Lecture

Wireless Receivers: Parameter Estimation

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In course

EE-442: Wireless receivers: algorithms and architectures

The students will learn about the basic principles of wireless communication systems, including transmission and modulation schemes as well as the basic components and algorithms of a wireless receiver. They develop an understanding for the wireless channe ...

Description

This lecture covers the topic of parameter estimation in wireless receivers, focusing on formulating the conditions, unknowns, time offsets, blind estimation, and constructing signals from samples. It also delves into phase estimation, ambiguity, and signal modeling.

Instructor

Andreas Peter Burg

Andreas Burg was born in Munich, Germany, in 1975. He received his Dipl.-Ing. degree in 2000 from the Swiss Federal Institute of Technology (ETH) Zurich, Zurich, Switzerland. He then joined the Integrated Systems Laboratory of ETH Zurich, from where he graduated with the Dr. sc. techn. degree in 2006. In 1998, he worked at Siemens Semiconductors, San Jose, CA. During his doctoral studies, he was an intern with Bell Labs Wireless Research for a total of one year. From 2006 to 2007, he held positions as postdoctoral researcher at the Integrated Systems Laboratory and at the Communication Theory Group of the ETH Zurich. In 2007 he co-founded Celestrius, an ETH-spinoff in the field of MIMO wireless communication, where he was responsible for the ASIC development as Director for VLSI. In January 2009, he joined ETH Zurich as SNF Assistant Professor and as head of the Signal Processing Circuits and Systems group at the Integrated Systems Laboratory. In January 2011, he became a Tenure Track Assistant Professor at the Ecole Polytechnique Federale de Lausanne (EPFL) where he is leading the Telecommunications Circuits Laboratory in the School of Engineering. In June 2018 he was promoted to the role of a Tenured Associate Professor. In 2000, Mr. Burg received the Willi Studer Award and the ETH Medal for his diploma and his diploma thesis, respectively. Mr. Burg was also awarded an ETH Medal for his Ph.D. dissertation in 2006. In 2008, he received a 4-years grant from the Swiss National Science Foundation (SNF) for an SNF Assistant Professorship. In his professional career, Mr. Burg was involved in the development of more than 25 ASICs. He is a member of the IEEE and of the European Association for Signal Processing (EURASIP). Research interests and expertise

Circuits and systems for telecommunications (wireless and wired)
Prototyping and silicon implementation of new communication technologies
Development of communication algorithms and optimization for hardware implementation
Low-power VLSI signal processing for communications and other applications
Digital integrated circuits
Circuits for image and video processing

Official source

Ontological neighbourhood

Statistics

Data collection: Statistical sampling

Related concepts (32)

Signal

In signal processing, a signal is a function that conveys information about a phenomenon. Any quantity that can vary over space or time can be used as a signal to share messages between observers. The IEEE Transactions on Signal Processing includes audio, video, speech, , sonar, and radar as examples of signals. A signal may also be defined as observable change in a quantity over space or time (a time series), even if it does not carry information.

Analog signal processing

Analog signal processing is a type of signal processing conducted on continuous analog signals by some analog means (as opposed to the discrete digital signal processing where the signal processing is carried out by a digital process). "Analog" indicates something that is mathematically represented as a set of continuous values. This differs from "digital" which uses a series of discrete quantities to represent signal. Analog values are typically represented as a voltage, electric current, or electric charge around components in the electronic devices.

Quantization (signal processing)

Quantization, in mathematics and digital signal processing, is the process of mapping input values from a large set (often a continuous set) to output values in a (countable) smaller set, often with a finite number of elements. Rounding and truncation are typical examples of quantization processes. Quantization is involved to some degree in nearly all digital signal processing, as the process of representing a signal in digital form ordinarily involves rounding. Quantization also forms the core of essentially all lossy compression algorithms.

Sampling (statistics)

In statistics, quality assurance, and survey methodology, sampling is the selection of a subset or a statistical sample (termed sample for short) of individuals from within a statistical population to estimate characteristics of the whole population. Statisticians attempt to collect samples that are representative of the population. Sampling has lower costs and faster data collection compared to recording data from the entire population, and thus, it can provide insights in cases where it is infeasible to measure an entire population.

Stratified sampling

In statistics, stratified sampling is a method of sampling from a population which can be partitioned into subpopulations. In statistical surveys, when subpopulations within an overall population vary, it could be advantageous to sample each subpopulation (stratum) independently. Stratification is the process of dividing members of the population into homogeneous subgroups before sampling. The strata should define a partition of the population.

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