The explosive growth of data traffic and heterogeneous service requirements of 5G networks—covering Enhanced Mobile Broadband(eMBB),Ultra-Reliable Low Latency Communication(URLLC),and Massive Machine Type Communicati...The explosive growth of data traffic and heterogeneous service requirements of 5G networks—covering Enhanced Mobile Broadband(eMBB),Ultra-Reliable Low Latency Communication(URLLC),and Massive Machine Type Communication(mMTC)—present tremendous challenges to conventional methods of bandwidth allocation.A new deep reinforcement learning-based(DRL-based)bandwidth allocation system for real-time,dynamic management of 5G radio access networks is proposed in this paper.Unlike rule-based and static strategies,the proposed system dynamically updates itself according to shifting network conditions such as traffic load and channel conditions to maximize the achievable throughput,fairness,and compliance with QoS requirements.By using extensive simulations mimicking real-world 5G scenarios,the proposed DRL model outperforms current baselines like Long Short-Term Memory(LSTM),linear regression,round-robin,and greedy algorithms.It attains 90%–95%of the maximum theoretical achievable throughput and nearly twice the conventional equal allocation.It is also shown to react well under delay and reliability constraints,outperforming round-robin(hindered by excessive delay and packet loss)and proving to be more efficient than greedy approaches.In conclusion,the efficiency of DRL in optimizing the allocation of bandwidth is highlighted,and its potential to realize self-optimizing,Artificial Intelligence-assisted(AI-assisted)resource management in 5G as well as upcoming 6G networks is revealed.展开更多
Vehicular Ad Hoc Networks (VANETs) play a pivotal role in advancing Intelligent Transportation Systems (ITS), facilitating real-time communication among vehicles and infrastructure. However, VANETs face challenges ari...Vehicular Ad Hoc Networks (VANETs) play a pivotal role in advancing Intelligent Transportation Systems (ITS), facilitating real-time communication among vehicles and infrastructure. However, VANETs face challenges arising from high mobility, dynamic topologies, and significant interference levels. This study proposes a novel cross-layer framework incorporating channel prediction and adaptive resource management to address these challenges. By leveraging a Software-Defined Radio (SDR) platform, the framework is evaluated under diverse mobility and interference conditions. Key contributions include an analysis of multi-code and multi-modulation schemes, identification of critical trade-offs in receiver diversity, and the introduction of mechanisms to optimize Quality of Service (QoS). Simulation results demonstrate significant improvements in throughput, packet delivery ratio, and network resilience, highlighting the framework’s potential for real-world applications such as autonomous vehicles and smart city communication networks. The study concludes with actionable recommendations for future research, emphasizing scalability, real-time adaptation, and hardware implementation to further enhance VANET performance.展开更多
Large-scale service composition has become an important research topic in Service-Oriented Computing（SOC）. Quality of Service（Qo S） has been mostly applied to represent nonfunctional properties of web services and...Large-scale service composition has become an important research topic in Service-Oriented Computing（SOC）. Quality of Service（Qo S） has been mostly applied to represent nonfunctional properties of web services and to differentiate those with the same functionality. Many studies for measuring service composition in terms of Qo S have been completed. Among current popular optimization methods for service composition, the exhaustion method has some disadvantages such as requiring a large number of calculations and poor scalability. Similarly,the traditional evolutionary computation method has defects such as exhibiting slow convergence speed and falling easily into the local optimum. In order to solve these problems, an improved optimization algorithm, WS FOA（Web Service composition based on Fruit Fly Optimization Algorithm） for service composition, was proposed, on the basis of the modeling of service composition and the FOA. Simulated experiments demonstrated that the algorithm is effective, feasible, stable, and possesses good global searching ability.展开更多
文摘The explosive growth of data traffic and heterogeneous service requirements of 5G networks—covering Enhanced Mobile Broadband(eMBB),Ultra-Reliable Low Latency Communication(URLLC),and Massive Machine Type Communication(mMTC)—present tremendous challenges to conventional methods of bandwidth allocation.A new deep reinforcement learning-based(DRL-based)bandwidth allocation system for real-time,dynamic management of 5G radio access networks is proposed in this paper.Unlike rule-based and static strategies,the proposed system dynamically updates itself according to shifting network conditions such as traffic load and channel conditions to maximize the achievable throughput,fairness,and compliance with QoS requirements.By using extensive simulations mimicking real-world 5G scenarios,the proposed DRL model outperforms current baselines like Long Short-Term Memory(LSTM),linear regression,round-robin,and greedy algorithms.It attains 90%–95%of the maximum theoretical achievable throughput and nearly twice the conventional equal allocation.It is also shown to react well under delay and reliability constraints,outperforming round-robin(hindered by excessive delay and packet loss)and proving to be more efficient than greedy approaches.In conclusion,the efficiency of DRL in optimizing the allocation of bandwidth is highlighted,and its potential to realize self-optimizing,Artificial Intelligence-assisted(AI-assisted)resource management in 5G as well as upcoming 6G networks is revealed.
文摘Vehicular Ad Hoc Networks (VANETs) play a pivotal role in advancing Intelligent Transportation Systems (ITS), facilitating real-time communication among vehicles and infrastructure. However, VANETs face challenges arising from high mobility, dynamic topologies, and significant interference levels. This study proposes a novel cross-layer framework incorporating channel prediction and adaptive resource management to address these challenges. By leveraging a Software-Defined Radio (SDR) platform, the framework is evaluated under diverse mobility and interference conditions. Key contributions include an analysis of multi-code and multi-modulation schemes, identification of critical trade-offs in receiver diversity, and the introduction of mechanisms to optimize Quality of Service (QoS). Simulation results demonstrate significant improvements in throughput, packet delivery ratio, and network resilience, highlighting the framework’s potential for real-world applications such as autonomous vehicles and smart city communication networks. The study concludes with actionable recommendations for future research, emphasizing scalability, real-time adaptation, and hardware implementation to further enhance VANET performance.
基金supported by the National Natural Science Foundation of China (Nos. 61402006 and 61202227)the Natural Science Foundation of Anhui Province of China (No. 1408085MF132)+2 种基金the Science and Technology Planning Project of Anhui Province of China (No. 1301032162)the College Students Scientific Research Training Program (No. KYXL2014060)the 211 Project of Anhui University (No. 02303301)
文摘Large-scale service composition has become an important research topic in Service-Oriented Computing（SOC）. Quality of Service（Qo S） has been mostly applied to represent nonfunctional properties of web services and to differentiate those with the same functionality. Many studies for measuring service composition in terms of Qo S have been completed. Among current popular optimization methods for service composition, the exhaustion method has some disadvantages such as requiring a large number of calculations and poor scalability. Similarly,the traditional evolutionary computation method has defects such as exhibiting slow convergence speed and falling easily into the local optimum. In order to solve these problems, an improved optimization algorithm, WS FOA（Web Service composition based on Fruit Fly Optimization Algorithm） for service composition, was proposed, on the basis of the modeling of service composition and the FOA. Simulated experiments demonstrated that the algorithm is effective, feasible, stable, and possesses good global searching ability.
