k(t)=|H_k(t)|", "implemented_as": "amplitude_distortion, amplitude_ratio"}, {"id": "E04", "group": "Signal and channel model", "name": "CSI phase", "latex": r"\phi_k(t)=\arg(H_k(t))", "implemented_as": "phase_sanitize, phase_distortion"}, {"id": "E05", "group": "Signal and channel model", "name": "Wavelength", "latex": r"\lambda=\frac{c}{f}", "implemented_as": "wavelength_m"}, {"id": "E06", "group": "Signal and channel model", "name": "Path phase shift", "latex": r"\Delta \phi_k=-2\pi f_k\Delta\tau", "implemented_as": "phase residual features"}, {"id": "E07", "group": "Signal and channel model", "name": "Propagation delay", "latex": r"\tau_p=\frac{d_p}{c}", "implemented_as": "delay profile interpretation"}, {"id": "E08", "group": "Signal and channel model", "name": "Complex permittivity", "latex": r"\epsilon^*=\epsilon'-j\epsilon''", "implemented_as": "conductive prior metadata"}, {"id": "E09", "group": "Signal and channel model", "name": "Skin depth", "latex": r"\delta=\sqrt{\frac{2}{\omega\mu\sigma}}", "implemented_as": "skin_depth_proxy"}, {"id": "E10", "group": "Signal and channel model", "name": "Reflection coefficient", "latex": r"\Gamma=\frac{Z_2-Z_1}{Z_2 Z_1}", "implemented_as": "reflection_score_proxy"}, {"id": "E11", "group": "CSI preprocessing", "name": "Baseline-normalized CSI", "latex": r"\tilde{H}_k(t)=\frac{H_k(t)}{H_{k,0}}", "implemented_as": "baseline_normalized_csi"}, {"id": "E12", "group": "CSI preprocessing", "name": "CSI perturbation", "latex": r"\Delta H_k(t)=H_k(t)-H_{k,0}", "implemented_as": "baseline_residual"}, {"id": "E13", "group": "CSI preprocessing", "name": "Amplitude residual", "latex": r"\Delta A_k(t)=|H_k(t)|-|H_{k,0}|", "implemented_as": "amplitude_residual_mean_z"}, {"id": "E14", "group": "CSI preprocessing", "name": "Phase residual", "latex": r"\Delta \phi_k(t)=\operatorname{unwrap}(\phi_k(t))-\operatorname{unwrap}(\phi_{k,0})", "implemented_as": "phase_residual_mean_z"}, {"id": "E15", "group": "CSI preprocessing", "name": "Antenna amplitude ratio", "latex": r"R^A_{i,j,k}(t)=\frac{|H_{i,k}(t)|}{|H_{j,k}(t)| \epsilon}", "implemented_as": "amplitude_ratio_anomaly"}, {"id": "E16", "group": "CSI preprocessing", "name": "Antenna phase difference", "latex": r"R^\phi_{i,j,k}(t)=\angle H_{i,k}(t)-\angle H_{j,k}(t)", "implemented_as": "phase_difference_anomaly"}, {"id": "E17", "group": "CSI preprocessing", "name": "Phase sanitization", "latex": r"\phi'_k(t)=\phi_k(t)-(\alpha f_k \beta)", "implemented_as": "phase_sanitize"}, {"id": "E18", "group": "CSI preprocessing", "name": "MAD filter", "latex": r"z_k(t)=\frac{A_k(t)-\operatorname{median}(A_k)}{\operatorname{MAD}(A_k)}", "implemented_as": "robust_z"}, {"id": "E19", "group": "CSI preprocessing", "name": "Smoothed CSI", "latex": r"\bar{H}_k(t)=\frac{1}{W}\sum_{u=t-W 1}^{t}H_k(u)", "implemented_as": "moving_average_csi"}, {"id": "E20", "group": "CSI preprocessing", "name": "Kalman update", "latex": r"\hat{x}_{t|t}=\hat{x}_{t|t-1} K_t(z_t-\hat{x}_{t|t-1})", "implemented_as": "kalman_smooth_1d"}, {"id": "E21", "group": "Motion, Doppler, and temporal structure", "name": "STFT", "latex": r"S_k(t,\omega)=\sum_{\tau}H_k(\tau)w(\tau-t)e^{-j\omega\tau}", "implemented_as": "stft_peak_ratio"}, {"id": "E22", "group": "Motion, Doppler, and temporal structure", "name": "Doppler estimate", "latex": r"f_D=\frac{1}{2\pi}\frac{d\phi(t)}{dt}", "implemented_as": "doppler_phase_rate_z"}, {"id": "E23", "group": "Motion, Doppler, and