Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:
Zhang Ping;Shi Guangming;Cui Shuguang;Zhang Zhaoyang;Niu Kai;Xiao Yong;Qin Zhijin;Dai Jincheng;Shao Shuo;Deniz Gündüz;Eleonora Grassucci;
Pages: iii - vii Abstract: The last seventy years have witnessed the transition of communication from Shannon's theoretical concept to current high-efficiency practical systems. With respect to Shannon information theory, to fulfill the high-bandwidth utilization, any further increase in the data rate requires a significant augmentation in the received signal power unless the bandwidth is extended in proportion to the incremental data rate. Although many such technologies have achieved tremendous success in today's communication systems, they also lead to severe high-frequency coverage costs, complicated signal processing, high energy consumption, etc. Together with extraordinary promises, naively increasing channel capacity cannot address all communication problems, especially in future intelligent eras. It is the very time for radical rethinking of classical communication mechanisms. PubDate:
MON, 22 JUL 2024 09:16:23 -04 Issue No:Vol. 21, No. 7 (2024)
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:
Tang Jiancheng;Yang Qianqian;Zhang Zhaoyang;
Pages: 1 - 16 Abstract: As conventional communication systems based on classic information theory have closely approached Shannon capacity, semantic communication is emerging as a key enabling technology for the further improvement of communication performance. However, it is still unsettled on how to represent semantic information and characterise the theoretical limits of semantic-oriented compression and transmission. In this paper, we consider a semantic source which is characterised by a set of correlated random variables whose joint probabilistic distribution can be described by a Bayesian network. We give the information-theoretic limit on the lossless compression of the semantic source and introduce a low complexity encoding method by exploiting the conditional independence. We further characterise the limits on lossy compression of the semantic source and the upper and lower bounds of the rate-distortion function. We also investigate the lossy compression of the semantic source with two-sided information at the encoder and decoder, and obtain the corresponding rate distortion function. We prove that the optimal code of the semantic source is the combination of the optimal codes of each conditional independent set given the side information. PubDate:
MON, 22 JUL 2024 09:16:24 -04 Issue No:Vol. 21, No. 7 (2024)
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:
Liang Zijian;Niu Kai;Zhang Ping;
Pages: 17 - 36 Abstract: As a novel paradigm, semantic communication provides an effective solution for breaking through the future development dilemma of classical communication systems. However, it remains an unsolved problem of how to measure the information transmission capability for a given semantic communication method and subsequently compare it with the classical communication method. In this paper, we first present a review of the semantic communication system, including its system model and the two typical coding and transmission methods for its implementations. To address the unsolved issue of the information transmission capability measure for semantic communication methods, we propose a new universal performance measure called Information Conductivity. We provide the definition and the physical significance to state its effectiveness in representing the information transmission capabilities of the semantic communication systems and present elaborations including its measure methods, degrees of freedom, and progressive analysis. Experimental results in image transmission scenarios validate its practical applicability. PubDate:
MON, 22 JUL 2024 09:16:23 -04 Issue No:Vol. 21, No. 7 (2024)
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:
Yue Weijie;Si Zhongwei;
Pages: 37 - 49 Abstract: Video transmission requires considerable bandwidth, and current widely employed schemes prove inadequate when confronted with scenes featuring prominently. Motivated by the strides in talking-head generative technology, the paper introduces a semantic transmission system tailored for talking-head videos. The system captures semantic information from talking-head video and faithfully reconstructs source video at the receiver, only one-shot reference frame and compact semantic features are required for the entire transmission. Specifically, we analyze video semantics in the pixel domain frame-by-frame and jointly process multi-frame semantic information to seamlessly incorporate spatial and temporal information. Variational modeling is utilized to evaluate the diversity of importance among group semantics, thereby guiding bandwidth resource allocation for semantics to enhance system efficiency. The whole end-to-end system is modeled as an optimization problem and equivalent to acquiring optimal rate-distortion performance. We evaluate our system on both reference frame and video transmission, experimental results demonstrate that our system can improve the efficiency and robustness of communications. Compared to the classical approaches, our system can save over 90% of bandwidth when user perception is close. PubDate:
