Search Results (156)

To enhance the spectral efficiency of hybrid beamforming in millimeter-wave massive MIMO systems, the problem is formulated as a high-dimensional non-convex optimization under constant modulus constraints. A novel algorithm based on fourth-order tensor Tucker decomposition is proposed. Specifically, the frequency-domain channel matrices are structured into a fourth-order tensor to explicitly capture the couplings across the spatial, frequency, and user domains. To tackle the non-convexity induced by constant modulus constraints, the analog precoder and combiner are derived by solving a truncated-rank Tucker decomposition problem through the Alternating Direction Method of Multipliers and Alternating Least Squares schemes. Subsequently, in the digital domain, the Regularized Block Diagonalization algorithm is integrated with the subcarrier and user factor matrices—obtained from the tensor decomposition—along with the water-filling strategy to design the digital precoder and combiner, thereby achieving a balance between multi-user interference suppression and noise enhancement. The proposed tensor-based algorithm is demonstrated through simulations to outperform existing state-of-the-art schemes. This work provides an efficient and mathematically sound solution for hybrid beamforming in dense multi-user scenarios envisioned for sixth-generation mobile communications. Full article

Enhancing user access capacity in satellite beam-hopping systems remains challenging due to dynamic traffic and limited beam dwell times. Conventional Multi-Frequency Time-Division Multiple Access (MF-TDMA) proves highly inefficient under such constraints. To overcome this, we propose a novel scheme that integrates power-domain Non-Orthogonal Multiple Access (NOMA) with MF-TDMA, employing Successive Interference Cancelation (SIC) for multi-user signal separation. A bi-directional adaptive carrier synchronization method and optimized burst structure are introduced, which collectively reduce synchronization overhead by over 40% compared to MF-TDMA. Simulations demonstrate a dramatically improved frame error rate of 0.0005% at 4 dB SNR—30 times lower than the 0.016% achieved by MF-TDMA—and a transmission efficiency of 92–97%, significantly outperforming conventional MF-TDMA. These results validate the proposed method’s substantial gains in capacity and efficiency for next-generation satellite systems. Full article

With the evolution of 6G technology toward global coverage and multidimensional integration, OTFS modulation has become a research focus due to its advantages in high-mobility scenarios. However, existing OTFS signal detection algorithms face challenges such as pilot contamination, Doppler spread degradation, and diverse interference in complex environments. This paper proposes the SPARSE-OTFS-Net algorithm, which establishes a comprehensive signal detection solution by innovatively integrating sparse random pilot design, compressive sensing-based frequency offset estimation with closed-loop cancellation, and joint denoising techniques combining an autoencoder, residual learning, and multi-scale feature fusion. The algorithm employs deep learning to dynamically generate non-uniform pilot distributions, reducing pilot contamination by 60%. Through orthogonal matching pursuit algorithms, it achieves super-resolution frequency offset estimation with tracking errors controlled within 20 Hz, effectively addressing Doppler spread degradation. The multi-stage denoising mechanism of deep neural networks suppresses various interferences while preserving time-frequency domain signal sparsity. Simulation results demonstrate: Under large frequency offset, multipath, and low SNR conditions, multi-kernel convolution technology achieves significant computational complexity reduction while exhibiting outstanding performance in tracking error and weak multipath detection. In 1000 km/h high-speed mobility scenarios, Doppler error estimation accuracy reaches ±25 Hz (approaching the Cramér-Rao bound), with BER performance of 5.0 × 10⁻⁶ (7× improvement over single-Gaussian CNN’s 3.5 × 10⁻⁵). In 1024-user interference scenarios with BER = 10⁻⁵ requirements, SNR demand decreases from 11.4 dB to 9.2 dB (2.2 dB reduction), while maintaining EVM at 6.5% under 1024-user concurrency (compared to 16.5% for conventional MMSE), effectively increasing concurrent user capacity in 6G ultra-massive connectivity scenarios. These results validate the superior performance of SPARSE-OTFS-Net in 6G ultra-massive connectivity applications and provide critical technical support for realizing integrated space–air–ground networks. Full article

