与国际学者共建高水平研究生课程-- 《Emerging Technologies in Machine Learning and Energy Detection》授课通知

应陕西省111 “交通-能源-信息”多网融合与自洽技术学科创新引智基地邀请，马德里卡洛斯三世大学/伊斯兰大学的MdSipon Miah教授将于2023年12月22日至29日在长安大学渭水校区交通运输及智能装备创新平台3号楼能源与电气工程学院6楼612会议室线下亲自讲授“与国际学者共建高水平研究生课程”—— Emerging Technologies in Machine Learning and Energy Detection《机器学习与能量检测新技术》。真诚欢迎老师和各位同学前来体验世界一流大学的授课方式，并与名师当场互动交流。

课程主要安排：

课程内容摘要：

Lecture 1-2:  Cluster-based Cooperative Spectrum Sensing in Cognitive Radio Networks (CRNs)

Intro: Cognitive radio (CR) networks have been active area of research because of its ability to opportunistically share the spectrum. A cluster-based cooperative spectrum sensing (CCSS) has a tremendous impact on sensing reliability compared with cooperative spectrum sensing. The energy detection (ED) technique requires perfect knowledge of noise power. An eigenvalue-based spectrum sensing has mitigated the noise uncertainty problem. Sensing and reporting time slots are rigidly separated in the conventional ED and eigenvalue-based detection (EVD) schemes. In CCSS, more reporting time slots are required as the number of CR users (CRUs) increases. If the reporting time slots of other CRUs as sensing time slots with a superposition allocation, the more reliable channel sensing can be achieved. In this paper, we propose CCSS using EVD technique with a superposition approach scheme where the reporting time slot is properly utilized to sense the primary user's (PU's) signal more accurately by rescheduling the reporting time slot for CRUs and cluster heads (CHs). Simulation result shows that the proposed EVD scheme has better detection probability than the conventional CCSS using both ED and EVD techniques.

Lecture 3-4:  Sensing and Throughput Analysis of a MU-MIMO based Cognitive Radio-based Internet of Things (CR-IoT)

Intro: State-of-the-art energy detection (ED) based spectrum sensing requires perfect knowledge of noise power and is vulnerable to noise uncertainty. An eigenvalue-based spectrum sensing approach performs well in such an uncertain environment, but does not mitigate the spectrum scarcity problem, which evolves with the future Internet of Things (IoT) rollout. In this paper, we propose a multi-user multiple-input and multiple-output (MU-MIMO) based cognitive radio scheme for the Internet of Things (CR-IoT) with weighted-eigenvalue detection (WEVD) for the analysis of sensing, system throughput, energy efficiency and expected lifetime. In this scheme, each CR-IoT user is being equipped with MIMO antennas; we calculate the WEVD ratio, which is defined as the ratio between the difference of the maximum eigenvalue and minimum eigenvalue to the sum of the maximum eigenvalue and minimum eigenvalue. This mitigates against the spectrum scarcity problem, enhances system throughput, improves energy efficiency, prolongs expected lifetime and lowers error probability. Simulation results confirm the effectiveness of the proposed scheme; here the WEVD technique demonstrates a better detection gain and enhanced system throughput in comparison to the conventional scheme with eigenvalue based detection (EVD) and ED techniques in a noise uncertainty environment (i.e. SNR <-28). Furthermore, the proposed scheme has lower energy consumption, prolonged expected lifetime and achieves a low error probability when compared with other schemes like the conventional single-input and single-output (SISO) based CR-IoT scheme with EVD and ED spectrum sensing.

Lecture 5-6:  Energy Harvesting-enabled Cognitive Radio-Internet of Things (CR-IoT) using Machine Learning

Intro: The cognitive radio (CR) is a modern technology in cognitive radio-Internet of things (CR-IoT) networks. In contrast, each CR-IoT user is unable to achieve both a better sensing gain, and an enhanced system sum rate in conventional energy detection technique based CR-IoT networks with the present energy harvesting (EH) and security threats due to under-utilized the reporting framework. For this reason, we proposed EH-enabled CR-IoT networks using machine learning (ML) algorithms in which each normal CR-IoT user is assisted by finite capacity battery systems and energy harvested. In this paper, Firstly, the proposed hybrid detection technique based on EH-enabled CR-IoT networks using ML algorithms is separating the trusted (normal) and untrusted (malicious users) CR-IoT users where all untrusted CR-IoT users are not participating in spectrum sensing due to they degraded the performance like sensing gain and system sum rate; Secondly, the proposed scheme is utilized the reporting framework where only trusted CR-IoT users are obtained longer sensing time slot which enhanced the sensing gain, the EH, and the sum rate; and Finally, the simulation results show that the proposed hybrid scheme outperformed the conventional schemes in terms of security, sensing gain, EH, and system sum rate.

Lecture 7-8:  MU-MIMO Based Spectrum Sensing and Sum Rate in a Cognitive Radio for Internet of Vehicular Things

Intro: Cognitive radio CR is applied to vehicular ad-hoc networks (VANETs) which is utilized the unused licensed spectrum more efficiently. However, the vehicles in the CR-VANETs era are constrained regarding spectrum, sum rate, energy consumption, error probability, and antenna size to meet the demands, resulting in a growing number of interconnected vehicles for Internet of things (IoT) networks. To overcome these constrained in IoT networks that is needed a revision of the IoT architecture for the Internet of vehicular things (IoVT). In this article, we present a multi-user multiple-input and multiple-output (MU-MIMO) antennas aided cooperative spectrum sensing (CSS) for a CR based IoVT (CR-IoVT) network where firstly, each cognitive radio vehicle (CRV) is assumed to be equipped with MIMO antennas; secondly, the detection gain of the secondary network i.e., (CR-IoVT) is measured based on the energy detection method; and finally, the fusion center makes a global decision about the status of the  primary user signal in a CR-IoVT network by applying the soft data fusion rules i.e., equal gain combining (EGC) and maximal ratio combining (MRC) rules. Simulation results are presented to demonstrate that the proposed scheme achieves an improved detection gain, an enhanced sum rate, and a fewer error probability when compared to other conventional schemes like the non-CSS (NCSS) scheme with a single-input and single-output (SISO) antenna and CSS scheme with a SISO antenna.

    主讲人简介：

        Md. Sipon Miah教授曾在多个国际知名实验室担任科学研究员，并在爱尔兰国立大学担任导师。在国外各大期刊以第一作者或合作通讯作者发表学术论文约80篇、并被国际同行大量引用；多次在国际学术会议上作大会报告。担任人工智能与制造国际会议特约编委、24个国际期刊以及会议审稿者，并担任多个国际会议的TPC委员。Miah教授曾在伊斯兰大学获得信息和通信技术的博士研究生学位并荣获哈迪曼奖学金；在获得信息和通信技术的博士学位后，Miah教授在爱尔兰国立大学戈尔韦分校(NUIG)获得计算机科学的第二博士学位， 他是西班牙马德里卡洛斯三世大学(UC3M)信号理论与通信系的研究人员和伊斯兰大学终身教授。2016年，他在NUIG获得了著名的Hardiman奖学金，后在UC3M攻读博士后期间获得著名的30博士后基金。研究领域包括能量检测、无线传感器网络、物联网（loT）、工业物联网(lloT)、基于认知无线电的车联技术、云计算与数据安全、基于机器学习或人工智能的新技术应用等多个热门领域与前沿学科。