MON, 22 JUL 2024 09:16:24 -04 Issue No:Vol. 21, No. 7 (2024)
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:
Zhang Xi;Zhang Yiqian;Li Congduan;Ma Xiao;
Pages: 50 - 64 Abstract: Recently, deep learning-based semantic communication has garnered widespread attention, with numerous systems designed for transmitting diverse data sources, including text, image, and speech, etc. While efforts have been directed toward improving system performance, many studies have concentrated on enhancing the structure of the encoder and decoder. However, this often overlooks the resulting increase in model complexity, imposing additional storage and computational burdens on smart devices. Furthermore, existing work tends to prioritize explicit semantics, neglecting the potential of implicit semantics. This paper aims to easily and effectively enhance the receiver's decoding capability without modifying the encoder and decoder structures. We propose a novel semantic communication system with variational neural inference for text transmission. Specifically, we introduce a simple but effective variational neural inferer at the receiver to infer the latent semantic information within the received text. This information is then utilized to assist in the decoding process. The simulation results show a significant enhancement in system performance and improved robustness. PubDate:
MON, 22 JUL 2024 09:16:24 -04 Issue No:Vol. 21, No. 7 (2024)
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:
Chen Mingkai;Liu Minghao;Zhang Zhe;Xu Zhiping;Wang Lei;
Pages: 65 - 77 Abstract: In the future development direction of the sixth generation (6G) mobile communication, several communication models are proposed to face the growing challenges of the task. The rapid development of artificial intelligence (AI) foundation models provides significant support for efficient and intelligent communication interactions. In this paper, we propose an innovative semantic communication paradigm called task-oriented semantic communication system with foundation models. First, we segment the image by using task prompts based on the segment anything model (SAM) and contrastive language-image pretraining (CLIP). Meanwhile, we adopt Bezier curve to enhance the mask to improve the segmentation accuracy. Second, we have differentiated semantic compression and transmission approaches for segmented content. Third, we fuse different semantic information based on the conditional diffusion model to generate high-quality images that satisfy the users' specific task requirements. Finally, the experimental results show that the proposed system compresses the semantic information effectively and improves the robustness of semantic communication. PubDate:
MON, 22 JUL 2024 09:16:24 -04 Issue No:Vol. 21, No. 7 (2024)
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:
Ma Kangning;Shi Yuxuan;Shao Shuo;Tao Meixia;
Pages: 78 - 94 Abstract: We consider an image semantic communication system in a time-varying fading Gaussian MIMO channel, with a finite number of channel states. A deep learning-aided broadcast approach scheme is proposed to benefit the adaptive semantic transmission in terms of different channel states. We combine the classic broadcast approach with the image transformer to implement this adaptive joint source and channel coding (JSCC) scheme. Specifically, we utilize the neural network (NN) to jointly optimize the hierarchical image compression and superposition code mapping within this scheme. The learned transformers and codebooks allow recovering of the image with an adaptive quality and low error rate at the receiver side, in each channel state. The simulation results exhibit our proposed scheme can dynamically adapt the coding to the current channel state and outperform some existing intelligent schemes with the fixed coding block. PubDate:
MON, 22 JUL 2024 09:16:23 -04 Issue No:Vol. 21, No. 7 (2024)
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:
Wang Yining;Han Shujun;Xu Xiaodong;Meng Rui;Liang Haotai;Dong Chen;Zhang Ping;
