Changchun Zhang

Massive MIMO as a Big Data System: Random Matrix Models and Testbed

This paper has two parts. The first one deals with how to use large random matrices as building blocks to model the massive data arising from the massive (or large-scale) multiple-input, multiple-output (MIMO) system. As a result, we apply this model for distributed spectrum sensing and network monitoring. The part boils down to the streaming, distributed massive data, for which a new algorithm is obtained and its performance is derived using the central limit theorem that is recently obtained in the literature. The second part deals with the large-scale testbed using software-defined radios (particularly, universal software radio peripheral) that takes us more than four years to develop this 70-node network testbed. To demonstrate the power of the software-defined radio, we reconfigure our testbed quickly into a testbed for massive MIMO. The massive data of this testbed are of central interest in this paper. For the first time, we have modeled the experimental data arising from this testbed. To our best knowledge, there is no other similar work.

Cognitive radio network as wireless sensor network (II): Security consideration

This is the second paper in a series of using cognitive radio network as wireless sensor network. The motivation of the paper is to push the convergence of radar and communication systems into a unified cognitive network. This paper studies this vision from a secure point of view. We propose two methods for robust spectrum sensing in the same framework of cognitive radio network. The first method is based on robust principal component analysis (PCA), to separate spectrum sensing results into the low rank signal matrix and the sparse attack matrix. Using sparse attack cancellation in least squares, the second method iteratively estimates the relative transmitted power of primary user under the threats of attackers. Then the relative transmitted power of primary user can be calculated from the recovered signal matrix. Both two methods can detect the sparse compromised cognitive radio nodes and effectively obtain the relative transmitted power.

Towards A Large-Scale Cognitive Radio Network Testbed: Spectrum Sensing, System Architecture, and Distributed Sensing

This paper presents a comprehensive review of the cognitive radio network (CRN) testbed built at TTU. Our goals are (1) to use our CRN testbed as a data acquisition tool; (2) to use random matrix theory to model the collect data and apply the new models in the context of quantum information. We attempt to achieve a balance between experimental work and theoretical work. We first spell out the vision and concrete tasks for our research in the near future. Second, we review our latest results in an more accessible manner than the conference version.

Towards a large-scale cognitive radio network: Testbed, intensive computing, frequency agility and security

Cognitive radio (CR) is a promising technique for improving the efficiency of utilizing the precious radio spectrum. A cognitive radio network (CRN) testbed not only can verify concepts, algorithms, and protocols for CR, but also can reveal practical problems and guide future research. Vision, architecture, intensive computing, frequency band shifting, security, ultra-wideband receiver and potential applications are discussed in this paper. In support of our arguments, experiments and demonstrations are reported as well.

Cognitive radio network as wireless sensor network (I): Architecture, testbed, and experiment

This paper explores the vision of a dual-use sensing/communication system based on Cognitive Radio Network (CRN). The motivation of the paper is to push the convergence of sensing and communication systems into a unified cognitive network. The concept design of this sensing/communication system is presented along with potential functions and challenges. A through tree target detection using real data collected by CRN testbed is demonstrated. The CRN testbed will be built based on Rice WARP nodes. To further exploit the advantages of dynamic spectrum access and frequency diversity, multi-frequency signal, which is similar to OFDM signal, are employed to for detection experiment in the harsh radio environment. The experiment results illustrate the vision of employing a CRN as wireless sensor network.

Robust non-negative matrix factorization for joint spectrum sensing and primary user localization in cognitive radio networks

In this paper, a novel approach based on non-negative matrix factorization is applied for joint spectrum sensing and primary user localization in cognitive radio networks. This approach is robust and tolerant to sparse, yet strong interference caused by malicious attack or false data injection. Simulation results clearly indicate that the proposed method is highly effective in yielding low localization error for various strengths and degrees of sparsity of interferer. It is also shown that the localization performance significantly increases with the number of cognitive radios deployed.

Cognitive Radio Network as Wireless Sensor Network (III): Passive target intrusion detection and experimental demonstration

A Cognitive Radio Network (CRN) based Wireless Sensor Network (WSN), as an extension of CRN, is explored for radio frequency (RF) passive target intrusion detection. Compared to a cheap WSN, the CRN based WSN is expected to deliver better results due to its strong communication functions and powerful computing ability. Issues addressed in this paper include experimental architecture, waveform design, and machine learning algorithm for classification. In particular, passive target intrusion is experimentally demonstrated using multiple WARP platforms that serve as the cognitive/sensor nodes. In contrast to traditional localization methods relying on radio propagation properties, the technique used in this research is based on machine learning with measured data, considering complicated multipath environment and high dimensional sensing data collected by the CRN based WSN. Preliminary experimental results are quite encouraging, suggesting that a large-scale CRN based WSN supported by machine learning techniques has promising potential for passive target intrusion detection in harsh RF environments.