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

/pdf/new-improved-system-for-wwvb-broadcast-nist-iyfhshy0lx.pdf

New Improved System for WWVB Broadcast | NIST

Radio controlled clocks (RCCs) based on the WWVB broadcast, commonly referred to as “atomic clocks”, might experience relatively high levels of on-frequency interference from the equivalent MSF time-code broadcast from the UK, particularly on the East Coast. The problem may be further exacerbated by a random orientation of the RCC device, which might favor the reception of the interfering signal. While the legacy receivers for WWVB, based on envelope detection, are particularly vulnerable to such interference, a basic coherent BPSK receiver for the enhanced broadcast demonstrates a 25 dB performance advantage in interference immunity. An interference cancelation technique is proposed, to further enhance the performance of the coherent BPSK receiver, achieving an additional 14 dB improvement in interference rejection.

Interference-robustness improvement in BPSK receivers for the enhanced WWVB broadcast

Existing rate adaptation protocols have advocated training to establish the relationship between channel conditions and the optimal modulation and coding scheme. However, wireless devices for outdoor and vehicular communications frequently enter environments they have not yet encountered and therefore, have insufficient training for rate adaptation decisions. In addition, protocols are often optimally tuned for indoor environments but, when taken outdoors, perform poorly. In both cases, the decision structure formed offline lacks the ability to acclimate to a new situation on the fly. The diverse and ever-changing environments of increasingly mobile wireless devices call for a rate adaption scheme that can quickly adjust accordingly to form a unique environment set established by the user. In this paper, we propose an on-the-fly, in-situ training (FIT) mechanism which addresses the challenges of making rate decisions with unpredictable fluctuation and lack of repeatability of real wireless channels. We design and conduct extensive experiments on emulated and in-field wireless channels to evaluate the in-situ training process, showing that the rate decision structure can be updated as channel conditions change using existing traffic flows.

http://lyle.smu.edu/~camp/pubs/FIT_wcnc2015.pdf

FIT: On-the-fly, in-situ training with sensor data for SNR-based rate selection

An optimization scheme is developed for a cosine modulated perfect- reconstruction (PR) filter bank that is tailored to mitigate the effects of narrowband radio frequency interference (RFI). The conventionally used optimization criterion for bandpass filtering is to maximize the sidelobe attenuation by minimizing the stopband energy and minimizing the maximum stopband ripple. The proposed optimization scheme is designed particularly to combat RFI with completely known or partially known statistics. Simulation results for the scenario of strong narrowband RFI show that the proposed optimization scheme offers about 14% data rate improvement in the system's data throughput when compared to the traditional schemes.

/pdf/optimization-of-cosine-modulated-filter-bank-for-narrowband-3947o08ydc.pdf

Optimization of Cosine Modulated Filter Bank for Narrowband RFI

Environmental factors that lead to the movement and type of obstacles in and around wireless links are well-known to directly affect channel characteristics. However, while mobile users typically have repeatable daily or weekly patterns with common locations being frequently visited, many protocols along the network stack do not attempt to identify when physical locations are revisited. If wireless channels could be recognized as previously visited, the observance of good and bad decisions in that particular context could dramatically improve some network protocols. In this paper, we present a channel recognition framework which uses the geometrical shape of the link-level performance in a particular context across transmission modes and channel qualities. When attempting to recognize a channel condition, the performance of data transmissions is observed and compared against known channel types to detect similar behavior. The matching channel type can be used as an input to the link adaptation training and resulting decision structure. We perform extensive experimentation on controlled repeatable channels as well as in-field channels to show the validity of the classification algorithm.

Dinesh Rajan

Papers

New Improved System for WWVB Broadcast | NIST

Interference-robustness improvement in BPSK receivers for the enhanced WWVB broadcast

FIT: On-the-fly, in-situ training with sensor data for SNR-based rate selection

Optimization of Cosine Modulated Filter Bank for Narrowband RFI

Geometry-based channel recognition for context-aware applications