Pages: 95 - 112 Abstract: To facilitate emerging applications and demands of edge intelligence (EI)-empowered 6G networks, model-driven semantic communications have been proposed to reduce transmission volume by deploying artificial intelligence (AI) models that provide abilities of semantic extraction and recovery. Nevertheless, it is not feasible to preload all AI models on resource-constrained terminals. Thus, in-time model transmission becomes a crucial problem. This paper proposes an intellicise model transmission architecture to guarantee the reliable transmission of models for semantic communication. The mathematical relationship between model size and performance is formulated by employing a recognition error function supported with experimental data. We consider the characteristics of wireless channels and derive the closed-form expression of model transmission outage probability (MTOP) over the Rayleigh channel. Besides, we define the effective model accuracy (EMA) to evaluate the model transmission performance of both communication and intelligence. Then we propose a joint model selection and resource allocation (JMSRA) algorithm to maximize the average EMA of all users. Simulation results demonstrate that the average EMA of the JMSRA algorithm outperforms baseline algorithms by about 22%. PubDate:
MON, 22 JUL 2024 09:16:23 -04 Issue No:Vol. 21, No. 7 (2024)
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:
Cheng Yukun;Chen Wei;Ai Bo;
Pages: 113 - 124 Abstract: The concept of semantic communication provides a novel approach for applications in scenarios with limited communication resources. In this paper, we propose an end-to-end (E2E) semantic molecular communication system, aiming to enhance the efficiency of molecular communication systems by reducing the transmitted information. Specifically, following the joint source channel coding paradigm, the network is designed to encode the task-relevant information into the concentration of the information molecules, which is robust to the degradation of the molecular communication channel. Furthermore, we propose a channel network to enable the E2E learning over the non-differentiable molecular channel. Experimental results demonstrate the superior performance of the semantic molecular communication system over the conventional methods in classification tasks. PubDate:
MON, 22 JUL 2024 09:16:23 -04 Issue No:Vol. 21, No. 7 (2024)
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:
Zhu Hongfei;Cao Zhiwei;Zhao Yuping;Li Dou;
Pages: 125 - 134 Abstract: In this paper, we innovatively associate the mutual information with the frame error rate (FER) performance and propose novel quantized decoders for polar codes. Based on the optimal quantizer of binary-input discrete memoryless channels (B-DMCs), the proposed decoders quantize the virtual subchannels of polar codes to maximize mutual information (MMI) between source bits and quantized symbols. The nested structure of polar codes ensures that the MMI quantization can be implemented stage by stage. Simulation results show that the proposed MMI decoders with 4 quantization bits outperform the existing nonuniform quantized decoders that minimize mean-squared error (MMSE) with 4 quantization bits, and yield even better performance than uniform MMI quantized decoders with 5 quantization bits. Furthermore, the proposed 5-bit quantized MMI decoders approach the floating-point decoders with negligible performance loss. PubDate:
TUE, 09 APR 2024 09:16:40 -04 Issue No:Vol. 21, No. 7 (2024)
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:
Qasim Jan;Yin Chao;Pan Zhiwen;Muhammad Furqan;Zakir Ali;You Xiaohu;
Pages: 135 - 148 Abstract: Though belief propagation bit-flip (BPBF) decoding improves the error correction performance of polar codes, it uses the exhaustive flips method to achieve the error correction performance of CA-SCL decoding, thus resulting in high decoding complexity and latency. To alleviate this issue, we incorporate the LDPC-CRC-Polar coding scheme with BPBF and propose an improved belief propagation decoder for LDPC-CRC-Polar codes with bit-freezing (LDPC-CRC-Polar codes BPBFz). The proposed LDPC-CRC-Polar codes BPBFz employs the LDPC code to ensure the reliability of the flipping set, i.e., critical set (CS), and dynamically update it. The modified CS is further utilized for the identification of error-prone bits. The proposed LDPC-CRC-Polar codes BPBFz obtains remarkable error correction performance and is comparable to that of the CA-SCL (L = 16) decoder under medium-to-high signal-to-noise ratio (SNR) regions. It gains up to 1.2dB and 0.9dB at a fixed BLER = 10−4 compared with BP and BPBF (CS-1), respectively. In addition, the proposed LDPC-CRC-Polar codes BPBFz has lower decoding latency compared with CA-SCL and BPBF, i.e., it is 15 times faster than CA-SCL (L = 16) at high SNR regions. PubDate:
MON, 22 JUL 2024 09:16:23 -04 Issue No:Vol. 21, No. 7 (2024)
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:
Zhang Hang;Li Bo;Yan Zhongjiang;Yang Mao;Li Xinru;
