Channel aggregation (CA) is one of the newest concept which cognitive radio network is bringing to bear for the smooth role out of fifth/next generation wireless networks. This is the combining of several unused primary user spectrum holes into a logic usable channel. However, several of these strategies have been investigated considering the varying nature of wireless link and adaptive modulation and coding (AMC). Examples are the instant blocking strategy (IBS) and readjustment based strategy (RBS). This paper develops and compares two CA policies with queue, which are the IBS with queue (IBS + Q), and the RBS with queue (RBS+Q). This is in furtherance of previous proposed work. The aim is to identifying the impact of a queuing regime on the performance of the secondary network such that any secondary user (SU) that has not completed its service, as an alternative to dropping or forcibly terminating the service, it is queued in order to get another opportunity to access the primary user (PU) channels. The performance is evaluated through a simulation framework. The results validate that with a welldesigned queuing regime, capacity, access and other metrics can be improved with significant reduction in blocking and forced termination probabilities respectively.
Combined Channel Aggregation and Fragmentation Strategy in Cognitive Radio Networks
在认知无线电网络中，信道聚合（CA）和信道分段（CF）技术已经被提出来提高频谱利用率。针对现有文献中CA和CF各自独立研究的不足，本文创新性地将CA和CF结合起来，提出了一种新的频谱共享策略CAF（Channel Aggregation and Fragmentation）。我们详细阐述了建议的CAF策略，并推导出一个连续时间马尔可夫链（CTMC）模型的平衡方程。然后，各种系统性能指标，包括阻塞概率，丢弃概率，频谱利用率和吞吐量的次级网络进行评估。分析和仿真结果表明，该策略有效地降低了阻塞和丢弃概率，提高了频谱利用率和吞吐量。此外，通过调整每个次级用户的带宽需求，可以实现不同的系统性能。
Reinforcement Learning for Cognitive Delay/Disruption Tolerant Network Node Management in an LEO-based Satellite Constellation
University of Chinese Academy of Sciences（中国科学院大学）；Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences（中国科学院空间应用工程与技术中心）
PFAdapter: Hierarchical LoRA Decomposition for Personalized Federated MLLMs
College of Future Information Technology, Fudan University（复旦大学未来信息技术学院）；Division of Natural and Applied Sciences, Duke Kunshan University（昆山杜克大学自然科学与应用科学部）；Department of Electrical and Computer Engineering, The University of British Columbia（英属哥伦比亚大学电气与计算机工程系）；Ant Group（蚂蚁集团）；College of Information Science and Electronic Engineering, Zhejiang University（浙江大学信息科学与电子工程学院）；School of Data Science and Engineering, East China Normal University（华东师范大学数据科学与工程学院）；College of Intelligent Robotics and Advanced Manufacturing, Fudan University & Fysics AI（复旦大学智能机器人与先进制造学院及复肆智能科技（上海）有限公司）；Xiaomi EV, Xiaomi Campus（小米汽车、小米园区）；Information and Communications Technology Cluster, Singapore Institute of Technology (SIT)（新加坡理工学院信息通信技术集群）；College of Electronic and Information Engineering, Tongji University（同济大学电子与信息工程学院）；School of Computer Science and Informatics, Cardiff University（卡迪夫大学计算机科学与信息学院）
