Channel estimation and multiuser detection are enabling technologies for future generations of wireless applications. However, sophisticated algorithms are required for accurate channel estimation and multiuser detection, and real-time implementation of these algorithms is difficult. This paper presents architectural design methods for wireless channel estimation which can be leveraged to enable real-time multiuser detection. We redesign the matching pursuit (MP) channel estimation algorithm to reduce the complexity while maintaining the estimation accuracy. Furthermore, we develop a parameterized intellectual property (IP) core, which provides a hardware implementation of the MP algorithm. Experimental results demonstrate the effectiveness and ef- ficiency of the new algorithm and IP core for channel estimation. The implementation of our MP core on a modern, high performance reconfigurable system is about 216 times faster than running the algorithm on a state of the art microprocessor. The MP core possesses the speed required for performing true multiuser detection, enabling future generations of wireless communication applications.

Efficient distributed algorithms are an important enabling technology for large-scale ad-hoc wireless sensor and communications networks. In this paper, optimal Bayesian data fusion under the assumption of linear Gaussian state and measurement models is presented. Within this framework, an efficient algorithm for distributed state estimation in ad-hoc networks is developed. Approximate algorithms are then developed for further improvements in network resource efficiency. These include a parameterizable tradeoff of improved communications efficiency for increased latency in the rate at which information propagates through the network. It is also shown that the algorithms are well-suited for use with non-linear measurements. Finally, for distributed node position estimation in a mobile ad-hoc network, simulation results show that accurate, efficient node localization is achieved

Channel estimation and distributed positioning algorithms are presented for geolocation in a wireless ad hoc network. The network uses a direct-sequence code-division multiple-access-based handshaking protocol, in which nodes receive multiple acknowledgment packets in response to a request-to-send waveform. Round-trip travel time (RTT) and angle-of-arrival (AOA) measurements are obtained using the generalized successive interference cancellation/matching pursuits (GSIC/MP) algorithm. The performance of GSIC/MP is evaluated via simulation and comparison to the Crame/spl acute/r-Rao bound. Position estimates are initialized using linearized least-squares and updated by an extended Kalman filter-based algorithm that includes measurement validation for nonline-of-sight error mitigation. The method is generalized for distributed estimation in sparsely connected networks: at each node, position estimates from connected nodes are incorporated via a fusion algorithm and updated using locally processed RTT/AOA measurements. Finally, comprehensive ad hoc network simulations are presented including channel ray tracing, RTT/AOA estimation and validation, and distributed positioning.