Pages: 149 - 168 Abstract: In this paper, we propose a Multi-token Sector Antenna Neighbor Discovery (M-SAND) protocol to enhance the efficiency of neighbor discovery in asynchronous directional ad hoc networks. The central concept of our work involves maintaining multiple tokens across the network. To prevent mutual interference among multi-token holders, we introduce the time and space non-interference theorems. Furthermore, we propose a master-slave strategy between tokens. When the master token holder (MTH) performs the neighbor discovery, it decides which 1-hop neighbor is the next MTH and which 2-hop neighbors can be the new slave token holders (STHs). Using this approach, the MTH and multiple STHs can simultaneously discover their neighbors without causing interference with each other. Building on this foundation, we provide a comprehensive procedure for the M-SAND protocol. We also conduct theoretical analyses on the maximum number of STHs and the lower bound of multi-token generation probability. Finally, simulation results demonstrate the time efficiency of the M-SAND protocol. When compared to the Q-SAND protocol, which uses only one token, the total neighbor discovery time is reduced by 28% when 6 beams and 112 nodes are employed. PubDate:
TUE, 09 APR 2024 09:16:40 -04 Issue No:Vol. 21, No. 7 (2024)
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:
Mei Miao;Tang Miao;Zhou Long;
Pages: 169 - 185 Abstract: The telecommunications industry is becoming increasingly aware of potential subscriber churn as a result of the growing popularity of smartphones in the mobile Internet era, the quick development of telecommunications services, the implementation of the number portability policy, and the intensifying competition among operators. At the same time, users' consumption preferences and choices are evolving. Excellent churn prediction models must be created in order to accurately predict the churn tendency, since keeping existing customers is far less expensive than acquiring new ones. But conventional or learning-based algorithms can only go so far into a single subscriber's data; they cannot take into consideration changes in a subscriber's subscription and ignore the coupling and correlation between various features. Additionally, the current churn prediction models have a high computational burden, a fuzzy weight distribution, and significant resource economic costs. The prediction algorithms involving network models currently in use primarily take into account the private information shared between users with text and pictures, ignoring the reference value supplied by other users with the same package. This work suggests a user churn prediction model based on Graph Attention Convolutional Neural Network (GAT-CNN) to address the aforementioned issues. The main contributions of this paper are as follows: Firstly, we present a three-tiered hierarchical cloud-edge cooperative framework that increases the volume of user feature input by means of two aggregations at the device, edge, and cloud layers. Second, we extend the use of users' own data by introducing self-attention and graph convolution models to track the relative changes of both users and packages simultaneously. Lastly, we build an integrated offline-online system for churn prediction based on the strengths of the two models, and we experimentally validate the efficacy of cloud-side collaborative training and inference. In summary, the churn prediction model based on Graph Attention Convolutional Neural Network presented in this paper can effectively address the drawbacks of conventional algorithms and offer telecom operators crucial decision support in developing subscriber retention strategies and cutting operational expenses. PubDate:
MON, 22 JUL 2024 09:16:23 -04 Issue No:Vol. 21, No. 7 (2024)
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:
Chen Yating;Wen Cai;Huang Yan;Liang Le;Li Jie;Zhang Hui;Hong Wei;
Pages: 186 - 211 Abstract: In this paper, we formulate the precoding problem of integrated sensing and communication (ISAC) waveform as a non-convex quadratically constrained quadratic programming (QCQP), in which the weighted sum of communication multi-user interference (MUI) and the gap between dual-use waveform and ideal radar waveform is minimized with peak-to-average power ratio (PAPR) constraints. We propose an efficient algorithm based on alternating direction method of multipliers (ADMM), which is able to decouple multiple variables and provide a closed-form solution for each subproblem. In addition, to improve the sensing performance in both spatial and temporal domains, we propose a new criteria to design the ideal radar waveform, in which the beam pattern is made similar to the ideal one and the integrated sidelobe level of the ambiguity function in each target direction is minimized in the region of interest. The limited memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithm is applied to the design of the ideal radar waveform which works as a reference in the design of the dual-function waveform. Numerical results indicate that the designed dual-function waveform is capable of offering good communication quality of service (QoS) and sensing performance. PubDate:
MON, 22 JUL 2024 09:16:23 -04 Issue No:Vol. 21, No. 7 (2024)
