Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

It is shown that, particularly for terrestrial cellular telephony, the interference-suppression feature of CDMA (code division multiple access) can result in a many-fold increase in capacity over analog and even over competing digital techniques. A single-cell system, such as a hubbed satellite network, is addressed, and the basic expression for capacity is developed. The corresponding expressions for a multiple-cell system are derived. and the distribution on the number of users supportable per cell is determined. It is concluded that properly augmented and power-controlled multiple-cell CDMA promises a quantum increase in current cellular capacity. >

On the capacity of a cellular CDMA system

Cognitive radios hold tremendous promise for increasing spectral efficiency in wireless systems. This paper surveys the fundamental capacity limits and associated transmission techniques for different wireless network design paradigms based on this promising technology. These paradigms are unified by the definition of a cognitive radio as an intelligent wireless communication device that exploits side information about its environment to improve spectrum utilization. This side information typically comprises knowledge about the activity, channels, codebooks, and/or messages of other nodes with which the cognitive node shares the spectrum. Based on the nature of the available side information as well as a priori rules about spectrum usage, cognitive radio systems seek to underlay, overlay, or interweave the cognitive radios' signals with the transmissions of noncognitive nodes. We provide a comprehensive summary of the known capacity characterizations in terms of upper and lower bounds for each of these three approaches. The increase in system degrees of freedom obtained through cognitive radios is also illuminated. This information-theoretic survey provides guidelines for the spectral efficiency gains possible through cognitive radios, as well as practical design ideas to mitigate the coexistence challenges in today's crowded spectrum.

Breaking Spectrum Gridlock With Cognitive Radios: An Information Theoretic Perspective

Information Theory and Reliable Communication

Most of the signal processing that we will study in this course involves local operations on a signal, namely transforming the signal by applying linear combinations of values in the neighborhood of each sample point. You are familiar with such operations from Calculus, namely, taking derivatives and you are also familiar with this from optics namely blurring a signal. We will be looking at sampled signals only. Let's start with a few basic examples. Local difference Suppose we have a 1D image and we take the local difference of intensities, DI(x) = 1 2 (I(x + 1) − I(x − 1)) which give a discrete approximation to a partial derivative. (We compute this for each x in the image.) What is the effect of such a transformation? One key idea is that such a derivative would be useful for marking positions where the intensity changes. Such a change is called an edge. It is important to detect edges in images because they often mark locations at which object properties change. These can include changes in illumination along a surface due to a shadow boundary, or a material (pigment) change, or a change in depth as when one object ends and another begins. The computational problem of finding intensity edges in images is called edge detection. We could look for positions at which DI(x) has a large negative or positive value. Large positive values indicate an edge that goes from low to high intensity, and large negative values indicate an edge that goes from high to low intensity. Example Suppose the image consists of a single (slightly sloped) edge:

http://ieeexplore.ieee.org/iel5/5/31307/01456462.pdf

Linear systems

Previous work has shown that for super-resolution image reconstruction from low resolution images, image acquisition with a diverse optical system improves reconstructed image quality as measured by the expected and actual mean squared error. However, other measures of image fidelity should also be considered. An alternative performance measure might be based on edge errors, since edges are often the first step in more complex image analysis for both image processing systems and biological systems. This paper explores the behavior of edge errors and intensity errors for super-resolution image reconstruction applications in which ill-posed inversions may cause the actual mean squared error to be highly dependent of image content and thus poorly predicted by the expected mean squared error.

/pdf/edge-detection-performance-in-super-resolution-image-4kafdec6w3.pdf

Edge Detection Performance in Super-Resolution Image Reconstruction from Camera Arrays

In this paper, we introduce the model for a Gaussian soft-handover channel (SHC), which adds a new dimension of flexibility to the well-known interference channel (IC). We provide a unified framework for computing achievable rate regions for the SHC in both the uplink and downlink cases. This achievable rate region for SHC is given by the convex hull of the union of certain multiple-access, interference, broadcast, and Z-channels. Some properties of the achievable region are studied. Specifically, we show the following key results: 1) in an uplink SHC, there are channel conditions under which decoding at a single receiver based, for example, on a maximum SNR condition does not achieve the entire boundary of the achievable region; 2) in a downlink SHC, multiple base stations should transmit independent information to all users to achieve the boundary points of the achievable region; and 3) when a mobile communicates with multiple base stations, the ratio of the uplink rates with different base stations could be different from the ratio of the downlink rates with those base stations. A simple outer bound on SHC capacity based on the capacity of multiple-input-multiple-output (MIMO) systems is also given.

/pdf/bounds-on-the-capacity-of-the-gaussian-soft-handover-channel-4v4v85h30g.pdf

Bounds on the Capacity of the Gaussian Soft-Handover Channel

We propose a new scheme for multiple antenna transmission in the context of spread spectrum signaling. The new scheme consists of using shifted Gold sequences to modulate independent information on the multiple antennas. We show that this new scheme greatly improves the throughput over currently known multiple antenna methods. We also find the optimal power allocation strategy among multiple transmit antennas for a fixed feedback rate.

/pdf/new-spread-spectrum-techniques-for-multiple-antenna-transmit-50xo5bvnhc.pdf

New spread spectrum techniques for multiple antenna transmit diversity

In this paper, we propose power efficient schedulers for transmitting bursty traffic sources over Gaussian wireless channels that provide guarantees on absolute delays experienced by the source packets. The proposed schedulers find the transmission rate and power using temporal water-filling techniques without any knowledge of the arrival traffic statistics. We also introduce an iterative process to compute a lower bound on the transmit power of any scheduler that provides absolute delay guarantees. The proposed schedulers and iterative method of computing the lower bound are also shown to provide statistical guarantees on packet delays.

Towards universal power efficient scheduling in wireless channels

We propose a new technique for the estimation of chaotic signals in the presence of additive noise. The new method uses statistical measures to restrict the space over which the signals are received. The proposed technique is applicable to a large variety of chaotic signals and has good performance indicated by the low estimation error bias and variance. The complexity of the algorithm is shown to be low.

Dinesh Rajan

Papers

Edge Detection Performance in Super-Resolution Image Reconstruction from Camera Arrays

Bounds on the Capacity of the Gaussian Soft-Handover Channel

New spread spectrum techniques for multiple antenna transmit diversity

Towards universal power efficient scheduling in wireless channels

New estimation technique for a class of chaotic signals