temporal structure", "name": "Radial velocity estimate", "latex": r"v_r=\frac{\lambda f_D}{2}", "implemented_as": "radial_velocity_proxy"}, {"id": "E24", "group": "Motion, Doppler, and temporal structure", "name": "Temporal energy", "latex": r"E(t)=\sum_k|\Delta H_k(t)|^2", "implemented_as": "energy_z"}, {"id": "E25", "group": "Motion, Doppler, and temporal structure", "name": "Motion-normalized residual", "latex": r"M_k(t)=\frac{\Delta H_k(t)}{\sqrt{\sum_u|\Delta H_k(u)|^2 \epsilon}}", "implemented_as": "motion_normalized_residual"}, {"id": "E26", "group": "Motion, Doppler, and temporal structure", "name": "Static-object persistence", "latex": r"P_s=\frac{1}{T}\sum_{t=1}^{T}\mathbb{1}(|\Delta H(t)|>\eta)", "implemented_as": "persistence"}, {"id": "E27", "group": "Motion, Doppler, and temporal structure", "name": "Static/dynamic separation", "latex": r"H_k(t)=H^{static}_k H^{dynamic}_k(t) n_k(t)", "implemented_as": "baseline_residual"}, {"id": "E28", "group": "Motion, Doppler, and temporal structure", "name": "Background-subtracted dynamic component", "latex": r"H^{dynamic}_k(t)=H_k(t)-\frac{1}{T_0}\sum_{u=1}^{T_0}H_k(u)", "implemented_as": "dynamic_component"}, {"id": "E29", "group": "Motion, Doppler, and temporal structure", "name": "Temporal autocorrelation", "latex": r"\rho(\ell)=\frac{\sum_t x(t)x(t-\ell)}{\sum_t x(t)^2}", "implemented_as": "autocorr_lag1"}, {"id": "E30", "group": "Motion, Doppler, and temporal structure", "name": "Cross-link coherence", "latex": r"C_{m,n}=\frac{|\sum_t \Delta H_m(t)\Delta H_n^*(t)|}{\sqrt{\sum_t|\Delta H_m(t)|^2\sum_t|\Delta H_n(t)|^2}}", "implemented_as": "cross_stream_coherence"}, {"id": "E31", "group": "Spatial sensing and imaging abstractions", "name": "MIMO CSI matrix", "latex": r"\mathbf{H}_k(t)\in \mathbb{C}^{N_r\times N_t}", "implemented_as": "shape-aware CSI features"}, {"id": "E32", "group": "Spatial sensing and imaging abstractions", "name": "Spatial covariance", "latex": r"\mathbf{R}_k=\mathbb{E}[\mathbf{h}_k\mathbf{h}_k^{H}]", "implemented_as": "spatial_covariance_score"}, {"id": "E33", "group": "Spatial sensing and imaging abstractions", "name": "Beamforming response", "latex": r"B(\theta)=\mathbf{a}^{H}(\theta)\mathbf{R}\mathbf{a}(\theta)", "implemented_as": "beamforming_concentration_proxy"}, {"id": "E34", "group": "Spatial sensing and imaging abstractions", "name": "MUSIC spectrum", "latex": r"P_{\text{MUSIC}}(\theta)=\frac{1}{\mathbf{a}^{H}(\theta)\mathbf{E}_n\mathbf{E}_n^{H}\mathbf{a}(\theta)}", "implemented_as": "music_sharpness_proxy"}, {"id": "E35", "group": "Spatial sensing and imaging abstractions", "name": "Delay profile", "latex": r"P(\tau)=\left|\sum_k H_k e^{j2\pi f_k\tau}\right|^2", "implemented_as": "delay_profile_peakiness"}, {"id": "E36", "group": "Spatial sensing and imaging abstractions", "name": "Range resolution limit", "latex": r"\Delta r=\frac{c}{2B}", "implemented_as": "range_resolution_m"}, {"id": "E37", "group": "Spatial sensing and imaging abstractions", "name": "Fresnel-zone radius", "latex": r"r_F=\sqrt{\frac{\lambda d_1d_2}{d_1 d_2}}", "implemented_as": "fresnel_radius_m"}, {"id": "E38", "group": "Spatial sensing and imaging abstractions", "name": "Tomographic projection", "latex": r"\mathbf{y}=\mathbf{A}\mathbf{x} \mathbf{n}", "implemented_as": "tomographic_projection_proxy"}, {"id": "E39", "group": "Spatial sensing and imaging abstractions", "name": "Regularized