Telecommunication Systems最新文献

As a revolution in the field of transportation, the demand for communication of vehicles is increasing. Therefore, how to improve the success rate of vehicle spectrum access has become a major problem to be solved. The case of a single vehicle accessing a channel was only considered in the previous research on dynamic spectrum access in cognitive vehicular networks (CVNs), and the spectrum resources could not be fully utilized. In order to fully utilize spectrum resources, a model for spectrum sharing among multiple secondary vehicles (SVs) and a primary vehicle (PV) is proposed. This model includes scenarios where multiple SVs share spectrum to maximize the average quality of service (QoS) for vehicles. And the condition is considered that the total interference generated by vehicles accessing the same channel is less than the interference threshold. In this paper, a deep Q-network method with a modified reward function (IDQN) algorithm is proposed to maximize the average QoS of PVs and SVs and improve spectrum utilization. The algorithm is designed with different reward functions according to the QoS of PVs and SVs under different situations. Finally, the proposed algorithm is compared with the deep Q-network (DQN) and Q-learning algorithms under the Python simulation platform. The average access success rate of SVs in the IDQN algorithm proposed can reach 98(%), which is improved by 18(%) compared with the Q-learning algorithm. And the convergence speed is 62.5(%) faster than the DQN algorithm. At the same time, the average QoS of PVs and the average QoS of SVs in the IDQN algorithm can reach 2.4, which is improved by 50(%) and 33(%) compared with the DQN algorithm, and improved by 60(%) and 140(%) compared with the Q-learning algorithm.

In modern warfare, the use of UAVs for reconnaissance, search and rescue missions is very common, and it is essential to plan the flight path of UAVs. However, in the face of complex battlefield environment, the existing flight path planning algorithms have the problems of long time consumption and unstable path. Therefore, this paper studies the UAV flight path planning optimization in complex battlefield environment. First, we construct the battlefield environment model. Then, by analyzing the UAV flight constraints existing in battlefield environment, the objective function is obtained. And the problem of UAV flight path planning optimization is transformed into a nonlinear combinatorial optimization problem. On this basis, an Adaptive Adjustment Flight Path Planning algorithm (AA-FPP) is proposed. The AA-FPP algorithm adaptively adjusts the absorption coefficient of fireflies by using chaotic strategy. It adjusts the control position updating formula by using time-varying inertia weight to enhance its global searching ability. Then, random factors based on Boltzmann selection strategy are introduced to perturb the iterative solutions in AA-FPP. It expands the search space of the path and enhances the convergence efficiency. Finally, simulation results show that the AA-FPP algorithm can successfully plan a flight path that reduces static/dynamic threat intensity. And it has greater advantages in path stability and planning time consumption.

Higher energy consumption and poor spectrum sharing were identified as the common problems in spectrum sharing at cognitive radio networks. So to overcome these issues in the present research, a novel Horse herd-based Elman spectrum sharing model is developed. The current study found lower energy consumption and a higher spectrum sharing rate in less time. Subsequently, a channel was created for sharing the spectrum over the primary and secondary users. After that, through the proposed model, the sharing system is monitored. Moreover, the spectrum is transmitted until the false alarm is detected. The system stops the users' spectrum sharing function if a false alarm is detected. However, the spectrum is shared with every user in the designed channel. Then a case study is developed to explain the working process of the model. After that, the performance of the proposed design is detailed, and in the comparison analysis, the performance of the proposed model is compared with the existing models. Consequently, the performance of the proposed model is validated based on the throughput, outage probability, bit error rate, energy consumption, data transmission, and spectral efficiency.

In the wireless communication, the shortage of bandwidth has motivated the investigation and study of the wireless access technology called massive Multiple-Input Multiple-Output (MIMO). In multi-tier heterogeneous Fifth Generation (5G) networks, energy efficiency is a severe concern as the power utilization of macro base stations' is comparatively higher and proportional to their traffic load. In this paper, a novel African Vulture Shepherd Optimization Algorithm (AVSOA) is established that relies on macro cells and small cell system load information to determine the highly energy-efficient traffic offloading system. The proposed AVSOA model is a combination of the African Vulture Optimization Algorithm (AVOA) and the Shuffled Shepherd Optimization Algorithm (SSOA). The system load is predicted here by exploiting a Deep Quantum Neural Network (DQNN) algorithm to perform the conditional traffic offloading in that every macro-Base Station (BS) conjectures the offloading systems of other macro cells. The experimental evaluation of the adopted model is contrasted with the conventional models considering the energy efficiency, spectral efficiency, throughput, and system load. Finally, the performance analysis of the proposed model achieved better energy efficiency, spectral efficiency, and throughput of 0.250598, 0.184527, and 0.820354 Mbps and a minimum system load of 697.

