Reduced Complexity Belief Propagation Research Articles

Reduced Complexity Belief Propagation Decoding Algorithm for Polar Codes Based on the Principle of Equal Spacing

Reduced Complexity Belief Propagation Decoding Algorithm for Polar Codes Based on the Principle of Equal Spacing

A new framework for soft decision equalization in frequency selective MIMO channels

We introduce a novel framework for soft-input, soft-output (SISO) equalization in frequency selective multipleinput multiple-output (MIMO) channels based on the well-known belief propagation (BP) algorithm. As in the BP equalizer, we model the multipath channels using factor graphs (FGs) where the transmitted and received signals are represented by the function and variable nodes respectively. The edges connecting the function and variable nodes illustrate the dependencies of the multipath channel and soft decisions are developed by exchanging information on these edges iteratively. We incorporate powerful techniques such as groupwise iterative multiuser detection (IMUD), probabilistic data association (PDA) and sphere decoding (SD) in order to reduce the computational complexity of BP equalizer with relatively small degradation in performance. The computational complexity of this new reduced-complexity BP (RCBP) equalizer grows linearly with block size and memory length of the channel. The proposed framework has a flexible structure that allows for parallel as well as serial detection. We will illustrate through simulations that the RCBP equalizer can even handle overloaded scenarios where the channel matrix is rank deficient, and it can achieve excellent performance by applying iterative equalization using the low-density parity check codes (LDPC).

Reduced-Complexity Belief Propagation for System-Wide MUD in the Uplink of Cellular Networks

System-wide multiuser detection (MUD), in which all base stations (BSs) of a cellular system cooperate to detect the data of all mobile stations (MSs), has much promise. However, little is known at present about practical techniques or their performance. An attractive method is belief propagation (BP), with message exchange between nearby cooperating BSs over a backbone landline network, but its performance is known only for a greatly simplified network model and its computation load grows exponentially with the number of interfering MS symbols at each BS. In this paper, we present a reduced complexity variation of BP (RCBP) and show that its performance is close to or identical to that of BP in the simplified network. We also observe excellent performance by iterative multiuser detection and decoding of low-density parity check codes (LDPC). Furthermore, we examine RCBP performance in a realistic wireless network model, with path loss, shadowing, fading and power control. These results, though poorer than those of the simplified network, show that system-wide MUD with cooperating BSs provides great improvement compared with conventional systems.