To address the challenges of error accumulation and long-horizon divergence in multi-step network forecasting for 5 G core and backbone transport networks, this paper proposes a method based on Future-Guided Learning (FGL) and Multi-Teacher Distillation. The proposed approach introduces a causally consistent framework that leverages futurewindow teachers and a history-window student model. More specifically, multiple heterogeneous teacher models generate predictive distributions based on short-horizon windows that are closer to the forecasting targets. Then time-shifted probabilistic distillation transfers the resulting future soft labels to the student model, which is trained solely on historical data. This method significantly improves the accuracy and stability of long-horizon predictions. It does not incorporate future data during inference. For the student model, we adopt iTransformer-Lite, a lightweight multivariate time-series architecture, which reduces model size while enhancing the efficiency of multivariate interaction modeling. Experiments on the real-world CESNET-TimeSeries24 dataset demonstrate that the proposed method achieves lower RMSE and higher R-squared values for long-horizon forecasting, while maintaining a lightweight inference process. This approach provides reliable forward-looking insights, supporting proactive operations, traffic engineering, and slice resource orchestration in 5G and beyond networks.
Affine frequency division multiplexing (AFDM) is a promising multicarrier waveform for high-mobility scenarios due to its robustness against doubly-dispersive channels. However, accurate channel estimation remains challenging because delay and Doppler shifts are coupled in the discrete affine Fourier transform (DAFT) domain. In this paper, unlike conventional AFDM channel estimation schemes that rely on a relatively large chirp parameter to maintain path separability in doubly-dispersive channels, we develop a CP-based channel estimation scheme without this requirement. This makes the proposed method more suitable for channels with large delay spreads, where conventional designs would otherwise suffer from high pilot overhead. Specifically, the cross-correlation between the cyclic prefix (CP) and the corresponding symbol tail is computed over multiple symbols and averaged to estimate the Doppler shifts. The path delays are then identified using pilots in the DAFT domain, based on which an equivalent channel matrix is constructed for DAFT-domain equalization. Crucially, we show that under random signaling, the phase of the CP cross-correlation depends only on the Doppler shifts and is invariant to multipath interference. Simulation results demonstrate that the proposed method achieves a Doppler normalized mean square error (NMSE) of $10^{-3}$ at an SNR of 17 dB. Moreover, the proposed CP-AFDM scheme demonstrates superior BER performance over conventional multicarrier waveforms.
We consider a hybrid near-field and far-field simultaneous wireless information and power transfer (SWIPT) system assisted by simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS), where a fullduplex hybrid access point (HAP) serves multiple users via SWIPT. We formulate a sum-rate maximization problem, by jointly optimizing receive beamforming, energy beamforming and STAR-RIS coefficients whose solution is achieved via alternating optimization (AO) that combines penalty-based semidefinite relaxation (SDR), and an interior point method. Via simulations, the sum-rate performance of the proposed algorithmic solution is verified with the convergence analysis.
This work exploits the degrees of freedom offered by the null space of the time-varying cascaded channel between the base station and information users (IUs), as mediated by an intermediate reconfigurable intelligent surface (RIS), to transmit an additional energy signal to energy users (EUs) concurrently with the data transmission to the IUs. Specifically, an extended null space (ENS) based design is proposed for a RISassisted simultaneous wireless information and power transfer (SWIPT) system, enabling simultaneous information and energy transfer. The proposed framework formulates a multi-objective optimization problem aiming to jointly maximize the harvested power of the EUs and the data rates of the IUs. To solve this problem, a deep deterministic policy gradient (DDPG) algorithm is developed and implemented. Simulation results demonstrate that the DDPG-based ENS design consistently outperforms state-of-the-art SWIPT schemes by improving the IUs' sum-rate and the total equivalent sum-rate (ESR) of the entire system, while maintaining competitive performance for the wireless power transfer.
