The discrepancy between perceived spectrum shortage and the actual availability by measurements motivates the use of cognitive radio concepts. In this approach the radio locates and then transmits in unused or lightly used bands. If a wideband digital approach (on the order of GHz) to channel selection is taken to provide the necessary radio flexibility, there is a stringent dynamic range and speed requirement in the analog to digital conversion process. This arises from the large interfering signals which are effectively in-band since they are not removed by analog pre-filters. Given this extremely challenging wideband dynamic range goal, a fundamentally different mixed signal architecture has been pursued which is based on time domain signal cancellation. The objective of this thesis is therefore to analyze and implement critical aspects of this approach, with particular focus on the power requirements and silicon area.

This approach explores the use of a mixed analog with digital assistance architecture which uses multiple low to medium resolution ADCs with digital adaptive filters. The effective dynamic range of the front-end is enhanced by cancelling the unwanted interference in the time domain.

Interference cancellation performance is improved using oversampling and a digital dual adaptive signal processing technique that provides a low mean squared error for cancellation together with a large processing gain. The system could achieve at least 11 bit equivalent dynamic range for the desired weak signals using two 5-bit ADC's and a 7-bit DAC. In general, the Effective Bits from Dynamic Range Reduction (EBDR) for the system is equal to, or more than N+M bits, where N and M are resolutions of the two ADC's used.

The key components of the system are high speed medium resolution ADC's and they have been implemented to demonstrate the path to a low power small area solution. An asynchronous 1GS/s ADC with a peak SNDR of 31.5dB, ENOB 5.0 bits, is achieved by time interleaving two ADC's based on the binary equivalent successive approximation (SA) algorithm using a series capacitive ladder with input capacitance of 84fF. A simple extension of the SA algorithm essentially removes the ENOB degradation due to metastability from the comparator. The ADC's are fabricated in 65nm CMOS with an active area of 0.11mm², with a total power consumption of 6.7mW.

It is believed that the time domain approach for wideband, high dynamic range applications which has been explored in this thesis is a step towards the goal of a future radio system, in which the A/D conversion process is directly after the front-end low noise amplifiers.

Sensitivity-based methodology is applied to optimization of performance, power and area across several levels of design abstraction for a complex wireless baseband signal processing algorithm. The design framework is based on a unified, block-based graphical description of the algorithm to avoid design re-entry in various phases of chip development. The use of architectural techniques for minimization of power and area for complex signal processing algorithms is demonstrated using this framework. As a proof of concept, an ASIC realization of the MIMO baseband signal processing for a multi-antenna WLAN is described. The chip implements a 4 x 4 adaptive singular value decomposition (SVD) algorithm with combined power and area minimization achieving a power efficiency of 2.1 GOPS/mW (12-bit add equivalent) in just 3.5 mm(2) in a standard 90 nm CMOS process. The computational throughput of 70 GOPS is implemented with 0.5 M cells at a 100 MHz clock and 385 mV supply, dissipating 34 mW of power. With optimal channel conditions the algorithm implemented can deliver up to 250 Mb/s over 16 sub-carriers.

Cognitive radios have been proposed to address the dual problem of spectrum under-utilization and the need for vast swathes of new spectrum in the 0-3GHz band for wireless data services. Previous research in the area of cognitive radios has concentrated on signal processing (SP) innovations for improved detection with a limited number of samples (a.k.a detection sensitivity). However the SP perspective alone is unable to recover much unused spectrum. In this thesis we focus on two fronts to maximize the recovery of unused spectrum. First, we take a spatial perspective on spectrum usage. Second, we will look at advanced algorithms which use other radios and frequencies to aid the detection process.

