TIT

The sum-product algorithm (belief/probability propagation) can be naturally mapped into analog transistor circuits. These circuits enable the construction of analog-VLSI decoders for turbo codes, low-density parity-check codes, and similar codes.

Abstract: Traditional approaches in the analysis of downlink systems decouple the precoding and channel estimation problems. However, in cellular systems with mobile users, these two problems are in fact tightly coupled. In this paper, this coupling is ...

We analyze the capacity of a multiple-antenna wireless link with M antennas at the transmitter and N antennas at the receiver in a Rician fading channel when the channel is unknown at both the transmitter and the receiver. The Rician model is a nonstandard model with a Rayleigh component and an isotropically random rank-one specular component. The Rayleigh and specular components remain constant for T symbol periods, after which they change to completely independent realizations, and so on. To maximize mutual information over the joint density of T·M complex transmitted signals it is sufficient to maximize over a joint density of min{T,M} real transmitted signal magnitudes. The capacity-achieving signal matrix is equal to the product of two independent matrices, a T×T isotropically random unitary matrix and a T×M real nonnegative diagonal matrix. If M>T, optimum signaling uses only T out of the M transmit antennas. We derive a novel lower bound on capacity which enables us to compute achievable rate regions for many cases. This lower bound is also valid for the case of purely Rayleigh-fading channels, where it has not been feasible, in general, to compute capacity, or mutual information. Our numerical results also indicate that the Rayleigh model is surprisingly robust: under our Rician model, up to half of the received energy can arrive via the specular component without significant reduction in capacity compared with the purely Rayleigh case.

this paper. We examine the data reconciliation problem within a generalized framework in which differences between multi-sets correspond to evaluations of arbitrary error functions. We limit communication between reconciling hosts to a single message and, under such conditions, the problem of data reconciliation is equivalent to a variation of the problem of graph coloring: second-order coloring or distance-2 coloring, from

We construct new families of error-correcting codes based on Gallager’s low-density parity-check codes, which we call irregular codes. When decoded using belief propagation, our codes can correct more errors than previously known low-density parity-check codes. For example, for rate 1/4 codes on 16,000 bits over a binary symmetric channel, previous low-density parity-check codes can correct up to approximately 16 % errors, while our codes can correct over 17%. Our improved performance comes from using codes based on irregular random bipartite graphs, based on the work of [7]. Previously studied low-density parity-check codes have been derived from regular bipartite graphs. We report experimental results for our irregular codes on both binary symmetric channels and Gaussian channels. In some cases our results come very close to reported results for turbo codes, suggesting that, with improvements, irregular codes may be able to match turbo code performance. 1

The problem of constructing statistically synchronizable codes over arbitrary alphabets and for any finite source is considered. It is shown how to efficiently construct a statistically synchronizable code whose average codeword length is within the least likely codeword probability from that of the Huffman code for the same source. Moreover, a method is given for constructing codes having a synchronizing codeword. The method yields synchronous codes that exhibit high synchronizing capability and low redundancy.>