Xiaochen Li

Bio: Xiaochen Li is an academic researcher from Stevens Institute of Technology. The author has contributed to research in topics: Cognitive radio & Self-organizing map. The author has an hindex of 3, co-authored 3 publications receiving 27 citations.

An integrated incremental self-organizing map and hierarchical neural network approach for cognitive radio learning

Abstract: In this paper, an incremental self-organizing map integrated with hierarchical neural network (ISOM-HNN) is proposed as an efficient approach for signal classification in cognitive radio networks. This approach can effectively detect unknown radio signals in the uncertain communication environment. The adaptability of ISOM can improve the real-time learning performance, which provides the advantage of using this approach for on-line learning and control of cognitive radios in many real-world application scenarios. Furthermore, we propose to integrate the ISOM with the hierarchical neural network (HNN) to improve the learning and prediction accuracy. Detailed learning algorithm and simulation results are presented in this work to demonstrate the effectiveness of this approach.

Reinforcement learning based adaptive rate control for delay-constrained communications over fading channels

Abstract: In this paper, we study efficient rate control schemes for delay sensitive communications over wireless fading channels based on reinforcement learning Our objective is to find a rate control scheme that optimizes the link layer performance, specifically, maximizes the system throughput subject to a fixed bit error rate (BER) constraint and longterm average power constraint We assume the buffer at the transmitter is finite; hence packet drop happens when the buffer is full We assume the fading channel under our study can be modeled as a finite state Markov chain, however the transition probability of channel states is not known, and the only information available about the wireless channel is the instantaneous channel gain, which is estimated and fed back from receiver side to the transmitter side on the fly In this paper, we use reinforcement learning approach to learn the time-varying channel environment and search for the optimal control policy on line Simulation results show that starting from an arbitrary control policy, the learning agent gradually modifies its estimation about the system model and adjusts the control policy to its optimality

Classification and Control of Cognitive Radios Using Hierarchical Neural Network

Abstract: This paper proposes a method to protect the communication band through machine learning in cognitive networks. A machine learning cognitive radio (MLCR) extracts features from the signal waveforms received from various radios. A machine learning radio user (MLRU) assigns the states, i.e., unauthorized/authorized, and the associated actions, i.e., interfering/no interfering, to each waveform. The MLCR learns through a proposed hierarchical neural network to classify the signal states based on their features. The {signal, action} pairs are stored in the knowledge base and can be retrieved by MLCR automatically based on its prediction of the signal state related to the presented signal waveform. A case study of protecting the band of a legacy radio using our proposed method is provided to validate the effectiveness of this work.

The Self-Organizing Map

Abstract: An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

Machine learning for wireless communications in the Internet of Things: A comprehensive survey

Abstract: The Internet of Things (IoT) is expected to require more effective and efficient wireless communications than ever before. For this reason, techniques such as spectrum sharing, dynamic spectrum access, extraction of signal intelligence and optimized routing will soon become essential components of the IoT wireless communication paradigm. In this vision, IoT devices must be able to not only learn to autonomously extract spectrum knowledge on-the-fly from the network but also leverage such knowledge to dynamically change appropriate wireless parameters ( e.g. , frequency band, symbol modulation, coding rate, route selection, etc.) to reach the network’s optimal operating point. Given that the majority of the IoT will be composed of tiny, mobile, and energy-constrained devices, traditional techniques based on a priori network optimization may not be suitable, since (i) an accurate model of the environment may not be readily available in practical scenarios; (ii) the computational requirements of traditional optimization techniques may prove unbearable for IoT devices. To address the above challenges, much research has been devoted to exploring the use of machine learning to address problems in the IoT wireless communications domain. The reason behind machine learning’s popularity is that it provides a general framework to solve very complex problems where a model of the phenomenon being learned is too complex to derive or too dynamic to be summarized in mathematical terms. This work provides a comprehensive survey of the state of the art in the application of machine learning techniques to address key problems in IoT wireless communications with an emphasis on its ad hoc networking aspect. First, we present extensive background notions of machine learning techniques. Then, by adopting a bottom-up approach, we examine existing work on machine learning for the IoT at the physical, data-link and network layer of the protocol stack. Thereafter, we discuss directions taken by the community towards hardware implementation to ensure the feasibility of these techniques. Additionally, before concluding, we also provide a brief discussion of the application of machine learning in IoT beyond wireless communication. Finally, each of these discussions is accompanied by a detailed analysis of the related open problems and challenges.

Cloning and characterization of PR5 gene from Curcuma amada and Zingiber officinale in response to Ralstonia solanacearum infection.

Abstract: Ginger (Zingiber officinale Roscoe), is an important spice crop that is badly affected by Ralstonia solanacearum wilt. Ginger does not set seed and sexual recombination has never been reported. In spite of extensive search in its habitats, no resistance source to Ralstonia induced bacterial wilt, could be located in ginger. Curcuma amada Roxb. is a potential donor for bacterial wilt resistance to Z. officinale, if the exact mechanism of resistance is understood. Pathogenesis-related (PR)-5 proteins are a family of proteins that are induced by different phytopathogens in many plants and share significant sequence similarity with thaumatin. Two putative PR5 genes, CaPR5 and ZoPR5, were amplified from C. amada and ginger, which encode precursor proteins of 227 and 224 amino acid residues, respectively, and share high homology with a number of other PR5 genes. The secondary and three-dimensional structure comparison did not reveal any striking differences between these two proteins. The expression of Ca and ZoPR5s under R. solanacearum inoculation was analyzed at different time points using quantitative real-time PCR (qRT-PCR). Our results reveal that CaPR5 is readily induced by the bacterium in C. amada, while ZoPR5 induction was very weak and slow in ginger. These results suggest that the CaPR5 could play a role in the molecular defense response of C. amada to pathogen attack. This is the first report of the isolation of PR5 gene from the C. amada and Z. officinale. Promoter analysis indicates the presence of a silencing element binding factor in ZoPR5-promoter, but not in CaPR5. Prospective promoter elements, such as GT-1 box and TGTCA, implicated as being positive regulatory elements for expression of PR proteins, occur in the 5′-flanking sequences of the CaPR5. Transient GUS expression study confirms its action with a weaker GUS expression in ginger, indicating that the PR5 expression may be controlled in the promoter.