Telecommunication Systems最新文献

This research focuses on how to improve benefit of downlink non-orthogonal multiple access (NOMA) concept in a cellular wireless system. The issues related with a user grouping in NOMA and an allocation of power among users are crucial to maximize the system performance and efficiency. Power allocation at transmitter side, as well as successive interference cancellation (SIC) at receiver side, are key operations which time consumption and computational complexity increase with increase of number of users in a NOMA group. In addition, accommodating more users in a group requires an increase in transmit power. As a result of trade-off between practicability and capacity gain provided by NOMA, we examine a user grouping scheme in which the number of users in a group does not exceed three. Namely, the idea is to avoid orthogonal multiple access (OMA) users and additional communication resources employing hybrid three-users NOMA grouping and NOMA pairing. The application of power allocation algorithm maximizes achievable sum rate (ASR) of NOMA group with restriction that individual user rates in NOMA must be higher than in the case of OMA system providing user fairness. Under these constraints, expressions for optimal values of power allocation coefficients are obtained in closed-form. Computer simulations are carried out for the system with perfect SIC over Rayleigh fading channels taking into account the influence of interferences from nearby base stations (BSs). The results demonstrate the effectiveness of the proposed framework for user grouping and power allocation compared with conventional NOMA pairing algorithm and OMA transmission.

Multimedia content represents a significant portion of the traffic in computer networks, and COVID-19 has only made this portion bigger, as it now represents an even more significant part of the traffic. This overhead can, however, be reduced when many users access the same content. In this context, Wi-Fi, which is the most popular Radio Access Technology, introduced the Group Addressed Transmission Service (GATS) with the amendment IEEE 802.11aa. GATS defines a set of policies aiming to make multicast traffic more robust and efficient. However, Wi-Fi is constantly evolving, and as it improves and greater bandwidths and data rates become available, it is necessary to reevaluate the behavior of mechanisms introduced in past amendments. This is also the case with GATS, whose policies have different behaviors and adapt better to different channel conditions. These policies have been evaluated in the past on High Throughput networks. Still, none of the evaluations provided insights into the behavior of GATS policies in Very-High Throughput (VHT) physical layers in a realistic manner. This is extremely relevant as a greater available bandwidth can impact the decisions of the GATS policy configuration. Thus, in this work, we present an evaluation of the IEEE 802.11aa amendment with a VHT physical layer in a realistic scenario that uses Minstrel as a rate adaptation algorithm simulated in NS-3.

Device-to-device (D2D) communications, regarded as a crucial technology for the Beyond Fifth-Generation (B5G) wireless networks, provide substantial benefits including elevated spectrum effectiveness, improved coverage, and traffic offloading. The mode selection and channel allocation play an important role in ensuring the data rate and enhancing user experience in D2D communications. The dynamic switching of communication modes by D2D User Equipments (UEs) depends on the extent of shared resources among D2D pairs. In this paper, we examine the issue of joint mode selection and resource allocation for D2D communication within a cellular network in an uplink scenario. The main objective of this study is to optimize the overall sum rates of the network, while simultaneously guaranteeing the Quality of Service (QoS) requirements. Three communication modes are considered which are Direct Mode (MD), Relay-assisted Mode (RM), and Local route Mode (LM). In addition, each D2D pair has the option to be allocated either a dedicated channel or a reused channel. We present an innovative approach for the simultaneous determination of mode selection and channel allocation in D2D communication. The proposed approach is based on a greedy strategy and a modified many-to-many matching technique that effectively selects the best communication mode and assigns the optimal channel for each D2D pair, respectively. The simulation results illustrate that the presented approach exhibits a significant improvement in the network performance when compared to the benchmark algorithms. The proposed scheme is evaluated with perfect and imperfect Channel State Information (CSI) conditions.

