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Research at the Broadband Access Wireless Communication (BAWC) Lab at MSU is rooted in signal processing and wireless communications, and expands interactively to wireless security, ad-hoc and sensor networks, and interoperable wireless networks, with emphases on power and spectral efficiency, security and interoperability. The BAWC Lab has both software and hardware platforms for wireless communication system design, implementation and networking. |
Security has become an urgent issue and a great concern in both civilian and military wireless communications. While people are relying more and more on wireless networks for critical information transmission, the ubiquitous wireless interconnectivity has also provided a primary conduit for malicious agents to exploit vulnerabilities on a widespread basis. Driven by the ever-increasing demand on secure wireless networking, development of novel wireless systems with built-in security has turned out to be the next impetus in communications.
How to strengthen wireless security and provide an ideal platform for promising wireless services such as mobile Internet and e-commerce? Patching or add-on security may be effective in the short run, but is far from adequate for addressing the needs on wireless security and can greatly complicate the communication systems. As pointed out by the President's Information Technology Advisory Committee:
"We urgently need to expand our focus on short-term patching to also include longer-term development of new methods for designing and engineering secure systems."
This research is devoted to the fundamental study of new wireless security models and methods, and to design minimally intrusive wireless systems with built-in security.
Non-uniformity exists in both the source coding and information transmission, and this non-uniformity can be exploited to construct information transmission strategies and minimize the input-output distortion. Transmission efficiency improvement will be explored from space-time-frequency domains based on a mixed-signal perspective. The results will benefit any wireless systems with analog inputs, especially sensor networks which are strictly energy constrained.
As transmission at higher frequencies became possible, FCC (the Federal Communications Commission) has assigned frequencies in different bands for different purposes. The radio frequency spectrum within the United States extends from 9KHz to 300GHz and is allocated into more than 450 frequency bands. The fragmented spectrum and devices severely limit the spectrum sharing and the connectivity of wireless networks, resulting in ineffective use of the available spectrum, limited interoperability and incompatible wireless communication systems. Based on programmable radios and dynamic network management, we will develop schemes to improve the overall spectrum efficiency and the interoperability of wireless communication systems which are of critical strategic and economic interest to the United States.
The need for high-speed communications has motivated the extensive research on blind signal processing, which avoids using training/pilot symbols, and therefore makes full use of the bandwidth. Blind channel estimation refers to the problem of determining the system impulse response relying solely on the received signals and some a priori statistical knowledge of the input. Earlier techniques have been concentrated on single-input single-output (SISO) systems and are generally based on higher-order statistics (HOS), since SISO channels driven by stationary input signal could not be identified from the second-order statistics (SOS) of baud-rate sampled channel outputs. Later by the work of Tong, Xu and Kailath, it has been shown that with the space/time diversity (multiple antennas or oversampling) at the receiver ( later generalized to the single-input multiple-output (SIMO) systems ), SISO channels can be identified uniquely from the second-order statistics of the channel output. Since then, a number of SOS-based algorithms have been proposed, especially for subspace-based methods and linear predictor (LP)-based approaches.
For SIMO system, blind channel identification generally relies on a restrictive condition that all the sub-channels do not share any common zeros. As increasing attention turns to the multiple-input and multiple-output (MIMO) systems, blind channel identification of MIMO systems has recently been widely investigated. The subspace-based methods and multistep linear predictor-based approaches generally require the system transfer function to be irreducible and column-reduced to ensure the inversibility of MIMO FIR channels. However, it should be noted that these restrictive conditions can be relaxed by exploiting space-time diversity at the transmitter end with the price of significantly reduced data rate. Our research is currently focused on relaxing the restrictive identifiability conditions on blind channel estimation algorithms for MIMO systems meanwhile achieving high transmission data rate through simple and flexible system design.