International Journal of Communication Systems最新文献

In Multiuser Multiple-Input Multiple-Output systems, the peak-to-average power ratio poses a significant constraint, which impacts power efficiency, signal integrity, and overall system reliability. For reducing peak-to-average power ratio, although conventional methods provide varying levels of effectiveness, these techniques frequently encounter issues related to requirements for side information, limited flexibility in adapting to changing channel conditions, and computational complexity. To address these challenges, this paper proposes a novel Proximal Policy Optimization (PPO)-based Graph Neural Network framework for dynamic precoding optimization, particularly focusing on minimizing the peak-to-average power ratio in Multiuser Multiple-Input Multiple-Output systems. Because of its effective balance between performance and training reliability, the PPO algorithm is employed, which promotes stable and efficient learning. The model uses the CSI data to build a graph, and then extracts features through a GNN to obtain spatial correlations. These extracted GNN features were used as the state of a PPO reinforcement learning agent. The PPO agent is trained to find optimal precoding policies to minimize PAPR without compromising signal quality. Then, the acquired policy is applied to precode at the transmitter that transmits OFDM signals with low PAPR and high spectral efficiency. Extensive MATLAB simulations confirm the efficacy of the proposed framework, demonstrating a notable peak-to-average power ratio reduction of 4.8 dB at a complementary cumulative distribution function of 10⁻³. Even in fluctuating channel conditions and varying user densities, the framework achieves improved signal-to-interference-plus-noise ratio and bit error rate performance, while keeping computational complexity low. The proposed model eliminates the necessity for side information, guaranteeing seamless compatibility with existing Multiuser Multiple-Input Multiple-Output transceiver designs and enabling real-time applications. These findings emphasize the proposed model's potential as an efficient, robust, and scalable method for future wireless communication systems.

A new compact N-shaped metasurface microstrip antenna (NM-MSA) is designed for the advanced 5G/B5G communications. The NM-MSA is featured with the elaborated metasurface patch, enabling its size to be electrically smaller than the conventional MSA. To demonstrate the NM-MSA's performances, a particular antenna sample is fabricated on a dielectric substrate with ε_r = 2.2. It has a patch size of 21 mm × 20 mm but is observed to resonate at 3.506 GHz. Considering the guided wavelength at 3.506 GHz is λ_g = 57.69 mm, the practical NM-MSA's patch size is normalized as 0.364λ_g × 0.347λ_g, which breaks the conventional MSA's 0.5λ_g limitation. In addition, the NM-MSA sample has an acceptably high antenna gain of 6.97 dBi and regular radiation patterns, which can well meet the demands of 5G/B5G communications.

This paper investigates the performance of a proposed orthogonal time-frequency space (OTFS) modulation scheme compared with the conventional orthogonal frequency division multiplexing (OFDM) system under various wireless channel conditions. OTFS is evaluated for its robustness against Doppler spread and multipath fading, particularly in high-mobility scenarios. Performance metrics such as bit-error rate (BER) and throughput are analyzed across multiple modulation schemes, different signal-to-noise ratio (SNR) levels, and varying numbers of scattered paths. Simulation results at a fixed SNR of 20 dB show that the proposed OTFS consistently achieves a lower BER than OFDM, even as the number of multipath components and normalized digital velocities increase. For example, at a velocity of 1 with five scattered paths, OTFS achieves a BER of 5.73 × 10⁻⁴, significantly outperforming OFDM's 1.76 × 10⁻³. Furthermore, the proposed OTFS demonstrates better resilience under dense multipath conditions. This advantage becomes even more pronounced under severe scattering and high-mobility conditions, where OFDM performance degrades sharply due to Doppler effects. In addition to performance improvements, the proposed OTFS system introduces a computationally efficient matrix inversion strategy that significantly reduces the complexity of equalization. By applying block matrix inversion techniques, the proposed method avoids full matrix inversion and achieves over 56% reduction in running time at high subcarrier counts (e.g., $��N�=�512�$), and more than 2.3× speedup at $��N�=�1024�$ compared with the conventional OTFS. These results highlight the proposed OTFS system as a reliable, efficient and scalable modulation scheme for future wireless communication systems, particularly in environments with high Doppler and channel variation.

