Telecommunication Systems最新文献

At present, confrontations in cyberspace are becoming increasingly fierce, and network attacks and defenses have become the main form of confrontation between parties in cyberspace. The maximum benefit can be obtained by using scientific decision-making. Traditional network attack and defense research mainly focuses on the relationship between network attackers and network defenders, but more is need to explain the benefits of the evolution of network attacks and defenses. Given this, this paper constructs a tripartite evolutionary game model among network attackers, network defenders, and government regulators, analyzes the evolutionary stability of each participant’s strategy choice, discusses the influence of each element on the tripartite strategic choices, and further analyzes the stability of the equilibrium point of the tripartite evolutionary system. The research results show that (1) the timely supervision of the network by the government supervision department is conducive to enhancing the construction of network security and has strategic significance for the security of cyberspace; (2) the network defense department can obtain more effective defense measures when being attacked by the network; (3) the network defense party can analyze the strategic defense requirements that are more in line with its own benefits. Finally, we use Matlab2021a to conduct simulation analysis, verify the effectiveness of the evolutionary game model under different initial conditions, and put forward reasonable suggestions for network defenders and government regulators according to the conclusions of the analysis.

In this paper, we propose a novel watermarking method in order to enhance the security in biometric voice/speech transmission systems basing on sub-sampling, discrete cosine transform (DCT) and adaptive watermark embedding. To improve imperceptibility, we use sub-sampling and adaptive embedding in DCT high energy coefficients. We employ a significant watermark represented in a biometric unique fingerprint. Introducing bits, only in high energy fraction, provides us with further agreeing element depicted by decreasing running time, both in embedding and extraction processes, which can help minimizing hardware consumption. Achieved results reveal the stability and flexibility of our proposed scheme and confirm its robustness against additive noise. In addition, we enhanced our previous published approaches and the expected limitations of our proposed model will appear in the hardware implementation where the attacks will be more considered. Another limitation will be the number of quantization bits used for high energy DCT coefficients. All these may affect the speaker secured identification and verification system.

In this paper, we optimize the instantaneous and average throughput when wireless communications uses Reconfigurable Intelligent Surfaces (RIS) and the source harvests power from sun using a Photo-Voltaic (PV) system. Instantaneous throughput optimization offers up to 6 dB gain versus average throughput optimization. Average throughput optimization offers a higher throughput than any packet length. Performance gain is up to 1 dB gain using average throughout optimization versus other values of packet length.

Non-Orthogonal Multiple Access (NOMA) is an emerging multiple access scheme for the fifth-generation (5G) and beyond 5G mobile communications. Since NOMA supports a massive number of users simultaneously in a single time/frequency resource block through power domain multiplexing. However, the “Successive Interference Cancellation (SIC)” decoding technique at the receiver is an intricate task in the NOMA systems. Even though SIC operates in a sequential manner, multiple parallel SIC operations can be performed when deploying user pairing. Hence, outsourcing the efficient parallel processing devices is a suitable solution to compute all clusters SIC swiftly. In this paper, we design an efficient high-speed FPGA-based hardware accelerator for SIC implementation in uplink NOMA systems with user pairing on the 32-bit low-memory edge device (PYNQ-Z2 board). Ultimately, experimental results corroborate that the proposed accelerator design provides better SIC computation time than the NVIDIA Tesla K80 Graphics Processing Unit, Central Processing Unit, and ARM-Processor on PYNQ-Z2 board.

In this work, the performance of layered transmission with optimum decoding order and multiuser diversity is analyzed in a K-user downlink network. The transmitter is equipped with two antennas whereas every user has r ((rge 2)) antennas. In each training period, the transmitter opportunistically schedules a single user based on a set of scalar feedback (FB) values sent back from the users. Investigating on what should be fed back, three distinct FB strategies are studied and an exact joint distribution expression is derived in each case for the evaluation of the average outage probability. Analytical results are obtained without disregarding the impact of error propagation and broadly differ from the former approximated analyses. It is shown that one of the inspected strategies clearly outperforms the others and can achieve full multiuser diversity gain of K. We show that by just a scalar FB from the users to the transmitter, which can easily be quantized in accordance with the desired accuracy, a significant power gain can be accomplished at the transmitter as compared to a single-user scenario. For the mathematical analysis, we rely on the fact that all orderings are equiprobable under independent and identical fading. To that end, we obtain the joint distribution of the ordered gains from the joint distribution of the unordered gains.

The architecture of integrating Software Defined Networking (SDN) with Network Function Virtualization (NFV) is excellent because the former virtualizes the control plane, and the latter virtualizes the data plane. As Programming Protocol-independent Packet Processors (P4) become popular, the architecture integrating SDN with NFV may shift from traditional switches to P4 switches. In this architecture, which integrates P4 switch and NFV (P4 + NFV), network functions can be provided in both P4 switches (PNF) and NFV (VNF). Thus, to minimize packet delay, an offloading problem between P4 switches and NFV in this P4 + NFV should be addressed. This paper tackles this offloading problem and figures out the prioritization mechanism between newly arriving packets and packets that require VNF for minimizing packet delay. We model and analyze the P4 + NFV architecture using an M/M/1 queuing model with non-preemptive priority. Also, we propose an optimization solution based on gradient descent to find the optimal offloading probability of going to VNF. Results show that optimal offloading from P4 switch to NFV can reduce the average packet delay from 13.74 to 40.73%, when packets requiring VNF are given higher priority than newly arriving packets.