With the development of super computation ability and invention of quantum computer, safeguarding network layer data confidentiality by computation based encryption method suffers great impact. As a result, people pay more attention to safeguarding data confidentiality in physical layer of wireless satellite communication system. In this paper, a groundbreaking method for enhancing physical layer security (PLS) by information hiding technology is proposed, that is, embedding secret information into chaotic frequency-hopping signal by multilevel frequency shift keying (MFSK) modulation. First, the secret information to be transmitted is encrypted with chaotic binary sequence. Secondly, according to frequency-hopping pattern and chaotic address sequence, select suitable modulation order and specific frequency point corresponding to each encrypted data block. Finally, the encrypted information is embedded into frequency-hopping Sine signal by MFSK modulation. Experimental results indicate that the proposed scheme is robust against various jamming attacks, such as power attenuation, noise adding, single-tone jamming, key exhaustive search, etc.

Due to its ability for flexible placement, the Unmanned Aerial Vehicle (UAV) has been widely utilized as an aerial relay to transmit the video streaming data, which is particularly useful in emergency communication networks that lack the communication infrastructure. In this paper, we examine the combined resource optimization and trajectory planning for video services using dynamic adaptive streaming over HTTP (DASH) in the uplink transmission of UAV-based emergency indoor-outdoor communication networks. In detail, a rotary-wing UAV works as an aerial relay, forwarding emergency video collected by indoor users to a remote Base Station (BS). To ensure that the UAV can forward the emergency video data from disaster area with stable video quality, we formulate an optimization problem of maximizing the uplink throughput and video streaming utility for all indoor users, by jointly optimizing the 3D flight trajectory of UAV, playback rate of video, communication time as well as bandwidth allocation, subject to the UAV trajectory constraints, total available bandwidth limitation, video quality variation constraints, as well as information-causality constraints for both UAV relaying and video playing. To tackle the formulated non-convex problem, we present a joint UAV trajectory, bandwidth allocation and video utility (JTBU) algorithm. Specifically, we first decouple the original problem into two sub-issues: 3D UAV trajectory optimization subproblem and resource allocation optimization subproblem. Then the JTBU algorithm alternately optimizes the two subproblems until the convergency is reached. Finally, the numerical results confirm that the proposed algorithm had a higher video streaming utility and system throughput than the baseline methods.

Wide usage of Orthogonal frequency division multiplexing (OFDM) in visible light communication (VLC) systems aids in sharing immense multimedia files. But, an increase in the peak-to-average power ratio (PAPR) becomes a setback for OFDM. This paper focuses on PAPR reduction and encrypted image transmission in OFDM-based VLC systems using masking approach. The proposed technique generates a mask with the random sequence from a Pi-based coupled chaotic map (PCCM). The ciphering of the transmitted image data involves twofold administration of the PCCM mask. First, the image data is confused using Arnold’s Cat Map (ACM), and then the PCCM mask is applied. For the second time, the PCCM masks are applied on the equal-sized blocks of the subdivided image data at the Quadrature Amplitude Modulation (QAM) mapper stage, hence secures the transmission path. The proposed masking scheme frustrates statistical, brute-force and differential attacks and has a large key space of about (10^{340}). The PCCM based masking scheme is able to reduce PAPR from 13.5 dB to 6 dB, with 56% reduction percentile when compared to the normal OFDM system, and the same is validated by Complementary cumulative distribution function (CCDF). The decryption quality of the PCCM mask based encryption approach is proven better with high correlation coefficient and output signal-to-noise ratio (SNR) values. This scheme also maintains the condition of the image shared with a Bit error rate (BER) improvement of (thicksim )1.2 to 1.5 dB compared to the normal OFDM system. This proposed technique is evaluated by simulation experiments.

