Performance Analysis of Iterative Channel Estimation and Multiuser Detection in Multipath DS-CDMA Channels

This paper examines the performance of decision feedback based iterative channel estimation and multiuser detection in channel coded aperiodic DS-CDMA systems operating over multipath fading channels. First, explicit expressions describing the performance of channel estimation and parallel interference cancellation based multiuser detection are developed. These results are then combined to characterize the evolution of the performance of a system that iterates among channel estimation, multiuser detection and channel decoding. Sufficient conditions for convergence of this system to a unique fixed point are developed.Comment: To appear in the IEEE Transactions on Signal Processin

GPS Carrier Tracking Loop Performance in the presence of Ionospheric Scintillations

The performance of several GPS carrier tracking loops is evaluated using wideband GPS data recorded during strong ionospheric scintillations. The aim of this study is to determine the loop structures and parameters that enable good phase tracking during the power fades and phase dynamics induced by scintillations. Constant-bandwidth and variable-bandwidth loops are studied using theoretical models, simulation, and tests with actual GPS signals. Constant-bandwidth loops with loop bandwidths near 15 Hz are shown to lose phase lock during scintillations. Use of the decision-directed discriminator reduces the carrier lock threshold by ∼1 dB relative to the arctangent and conventional Costas discriminators. A proposed variablebandwidth loop based on a Kalman filter reduces the carrier lock threshold by more than 7 dB compared to a 15-Hz constant-bandwidth loop. The Kalman filter-based strategy employs a soft-decision discriminator, explicitly models the effects of receiver clock noise, and optimally adapts the loop bandwidth to the carrier-to-noise ratio. In extensive simulation and in tests using actual wideband GPS data, the Kalman filter PLL demonstrates improved cycle slip immunity relative to constant bandwidth PLLs.Aerospace Engineering and Engineering Mechanic

Joint Structure Learning of Multiple Non-Exchangeable Networks

Several methods have recently been developed for joint structure learning of multiple (related) graphical models or networks. These methods treat individual networks as exchangeable, such that each pair of networks are equally encouraged to have similar structures. However, in many practical applications, exchangeability in this sense may not hold, as some pairs of networks may be more closely related than others, for example due to group and sub-group structure in the data. Here we present a novel Bayesian formulation that generalises joint structure learning beyond the exchangeable case. In addition to a general framework for joint learning, we (i) provide a novel default prior over the joint structure space that requires no user input; (ii) allow for latent networks; (iii) give an efficient, exact algorithm for the case of time series data and dynamic Bayesian networks. We present empirical results on non-exchangeable populations, including a real data example from biology, where cell-line-specific networks are related according to genomic features.Comment: To appear in Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics (AISTATS