Nowadays the communication of massive multi-user multiple-input multiple-output (MU-MIMO) takes an important role in wireless systems, as they facilitate the ultra-reliable transmission of data and high performance. In order to sustain massive user equipment (UE) with tremendous reliability and spectral efficiency, more antennas are deployed per base station (BS) in the MU-MIMO system. To overcome such problems, the recurrent neural network (RNN) with crossover-gradient based optimizer (GBO) model called RNN-crossover GBO is proposed for precoding in the MU-MIMO system. However, it is essential to diminish the complexity to attain the maximum sum rate for obtaining the optimal solution. Moreover, the kernel linear discriminant analysis (KLDA) dimensionality reduction technique is employed for mapping high dimensional data into a low dimension by considering a linear combination matrix. In order to obtain the best feature the GBO is employed that predict the optimal solution and restrict falling from the local solution. Furthermore, the crossover-GBO algorithm is applied with the RNN to estimate the output precoding matrix with considerable features to obtain the best search space. The experimental results revealed that the proposed method achieves higher performance with a higher sum rate and shows significant improvement in spectral efficiency (SE) values than the existing methods. SE rises due to the selection of closely associated features. This indicates the robustness and stability of the proposed model.

Named Data Network (NDN) is proposed for the Internet as an information-centric architecture. Content storing in the router’s cache plays a significant role in NDN. When a router’s cache becomes full, a cache replacement policy determines which content should be discarded for the new content storage. This paper proposes a new cache replacement policy called Discard of Fast Retrievable Content (DFRC). In DFRC, the retrieval time of the content is evaluated using the FIB table information, and the content with less retrieval time receives more discard priority. An impact weight is also used to involve both the grade of retrieval and stale parameters in the discard priority calculation. This weight adjusts the relative impact of these two parameters on the discard priority. The grade of retrieval reflects the content’s retrieval time, and the stale parameter indicates its popularity. The reinforcement Learning method is used to calculate the suitable weight dynamically and based on the network condition. Simulation results show that DFRC improves hit rate and round-trip time compared to Least Recently Used, Optimized Cache Replacement algorithm for Information-Centric Networks, Efficient Popularity-aware Probabilistic Caching, and Shortest Round-Trip Time.

In this paper, four-element Multiple-Input Multiple-Output (MIMO) antenna system has been proposed for Intelligent Internet of Everything (IIoE) and high-speed data applications. The presented antenna design operates at 4.9 GHz (5G millimeter wave applications) with high isolation in a small size. Defected Microstrip Structure (DMS), slot modification and orthogonal polarization are used to generate the desired frequency band, improve the impedance bandwidth, and enhance the isolation between antenna elements at 4.9 GHz, respectively. Furthermore, the usefulness of the proposed MIMO antenna has been verified by comparing the simulated and the measured results using a fabricated version of the considered 4 × 4 MIMO antenna. In addition, the results indicate a high diversity performance in terms of: Envelope Correlation Coefficient (ECC), Diversity Gain (DG), Efficiency, Gain, Total Active Reflection Coefficient (TARC) and Total Channel Capacity Loss (CCL_Total).

This paper proposes a multi-strategy modified seagull algorithm to optimize DV-Hop localization algorithm (DISO) to improve the precision of non-range-ranging localization algorithm in wireless sensor networks. Firstly, the algorithm analyzes the causes of errors in the positioning of the traditional non-ranging location algorithm DV-Hop, and improves these steps. Among them, the communication area of anchor nodes is divided by different radii, so as to reduce the influence of distance on hop number. The node distribution is stochastic, so the mean square error is used instead of the unbiased estimation, and the weight is introduced to calculate the average jump distance, which reduces the error caused by the random distribution of nodes. Secondly, the objective function optimization method is used to replace the trilateral measurement, and the improved seagull optimization algorithm is used for iterative optimization. Finally, the seagull optimization algorithm is modified in view of its shortcomings. The chaotic mapping was used to initialize the seagull population and increase its diversity. The flight parameters of seagull and the position update methods of the worst and best seagull are improved, and the optimization ability of the algorithm is improved by combining levy flight mechanism and T distribution variation strategy. The simulation results show that the initial population distribution of DISO algorithm is more uniform, which establishes a basic advantage for the subsequent optimization. Keeping the other parameters consistent, DISO algorithm has higher positioning accuracy than other comparison algorithms, no matter changing the number of anchor nodes or the total number of nodes or changing the communication radius. The positioning errors of DISO algorithm are reduced by 45.63%, 17.17%, 22.61% and 11.68% compared with DV-Hop algorithm and other comparison algorithms under different number of anchor nodes. Under different total number of nodes, the positioning error is reduced by 49.91%, 20.81%, 35.80% and 9.20%. Under different communication radius, the positioning error is reduced by 55.47%, 21.07%, 24.84% and 13.11%. It is proved that DISO algorithm has more accurate localization results.