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:
Wang Shuo;Chu Jiang;Pei Qingqi;Shao Feng;Yuan Shuai;Zhong Xiaoge;
Pages: 212 - 223 Abstract: The static and predictable characteristics of cyber systems give attackers an asymmetric advantage in gathering useful information and launching attacks. To reverse this asymmetric advantage, a new defense idea, called Moving Target Defense (MTD), has been proposed to provide additional selectable measures to complement traditional defense. However, MTD is unable to defeat the sophisticated attacker with fingerprint tracking ability. To overcome this limitation, we go one step beyond and show that the combination of MTD and Deception-based Cyber Defense (DCD) can achieve higher performance than either of them. In particular, we first introduce and formalize a novel attacker model named Scan and Foothold Attack (SFA) based on cyber kill chain. Afterwards, we develop probabilistic models for SFA defenses to provide a deeper analysis of the theoretical effect under different defense strategies. These models quantify attack success probability and the probability that the attacker will be deceived under various conditions, such as the size of address space, and the number of hosts, attack analysis time. Finally, the experimental results show that the actual defense effect of each strategy almost perfectly follows its probabilistic model. Also, the defense strategy of combining address mutation and fingerprint camouflage can achieve a better defense effect than the single address mutation. PubDate:
TUE, 09 APR 2024 09:16:39 -04 Issue No:Vol. 21, No. 7 (2024)
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:
Duan Pengfei;Ma Zhaofeng;Zhang Yuqing;Wang Jingyu;Luo Shoushan;
Pages: 224 - 236 Abstract: With the growth of requirements for data sharing, a novel business model of digital assets trading has emerged that allows data owners to sell their data for monetary gain. In the distributed ledger of blockchain, however, the privacy of stakeholder's identity and the confidentiality of data content are threatened. Therefore, we proposed a blockchain-enabled privacy-preserving and access control scheme to address the above problems. First, the multi-channel mechanism is introduced to provide the privacy protection of distributed ledger inside the channel and achieve coarse-grained access control to digital assets. Then, we use multi-authority attribute-based encryption (MAABE) algorithm to build a fine-grained access control model for data trading in a single channel and describe its instantiation in detail. Security analysis shows that the scheme has IND-CPA secure and can provide privacy protection and collusion resistance. Compared with other schemes, our solution has better performance in privacy protection and access control. The evaluation results demonstrate its effectiveness and practicability. PubDate:
TUE, 09 APR 2024 09:16:39 -04 Issue No:Vol. 21, No. 7 (2024)
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:
Feng Yiting;Ma Zhaofeng;Duan Pengfei;Luo Shoushan;
Pages: 237 - 251 Abstract: The widespread adoption of blockchain technology has led to the exploration of its numerous applications in various fields. Cryptographic algorithms and smart contracts are critical components of blockchain security. Despite the benefits of virtual currency, vulnerabilities in smart contracts have resulted in substantial losses to users. While researchers have identified these vulnerabilities and developed tools for detecting them, the accuracy of these tools is still far from satisfactory, with high false positive and false negative rates. In this paper, we propose a new method for detecting vulnerabilities in smart contracts using the BERT pre-training model, which can quickly and effectively process and detect smart contracts. More specifically, we preprocess and make symbol substitution in the contract, which can make the pre-training model better obtain contract features. We evaluate our method on four datasets and compare its performance with other deep learning models and vulnerability detection tools, demonstrating its superior accuracy. PubDate:
TUE, 09 APR 2024 09:16:39 -04 Issue No:Vol. 21, No. 7 (2024)
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:
Yang Lilin;Li Guyue;Guo Tao;Xu Hao;Hu Aiqun;
Pages: 252 - 266 Abstract: Physical-layer secret key generation (PSKG) provides a lightweight way for group key (GK) sharing between wireless users in large-scale wireless networks. However, most of the existing works in this field consider only group communication. For a commonly dual-task scenario, where both GK and pairwise key (PK) are required, traditional methods are less suitable for direct extension. For the first time, we discover a security issue with traditional methods in dual-task scenarios, which has not previously been recognized. We propose an innovative segment-based key generation method to solve this security issue. We do not directly use PK exclusively to negotiate the GK as traditional methods. Instead, we generate GK and PK separately through segmentation which is the first solution to meet dual-task. We also perform security and rate analysis. It is demonstrated that our method is effective in solving this security issue from an information-theoretic perspective. The rate results of simulation are also consistent with the our rate derivation. PubDate:
