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

Big AI Models for 6G Wireless Networks: Opportunities, Challenges, and Research Directions

Recently, big artificial intelligence models (BAIMs) represented by chatGPT have brought an incredible revolution. With the pre-trained BAIMs in certain fields, numerous downstream tasks can be accomplished with only few-shot or even zero-shot learning and exhibit state-of-the-art performances. As widely envisioned, the big AI models are to rapidly penetrate into major intelligent services and applications, and are able to run at low unit cost and high flexibility. In 6G wireless networks, to fully enable intelligent communication, sensing and computing, apart from providing other intelligent wireless services and applications, it is of vital importance to design and deploy certain wireless BAIMs (wBAIMs). However, there still lacks investigation on architecture design and system evaluation for wBAIM. In this paper, we provide a comprehensive discussion as well as some in-depth prospects on the demand, design and deployment aspects of the wBAIM. We opine that wBAIM will be a recipe of the 6G wireless networks to build high-efficient, sustainable, versatile, and extensible wireless intelligence for numerous promising visions. Then, we provide the core characteristics, principles, and pilot studies to guide the design of wBAIMs, and discuss the key aspects of developing wBAIMs through identifying the differences between the existing BAIMs and the emerging wBAIMs. Finally, related research directions and potential solutions are outlined