Wireless sensor networks (WSNs) are extensively utilized in applications such as environmental tracking and event monitoring without human intervention. The continual sensing and energy depletion limit the lifespan of sensor nodes in WSNs, particularly in high-traffic areas close to sink nodes, resulting in the energy-hole problem. The major challenges are the efficient energy utilization and routing. To address these, this paper proposes an energy-efficient cluster head selection with multipath routing using heterogeneous-hyper graph neural network and velocity paused particle swarm optimization (EECHS-HHGNN-VPPSO). The method employs a heterogeneous-hyper graph neural network (HHGNN) for intelligent cluster head (CH) selection by considering factors like node proximity, communication cost, residual energy, and coverage. Multipath routing is further optimized using velocity paused particle swarm optimization (VPPSO), which improves load balancing and minimizes redundant energy use during data transmission. The proposed technique significantly enhances network performance by achieving higher throughput, improved packet delivery ratio (PDR), reduced energy consumption, prolonged network lifetime, and lower node mortality. Experimental results demonstrate that the proposed EECHS-HHGNN-VPPSO achieves a throughput of 0.9 Mbps and energy consumption of just 0.4 mJ with 500 sensor nodes, outperforming existing methods in scalability, efficiency, and robustness for energy-constrained WSN environments.

The efficient cluster-based routing strategies are essential because of inherent limitations of wireless sensor networks (WSNs) such as limited bandwidth and energy resources. In WSN, the utilization of energy can be reduced by organizing nodes into clusters; thus, it prolongs the lifespan of the network. Additionally, it is essential to maintain data reliability and accuracy in environments prone to interference by utilizing reliable transmission strategies. Thus, a novel approach, namely, self-improved secretary bird optimization algorithm for cluster head optimization and reliable transmission (SISBOA-CORT), is proposed to address such issues. This approach encompasses two essential phases: optimal cluster head (CH) selection-based routing and reliable transmission to ensure efficient cluster-based routing and perform data transmission in a reliable manner. In the optimal CH selection-based routing phase, the nodes in WSN are grouped into clusters. In each cluster, an optimal CH is selected using the self-improved secretary bird optimization (SISBO) algorithm under consideration of constraints such as trust, link quality, latency, energy, and distance. An LSTM-based energy prediction model is employed in this approach to predict the energy levels of each node in the network. For the reliable transmission phase, Manhattan distance is employed to find the shortest distance between the source node and the destination node. The efficiency of the SISBOA-CORT approach is validated experimentally in terms of metrics such as delay, trust, RSSI, etc. The SISBOA-CORT achieved a delay rate of 1.187 s, a residual energy value of 3.097 J, and an RSSI of −139.265.

Wireless communication industry's explosive growth over the last 10 years caused a shortage of resources since its demand has greatly increased. A technology called cognitive radio (CR) was created to make efficient utilization of the spectrum from radio sources. The effectiveness of CR is significantly influenced by the spectrum sensing (SS) function, and it is the primary function of CR, which helps to discover available spectrum for better spectrum utilization and reduce detrimental conflict with approved users. In addition, the conventional models cause high computational complexity in the SS. In this work, an adaptive SS system for CR networks is developed to identify unused bands of frequencies in order to get outside of these constraints. Initially, the essential synthetic data are gathered manually, and these data are used by the suggested adaptive and residual hybrid network (A-RHN) for SS. The A-RHN network is developed by using an attention-based Autoencoder with a multi-scale capsule network (AA-MCN). Moreover, the effectiveness of this model is enhanced by optimizing parameters via the revised uniform variable-based addax optimization algorithm (RUV-AOA). The proposed model enables more efficient use of the available spectrum by avoiding transmitting on frequencies that are already in use.

With increasing autonomous nodes and expansion in network size leads to the continuous problem of routing scalability in mobile ad hoc networks (MANETs). In MANETs, efficient routing is critical for ensuring reliable communication, especially in large-scale, dynamic environments. Traditional routing protocols often struggle with scalability, energy consumption, and packet delivery reliability. In the paper, we introduce a ranking-based criteria route conscious path routing system. Based on residual energy, proximity, and transmission history considering their priorities, this system ranks nodes. By means of dynamically choosing the best paths inside larger networks, this ranking system promotes scalable transmission. Using ad hoc on-demand distance vector (AODV), the simulated annealing-based routing method can adapt to the growing number of nodes without compromising performance. This ensures the best energy economy and avoidance of maximum delay. Comparatively to conventional routing methods, simulations employing NS2 show a 35% increase in data transmission scalability along with a 10% decrease in energy consumption and this is observed even as the size of the network increases.