Computational complexity is the main challenge of wideband direction of arrival (DOA) estimation algorithms in practice. DOA estimation method using wideband modal orthogonality (WIMO) is a very efficient algorithm that does not assume a priori knowledge on the number of sources. In this paper, efficient subspace methods like Fourier domain-multiple signal classification (FD-MUSIC) and Root-MUSIC algorithms are developed for WIMO framework to improve the performance and computational complexity of the WIMO method. Simulation results confirm that these methods obtain superior performance regarding target resolution (probability of resolution). It is noteworthy, the FD-MUSIC based algorithm excels in target resolution and Root-Mean-Square-Error (RMSE), particularly as the signal bandwidth increases. In addition, the computational complexity of these methods are reduced compared to WIMO algorithm.

In multi-user cognitive-radio internet of things (CR-IoT) network, accurate estimations of data arrival are critical for secondary users to allocate channels. In the context of the data arrival model with long-term variations of rate, improving the accuracy of the performance evaluation of channel allocation protocols is an open issue. Thus, to evaluate the performance of various channel allocation protocols with predefined models of data arrival, a queuing analysis framework is developed using a probability allocation vector (PrA). The time-varying feature of data arrival is described by a Markov process including various data arrival states in the proposed framework. A dynamic probability allocation vector (DPrA) protocol capable of adjusting allocation strategy according to the arrival states by constructing the PrA is proposed. For comparative analysis, a maximum throughput allocation (MTA) protocol for conventional data arrival model is also evaluated under the proposed framework. Numerical results show that the DPrA protocol outperforms the MTA protocol in various performance metrics. Furthermore, the proposed modeling method for data traffic can provide convenience and effectiveness when designing channel allocation protocols in a CR-IoT network.

The Industrial Internet of Things (IIoT) attributes to intelligent sensors and actuators for better manufacturing and industrial operations. At the same time, IIoT devices must be secured from the potentially catastrophic effects of eventual attacks, and this necessitates real-time prediction and preventive strategies for cyber-attack vectors. Due to this, the objective of this investigation is to obtain a high-accuracy intrusion detection technique with a minimum payload. As the experimental process, we have utilized the IIoT network security dataset, namely WUSTL-IIOT-2021. The feature selection technique Particle Swarm Optimization (PSO) and feature reduction techniques such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and t-distributed stochastic neighbor embedding (t-SNE) are applied. Additionally, the Generalized Additive Model (GAM) and Multivariate Adaptive Regression Splines (MARS) are used to detect payloads that can interfere with the normal operation of an application. Both PSO and PCA combined with MARS have produced predictive results with an exceptional accuracy of 100%. Yet, the trained Machine Learning (ML) model is quantized with 4-bit and 8-bit, and it is deployed on Azure IoT Edge to simulate edge devices. Experimental results show that the latency of the model was reduced by 25% on quantization.

The employment of Intelligent Reflecting Surface (IRS) in MIMO systems has been extensively studied as a feasible alternative to relays. IRS can operate either actively or passively. In the case of a passive IRS configuration, no signal processor or amplifier is required, allowing for signal reflection in the desired phase shift with lower power consumption and cost. However, the passive IRS gain is significantly decreased due to the path loss in the IRS channel. In the active IRS structure, each reflective element is fitted with a power amplifier that enables it to reflect the signal to the intended destination at an appropriate power level. This confers a greater benefit to the system than a passive structure. Consequently, a hybrid active–passive IRS is recommended as it results in a higher sum rate, more energy gain, and a balance between the higher rate and lower energy consumption. In this paper, we examine a practical Rician channel hybrid IRS-assisted multiuser MIMO system with the goal of maximizing the sum rate. To accomplish this, we simplified the objective function of the main problem using the Fractional Programming (FP) method and modified the formulations in order to divide the main problem into a series of sub-problems. Then, using the Block Coordinate Descent (BCD) method and placing sub-problems into consecutive blocks, we solve each sub-problem to obtain the optimal solution for each block and, ultimately, the answer to the main problem. Finally, simulations were conducted for the proposed scenario, indicating that the sum rate of the hybrid IRS structure varied between the active and inactive IRS structure, depending on transmission power. However, adjusting the number of elements in the active and passive structures can achieve higher overall rates than traditional structures at low power levels.

