Telecommunication Systems最新文献

Massive multiple-input multiple-output (MMIMO) is essential to modern wireless communication systems, like 5G and 6G, but it is vulnerable to active eavesdropping attacks. One type of such attack is the pilot contamination attack (PCA), where a malicious user copies pilot signals from an authentic user during uplink, intentionally interfering with the base station’s (BS) channel estimation accuracy. In this work, we propose to use a Decision Tree (DT) algorithm for PCA detection at the BS in a multi-user system. We present a methodology to generate training data for the DT classifier and select the best DT according to their depth. Then, we simulate different scenarios that could be encountered in practice and compare the DT to a classical technique based on likelihood ratio testing (LRT) submitted to the same scenarios. The results revealed that a DT with only one level of depth is sufficient to outperform the LRT. The DT shows a good performance regarding the probability of detection in noisy scenarios and when the malicious user transmits with low power, in which case the LRT fails to detect the PCA. We also show that the reason for the good performance of the DT is its ability to compute a threshold that separates PCA data from non-PCA data better than the LRT’s threshold. Moreover, the DT does not necessitate prior knowledge of noise power or assumptions regarding the signal power of malicious users, prerequisites typically essential for LRT and other hypothesis testing methodologies.

Inter-satellite optical wireless communication (IsOWC) is frequently utilized for fast data transmission between various far away satellites in free space. Thousands of users are connected via broad-area applications that use network topologies with many frequencies per optical fiber and a range of transmission rates and capacities. Multiple frequencies being transmitted simultaneously on a fiber having a nanometer range (1300–1600) that is a powerful feature of an optical communications link. When coupled with optical amplifiers, the WDM idea creates communication lines that provide rapid user-to-user communication throughout national borders. A well-known drawback of free space optics (FSO) or Inter-Satellite Link (ISL) is degradation in optical signal power, in addition to its appealing advantages. The traditional system is improved in order to solve this issue. Using the optisystem-15 simulator, we analyzed the IsOWC link's capability for fast data transfer based on subjective factors including received power, Q-factor and BER. 16-channels multiplexed with modulation technique transmitters to the system are explained by the projected model. The link has been used at various data rates, distances, and external Match-Zehnder modulation. Applied a series hybrid optical amplifier of erbium-doped fiber amplifier (EDFA) and a laser amplifier that exists prior to and after the OWC line with the given parameters. Different OWC ranges with a loop control of 3 loops that made total link length 1600 km, and searching different data rates to show their impact on system efficiency, which showed a great enhancement in results that are crucial to the current inter-satellite OWC link.

Graphical abstract

Data transmission from sensor nodes is the biggest problem for IoT networks. Overusing communication power shortens node lifespans. Thus, network issues including QoS, security, network heterogeneity, congestion avoidance, reliable routing, and energy savings must be addressed. Routing protocols are essential for delivering data between organizations. Information gathering and consolidation require data aggregation to minimize traffic congestion, operating expenses, energy usage, and network lifespan. IoT data aggregation makes route planning dependable, energy-efficient, and difficult. Disjoint & Scalable Multipath Routing (D &SMR) is a new routing system developed using NS2 simulation. The method estimates delivery success using decision trees and neural networks. We evaluate characteristics such as (D &SMR) routing scheme predictability, node popularity, power consumption, speed, and location while training the model. Simulation results show that (D &SMR) outperforms a reliable routing system in terms of delivery success, lost messages, overhead, and hop count. The proposed hybrid routing method involves cluster construction and intra- and inter-cluster routing. The study found that (D &SMR) beats previous research in network resilience, packet transmission efficiency, end-to-end latency, and energy usage.

Deploying unmanned aerial vehicle base stations (UAV-BSs) to provide wireless access for terrestrial users is an alternative solution in emergency scenarios. Due to the dynamic nature of UAV-BSs and strong line-of-sight (LoS) transmission, co-frequency networking worsens the interference suffered by edge users, while the use of fully orthogonal channels for all UAV-BSs also faces the problem of frequency resource constraints. This paper develops a new deployment scheme by combining multiple UAV-BS locations, limited frequency reuse and power optimization. Thus, the nonlinear constrained optimization model is proposed to maximize user coverage. To reduce the computational complexity, the basic multi-UAV-BS network layout is first determined by polycentric clustering and connectivity adjustment based on user location distribution. Then, a genetic algorithm is used to optimize the multi-UAV-BS frequency arrangement and power adjustment to obtain the model solution. Simulations verify the effectiveness of the proposed scheme and evaluate the impact of co-channel interference.

Private information retrieval, which allows users to securely retrieve information stored in a single server or multiple servers without disclosing any query content to the server, has attracted much attention in recent years. However, most of the existing private information retrieval schemes cannot achieve data retrieval and data integrity authentication simultaneously. To address the above challenges, this paper proposes a verifiable private information retrieval scheme based on parity in a single-server architecture. Specifically, the data owner generates parity information for each data and extends the original database. Then the data owner generates hint information for the query client, and according to the inverse of the hint information, the matrix confusion and permutation of the extensible database are carried out on the database and the hint information is sent to the client. The client selects the corresponding element in the hint to generate the query vector and executes the reconstruction and verification phase after receiving the answer to accomplish the retrieval process. A series of security games prove that this scheme meets the privacy requirements defined by the PIR scheme, and experimental analysis shows that compared with related schemes, our scheme has certain advantages in time cost. The time of verification information generation is 0.3% of APIR and FMAPIR, the reconstruction time is 1.6% of APIR and 1.1% of FMAPIR and the query time is much less than them.