Wireless sensor networks (WSNs) are widely acknowledged for their potential as a robust infrastructure for collecting, processing, and transmitting information. Rezaeipour & Barati proposed a hierarchical key management scheme that manages key creation, distribution, and maintenance to provide services like message confidentiality, integrity, and authenticity to wireless sensor networks. As per the assertions posited by the authors, their scheme purportedly exhibits resilience against a spectrum of potential threats. Nevertheless, upon subjecting their scheme to meticulous scrutiny, our analysis revealed vulnerabilities to manifold adversarial strategies, notably including man-in-the-middle attacks, replay attacks, impersonation attacks, and node capture attacks. This scheme fails to establish a secure session key agreement also. In response to these identified shortcomings, this research devised an “Improved Hierarchical Key Management” (IHKM) Scheme aimed at rectifying the deficiencies of Rezaeipour and Barati’s scheme (RB scheme). This work proposed an enhanced key management scheme that utilizes a blend of symmetric and asymmetric key cryptography to enhance the security parameters of Hierarchical WSNs. A comprehensive security analysis of the proposed work shows that the IHKM scheme is secure against impersonation attacks, man-in-the-middle attacks, replay attacks node capture attacks, Denial-of-Service attacks, and base station bypassing attacks while establishing the mutual key agreement between the area managers and sensor nodes. Moreover, a comparative analysis of the IHKM scheme exhibits notable reductions in energy consumption (62.27% and 49.15%) and memory utilization (39.28%, 23.36%) when compared with the RB scheme and HISCOM schemes respectively. Simultaneously, IHKM contributes to the extension of the network’s overall lifespan by 48.78% as compared to HISCOM scheme and 32.63% as compared to compared with the RB scheme, thereby enhancing the efficiency and longevity of the WSN infrastructure.

The Internet of things (IoT) has become a cornerstone of the fourth industrial revolution. IoT sensor devices in the network are provisioned with limited resources, such as little processing speed, minimal computing capacity, and less power. Furthermore, IoT devices are battery-powered, which cannot provide battery sufficiently to some applications resulting in an energy scarcity problem. Clustering is an efficient method in IoT networks to save energy. Nodes can coordinate communication by selecting an optimal cluster head (CH) within the cluster and transmitting information to a central node or sink. The CH minimizes energy consumption associated with communication overhead and extends the overall lifespan of the network by facilitating coordination between clusters and the central server. Many existing optimization techniques have proposed CH selection to improve the network's lifespan but all the existing algorithms on CH selection are not practical due to the long convergence time. This research paper proposes a novel fuzzy-based Harris Hawks Optimization (FHHO) algorithm that chooses optimal CH considering Residual energy (RER) and distance between sink and node. The fitness function is evaluated using fuzzy logic over maximization and minimization network parameters. Extensive experimentations were conducted to test and validate the performance of proposed FHHO algorithm on MATLAB 2019a tool. And, the results stated that the proposed method FHHO has better results as compared to other CH selection techniques, namely, PSO-ECHS, FIGWO, and GWO-C, in network lifespan by 18–44% and throughput by 5–20%.

The fact that sensor nodes are situated in specific places is most obvious in wireless sensor networks. A wireless sensor network has been designed to address node localization through several solutions. Environmental monitoring, healthcare, and industrial automation are just some of the fields where wireless sensor networks (WSNs) find extensive application. This sort of WSN application is fundamentally dependent on the accurate localization of sensor nodes. The purpose of this paper is to propose an enhanced received signal strength indicator (RSSI) technique for determining unlocalized nodes using anchor nodes. An optimal communication range, aligning with the application needs, can be determined by assessing the dynamic correlation between communication distance and the RSSI throughout the localization procedure. The advanced RSSI-based node localization method proposed in this study involves two distinct stages: the distance determination phase and the computation phase. In this work, we assess the outcome using the Castalia simulation. The precision of wireless sensor network node location is greatly improved by integrating mobile anchor nodes with RSSI-based dynamic node localization. The results of the simulations and the subsequent analysis attest to the excellent accuracy and low energy consumption of the suggested method during the localization procedure.