This research focuses on integrating M-ary distributed detection and communications, and makes a significant impact on the theory and practice of wireless sensor networks (WSNs) and cognitive radio networks (CRNs). In particular, the following four problems are tackled.
(1) M-ary distributed detection: The PI develops fusion rules and local decision rules for M-ary distributed detection systems with different wireless channel models, and studies how the erroneous channels fundamentally limit the performance of these systems. Furthermore, two composite M-ary distributed detection problems that have applications in WSNs and CRNs are investigated: joint detection and localization of multiple acoustic sources and spectrum sensing in a multi-channel system. (2) Sequential methods for wireless distributed detection systems: Striving to gain a profound understanding of the tradeoffs between static and sequential distributed detection in wireless networks, the PI designs two sequential distributed detection systems with i) sequential tests only at the sensors and ii) sequential test only at the fusion center, and studies how the detection reliability and delay of static and sequential detection systems are affected by the adopted wireless channel model. (3) Impact of imperfect channel state information on the performance of wireless distributed detection systems: The PI studies the effects of channel estimation errors on the designs of static and sequential distributed detection systems and investigates how these errors further limit the system detection performance. (4) Cooperation in wireless distributed detection systems: For both static and sequential distributed detection systems cooperative schemes are developed, based on local information passing among neighboring nodes, and conditions under which cooperation provides gain, in terms of both detection reliability and delay, are explored and the relationships between the cooperative gain and different channel models are sought.