Sixth-generation (6G) communication systems, with ultra-wide bands, energy-autonomous end nodes, and dense connectivity, challenge existing network designs. Optimizing time resources with energy harvesting, backscatter communication, and relays is essential to maximize the total bit rate in multi-user symbiotic radio networks (SRNs) with blocked direct paths. The literature lacks a unified optimization treatment that explicitly accounts for imperfect successive interference cancellation (SIC). This study addresses this gap by proposing the first optimization framework to maximize total bit rate for energy-harvesting TDMA/PD–NOMA-based multi-cluster and relay-assisted peer-assisted SR networks. The two-phase architecture defines a tractable constrained optimization problem that jointly adjusts cluster-specific time slots ($\mathit{\tau }$ and $\lambda$). Incorporating QoS, signal power, and reflection coefficient constraints, it provides a compact formulation and numerical solutions for both perfect and imperfect SIC. Detailed simulations performed under typical 6G power levels, bandwidths, and energy-harvesting efficiencies demonstrate graphically that imperfect SIC significantly limits total throughput due to residual interference, while perfect SIC completely eliminates this ceiling under the same conditions, providing a significant capacity advantage. Furthermore, the gap between the two scenarios rapidly closes with increasing relay time margin. The findings demonstrate that network capacity is primarily determined by the triad of base station output power, channel noise, and SIC accuracy, and that the proposed framework achieves strong performance across the explored parameter space. Full article

In multi-user wireless communication scenarios, signal degradation caused by channel fading and co-channel interference restricts system capacity, while traditional enhancement schemes face challenges of high coordination complexity and hardware integration. This paper proposes an electromagnetic focusing method using a single-layer transmissive passive metasurface. A high-efficiency metasurface array is fabricated based on PCB technology, which utilizes subwavelength units for wide-range phase modulation to construct a multi-user energy convergence model in the WiFi band. By optimizing phase gradients through the geometric phase principle, the metasurface achieves collaborative wavefront manipulation for multiple target regions with high transmission efficiency, reducing system complexity compared to traditional multi-layer structures. Measurements in a microwave anechoic chamber and tests in an office environment demonstrate that the metasurface can simultaneously create signal enhancement zones for multiple users, featuring stable focusing capability and environmental adaptability. This lightweight design facilitates deployment in dense networks, providing an effective solution for signal optimization in indoor distributed systems and IoT communications. Full article

The demand for high data rates and large system capacity has posed significant challenges for medium access control (MAC) methods. Successive interference cancellation (SIC) is a classical multi-user detection (MUD) method; however, it suffers from an error propagation problem. To address this deficiency, we propose a method called Virtual Signal Processing-Based Integrated Multi-User Detection (VSP-IMUD). In VSP-IMUD, the received mixed multi-user signals are treated as an equivalent signal. The channel ambiguity corresponding to each user’s signal is then examined. For channels with non-zero ambiguity values, the signal components are detected using zero-forcing (ZF) reception. Next, the detected ambiguous signal components are reconstructed and subtracted from the received mixed signal using SIC. Once all the ambiguous signals are detected, the remaining signal components with zero ambiguity values are equated to a virtual integrated signal, to which a matched filter (MF) is applied. Finally, by selecting the signal with the highest channel gain and adopting its data as the reference symbol, the remaining signals’ dataset can be determined. Our theoretical analysis and simulation results demonstrate that VSP-IMUD effectively reduces the frequency of SIC applications and mitigates its error propagation effects, thereby improving the system’s bit-error rate (BER) performance. Full article

This paper proposes a novel Twin Delayed Deep Deterministic Policy Gradient (TDDPG)-based joint optimization algorithm for hybrid reconfigurable intelligent surface (RIS)-assisted integrated sensing and communication (ISAC) systems in Internet of Vehicles (IoV) scenarios. The proposed system model achieves deep integration of sensing and communication by superimposing the communication and sensing signals within the same waveform. To decouple the complex joint design problem, a dual-DDPG architecture is introduced, in which one agent optimizes the transmit beamforming vector and the other adjusts the RIS phase shift matrix. Both agents share a unified reward function that comprehensively considers multi-user interference (MUI), total transmit power, RIS noise power, and sensing accuracy via the CRLB constraint. Simulation results demonstrate that the proposed TDDPG algorithm significantly outperforms conventional DDPG in terms of sum rate and interference suppression. Moreover, the adoption of a hybrid RIS enables an effective trade-off between communication performance and system energy efficiency, highlighting its practical deployment potential in dynamic IoV environments. Full article

