This study proposes a downlink transmission scheme for coherent wavelength-division-multiplexed passive optical networks that introduces phase-amplitude multiplexing with asymmetric bit allocation and transmitter-side power reduction. Unlike conventional schemes that optimize only between in-phase and quadrature channels with symmetric bit mapping, the proposed method allocates unequal numbers of bits to phase and amplitude dimensions and extends power optimization to polarization multiplexing. This approach simultaneously addresses differences in transmission distance among optical network units and variations in their required transmission capacity. Numerical simulations confirm that transmitter-side power reduction can be achieved without degrading signal quality at the forward error correction limit. In contrast, digital signal processing at the receiver side remains fully functional. The scheme improves wavelength utilization by supporting up to four terminals per wavelength through polarization multiplexing. Analysis of the transmission budget further indicates that the maximum tolerable difference in transmission distance between terminals is approximately 20 kilometers. These results demonstrate the practicality and scalability of the proposed method for future mobile fronthaul access networks.

The emergence of federated learning as a novel distributed machine learning paradigm has found widespread applications in the field of data security. However, the model aggregation mechanism inherent in federated learning relies on centralised devices, and while the integration of blockchain technology effectively mitigates this issue, it simultaneously introduces several challenges. In practical scenarios, federated client devices have different performance levels and latencies, and the incorporation of blockchain’s consensus process significantly hinders the training efficiency of federated learning. Furthermore, clients are often untrustworthy and may exhibit malicious behaviour. To address these challenges, this paper proposes a practical and efficient Blockchain-based Dynamic Multi-Tier Federated Learning (B-DMTFL) framework. We consider the heterogeneity of clients in real-world network scenarios, and design a Dynamic FL computation scheme that facilitates reliable and efficient training within the federated learning process. At the same time, we integrate consistent hashing algorithm to formulate a new consensus protocol, thereby improving both the efficiency of consensus and the robustness of model aggregation. Extensive experiments demonstrate that this framework achieves accelerated convergence in applications that integrate blockchain with federated learning, while leveraging the blockchain’s verification mechanism to ensure secure model aggregation and maintain high model accuracy.

This paper presents a novel flexible ultra-wideband (UWB) antenna with notch characteristics in the WiMAX and WLAN frequency bands. The antenna employs flexible polyimide (PI) as the substrate material, offering excellent conformability to the human body. A coplanar waveguide (CPW) feeding structure is adopted to optimize impedance matching, while a defected ground structure (DGS) is introduced to achieve broadband performance. The antenna effectively suppresses interference from WiMAX and WLAN systems within the operating frequency range of 3.02-10.6 GHz. To mitigate electromagnetic interference when in contact with the human body and to enhance biocompatibility, a 2×2 electromagnetic bandgap (EBG) array is integrated on the backside of the antenna. This configuration significantly suppresses backward radiation and reduces the specific absorption rate (SAR). Simulation results incorporating a human body model demonstrate that the SAR values of the proposed UWB antenna are well below the limits specified by the FCC, ensuring its safety in wearable applications. Further studies confirm that the antenna maintains stable electrical performance under bending conditions, indicating its strong potential for applications in health monitoring and wearable biomedical devices.

Orthogonal Time Frequency Space (OTFS) modulation is an emerging multicarrier technique. It multiplexes and transmits signals in the Delay-Doppler (DD) domain, offering a robust solution for Vehicle-to-Everything (V2X) communication scenarios. Accurate channel estimation is critical for achieving high-performance OTFS communication within these environments. Traditional channel estimation methods often result in high Peak-to-Average Power Ratio (PAPR). They also tend to overlook the nonlinear effects of High-Power Amplifiers (HPAs) on the system. To address these challenges, this paper proposes a two-stage OMP-DL approach combining traditional algorithms with deep learning. Our proposed method first employs the traditional Orthogonal Matching Pursuit (OMP) algorithm for initial estimation of preambles and pilot signals. Next, a Deep Neural Network (DNN) standardizes these initial results. This standardized data then feeds into a Multi-Scale Gated Recurrent Unit (Multi-Scale GRU). The Multi-Scale GRU extracts features from its small-, medium-, and large-scale components for subsequent feature fusion. Finally, a denoising module produces the optimized estimation result. Simulation results demonstrate our algorithm’s effectiveness. It achieves lower Bit Error Rate (BER) and Normalized Mean Square Error (NMSE). Furthermore, it significantly mitigates the problem of high PAPR.