MON, 22 JUL 2024 09:16:23 -04 Issue No:Vol. 21, No. 7 (2024)
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:
Chen Guo;Peng Weijun;Wu Jing;Fang Youxuan;Ye Keke;Xin Yanshuang;
Pages: 267 - 277 Abstract: With the development of Internet of Things technology, intelligent door lock devices are widely used in the field of house leasing. In the traditional housing leasing scenario, problems of door lock information disclosure, tenant privacy disclosure and rental contract disputes frequently occur, and the security, fairness and auditability of the housing leasing transaction cannot be guaranteed. To solve the above problems, a blockchain-based proxy re-encryption scheme with conditional privacy protection and auditability is proposed. The scheme implements fine-grained access control of door lock data based on attribute encryption technology with policy hiding, and uses proxy re-encryption technology to achieve auditable supervision of door lock information transactions. Homomorphic encryption technology and zero-knowledge proof technology are introduced to ensure the confidentiality of housing rent information and the fairness of rent payment. To construct a decentralized housing lease transaction architecture, the scheme realizes the efficient collaboration between the door lock data ciphertext stored under the chain and the key information ciphertext on the chain based on the blockchain and InterPlanetary File System. Finally, the security proof and computing performance analysis of the proposed scheme are carried out. The results show that the scheme can resist the chosen plaintext attack and has low computational cost. PubDate:
MON, 22 JUL 2024 09:16:23 -04 Issue No:Vol. 21, No. 7 (2024)
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:
Chen Yong;Liao Naiwen;Wang Wei;Zhang Xianyu;Zhang Yu;
Pages: 278 - 290 Abstract: To improve the efficiency and fairness of the spectrum allocation for ground communication assisted by unmanned aerial vehicles (UAVs), a joint optimization method for on-demand deployment and spectrum allocation of UAVs is proposed, which is modeled as a mixed-integer non-convex optimization problem (MINCOP). An algorithm to estimate the minimum number of required UAVs is firstly proposed based on the pre-estimation and simulated annealing. The MINCOP is then decomposed into three sub-problems based on the block coordinate descent method, including the spectrum allocation of UAVs, the association between UAVs and ground users, and the deployment of UAVs. Specifically, the optimal spectrum allocation is derived based on the interference mitigation and channel reuse. The association between UAVs and ground users is optimized based on local iterated optimization. A particle-based optimization algorithm is proposed to resolve the subproblem of the UAVs deployment. Simulation results show that the proposed method could effectively improve the minimum transmission rate of UAVs as well as user fairness of spectrum allocation. PubDate:
MON, 22 JUL 2024 09:16:23 -04 Issue No:Vol. 21, No. 7 (2024)
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:
Zhang Han;Wang Yu;Liu Hao;Lin Hongyu;Chen Liquan;
Pages: 291 - 306 Abstract: The conventional dynamic heterogeneous redundancy (DHR) architecture suffers from the security threats caused by the stability differences and similar vulnerabilities among the executors. To overcome these challenges, we propose an intelligent DHR architecture, which is more feasible by intelligently combining the random distribution based dynamic scheduling algorithm (RD-DS) and information weight and heterogeneity based arbitrament (IWHA) algorithm. In the proposed architecture, the random distribution function and information weight are employed to achieve the optimal selection of executors in the process of RD-DS, which avoids the case that some executors fail to be selected due to their stability difference in the conventional DHR architecture. Then, through introducing the heterogeneity to restrict the information weights in the procedure of the IWHA, the proposed architecture solves the common mode escape issue caused by the existence of multiple identical error output results of similar vulnerabilities. The experimental results characterize that the proposed architecture outperforms in heterogeneity, scheduling times, security, and stability over the conventional DHR architecture under the same conditions. PubDate:
MON, 22 JUL 2024 09:16:23 -04 Issue No:Vol. 21, No. 7 (2024)
