Telecommunication Systems最新文献

Cyclic interleaved frequency division multiplexing (CIFDM) stands out as a promising solution for enhancing diversity reception over frequency-selective channels. Within the CIFDM framework, cyclic interleaving at the transmitter, followed by multipath detection at the receiver, collaboratively contributes to performance improvement. A recent advancement in this domain is the introduction of CIFDM-successive interference cancellation (CIFDM-SIC) (Anuthirsha and Stuwart in ETRI J, 1--11, 2024), a multipath multistage detection method. However, CIFDM-SIC leaves room for improvement, as it only partially mitigates multipath interference resulting from the semi-orthogonality of codes in CIFDM. To address this limitation, our current approach introduces a CIFDM-decorrelative successive interference cancellation (CIFDM-dSIC) method. In the proposed CIFDM-dSIC, a decorrelation process is employed to effectively eliminate non-orthogonal interference components arising from the cross-correlation of codes in the SIC output stages. Specifically, after the completion of the entire SIC operation, interference at each output stage, except the last one, is eliminated through path-specific decorrelation of the effective spreading codes. We have conducted a comprehensive evaluation of the performance of CIFDM-dSIC under various scenarios, comparing it with both existing CIFDM and traditional interleaved frequency division multiplexing methods. Simulation results unequivocally establish the dominance of the proposed CIFDM-dSIC, affirming its reliability in terms of bit error rate and diversity reception.

Virtual Machine Consolidation (VMC) in cloud computing refers to the process of optimizing resource utilization by consolidating multiple Virtual Machines (VMs) onto fewer physical servers. This approach aims to maximize the efficiency of resource allocation, reduce operational costs, and enhance overall system performance. In general, effective VMC remains a cornerstone of efficient cloud infrastructure management, balancing resource efficiency with operational complexities to deliver reliable and cost-effective services. In this paper, we undertake a systematic survey of the essential steps in VMC within cloud computing environments. We focus on three critical phases: Physical Machines (PMs) detection, VMs selection, and VMs placement. The review comprehensively explores various aspects of VMC in cloud computing, including motivations, benefits, techniques, challenges, limitations, and applications. It also delves into the techniques and algorithms used for VMC, providing insights into state-of-the-art approaches. Meanwhile, the paper serves as a valuable resource for researchers interested in VMC, and provides a foundation for future research endeavors.

Non-orthogonal multiple access (NOMA) enables the concurrent transmission of numerous user signals, ameliorating spectral efficiency by superimposing these signals. NOMA users utilize energy harvesting to efficiently utilize available energy sources. The energy harvesting process is non-linear in general. Additionally, innumerable antennas are used at the NOMA transmitter and power source to facilitate efficient energy transfer and information transmission with antenna selection (AS). Also, wireless channels cause path loss, fading, and shadowing effects, which directly influence harvested energy and communication reliability. Accordingly, the work assesses the outage performance and throughput of NOMA, examining realistic aspects like non-linear energy harvesting (nlEH), AS, multiple antennas, and (kappa -mu ) shadowed fading. The findings indicate a considerable performance degradation due to nlEH. Moreover, appropriate parameter selection is crucial for preventing complete outages in NOMA networks and achieving optimal performance. In addition, the performance of NOMA networks meliorates with an accreting number of antennas. Furthermore, the AS criterion dramatically impacts performance difference between NOMA users.

In the contemporary era, the Internet of Vehicles (IoV) plays a pivotal role in traffic management, especially in the context of highly scalable and dynamic 5 G networks. Safeguarding these networks presents numerous challenges, particularly in controlling access for unauthenticated users and establishing secure key agreements with fine-grained access control. Balancing these security measures is vital to prevent unauthorized information flow while aligning with the promising goals of future generation technologies. Despite numerous related studies being conducted, existing schemes face risks such as privacy breaches, identity tracing, and substantial computation overheads. Consequently, there is an urgent need to introduce a more secure and efficient scheme to counter various attacks. This paper introduces an authenticated key agreement scheme, tailored for fog-based IoV. The scheme’s security is established under the Random Oracle Model and verified using the ProVerif tool. Additionally, a performance assessment is conducted, demonstrating that the proposed protocol simultaneously meets both security and efficiency requirements when compared to several related schemes.

Pervasive Wireless Sensor Networks (PWSNs) are essential for collecting and transmitting real-time data from the physical world to various applications. Integrating blockchain technology with PWSNs enables decentralized data integration securely and immutably, although it requires energy-intensive computing processes. In this paper, the proposed model is designed to investigate the potential of blockchain technology to utilize acoustic signals in a pervasive acoustic wireless sensor network (PAWSN) to achieve energy efficiency. Considering the energy limitations of battery-operated sensor nodes, particularly acoustic sensors, and the energy consumption associated with blockchain technology, this study addresses these challenges by dividing the network into centralized and decentralized structures. In the proposed approach, acoustic sensor nodes operate within a centralized network structure, each assigned to a sensor zone with a central sink node. The sink nodes then participate in a decentralized network structure. To enhance energy efficiency, acoustic sensors in sensor zones are equipped with contextual information to minimize event listening. The primary objective of the proposed technique is to collect context-sensitive acoustic sensor data and integrate it into a decentralized blockchain while minimizing energy consumption at leaf nodes. The evaluation of the system architecture will employ blockchain technology, with a specific focus on leaf node energy efficiency (LNEN) as a primary performance metric. Analysis of experimental results reveals a substantial enhancement in energy efficiency, with LNEN approaching approximately 50% ((LNEN approx 50%)), attributed to the proposed model’s effectiveness.