The growing demand for high-speed wireless communication has driven significant advancements in millimeter-wave (mmWave) technologies, particularly in next-generation systems like 5G and beyond. Millimeter-wave Multiple-Input Multiple-Output (MIMO) systems have emerged as a promising solution to address the need for increased data rates, capacity, and spectral efficiency. This manuscript proposes a novel approach named Mitigating Channel Power Leakage in Millimeter-Wave MIMO Systems Using an Optimized Physically Consistent Neural Network (MCPL-MW-MIMO-PCNN). Here, transmitting as well as receiving beamformers are chosen via offline training of an analog beamforming (ABF) network, which takes the channel as input. To mitigate channel power leakage, the Physically Consistent Neural Network (PCNN) is employed, aligning the channel gains of selected beams in the same direction to maximize the received signal-to-noise ratio (SNR). Finally, the Multi Objective Fitness Dependent Optimizer (MOFDO) is considered to optimize the weight parameter of the PCNN classifier, which accurately mitigates channel power leakage. The proposed MCPL-MW-MIMO-PCNN is implemented in Python. The performance of the MCPL-MW-MIMO-PCNN approach attains 18.36%, 22.58%, and 29.99% lower bit error rate and 23.58%, 24.36%, and 30.72% higher spectral efficiency when compared to the existing techniques: DL dependent analog beam forming design for mmWave massive MIMO scheme (ABD-MWMMS-TNN), DL driven hybrid beam forming technique for mmWave MIMO system (HBM-MWMS-ANN), and DL framework for beam selection and power control in M-MIMO-mmWave communications (BSPC-MIMO-LSTM) methods.

The significant increase in cybercrimes increases the importance of digital forensics processes. Designing digital forensics regulations before cyber incidents occur is important in terms of compliance with the legislation, speed, and quality. The lack of systems meeting forensic requirements leads to various administrative and judicial problems. In this article, a novel platform named SARE has been developed to automate network forensic processes in a software-defined network (SDN) environment in a manner fully compatible with regulatory requirements. SARE was developed to meet the requirements arising from both Turkish legislation and certain international regulations. It automatically logs the required Internet access records specified in the legislation with the help of sFlow. It stores the log files in an electronically signed, backed-up, secure, accessible, and integrity-preserved manner and wipes them at the end of the specified period. Additionally, SARE can prevent attacks on the platform and prevent access to malicious targets. SARE was examined in experiments using real network traffic, and it successfully fulfilled all the requirements without the need for an extensive technical infrastructure. All necessary data can be stored with a file that is only 0.87% of the traffic size generated according to user habits. With 5.5% of the total number of packages, the regulation-compliant logging process has been completed. In addition, it has been shown that the same data in different file types create a size difference of more than three times. The use of SARE will enable compliance with regulations automatically without any intervention and improve the forensic readiness of organizations.

The evolution of wireless communication systems has driven the demand for efficient, compact, and versatile antenna designs. Among the various types of antennas, microstrip planar antennas have gained significant attention due to their low-profile structure, compatibility with modern electronic circuits, and ease of fabrication. Designing a microstrip planar antenna using optimization techniques involves finding the optimal parameters that result in the best performance for specific design criteria, which significantly improves the performance of the antenna while also minimizing costs and space requirements. Accordingly, this paper proposes a novel optimal design for a microstrip planar antenna. This study focused on designing an optimal microstrip planar antenna by a new optimization algorithm termed rhinopithecus swarm assisted hippopotamus optimization (RAHO) by carefully considering key parameters $��{\rho }_x�,�{\rho }_y�,�{\mathrm{wc}}_1�,�{\mathrm{wc}}_2�,�$ and $��{\mathrm{wc}}_3�$ that significantly influence the antenna's performance. The analysis proves the performance of the proposed antenna design with maximization of antenna gain with 6.41% enhancement than the RSOA algorithm.