The deployment of autonomous AI agents in smart environments has accelerated the need for accurate and privacy-preserving human identification. Traditional vision-based solutions, while effective in capturing spatial and contextual information, often face challenges related to high deployment costs, privacy concerns, and susceptibility to environmental variations. To address these limitations, we propose IdentiFi, a novel AI-driven human identification system that leverages WiFi-based wireless sensing and contrastive learning techniques. IdentiFi utilizes self-supervised and semi-supervised learning to extract robust, identity-specific representations from Channel State Information (CSI) data, effectively distinguishing between individuals even in dynamic, multi-occupant settings. The system’s temporal and contextual contrasting modules enhance its ability to model human motion and reduce multi-user interference, while class-aware contrastive learning minimizes the need for extensive labeled datasets. Extensive evaluations demonstrate that IdentiFi outperforms existing methods in terms of scalability, adaptability, and privacy preservation, making it highly suitable for AI agents in smart homes, healthcare facilities, security systems, and personalized services. Full article

Integrated Sensing and Communication (ISAC) has emerged as a key direction in 6G technology and is currently a major focus of research. This paper proposes a dynamic power allocation scheme that, under the constraints of transmission power and detection probability, formulates power allocation as an optimization problem aimed at maximizing the system sum rate. The approach accounts for varying target motion states, inter-user interference, and channel conditions. By jointly considering target motion sensing, inter-user interference, and channel conditions, our approach outperforms traditional methods that optimize communication and sensing separately. Numerical simulation results show that this scheme can achieve excellent performance in complex real-world environments. This work contributes to the development of efficient ISAC solutions and provides insights for future research into 6G technologies. Full article

Traditional sparse code multiple access (SCMA) systems, which transmit user codewords through fixed subcarrier allocations, exhibit vulnerability to external jamming and interference. To address this challenge, we propose a novel SCMA codebook design incorporating the frequency-hopping (FH) technique in this paper. The construction of FH-SCMA codebooks is developed by applying cyclic shifting operations to the factor graph matrix of the conventional SCMA codebooks, where the cyclic shifting patterns are governed by chaotic Bernoulli FH sequences. Through a comprehensive case study, the critical properties of the proposed FH-SCMA codebooks—the uniformity and the sparsity, along with its error-rate performance—are illustrated in detail. Through the proposed FH-SCMA codebooks, the subcarriers of FH-SCMA are randomly hopped over within the resource-block group, while retaining the sparsity requirement, thereby facilitating the multi-user detection at the receiver. The proposed FH-SCMA system (codebooks) achieves superior performance under jamming scenarios compared to both the traditional SCMA and the previous pseudo-random FH-SCMA. Full article

This study analyzes the performance of a multi-user digital communication system aided by reflecting intelligent surfaces (RIS) in terms of bit error probability and secrecy outage probability for a system sending symbols with M-QAM modulation passing through channels with Weibull fading, where RIS are employed to improve the signal-to-noise plus interference ratio (SINR) for each user. The performance analysis is conducted based on the statistical properties of the phase correction error of the transmitted signal, which follows a von Mises distribution. Furthermore, this study demonstrates that the resulting SINR follows a gamma distribution, with its parameters derived analytically. The RIS performance increases the line of sight strength and reduces the secrecy outage probability and error probability when the number of reflectors is sufficiently large, even without direct links between the users and the transmitter. Full article

With the rapid development of visible light communication (VLC) technology, traditional modulation schemes can no longer meet the high demands for bandwidth efficiency and signal stability in complex application scenarios. In particular, in orthogonal frequency division multiplexing (OFDM) systems, issues such as the nonlinearity of Light-Emitting Diodes (LEDs) and carrier frequency offset have worsened system performance. To address these challenges, this paper proposes an N-order Minimum Shift Keying (NMSK) OFDM system with Fast Hartley Transform (FHT) for signal mapping. Monte Carlo simulations systematically compare the performance of low-order and high-order NMSK modulations under various conditions. The results indicate that low-order NMSK exhibits superior robustness against bit errors and interference, while high-order NMSK can maintain a stable PAPR and provide higher spectral efficiency in high-bandwidth demand scenarios. Further experiments validate the stability of high-order NMSK in high-density multi-user and Industrial Internet of Things (IIoT) environments, proving its adaptability and effectiveness in such scenarios. The high-order NMSK modulation scheme provides strong support for the reliability and bandwidth efficiency of future 6G VLC networks, offering significant application prospects. Full article