This paper proposes a multiplexing scheme of control signals for initial access using a dedicated frequency bandwidth for the discrete Fourier transform-spread orthogonal frequency division multiplexing (DFT-S-OFDM) waveform in the sub-Terahertz (THz) bands. We employ a subcarrier spacing (SCS) in the dedicated frequency bandwidth that is narrower than that for other frequency bandwidths in which the shared channels used to convey end-user application data and layer 1/layer 2 control channels in the physical layer are multiplexed in the downlink (DL) and uplink (UL). The estimation accuracy of fractional frequency offset (FFO) based on the autocorrelation using a cyclic prefix (CP) that is attached to each DFT-S-OFDM symbol is improved due to the increase in the CP power as the SCS becomes narrower. Accordingly, the narrower SCS for the control signals in the initial access enable us to achieve a high physical layer cell ID detection probability by making effective use of the FFO estimation and joint estimation of the integer frequency offset (IFO) and primary synchronization signal (PSS) in the DL. Moreover, the narrow SCS achieves a relaxed round-trip time delay measurement based on the physical random access channel (PRACH). This enables a cyclic-shift based multiplexing scheme for the multiple PRACH preambles from the simultaneously accessing sets of user equipment (UE), which leads to a low miss-detection probability for the PRACH. We also present a synchronization signal block (SSB) structure in the DL and PRACH structure in the UL that are suited to the proposed multiplexing scheme for initial-access control signals in the sub-THz bands.

In this work, we make a case study on ratio-of-collapse evaluation after 2016 Kumamoto earthquakes by using ALOS2-PALSAR data. The worst-hit area Mashikimachi is selected as the observe area. We propose two types of methods for evaluating ratio-of-collapse after earthquake, i.e., supervised and unsupervised algorithm. The supervised method is developed by using quaternion neural network and Poincare sphere parameters. The unsupervised algorithm is based on seven models scattering power decomposition. The results of both methods well coincide with the survey result of ratio-of-collapse.

After the academic researches in recent years, integrated sensing and communication (ISAC) is now under study for the standardization in the 3rd Generation Partnership Project (3GPP) towards the fifth generation (5G)-Advanced (5G-A) and sixth generation (6G) wireless communication systems. In this paper, to promote the possibility of successful ISAC commercialization, we focus on communication-oriented ISAC system design. We firstly introduce the standardization progress of ISAC in 3GPP, and propose the concept of communication-oriented ISAC (CO-ISAC), and discuss the technologies under CO-ISAC framework including sensing modes, sensing waveform, multiple-input-multiple-output (MIMO) scheme, multiplexing methods for sensing and communication, and reference signal (RS). Then, taking RS design as one example, we propose a common RS for sensing and communication. Pseudo-random (PN) and Zadoff-Chu (ZC) sequences in communication systems are enhanced for sensing performance improvement. For PN, Binary Phase Shift Keying with $\tfrac{\pi}{2}$ rotation ($\tfrac{\pi}{2}$-BPSK) modulation is recommended via proving the low sidelobe in aperiodic ambiguity function (AF). For ZC, the closed-form expressions of aperiodic AF and peak-to-sidelobe ratio (PSLR) are derived, based on which the root index of ZC with low PSLR is designed. Evaluation results validate the gain of proposed PN and ZC sequences on sensing performance. Finally, our views on 6G Day-1 ISAC system design are provided, especially proposing that sensing technologies should be carefully designed by taking communication performance into consideration. Our research work shows the great potential of ISAC technologies to create new values even in CO-ISAC systems. In the future, more efforts on the researches, standardization and trials from academia and industry are needed to push ISAC into reality for 5G-A and 6G.