Beyond Countable Alphabets: An Extension of the Information-Spectrum Approach

A general approach is established for deriving one-shot performance bounds for information-theoretic problems on general alphabets beyond countable alphabets. It is mainly based on the quantization idea and a novel form of "likelihood ratio". As an example, one-shot lower and upper bounds for random number generation from correlated sources on general alphabets are derived.Comment: v0.5.1.20be8d, 7 page

Spin angular momentum transfer in magnetic nanostructure

The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file.Title from title screen of research.pdf file (viewed on March 4, 2008)Vita.Thesis (Ph. D.) University of Missouri-Columbia 2007.Spin angular momentum transfer, or spin transfer, is a short notion of the transfer of spin angular momentum between the spin polarized current and the magnetization of ferromagnetic condensates. Spin transfer effect in ferromagnetic nanostructures, such as Magnetic Tunnel Junctions (MTJ) and Spin Valves, is studied in this dissertation. Spin current generates spin transfer torque in ferromagnets, which can induce magnetization reversal, spin wave emission, as well as self-sustained magnetization precession in the presence of magnetic field.The magnetization oscillation in spin valves is referred as the spin transfer oscillator (STO). We investigated the magnetization dynamics in STO. We applied a universal method, Melnikov Integral, to determine three different dynamical phases in STO, that is, limit cycles, synchronization and chaos. Finite temperature may have significant effect on STO dynamics. We studied the thermal effect on limit cycles and chaos. In MTJ, in addition to spin transfer, energy transfer effect is studied on the basis of energy conservation. The effect of energy transfer on spin transfer induced magnetization switching is modeled in terms of an effective magnetic temperature.Includes bibliographical reference

Weight Distributions of Regular Low-Density Parity-Check Codes over Finite Fields

The average weight distribution of a regular low-density parity-check (LDPC) code ensemble over a finite field is thoroughly analyzed. In particular, a precise asymptotic approximation of the average weight distribution is derived for the small-weight case, and a series of fundamental qualitative properties of the asymptotic growth rate of the average weight distribution are proved. Based on this analysis, a general result, including all previous results as special cases, is established for the minimum distance of individual codes in a regular LDPC code ensemble.Comment: 15 pages, 5 figures, accepted for publication in IEEE Transactions on Information Theory, July 201

Over-the-Air Split Learning with MIMO-Based Neural Network and Constellation-Based Activation

This paper investigates a communication-efficient split learning (SL) over multiple-input multiple-output (MIMO) communication system. In particular, we mathematically decompose the inter-layer connection of a neural network (NN) to a series of linear precoding and combining transformations using over-the-air computation (OAC), which synergistically form a linear layer in NNs. The precoding and combining matrices are trainable parameters in such a system, whereas the MIMO channel is implicit. The proposed system eliminates the implicit channel estimation through exploiting the channel reciprocity and properly casting the backpropagation process, significantly saving the system costs and further improving the overall efficiency. The practical constellation diagrams are used as the activation function to avoid sending arbitrary analog signals as in the traditional OAC system. Numerical results are illustrated to demonstrate the effectiveness of the proposed scheme.Comment: IEEE MLS

Information-Theoretic Limits on Compression of Semantic Information

As conventional communication systems based on classic information theory have closely approached the limits of Shannon channel capacity, semantic communication has been recognized as a key enabling technology for the further improvement of communication performance. However, it is still unsettled on how to represent semantic information and characterise the theoretical limits. In this paper, we consider a semantic source which consists of a set of correlated random variables whose joint probabilistic distribution can be described by a Bayesian network. Then we give the information-theoretic limit on the lossless compression of the semantic source and introduce a low complexity encoding method by exploiting the conditional independence. We further characterise the limits on lossy compression of the semantic source and the corresponding upper and lower bounds of the rate-distortion function. We also investigate the lossy compression of the semantic source with side information at both the encoder and decoder, and obtain the rate distortion function. We prove that the optimal code of the semantic source is the combination of the optimal codes of each conditional independent set given the side information