John B. Shea, Ph.D. - US grants

EIA 0224410
PI(s): Wong, Tan F.
Fang, Yuguang; Shea, John M.
Institution: University of Florida

Title: CISE RR: Reconfigurable Multi-Node Wireless Communication Testbed

This proposal, developing a real-time wireless communication testbed of six reconfigurable transceiver nodes that operate in the 900MHz or 2.4GHz ISM band, enables conducting experiments on network protocols and communications techniques. Performing real-time experiments over real-life wireless communication channels, the infrastructure enables three projects.

Collaborative Communications,
Wireless Medium Access Control Protocol Design and Experiments, and
Reliability-Based Hybrid ARQ.

The first project concerns collaborative transmission and reception among multiple nodes in a wireless network. Techniques of distributed space-time coding and distributed and iterative decoding will be employed respectively to achieve transmit and receive diversity. The second project uses the test-bed to test new medium access control (MAC) protocols that employ a set of back-off algorithms designed to avoid potential "future" collisions and reduce the percentage of idle slots. These MAC protocols have the advantage of fast collision resolution and hence give much higher throughput. The last project develops and investigates a new hybrid automatic repeat request (ARQ) scheme that can take advantage of reliability estimates generated by soft-input, soft-output (SISO) decoders. This ARQ scheme transmits additional information for the unreliable bits, and this information is used to do additional decoding. The test-bed will be utilized to perform real-time experiments on a real-life channel to examine the throughput and delay performance of this ARQ scheme, which can be evaluated via simulations otherwise.
On the educational side, the platform provides integrated design training to undergraduate students. Conducting simulation studies on algorithms developed on the research projects, students will employ the test-bed to perform off-line experiments over a real communication channel to identify possible weaknesses of the algorithms developed based on theoretical models and then fine tune the algorithms to produce a real-time FPGA implementation.

In a wireless network, multiple communications devices in close proximity form a natural distributed antenna array. If a group of such devices transmit and receive in some cooperative manner, then the system performance can be significantly improved. This researchexplores techniques for cooperative transmission and reception using multiple devices. This cooperative communication approach is differentfrom traditional array processing because the distributed nature of
the communication nodes calls for network-oriented design approaches and processing algorithms. In particular, techniques that allow distributed and asynchronous processing that utilizes information provided by other nodes in a cluster will be considered.

For cooperative transmission, distributed space-time coding will be employed to take advantage of transmit diversity provided by the distributed array. One crucial issue for distributed space-time coding is that only rough synchronization between the nodes can be achieved. Hence space-time coding in an asynchronous setting needs to be considered. This leads to research on asynchronous diversity and coding gain analysis, symbol waveform design, and tradeoff between the complexity of transmitter synchronization and complexity of the decoding process.

For cooperative reception, distributed iterative decoding will be employed to obtain diversity advantage in reception. In this approach, multiple nodes form a distributed antenna array by collaboratively processing a received signal. By exchanging information in a distributed decoding process, the nodes are able to extract diversity from the channel and decode the message. The main obstacle to this approach is that there is a vast amount of information that can be
shared between the nodes. This problem can be solved by using iterative decoding to extract important information from the received signal at each node, and only this information is passed to other nodes. Each node will then utilize the information from other nodes to perform further decoding to obtain the diversity advantage provided by the additional information. The objective is to develop distributed processing techniques that allow us to obtain the maximum degree of
diversity advantage from the signals received at multiple receiving nodes, while requiring a minimum amount of information exchange between the nodes.

In order to make the above cooperative communication schemes work, a control signaling strategy has to be designed to allow sharing of information between nodes. More importantly, the physical-layer communication performance of the proposed system will be dependent on
the control signaling strategy. Therefore this signaling design problem will be attacked by a cross-layer design approach.

Analytical guidelines for designing systems employing the proposed cooperative communication schemes will be developed. The expected results of this research can be utilized in many different commercial and military communication networks, such as cellular and sensor networks.

NeTS-NBD Simulcast Enhanced Wireless Networks

Award 0626863

Tan F. Wong and John M. Shea

Information theory predicts that it is more efficient to simultaneously transmit (simulcast) signals carrying independent information to multiple users on a wireless channel than it is to time-, frequency- or code-share the channel among the users. In current wireless networks, this simulcasting capability of the wireless medium is not utilized. This research aims to develop practical simulcasting transmission techniques that exploit such hidden resources in both infrastructure and ad hoc networks. The application of simulcasting at the physical layer has many impacts on the higher-layer protocols. Under a cross-layer framework, the use of simulcasting in wireless networks is investigated through analysis, simulation, and experimentation. The expected analytical results will be used to investigate the performance limits of simulcasting. Simulation results will be used to test protocol designs and evaluate performance under more realistic models for channel, traffic, mobility, etc. A heterogeneous ad hoc network (HANET) testbed will be developed to emulate simulcasting in real networks.

