Abstract. Rapidly acquiring the code phase of the spreading sequence in an ultra-wideband system is a very difficult problem. In this paper, we present a new iterative algorithm and its hardware architecture in detail. Our algorithm is based on running iterative message passing algorithms on a standard graphical model augmented with multiple redundant models. Simulation results show that our new algorithm operates at lower signal to noise ratio than earlier works using iterative message passing algorithms. We also demonstrate an efficient hardware architecture for implementing the new algorithm. Specifically, the redundant models can be combined together so that substantial memory usage can be reduced. Our prototype achieves the cost-speed product unachievable by traditional approaches.

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Abstract-Iterative message passing algorithms on graphs, which are generalized from the well-known turbo decoding algorithm, have been studied intensively in recent years because they can provide near-optimal performance and significant complexity reduction. In this paper, we demonstrate that this technique can be applied to pseudorandom code acquisition problems as well. To do this, we represent good pseudonoise (PN) patterns using sparse graphical models, then apply the standard iterative message passing algorithms over these graphs to approximate maximum-likelihood synchronization. Simulation results show that the proposed algorithm achieves better performance than both serial and hybrid search strategies in that it works at low signal-to-noise ratios and is much faster. Compared with full parallel search, this approach typically provides significant complexity reduction.

The soft-input soft-output (SISO) module is the basic building block for established iterative detection (ID) algorithms for a system consisting of a network of finite state machines. The problem of performing ID for systems having parametric uncertainty has received relatively little attention in the open literature. Previously proposed adaptive SISO (A-SISO) algorithms are either based on an oversimplified channel model, or have complexity that grows exponentially with the observation length (or the smoothing lag). In this paper, the exact expressions for the soft metrics in the presence of parametric uncertainty modeled as a Gauss-Markov process are derived in a novel way that enables the decoupling of complexity and observation length. Starting from these expressions, a family of suboptimal (practical) algorithms is motivated, based on forward/backward adaptive processing with linear complexity in. Recently proposed A-SISO algorithms, as well as existing adaptive hard-decision algorithms are interpreted as special cases within this framework. Using a representative application-joint iterative equalization-decoding for trellis-based codes over frequency-selective channels-several design options are compared and the impact of parametric uncertainty on previously established results for ID with perfect channel state information is assessed.