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- Olsson, Mattias, 1977-
(author)
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Contributions to Delay, Gain, and Offset Estimation
- 2008
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Doctoral thesis (other academic/artistic)abstract
- The demand for efficient and reliable high rate communication is ever increasing. In this thesis we study different challenges in such systems, and their possible solutions.A goal for many years has been to implement as much as possible of a radio system in the digital domain, the ultimate goal being so called software defined radio (SDR) where the inner workings of a radio standard can be changed completely by changing the software. One important part of an SDR receiver is the high speed analog-to-digital converter (ADC) and one path to reach this high speed is to use a number of parallel, time-interleaved, ADCs. Such ADCs are, however, sensitive to sampling instant offsets, DC level offsets and gain offsets. This thesis discusses estimators based on fractional-delay filters and one application of these estimmators is to estimate and calibrate the relative delay, gain, and DC level offset between the ADCs comprising the time interleaved ADC.In this thesis we also present a technique for carrier frequency offset (CFO) estimation in orthogonal frequency division multiplexing (OFDM) systems. OFDM has gone from a promising digital radio transmission technique to become a mainstream technique used in several current and future standards. The main attractive property of OFDM is that it is inherently resilient to multipath reflections because of its long symbol time. However, this comes at the cost of a relatively high sensitivity to CFO. The proposed estimator is based on locating the spectral minimas within so-called null or virtual subcarriers embedded in the spectrum.~The spectral minimas are found iteratively over a number of symbols and is therefore mainly useful for frequency offset tracking or in systems where an estimate is not immediately required, such as in TV or radio broadcasting systems. However, complexity-wise the estimator is relatively easy to implement and it does not need any extra redundancy beside a nonmodulated subcarrier. The estimator performance is studied both in a channel with additive white Gaussian noise and in a multipath frequency selective channel environment.Interpolators and decimators are an important part of many systems, e.g. radio systems, audio systems etc. Such interpolation (decimation) is often performed using cascaded interpolators (decimators) to reduce the speed requirements in different parts of the system. In a fixed-point implementation, scaling is needed to maximize the use of the available word lengths and to prevent overflow. In the final part of the thesis, we present a method for scaling of multistage interpolators/decimators using multirate signal processing techniques. We also present a technique to estimate the output roundoff noise caused by the internal quantization.
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- Sun, Yi-Ran, 1976-
(author)
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Generalized Bandpass Sampling Receivers for Software Defined Radio
- 2006
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Doctoral thesis (other academic/artistic)abstract
- Based on different sampling theorem, for example classic Shannon’s sampling theorem and Papoulis’ generalized sampling theorem, signals are processed by the sampling devices without loss of information. As an interface between radio receiver front-ends and digital signal processing blocks, sampling devices play a dominant role in digital radio communications. Under the concept of Software Defined Radio (SDR), radio systems are going through the second evolution that mixes analog, digital and software technologies in modern radio designs. One design goal of SDR is to put the A/D converter as close as possible to the antenna. BandPass Sampling (BPS) enables one to have an interface between the RF or the higher IF signal and the A/D converter, and it might be a solution to SDR. However, three sources of performance degradation present in BPS systems, harmful signal spectral overlapping, noise aliasing and sampling timing jitter, hinder the conventional BPS theory from practical circuit implementations. In this thesis work, Generalized Quadrature BandPass Sampling (GQBPS) is first invented and comprehensively studied with focus on the noise aliasing problem. GQBPS consists of both BPS and FIR filtering that can use either real or complex coefficients. By well-designed FIR filtering, GQBPS can also perform frequency down-conversion in addition to noise aliasing reduction. GQBPS is a nonuniform sampling method in most cases. With respect to real circuit implementations, uniform sampling is easier to be realized compared to nonuniform sampling. GQBPS has been also extended to Generalized Uniform BandPass Sampling (GUBPS). GUBPS shares the same property of noise aliasing suppression as GQBPS besides that the samples are uniformly spaced. Due to the moving average operation of FIR filtering, the effect of sampling jitter is also reduced to a certain degree in GQBPS and GUBPS. By choosing a suitable sampling rate, harmful signal spectral overlapping can be avoided. Due to the property of quadrature sampling, the “self image” problem caused by I/Q mismatches is eliminated. Comprehensive theoretical analyses and program simulations on GQBPS and GUBPS have been done based on a general mathematic model. Circuit architecture to implementing GUBPS in Switched-Capacitor circuit technique has been proposed and analyzed. To improve the selectivity at the sampling output, FIR filtering is extended by adding a 1st order complex IIR filter in the implementation. GQBPS and GUBPS operate in voltage-mode. Besides voltage sampling, BPS can also be realized by charge sampling in current-mode. Most other research groups in this area are focusing on bandpass charge sampling. However, the theoretical analysis shows that our GQBPS and GUBPS in voltage mode are more efficient to suppress noise aliasing as compared to bandpass charge sampling with embedded filtering. The aliasing bands of sampled-data spectrum are always weighted by continuous-frequency factors for bandpass charge sampling with embedded filtering while discrete-frequency factors for GQBPS and GUBPS. The transmission zeros of intrinsic filtering will eliminate the corresponding whole aliasing bands of both signal and noise in GQBPS and GUBPS, while it will only cause notches at a limited set of frequencies in bandpass charge sampling. In addition, charge sampling performs an intrinsic continuous-time sinc function that always includes lowpass filtering. This is a drawback for a bandpass input signal.
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