The increasing demand for wireless services has resulted in significant pressure to develop new approaches to more efficiently utilize bandwidth resources. The simulcasting techniques developed in this project have the potential to significantly improve efficiency in cellular and wireless local area networks. Through this project, engineering students learn to design communication systems by understanding the analytical limitations, evaluating performance through simulations, and deploying and testing new protocols on the HANET testbed. The research results will be disseminated through the Internet and publications in conferences and technical journals.

Systems of autonomous vehicles under cooperative control provide versatile platforms for a variety of tasks including environmental monitoring, search and rescue, intelligent transportation, and cooperative surveillance or attack. Performing these tasks requires control or optimization of the system's formation while maintaining network connectivity. Such systems are also potential targets for attack, and so techniques to secure the communications and control processes are important. In this project, techniques from computer networks are coupled with controls techniques to reconfigure the physical formation of a system of autonomous vehicles while maintaining network connectivity by "routing" vehicles through the formation to new positions. This approach is an enabling technology that also allows the network formation to be optimized to increase the efficiency of networked communication. New approaches to securing communication signals are coupled with the ability to transform the physical formation of the system to provide new approaches to achieving communication that is secure against eavesdroppers and robust to hostile jamming. The results of this project will be new algorithms, protocols, and controls techniques for formation control of systems of autonomous vehicles that are more flexible, efficient, and secure than the techniques currently available. These results will be widely applicable to future sensing/monitoring, homeland security, and defense operations involving autonomous systems of ground, air, surface, and underwater vehicles.

Dynamic spectrum access (DSA) techniques offer the potential to use the radio spectrum more efficiently by allowing radios to sense which parts of the radio spectrum are not currently being used by the licensed users or other opportunistic users. However, detecting the presence of transmissions in a particular band is difficult because of randomness in the radio propagation environment and because radio signals can be received at very low power levels. Sensing the radio spectrum can be made more accurate by collecting and processing the sensing information from multiple radios. However, in combining this information, the locations and characteristics of the sensing radios may be revealed to the other radios or to various companies involved in collecting and combining the information.

This research project will develop novel techniques to protect the privacy of users involved in sensing the radio spectrum as part of a DSA system. The techniques are built on top of secure computing primitives, such as garbled circuits, which uses cryptographic techniques to allow users to compute a result without any of the parties being able to know the other parties inputs to the computation. Although complete privacy is not possible in a spectrum sensing system, this project aims to develop systems that achieve k-anonymity, in which a user's location and capabilities may only be reduced to one of k possibilities. A significant challenge in developing such techniques is that many secure computing primitives require high computational complexity, and thus cannot be implemented on many DSA devices, such as future generations of cellular phones. Thus, the project will develop privacy-preserving spectrum sensing techniques that have sufficiently low complexity to be implemented on such devices. One of the approaches to achieving this is to partition the computation between the devices and a cloud-computing server. These techniques will be implemented on a software-defined radio testbed that interfaces with a commercial cloud computing resource to allow testing using real radio signals and real computing platforms.

Current engineering practices and regulatory approaches on the use of the radio frequency (RF) spectrum are too antiquated to meet the ever
surging demand on the RF spectrum. A promising new solution to tackle this spectrum scarcity problem is to equip radio networks with artificial intelligence so that they can learn and predict the RF environment, as well as be social by interacting with other radio networks, leading to more collaborative use of the RF spectrum. This project will develop a software-defined radio system that can intelligently sense and adapt to others' use of the radio spectrum and collaborate with other radio networks in sharing the common RF spectrum. The developed system will be characterized by its flexibility to quickly and agile adaptability to changes in how others are using the RF spectrum. It will be also be characterized by how it uses machine-learning techniques to both extract the most relevant information about how the RF spectrum is being used and to adapt the communication strategies based on this information.

A dynamically reconfigurable system architecture will be developed in this project to make most efficient use of all the available computational resources in order to support all radio and ML functionalities. This highly flexible software-defined structure takes advantage of the learned knowledge about the RF environment by adapting the physical and medium access control layers use of spectrum and coordinating this utilization through carefully designed network protocols. A machine learning system is developed to identify the key information about the evolution of the communication scenario, and autonomously learn the state of the model. Reinforcement learning will be used to generate appropriate adaptive communication strategies based on the system state.