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:
K. Ramya;Senthilselvi Ayothi;
Pages: 307 - 324 Abstract: The cloud computing technology is utilized for achieving resource utilization of remote-based virtual computer to facilitate the consumers with rapid and accurate massive data services. It utilizes on-demand resource provisioning, but the necessitated constraints of rapid turnaround time, minimal execution cost, high rate of resource utilization and limited makespan transforms the Load Balancing (LB) process-based Task Scheduling (TS) problem into an NP-hard optimization issue. In this paper, Hybrid Prairie Dog and Beluga Whale Optimization Algorithm (HPDBWOA) is propounded for precise mapping of tasks to virtual machines with the due objective of addressing the dynamic nature of cloud environment. This capability of HPDBWOA helps in decreasing the SLA violations and Makespan with optimal resource management. It is modelled as a scheduling strategy which utilizes the merits of PDOA and BWOA for attaining reactive decisions making with respect to the process of assigning the tasks to virtual resources by considering their priorities into account. It addresses the problem of pre-convergence with well-balanced exploration and exploitation to attain necessitated Quality of Service (QoS) for minimizing the waiting time incurred during TS process. It further balanced exploration and exploitation rates for reducing the makespan during the task allocation with complete awareness of VM state. The results of the proposed HPDBWOA confirmed minimized energy utilization of 32.18% and reduced cost of 28.94% better than approaches used for investigation. The statistical investigation of the proposed HPDBWOA conducted using ANOVA confirmed its efficacy over the benchmarked systems in terms of throughput, system, and response time. PubDate:
MON, 22 JUL 2024 09:16:23 -04 Issue No:Vol. 21, No. 7 (2024)
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:
Wang Jigang;Cheng Shengyu;Cao Jicheng;He Meihua;
Pages: 325 - 333 Abstract: Although static program analysis methods are frequently employed to enhance software quality, their efficiency in commercial settings is limited by their high false positive rate. The EUGENE tool can effectively lower the false positive rate. However, in continuous integration (CI) environments, the code is always changing, and user feedback from one version of the software cannot be applied to a subsequent version. Additionally, people find it difficult to distinguish between true positives and false positives in the analytical output. In this study, we developed the EUGENE-CI technique to address the CI problem and the EUGENE-rank lightweight heuristic algorithm to rate the reports of the analysis output in accordance with the likelihood that they are true positives. On the three projects ethereum, go-cloud, and kuber-netes, we assessed our methodologies. According to the trial findings, EUGENE-CI may drastically reduce false positives while EUGENE-rank can make it much easier for users to identify the real positives among a vast number of reports. We paired our techniques with GoInsight1 and discovered a vulnerability. We also offered a patch to the community. PubDate:
MON, 22 JUL 2024 09:16:24 -04 Issue No:Vol. 21, No. 7 (2024)
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:
Zhang Zhikai;Gu Shushi;Zhang Qinyu;Xue Jiayin;
Pages: 334 - 345 Abstract: Due to the restricted satellite payloads in LEO mega-constellation networks (LMCNs), remote sensing image analysis, online learning and other big data services desirably need onboard distributed processing (OBDP). In existing technologies, the efficiency of big data applications (BDAs) in distributed systems hinges on the stable-state and low-latency links between worker nodes. However, LMCNs with high-dynamic nodes and long-distance links can not provide the above conditions, which makes the performance of OBDP hard to be intuitively measured. To bridge this gap, a multidimensional simulation platform is indispensable that can simulate the network environment of LMCNs and put BDAs in it for performance testing. Using STK's APIs and parallel computing framework, we achieve real-time simulation for thousands of satellite nodes, which are mapped as application nodes through software defined network (SDN) and container technologies. We elaborate the architecture and mechanism of the simulation platform, and take the Starlink and Hadoop as realistic examples for simulations. The results indicate that LMCNs have dynamic end-to-end latency which fluctuates periodically with the constellation movement. Compared to ground data center networks (GDCNs), LMCNs deteriorate the computing and storage job throughput, which can be alleviated by the utilization of erasure codes and data flow scheduling of worker nodes. PubDate:
MON, 22 JUL 2024 09:16:23 -04 Issue No:Vol. 21, No. 7 (2024)
Subscription journal
[228 journals]
