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

Dual Problems in Power Control

Accurate channel state information (CSI) is essential to increasing throughput in multi-input, multi-output (MIMO) systems with digital beamforming. CSI can be acquired by channel estimation and reported via feedback mechanisms. While the training and feedback overhead are typically proportional to the number of antennas, uplink measurements can be utilized to predict downlink CSI assuming perfect channel reciprocity in a time division duplex (TDD) mode. However, many works make the assumption that channels are perfectly reciprocal, which is often not the case in practice due to poor channel estimation and physical channel asymmetry. In this work, we investigate the key challenges in channel feedback performance, including TX-RX imbalance, channel coherence, and interference for mobile systems. We evaluate IEEE 802.11ac-based implicit and explicit feedback schemes with both emulated and in-field MIMO channels, particularly in regards to drone-based transmissions. Our analysis with channel emulation shows that implicit feedback is susceptible to channel reciprocity errors, while explicit feedback is more sensitive to Doppler effects. We propose a hybrid feedback mechanism that increases the throughput by 32% over conventional feedback methods. We additionally evaluate the impact of frequency offset asymmetry on the performance of distributed mobile systems. Our in-field experiments demonstrate that explicit feedback can provide better throughput improvement than implicit feedback in highly-mobile air-to-ground channels. Since our study spans many critical frequency bands, these results serve as a fundamental step towards understanding the impacts of asymmetric factors of channel reciprocity for drone-based beamforming systems.

Channel Reciprocity Analysis and Feedback Mechanism Design for Mobile Beamforming Systems

Multiplexed image reconstruction, estimating high resolution images from multiple low resolution images with highly overlapped fields of view, is improved when the magnification of the imagers is diverse. No assumptions of shift invariance or Toeplitz structure are required for computational manageability because localized reconstruction is possible and sensitivity to boundary conditions is reduced. Such multiplexed diverse image sensors have applications in flat sensor systems for surveillance and pervasive personal imaging.

Improved Multiplexed Image Reconstruction Performance Through Optical System Diversity Design

In this paper, we compute an achievable rate of a Gaussian Z-interference channel as shown in Fig. 1, when transmit node C has causal, imperfect cognitive knowledge of the signal sent by transmit node A. This achievable rate is derived using a two-phase transmission scheme in which node C uses a combination of a linear minimum mean square error (LMMSE) estimator and dirty paper code and node D employs a combination of LMMSE estimator and partial interference canceler. Numerical results indicate that the achievable rate of the Gaussian Z-interference channel increases significantly with cognition under certain channel conditions. We also derive an upper bound on the capacity of this channel with cognition and quantify the channel conditions under which the proposed achievable scheme equals the upper bound.

http://lyle.smu.edu/~rajand/Pubs/PengRajan_GLOBECOM_2009.pdf

Achievable Rate of Gaussian Cognitive Z-Interference Channel with Partial Side Information

In this paper, we show that packet blocking probability at the transmitter can be traded off with average queue delay for fading channels in environmentally powered wireless networks with bursty packet arrivals. Varying the size of the energy storage unit as well as that of the transmit buffer queue aids in the trade off. We cast the problem into a Markov Decision Process (MDP) and use Dynamic Programming to obtain the optimal scheduler. Further, we develop two different low complexity schedulers that achieve near optimal performance in different system settings.

/pdf/delay-bounded-rate-and-power-control-in-energy-harvesting-k1a3xc1vfo.pdf

Dinesh Rajan

Papers

Dual Problems in Power Control

Channel Reciprocity Analysis and Feedback Mechanism Design for Mobile Beamforming Systems

Improved Multiplexed Image Reconstruction Performance Through Optical System Diversity Design

Achievable Rate of Gaussian Cognitive Z-Interference Channel with Partial Side Information

Delay bounded rate and power control in energy harvesting wireless networks