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

In this paper, we investigate a mobile edge computing (MEC) system in which a set of users with intensive computation tasks, and a set of users with high downlink rate requirement, can cooperate to achieve a mutually-beneficial situation where the task completion time is reduced and the downlink users receive more information from the base station (BS). Specifically, by leveraging uplink and downlink non-orthogonal multiple access (NOMA), the user with an intensive computation task can offload its task bits to the edge cloud and the downlink user. Simultaneously this user relays information to the downlink user from the BS. We consider the joint optimization of computational resource allocation at the edge cloud, communication resource allocation, assignment among the two sets of users, the share of computation, and relay bits to minimize the overall completion time of the tasks while guaranteeing downlink users’ incentive requirement. A low complexity iterative algorithm is proposed to find efficient locally optimal solutions by utilizing convex optimization, a graph theory matching algorithm, and block coordinate descent technique. Numerical results show that the proposed technique leads to a significant reduction in users’ task completion time and increase in the downlink users rate.

NOMA Enabled Computation and Communication Resource Trade-off for Mobile Edge Computing

In this paper, we propose a framework for designing power efficient schedulers for transmitting bursty traffic sources over Gaussian wireless channels that provides deterministic and statistical guarantees on absolute delays experienced by the source packets. The proposed schedulers compute the transmission rate and power using temporal water-filling techniques without any knowledge of the arrival traffic statistics. The schedulers reduce the average transmission power substantially (55% in some scenarios) for small increases in delay. The framework allows us to design schedulers that artfully tradeoff the performance with the complexity of computing the schedulers. We also introduce an iterative process to compute a lower bound on the transmit power of any scheduler that provides absolute delay guarantees. The utility of having accurate traffic predictors is demonstrated; specifically, we show that a perfect one step predictor achieves near optimal performances for small delay bounds. The proposed schedulers and iterative method of computing the lower bound are also shown to provide statistical guarantees on packet delays.

/pdf/towards-universal-power-efficient-scheduling-in-gaussian-3awbhda3s8.pdf

Towards universal power efficient scheduling in Gaussian channels

The high mobility of vehicular networks makes channel estimation and phase tracking challenging and an important problem for OFDM receiver design. In IEEE 802.11p vehicular networks, the channel estimation based on a long preamble in the PHY frame performs poorly in fast-fading channels. Moreover, the pilot-based phase tracking usually suffers from large residual phase errors. In this paper, we propose and implement a novel phase tracking algorithm, leveraging the decoded data. We also provide a detailed hardware design for a decoder-based channel estimation algorithm with a pipeline structure on an FPGA-based platform. Through diverse experiments via an advanced channel emulator, our results show that the proposed algorithm could significantly improve the system performance in terms of packet error rate, while adding less than 3% of additional hardware resources. We also jointly evaluate the packet error rate versus the bitwidth of the data in the FPGA, which is important to achieve a good balance between the hardware cost and the system performance.

/pdf/implementation-and-evaluation-of-channel-estimation-and-m2z6fvrn82.pdf

Implementation and evaluation of channel estimation and phase tracking for vehicular networks

It is well known that channel state information at the transmitter (CSIT) leads to higher throughput in fading channels. We motivate the use of transmit buffer information at receiver (TBIR). The thesis of this paper is that having partial or complete instantaneous TBIR leads to a lower packet loss rate in block-fading channels assuming the availability of partial CSIT. We provide a framework for the joint design and analysis of feedback (FB) and feed-forward (FF) information in fading channels. We then introduce two forms of TBIR—statistical and instantaneous—and show the gains of each form of TBIR using a heuristic scheme. For a Rayleigh fading channel, we show that in certain cases the packet error rate reduces by nearly an order of magnitude with just one bit of feed-forward information of TBIR.

/pdf/exploiting-transmit-buffer-information-at-the-receiver-in-cqeytvbo1v.pdf

Exploiting Transmit Buffer Information at the Receiver in Block-Fading Channels

We study a cognitive radio network in which a group of secondary users sense the presence of a primary user and convey this information to a fusion center. The trade-off between the bit rate required and the accuracy of the sensed information is captured using a rate-distortion framework, which provides a lower bound on the overhead required for any spectrum sensing protocol. The effect of the number of sensors and the measurement noise at the sensors on the trade-off is quantified.

Dinesh Rajan

Papers

NOMA Enabled Computation and Communication Resource Trade-off for Mobile Edge Computing

Towards universal power efficient scheduling in Gaussian channels

Implementation and evaluation of channel estimation and phase tracking for vehicular networks

Exploiting Transmit Buffer Information at the Receiver in Block-Fading Channels

Estimation of centralized spectrum sensing overhead for cognitive radio networks