38131034
Comparison of multilevel coded modulations with different decoding methods for AWGN and Rayleigh fading channels
Multilevel coding (MLC) schemes based on channel capacity with multistage decoding (MSD) and parallel decoding on levels (PDL) are considered and compared. The channel models AWGN and Rayleigh fading are used in order to study the performance of MLC systems under different conditions. The investigation is done for 8ASK modulation and three set partitioning strategies. In each scheme BCH codes with different code lengths are used as component codes. Numerical results indicate that MSD is a sub-optimal decoding method of MLC for both channels, while PDL is most robust to varying channels if block partitioning (BP) is used. For Ungerboeck partitioning (UP) and mixed partitioning (MP) strategy, the MSD method is strongly recommended to use for the MLC system, while for the BP strategy, PDL is suggested to use as a simple decoding method compared with MSD.
3254735
Optimum design criterion and multilevel coding for radio systems over AWGN and Rayleigh fading channels
For the narrow band Wireless Code Division Multiple Access (WCDMA) system, there are some channel coding schemes proposed and applied like Turbo code and convolutional codes. But for the 4G Code Division Multiple Access (CDMA) wideband systems, we have to use new channel coding schemes with high bandwidth efficiency. In this case, multilevel coding (MLC) scheme is easy to map to Multiple Quardrature Amplitude Modulation (MQAM) modulation strategy to be used for 4G, and MLC+MQAM will be a potential channel coding scheme for the error correcting of next generation of mobile systems. 
 
 
 
A novel criterion, that is ‘capacity rule’ plus ‘mapping rule’, for the design of the optimum MLC scheme for radio systems over Rayleigh fading channels is proposed in this paper. Based on this theory, a few of key issues related to design an optimum MLC system are investigated. These include a novel optimum design criterion proposed, different mapping strategies, different decoding methods of MLC/MSD and MLC/Parallel Decoding on Levels (PDL) and their performance comparison over Additive White Gaussian Noise (AWGN) and Rayleigh fading channels respectively. Copyright © 2003 John Wiley & Sons, Ltd.
46393116
Learning Approximate MRFs from Large Transaction Data
In this paper we consider the problem of learning approximate Markov Random Fields (MRFs) from large transaction data. We rely on frequent itemsets to learn MRFs on the data. Since learning exact large MRFs is generally intractable, we resort to learning approximate MRFs. Our proposed modeling approach first employs graph partitioning to cluster variables into balanced disjoint partitions, and then augments important interactions across partitions to capture interdependencies across them. A novel treewidth based augmentation scheme is proposed to boost performance. We learn an exact local MRF for each partition and then combine all the local MRFs together to derive a global model of the data. A greedy approximate inference scheme is developed on this global model. We demonstrate the use of the learned MRFs on the selectivity estimation problem. Empirical evaluation on real datasets demonstrates the advantage of our approach over extant solutions.
1928330
A near-optimal broadcasting algorithm in all-port wormhole-routed hypercubes
For massively parallel computing mechanism, broadcasting is widely used in a variety of applications. When the computation is distributed among the powerful processors, communication overhead always limits the speedup. To reduce the influence of communication latency, a variety of routing methods have been discussed. In this paper, we propose an efficient routing method of broadcasting on all-port worrnhole-routed hybercubes. By exploiting the distance-insensitivity of wormhole switching, this efficient broadcasting algorithm reduce communication latency to l_rzl_logz(n+l )11steps in an n-dimensional hypercube, which is much better than previous results and very close to the theoretical optimum, [rz/log2(n+l )1.Further, by adopting the Hamming code, a well-known algebraic approach in coding theory, this proposed algorithm is contention-free in each broadcasting step.
386
Density evolution for asymmetric memoryless channels
Density evolution (DE) is one of the most powerful analytical tools for low-density parity-check (LDPC) codes and graph codes with message passing decoding algorithms. With channel symmetry as one of its fundamental assumptions, density evolution has been widely and successfully applied to different channels, including binary erasure channels (BECs), binary symmetric channels (BSCs), binary additive white Gaussian noise (BiAWGN) channels, etc. This paper generalizes density evolution for asymmetric memoryless channels, which in turn broadens the applications to general memoryless channels, e.g., z-channels, composite white Gaussian noise channels, etc. The central theorem underpinning this generalization is the convergence to perfect projection for any fixed-size supporting tree. A new iterative formula of the same complexity is then presented and the necessary theorems for the performance concentration theorems are developed. Several properties of the new density evolution method are explored, including stability results for general asymmetric memoryless channels. Simulations, code optimizations, and possible new applications suggested by this new density evolution method are also provided. This result is also used to prove the typicality of linear LDPC codes among the coset code ensemble when the minimum check node degree is sufficiently large. It is shown that the convergence to perfect projection is essential to the belief propagation (BP) algorithm even when only symmetric channels are considered. Hence, the proof of the convergence to perfect projection serves also as a completion of the theory of classical density evolution for symmetric memoryless channels.
6254649
Disjunctive Image Computation for Embedded Software Verification
Finite state models generated from software programs have unique characteristics that are not exploited by existing model checking algorithms. In this paper, we propose a novel disjunctive image computation algorithm and other simplifications based on these characteristics. Our algorithm divides an image computation into a disjunctive set of easier ones that can be performed in isolation. Hypergraph partitioning is used to minimize the number of live variables in each disjunctive component. We use the live variables to simplify transition relations and reachable state subsets. Our experiments on a set of real-world C programs show that the new algorithm achieves orders-of-magnitude performance improvement over the best known conjunctive image computation algorithm
22896034
Compiler Techniques for Extracting Loop-Level Parallelism