Device-to-Device (D2D) communication is proposed in cellular network as a vital technology to enhance spectral efficiency and system capacity, while reducing the energy consumption and the latency. Enabling D2D communications in multi-cell OFDMA systems poses two majors challenges: First, D2D equipements are characterizd by limited capacity of battery. Second, D2D devices inevitably suffer from co-channel interference (CCI) at cell edge. This paper addresses the mobility control (MC) for D2D in downlink multi-cell OFDMA system with cooperative relaying and Fractional Frequency Reuse (FFR) scheme. FFR and cooperative relaying schemes are proposed to improve energy efficiency (EE) and avoid CCI. In the edge region, according to a difference set and using the sectorization technique, the Frequency Reuse Factor (FRF) of 7/3 and 7/4 have been applied. The deployment of FFR with Amplify and Forward fixed relays improve significantly the system performance of FRF of 1 and 3. As shown in simulations results, the proposed cooperative MC scheme with FRF of 7/3 and 7/4 provides an improvement in term of EE nears ( 1,2.10^{6}) bits/s/J and (1,3.10^{6}) bits/s/J respectively in comparison with non-cooperative scheme with FRF=3.

To meet growing business needs and exponentially increasing development and maintenance costs, the concept of cloud computing has been proposed and developed rapidly. Cloud computing is a brand-new computing mode that can meet the needs of on-demand distribution and the rapid deployment of computing resources. It can provide strong scalability and applicability through virtualisation technology and elastic technology, and it can adapt to the needs of users in different environments and resources. Through the use of hardware such as cloud sensors, the data collected by various types of sensors can be directly uploaded to the cloud for processing and analysis, so that applications such as management, medical treatment and human–machine cooperation can be provided. However, applications in the cloud have upended traditional security boundaries and will face some unique security challenges. Due to the advantages of generating real data, generative adversarial networks (GANs) have attracted extensive attention in the field of cloud computing, such as data augmentation and encryption. Therefore, this paper reviews GAN-based security and applications in cloud computing. We compare the role of GANs in security and applications in the cloud from multiple dimensions. In addition, we analyse the research trends and future work prospects from the perspective of the algorithm itself, algorithm performance evaluation and cloud computing hardware.

Wireless ad hoc networks play a pivotal role in wireless communication systems. MANETs find extensive applications across various domains, encompassing real-time information exchange, network partitioning, rescue operations, interpersonal communication, and data sharing. MANET works as dynamic wireless networks without a fixed infrastructure in which nodes freely join or leave the network at any time. The absence of fixed infrastructure coupled with openness characteristics of MANET poses significant security issues. This paper proposes a technique called as Anti-blackhole, Gray-hole, and Flooding attack-Ad-hoc On-Demand Distance Vector (ABGF-AODV) to identify and thwart the impact of attacks in MANETs. Through extensive evaluation utilizing the NS-2 simulator, the performance of the proposed protocol is thoroughly examined. The results showcase the robustness of the ABGF-AODV protocol against various attacks, yielding better performance as compared with existing state of art technique.

Tuna swarm optimization algorithm (TSO) is an innovative swarm intelligence algorithm that possesses the advantages of having a small number of adjustable parameters and being straightforward to implement, but the TSO exhibits drawbacks including low computational accuracy and susceptibility to local optima. To solve the shortcomings of TSO, a TSO variant based on behavioral evaluation and simplex strategy is proposed by this study, named SITSO. Firstly, the behavior evaluation mechanism is used to change the updating mechanism of TSO, thereby improving the convergence speed and calculation accuracy of TSO. Secondly, the simplex method enhances the exploitation capability of TSO. Then, simulations of different dimensions of the CEC2017 standard functional test set are performed and compared with a variety of existing mature algorithms to verify the performance of all aspects of the SITSO. Finally, numerous simulation experiments are conducted to address the optimization of wireless sensor network coverage. Based on the experimental results, SITSO outperforms the remaining six comparison algorithms in terms of performance.

Recently, the ever-increasing demands including higher rate and connection stability have bottlenecked the user experience of the traditional cellular networks. To this end, the Cell-Free (CF) network, which is characterized as a user-centric architecture, is viewed as a promising paradigm to enhance the user experience. Aimed at this point, this paper investigates the joint optimization problem including user matching, sub-channel allocation and power controlling for the mmWave CF network to maximize the revenue of the operators. Firstly, the revenue maximization oriented joint optimization problem of the mmWave CF network is formulated as mixed integer non-convex and non-linear programming, which is NP-hard problem and is intractable to search an optimal solution in within polynomial time. Secondly, the original problem is decomposed into three sub-problems, i.e., user association sub-problem, the sub-channel allocation sub-problem and the power controlling sub-problem under the consideration of the matching quotas, rate demand and transmission power, etc. Thirdly, a many-to-many matching based user association algorithm and an alternating iterative joint resource management algorithm, which is composed of the harmony search based sub-channel allocation sub-algorithm and the interior-point method based power controlling sub-algorithm, are proposed to obtain a sub-optimal solution, and the computational complexity of the proposed algorithms are also analyzed. Finally, the performance superiorities of the proposed algorithms are demonstrated through extensive simulations, and it is demonstrated that the proposed algorithms can outperform the proposed algorithm outperforms the PUAA algorithm by 5.73% and the SRAA algorithm by 11.25% in terms of operator revenue.