Non-orthogonal multiple access (NOMA) has emerged as a key enabler of massive connectivity in next-generation wireless networks. However, conventional NOMA studies predominantly focus on two-user scenarios, limiting their scalability in practical multi-user environments. A critical challenge in these systems is error propagation in successive interference cancellation (SIC), which is further exacerbated by hardware distortions (HWDs). Hybrid NOMA (HNOMA) mitigates SIC errors and reduces system complexity, yet cell-edge users (CEUs) continue to experience degraded sum spectral efficiency (SSE) and throughput. Cooperative NOMA (C-NOMA) enhances CEU performance through retransmissions but incurs higher energy consumption. To address these limitations, this study integrates intelligent omni-surfaces (IOSs) into a cooperative hybrid NOMA (C-HNOMA) framework to enhance retransmission efficiency and extend network coverage. The closed-form expressions for average outage probability and throughput are derived, and a power allocation (PA) optimization framework is proposed to maximize SSE, with validation through Monte Carlo simulations. The introduction of a novel strong–weak strong–weak (SW-SW) user pairing strategy capitalizes on channel diversity, achieving an SSE improvement of ∼0.48% to ∼3.81% over conventional pairing schemes. Moreover, the proposed system demonstrates significant performance gains as the number of IOS elements increases, even under imperfect SIC (iSIC) and HWD conditions. By optimizing PA values, SSE is further enhanced by at least 2.24%, even with an SIC error of 0.01 and an HWD level of 8%. These results underscore the potential of an IOS-assisted C-HNOMA system with SW-SW pairing as a viable solution for improving multi-user connectivity, SSE, and system robustness in future wireless communication networks. Full article

Reconfigurable Intelligent Surfaces (RISs) and Non-Orthogonal Multiple Access (NOMA) have emerged as key technologies for next-generation (6G) wireless networks, attracting significant attention from researchers. As an advanced extension of RISs, Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surfaces (STAR-RISs) offer superior geometric and functional symmetry due to their capability to simultaneously reflect and transmit signals, thereby achieving full 360° spatial coverage. This symmetry not only ensures balanced energy distribution between the Transmission (T) and Reflection (R) regions but also facilitates interference cancellation through phase alignment. Furthermore, in NOMA networks, the symmetric allocation of power coefficients and the tunable transmission and reflection coefficients of STAR-RIS elements aligns with the principle of resource fairness in multi-user systems, which is crucial for maintaining fairness under asymmetric channel conditions. In this study, key factors, such as interference sources and distance effects, are considered in order to conduct a detailed analysis of the performance of STAR-RIS-assisted NOMA wireless education networks under multiple interference devices. Specifically, a comprehensive analysis of the Signal-to-Interference-plus-Noise Ratio (SINR) for both near-end and far-end devices is conducted, considering various scenarios, such as whether or not the direct communication link exists between the base station and the near-end device, and whether or not the near-end device is affected by interference. Based on these analyses, closed-form approximate expressions for the outage probabilities of the near-end and far-end devices, as well as the closed-form approximation for the system’s Spectral Efficiency (SE), are derived. Notably, the Gamma distribution is used to approximate the square of the composite channel amplitude between the base station and the near-end device, effectively reducing computational complexity. Finally, simulation results validate the accuracy of our analytical results. Both numerical and simulation results show that adjusting the base station’s power allocation, and the transmission and reflection coefficients of the STAR-RIS, can effectively mitigate the impact of interference devices on the near-end device and enhance the communication performance of receiving devices. Additionally, increasing the number of STAR-RIS elements can effectively improve the overall performance of the near-end device, far-end device, and the entire system. Full article

Power line communication (PLC) technology is investigated in this research. A PLC system model combining Orthogonal Frequency Division Multiplexing (OFDM) and Non-Orthogonal Multiple Access (NOMA) technologies is proposed to enhance spectral efficiency, extend transmission distance, and improve signal quality. We construct detailed models for the system, signal, and noise. Future Channel State Information (CSI) is predicted using a Long Short-Term Memory (LSTM) network, and an improved simulated annealing algorithm is employed to optimize power allocation and relay positioning in the system. Experiments validate the effectiveness of the LSTM model in predicting CSI data in a NOMA communication system, demonstrating generally good performance despite some prediction errors. Simulation results show that this approach significantly enhances system performance, reduces power consumption, and meets constraints on system capacity, bit error rate (BER), and signal-to-interference-plus-noise ratio (SINR) in complex PLC environments. Future research should focus on optimizing model parameters, expanding datasets, exploring alternative optimization algorithms, and testing the model in real-world scenarios to improve generalizability and practicality. In conclusion, the proposed multi-user PLC system provides an effective technical solution for future smart grid and Internet of Things (IoT) applications. Full article