The spatial perspective is necessitated by the FCC's decision to open up TV bands for ‘white space’/opportunistic spectrum use. Examination of the FCC's choice of parameters reveals the political and engineering tradeoffs made by the FCC. The FCC's rules applied to the database of TV transmitters and the population density of the United States as per the census of 2000 reveal that the geo-location rules enable an average of 9 white space channels per person. This number does not change significantly even if the secondaries take a purely selfish approach and operate only where the pollution from TV transmitters is low. The corresponding political tradeoff sacrifices 1 people-channel for broadcast use to gain 8 people-channels for white space use. Spectrum sensing is another mechanism to recover the available opportunity. Here, the FCC's sensing rule of -114dBm is very conservative and yields only a average of 1 white space channel per person.

This spatial analysis points to the inability of traditional detection metrics in predicting the spatial performance of sensing. To overcome this problem, we propose the twin metrics of Fear of Harmful Interference (FHI) and Weighted Probability of Area Recovered (WPAR) to quantify the performance of sensing relative to geo-location. Fear of Harmful interference (FHI), captures the safety to the primary users and is largely the fading-aware probability of missed detection with modifications to allow easier incorporation of system-level uncertainty. Weighted Probability of Area Recovered (WPAR), captures the performance of spectrum sensing by appropriately weighting the probability of false alarm (PFA) across different spatial locations. These metrics give a unifying framework in which to compare different spectrum-sensing algorithms. Reasonable parameters for this metric (most crucially, something which can be interpreted as a spatial discount factor) are obtained from the TV database and US population data.

Cooperative Sensing, in which secondaries use sensing results from multiple nearby radios to decide on whether the band is free to use, has been proposed as a mechanism to improve the performance over a single radio. However, the motivation for using cooperative sensing is different for recovering a temporal as opposed to recovering a spatial hole. For a temporal hole, cooperative sensing is used to gather as much energy in the primary signal as possible, thus maximizing the gap between the means of the signal present and signal absent hypotheses. For a spatial hole there is a mapping between the nominal signal strength at a given distance from the transmitter and the distance itself. By cooperating the hope is to converge to this nominal signal level. This difference has major implications on the choice of cooperation rule for different scenarios. A median rule is proposed as a robust cooperation rule for spatial holes. This rule can be implemented as a hard combining rule and is robust to uncertainties in the fading models and untrusted radio behavior.

Unfortunately all cooperation suffers when there is a loss in spatial diversity. To combat shadowing correlation across space, assisted/calibrated detection in the form of multiband sensing is proposed. In multiband sensing, detection results from nearby frequencies is used to aid detection in the frequency band of interest. Cooperation across multiband radios is robust against channel correlation and provides gains by weeding out radios afflicted by adverse propagation environments. A mobile, wideband testbed was designed and used to capture TV signals in the 500-700MHz band at various locations in Berkeley, CA. Analysis of these measurements reveals high shadowing and multipath spread correlation across frequencies. These new insights into the spectral environment are used to design detectors that perform better than a single band detector.

The ability to coexist with primaries of different scales (high power TV transmitters and low power Wireless Microphones) is an important requirement for a cognitive radio system. When sensing a large scale primary, a small scale secondary user can make its own decision about transmission based on the sensing results from its neighborhood. This assumption fails when the scale of the primary is comparable to the scale of the secondary user. In this scenario, we need to decouple sensing from admission control -- a sensor network is required to perform the sensing and localize the primary. For small primaries, the environment over which the sensing results are valid is small which imposes certain minimum density requirements for sensor nodes. Location information of the primary and secondary users is key for such an admission control algorithm to operate successfully.

In ultra-wideband (UWB) systems, antennas act as filters that introduce a frequency dependent response from the transmitter to receiver. To capture the waveform dispersion so that one can equalize/compensate at the transmitter/receiver, a new circuit modeling methodology that handles onmidirectional small antennas is proposed. By transforming the antennas into the degenerated Foster canonical forms and utilizing the waveform-onmidirectional property, it is shown that the transmitted far field waveform is a scaled version of the voltage across the radiation resistor in the model. Extended Thevenin/Norton equivalent circuits with dependent sources tracking the frequency dependence of the antenna effective length are also built for UWB receiving antennas. Simulation and experimental results show that this methodology is effective over a wide bandwidth and suitable for modeling most UWB antennas.