The paper investigates the effect of controlling the transmission power used for communication of data packets at physical layer to prolong longevity of network and adaptive learning rates in a reinforcement-learning algorithm working at network layer for dynamic and quick decision making. A routing protocol is proposed for data communication, which works in tandem with physical layer, to improve performance of Wireless Sensor Networks used in IoT applications. The proposed methodology employs Q-learning, a form of reinforcement learning algorithm at network layer. Here, an agent at each sensor node employs the Q-learning algorithm to decide on an agent which is to be used as packet forwarder and also helps in mitigating energy-hole problem. On the other hand, the transmission power control method saves agents’ battery energy by determining the appropriate power level to be used for packet transmission, and also achieving reduction in overhearing among neighboring agents. An agent derives its learning rate from its environment comprising of its neighboring agents. Each agents determines its own learning rate by using the hop distance to sink, and the residual energy (RE) of neighboring agents. The proposed method uses a higher learning rate at first, which is gradually decreased with the reduction in energy levels of agents over time. The proposed protocol is simulated to work in high-traffic scenarios with multiple source-sink pairs, which is a common feature of IoT applications in the monitoring and surveillance domain. Based on the NS3 simulation results, the proposed strategy significantly improved network performance in comparison with other routing protocols using Q-learning.

The data aggregation with the aid of mobile sink in wireless sensor networks (WSNs) is a promising solution to the recent hot-spot or sink-hole issues induced by multi-hop routing employing the static sink. Despite everything, most of the baseline models concentrate on energy-efficient data aggregation issues but struggle to maintain a tradeoff between energy energy-efficient and load-balanced data collection. In this research, we propose a novel Dynamic Levy Flight-enabled PSO (Dynamic LFPSO) optimization algorithm for addressing the load-balanced data aggregation problem with mobile sinks in WSNs. The Dynamic LFPSO algorithm incorporates a structured tree path for efficient data collection, where mobile sinks traverse the network following an optimized path. The algorithm leverages the benefits of the PSO algorithm combined with Levy Flight and dynamic inertia weight to achieve energy-efficient and load-balanced data collection while minimizing data collection delay. The comprehensive simulations are conducted using an NS-3 network simulator which demonstrates that the Dynamic LFPSO algorithm achieves a lower data collection delay of 55.4 ms, a higher network lifetime of 461 rounds, an improved Packet delivery ratio of 97.2%, and a better throughput of 50 kbps. Overall, the Dynamic LFPSO algorithm leads to better usage of network resources and prolonged network lifetime and also offers a practical solution to the challenges in WSNs, providing a foundation for further research and advancements in the field.

This research addresses the challenge of accurate load forecasting in cluster microgrids, where distributed energy systems interlink to operate seamlessly. As renewable energy sources become more widespread, ensuring a consistent and reliable power supply in the face of variable weather conditions is a significant challenge for power providers. The variability in energy consumption patterns, influenced by human behavior and environmental conditions, further complicates load prediction. The inherent instability of solar and wind energies adds complexity to forecasting load demand accurately. This paper suggests a solution in addressing some challenges by proposing a Modified Temporal Convolutional Feed Forward Network (MTCFN) for load forecasting in cluster microgrids. The Fire Hawk Optimization algorithm is employed to determine optimal configurations, addressing the intricacies of this complex optimization problem. Data collected from the Microgrid Market Share and Forecast 2024–2032 report, the efficiency of the proposed approach is evaluated through metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Square Error (MSE), and R-squared. The RMSE, MSE, MAE, MAPE, and R-squared values of the MTCFN are 0.4%, 1.5%, 0.6%, 6.8%, and 0.8%, respectively. The optimization algorithm's effectiveness is cross-validated through rigorous testing, training, and validation processes, revealing that the FFNN model based on the Fire Hawk Optimization algorithm yields superior load forecasting results. This research contributes to the advancement of signal, image, and video processing in the context of resilient and accurate energy management in cluster microgrids.