Slotted ALOHA is a well-known multiple access control protocol that devices use to access a radio channel in wireless networks. Age of information (AoI) is a key network performance indicator that represents the elapsed time between the time data is generated and the time data is received. Threshold-based ALOHA, a variant of the slotted ALOHA, uses a threshold and a transmission probability to send data and meet its requirement of average AoI (AAoI), in which however energy consumption is not discussed. It is crucial that a device operates energy-efficiently to reduce energy consumption, which is conducive to extending the life of the device and reducing greenhouse gas emissions. Therefore, this paper proposes a modified threshold ALOHA in low-duty-cycle wireless networks, called LBG-threshold-ALOHA, in which a device looks up whether sensing data is generated in prior N slots before its data generation/transmission. Instead of generating data in each transmission, the LBG-threshold ALOHA uses data generated in prior N slots to send, which reduces the activity of data generation and thus yields lower energy consumption. The characteristic of lower energy consumption is beneficial to produce a lower AAoI on a network constrained by low energy consumption. Simulation results show that, under an upper limit on the mean energy consumption, the LBG-threshold-ALOHA yields a lower minimum AAoI compared to the original threshold ALOHA.

Maximizing the communication coverage with the minimum number of unmanned aerial vehicles (UAVs) in a telecommunication system is investigated in this paper. In particular, the problem of maximizing the coverage area in stadium environments using UAVs is modeled mathematically as a multi-objective optimization problem. While the problem is solved using state-of-the-art solvers, to address the problem complexity and achieve the results for real-time applications, we propose a heuristic algorithm. The performance evaluation done in three crowding levels demonstrates that the performance with the heuristic algorithm is comparable to the mathematical model in terms of the number of coverage users. Moreover, the running time is significantly smaller in the proposed heuristic algorithm. This shows the efficiency of the model and solution. Moreover, we compare the heuristic algorithm with the non-dominated sorting genetic algorithm (NSGAII). The results of the paper show that the use of the heuristic algorithm speeds up the processing and decision making, and at the same time maximizes the communication coverage in stadium environments.

Dense network deployment is now being evaluated as one of the viable solutions to meet the capacity and connectivity requirements of the fifth-generation (5G) cellular system. The goal of 5G cellular networks is to offer clients with faster download speeds, lower latency, more dependability, broader network capacities, more accessibility, and a seamless client experience. However, one of the many obstacles that will need to be overcome in the 5G era is the issue of energy usage. For energy efficiency in 5G cellular networks, researchers have been studying at the sleeping strategy of base stations. In this regard, this study models a 5G BS as an (M^{[X]}/G/1) feedback retrial queue with a sleeping strategy to reduce average power consumption and conserve power in 5G mobile networks. The probability-generating functions and steady-state probabilities for various BS states were computed employing the supplementary variable approach. In addition, an extensive palette of performance metrics have been determined. Then, with the aid of graphs and tables, the resulting metrics are conceptualized and verified. Further, this research is accelerated in order to bring about the best possible (optimal) cost for the system by adopting a range of optimization approaches namely particle swarm optimization, artificial bee colony and genetic algorithm.

Remote sensing technology is a vital component of disaster management, poised to revolutionize how we safeguard lives and property through enhanced prediction, mitigation, and recovery efforts. Disaster management hinges on continuous monitoring of various environments, from urban areas to forests and farms. Data from these observations are relayed to servers, where sophisticated processing algorithms forecast impending disasters. Remote sensing technology operates through a layered framework. The sensing layer acquires raw data, the network layer facilitates data transmission, and the data processing layer extracts meaningful insights. The application layer then leverages these insights to make informed decisions. Elevating the intelligence of remote sensing technology necessitates advancements across these layers. This paper delves into disaster management concepts and highlights the pivotal role played by remote sensing technology. It offers a comprehensive exploration of each layer within the remote sensing technology framework, detailing foundational principles, tools, and methodologies for enhancing intelligence. Addressing challenges inherent to this technology, the paper also presents future-oriented solutions. Furthermore, it examines the influence of wireless network infrastructure, alongside emerging technologies like the Internet of Things, cloud computing, virtual machines, and low-power wireless networks, in nurturing the evolution and sustainability of remote sensing technology.

For 6G communications, the Ultra Massive Multiple Input Multiple Output (UM-MIMO) systems with Intelligent Reflecting Surface (IRS) assistance are capable since they can efficiently get beyond the limitations of restricted blockage and coverage. However, in the far field, a robust THz channel sparsity is unfavorable to spatial multiplexing, whereas excessive UM-MIMO and IRS dimensions extend the near field region. To address these issues, a hybrid beamforming IRS assisted UM-MIMO THz system with Deep Siamese Capsule Network is designed with the cascaded channel. The near and far field codebook-based beamforming is developed to model the proposed communication channel. The channel estimation is done based on the deep siamese capsule adaptive beluga whale neural network. The simulation results of the bit error rate, Normalized Mean Square Error (NMSE), spectral efficiency, sum rate, data rate, normalized channel gain, beamforming gain, and array gain loss shows that the proposed system achieves reliable performances compared with existing techniques. The suggested approach also demonstrates the outstanding adaptability to various network configurations and good scalability. The method provides a better channel estimation accuracy and less complexity which shows an NMSE of − 11.2 dB at an SNR of 10 dB.