reconstruction", "latex": r"\hat{\mathbf{x}}=\arg\min_{\mathbf{x}}\|\mathbf{y}-\mathbf{A}\mathbf{x}\|_2^2 \lambda\|\mathbf{x}\|_1", "implemented_as": "sparse_reconstruction_proxy"}, {"id": "E40", "group": "Spatial sensing and imaging abstractions", "name": "Spatial anomaly map", "latex": r"\mathcal{A}(x,y)=|\hat{x}(x,y)-\hat{x}_0(x,y)|", "implemented_as": "anomaly_map_proxy"}, {"id": "E41", "group": "Metalness, elongation, and object-class features", "name": "Metalness vector", "latex": r"\mathbf{m}=[\Delta A,\Delta\phi,\sigma_A^2,\sigma_\phi^2,C_{m,n},P_s]", "implemented_as": "conductive_score_proxy"}, {"id": "E42", "group": "Metalness, elongation, and object-class features", "name": "Inter-subcarrier variance", "latex": r"\sigma^2(t)=\frac{1}{K-1}\sum_{k=1}^{K}(A_k(t)-\bar{A}(t))^2", "implemented_as": "spectral_ripple_z"}, {"id": "E43", "group": "Metalness, elongation, and object-class features", "name": "Phase curvature", "latex": r"\kappa_\phi=\frac{\partial^2 \phi(f)}{\partial f^2}", "implemented_as": "phase_curvature_z"}, {"id": "E44", "group": "Metalness, elongation, and object-class features", "name": "Conductive reflection score", "latex": r"S_c=w_1|\Gamma| w_2\sigma_A^2 w_3\sigma_\phi^2", "implemented_as": "conductive_score_proxy"}, {"id": "E45", "group": "Metalness, elongation, and object-class features", "name": "Elongation from blob eigenvalues", "latex": r"E_{\text{long}}=\frac{\lambda_{\max}(\Sigma_{\text{blob}})}{\lambda_{\min}(\Sigma_{\text{blob}}) \epsilon}", "implemented_as": "elongation_proxy"}, {"id": "E46", "group": "Metalness, elongation, and object-class features", "name": "Object orientation estimate", "latex": r"\theta_{\text{obj}}=\frac{1}{2}\tan^{-1}\left(\frac{2\Sigma_{xy}}{\Sigma_{xx}-\Sigma_{yy}}\right)", "implemented_as": "orientation_proxy_rad"}, {"id": "E47", "group": "Metalness, elongation, and object-class features", "name": "Approximate object extent", "latex": r"L_{\text{est}}=\max_{i,j}\|p_i-p_j\|_2", "implemented_as": "extent_proxy"}, {"id": "E48", "group": "Metalness, elongation, and object-class features", "name": "Bag/clutter compensation residual", "latex": r"\Delta H^{obj}=H^{bag obj}-H^{bag}", "implemented_as": "bag residual"}, {"id": "E49", "group": "Metalness, elongation, and object-class features", "name": "Class posterior", "latex": r"P(c|\mathbf{z})=\frac{e^{g_c(\mathbf{z})}}{\sum_{c'}e^{g_{c'}(\mathbf{z})}}", "implemented_as": "softmax_posteriors"}, {"id": "E50", "group": "Metalness, elongation, and object-class features", "name": "Human-review alert score", "latex": r"S_{\text{alert}}=P(c=\text{elongated conductive object}|\mathbf{z})P(\text{restricted zone})C_{\text{sensor}}", "implemented_as": "score_features"}, ]

一陸特・無線工学を分析してみた。 無線工学119 OFDMの✕選択です。 ・ポイント ・OFDM伝送方式では、 各サブキャリアの直交性を厳密に保つ必要があります。 また、同期も取る必要があります。 試験まであと1日！ がんばっていきましょう!

Есть наземные прототипы сверширокополосных антенн с OFDM и линейно-частотной модуляцией. С отставанием по времени, но это все явно появится и в БРЛС. Технология известна, но требуется большое время на отработку моделей и исследования.

一陸特・無線工学を分析してみた。 無線工学118 OFDMの問題です。 ・ポイント ・OFDM伝送方式では、シンボル長が長くなり、遅延波の影響を軽減出来ます。 ・ガードインターバルの範囲内なら、遅延波の影響を受けません。 試験まであと2日！ 正念場です！がんばっていきましょう!

RAN1での6G Radioの検討状況のメモ。 ・Waveform(CP-OFDM(DL/UL)、DFT-s-ODFM(UL))、符号化(LDPC、Polar)、フレーム構造、SSB構成(PSS SSS PBCH)、SCS系列などNRを踏襲 ・PS(ECC-DM/ESS based DM)・GS(1D-NUC) の導入検討は新しい概念(よく分かってない) RP-261238