The Wireless Communications and Mobile Computing Lab (WiMoLab) focuses on performance analysis, algorithm development, and experimentation within wireless and mobile systems. It places a strong emphasis on systems-level solutions to expand system capacity.
Wireless Networking
Cognitive Radio
Signal Processing
Wireless Security
RF Antenna
AI and Machine Learning
Funded by NSF
A promising approach for reducing spectrum congestion in wireless networks is to introduce programmable radio platforms equipped with Software Defined Radios (SDR) that can apply cross-layer as well as cross-network optimization to maximize performance over changing RF environments. These programmable radios can independently adjust a variety of communications parameters including, modulation, power-level, and antenna beam pattern. They can also load-share traffic as well as integrate the individual capacities of available wireless networks to meet instantaneous performance requirements and improve coverage. While these capabilities are highly desirable, they create new challenges for the wireless network designer, since a programmable radio has increased capability to influence not only communications in which it is directly involved but also communications between unrelated nodes. How should programmable radios adapt their communications parameters to maximize performance while at the same time, minimize interference? This project addresses this question at several levels. First, we define new sub-layer structures within the physical layer for synchronizing pair-wise performance. We introduce primitives, ''adjectives and adverbs," for integrating software radios with higher OSI layers. These primitives define the environmental sensing and channel parameter adjustments that will be supported in an SDR. Finally, we define rules (i.e., the etiquette) that should be adopted and enforced to encourage ''proper behavior" by groups of programmable radios to minimize the effects on unrelated communications.
This project studies denial-of-service (DoS) attacks that are unique to dynamic spectrum access (DSA) networks: (a) DoS attacks by incumbent user emulation; (b) DoS attacks by protocol manipulation. In the first case, one or more malicious nodes pretend to be the primary by mimicking the power and/or signal characteristics to deceive legitimate secondary nodes into vacating the white space unnecessarily. In the second case, the malicious users either modify spectrum sensing related information or falsify their own sensing data thereby affecting the final decision. A number of mathematical models for the DoS attacks and several counter measures based on game theory, decision theory, stochastic learning, cryptography and Byzantine fault tolerance are developed in this project. Some defense mechanisms and protocols developed through this project will be tested on SpiderRadio (a cognitive radio test-bed being developed in the PIs’ laboratory). Broader Impact: Since DSA networks are expected to play an important role in first responder networks, the solutions proposed here are expected to impact design of such networks. Since research in DSA network protocols and architectures are still in the formative stages, the proposed security solutions can be incorporated into the design phase of the system rather than being added on as an afterthought.
Wireless networks play an increasingly crucial role in supporting tactical applications. Significant research has conducted to assure secure communication and trustworthy data delivery to enable proper response to scenarios that threaten the availability of wireless communications. This is especially critical for Army as this force consists of heterogeneous nodes that operate in a complex wireless environment. In such a highly dynamic, network centric environment, secure communication and trustworthy data delivery remain challenging because mobile nodes operate in the absence of supporting infrastructure, requiring nodes to collaborate in order to provide secure data exchange in the mission critical applications. Existing research has made great advancement in theoretic foundations and protocol design. Few efforts have shown the applicability and scalability of the theoretic-based research to real-world scenarios. This proposal aims to build an infrastructure that measures, tests, and evaluates various practical parameters when applying security problems for mission-critical tasks to real environments. It targets to bridge the gap between theoretic study and system implementation and further provide valuable performance evaluation feedback to enhance the theoretic study in Army’s future wireless systems. More specifically, the proposed infrastructure will allow us to address key challenges limiting current small-scale wireless security prototypes in lab environments.
This project considers passive radio frequency (RF) sensing that employs wireless communication signals as illuminators of opportunities (IOs) to detect, locate, and track objects of interest. Such capabilities are useful for a wide range of applications, e.g., indoor localization, health monitoring, vehicle tracking, and many more. Passive RF sensing has many advantages over its active counterpart: no dedicated transmitter and RF pollution free; covert operation; order of magnitude cheaper to build, deploy and operate; and the ability to simultaneously access several IOs to obtain multiple views and spatial diversity of the surveillance area. Despite the advantages, there are fundamental technical issues that need to be investigated for passive RF sensing. This project aims to develop novel signal processing techniques for passive RF sensing by taking into account impairments such as noisy reference, direct-path interference, and multi-path clutter, which are inherent in passive systems.
In this ongoing project, we investigate the fundamental challenges of inter-vehicle networking, including the theoretical foundation and constraints in practice that enable such networks to achieve their performance limits. Efficient algorithms will be designed to achieve fast neighbor discovery using reinforcement learning and case-based reasoning scheme. The results will advance the knowledge of opportunistic communications and facilitate engineering practice for much-needed applications in vehicular environments.