Wideband analog signals push contemporary analog-to-digital conversion systems to their performance limits. In many applications, however, sampling at the Nyquist rate is inefficient because the signals of interest contain only a small number of significant frequencies relative to the bandlimit, although the locations of the frequencies may not be known a priori. For this type of sparse signal, other sampling strategies are possible. This paper describes a new type of data acquisition system, called a random demodulator, that is constructed from robust, readily available components. Let K denote the total number of frequencies in the signal, and let W denote its bandlimit in Hz. Simulations suggest that the random demodulator requires just O(K log(W/K)) samples per second to stably reconstruct the signal. This sampling rate is exponentially lower than the Nyquist rate of W Hz. In contrast with Nyquist sampling, one must use nonlinear methods, such as convex programming, to recover the signal from the samples taken by the random demodulator. This paper provides a detailed theoretical analysis of the system’s performance that supports the empirical observations.

Abstract—Conventional sub-Nyquist sampling methods for analog signals exploit prior information about the spectral support. In this paper, we consider the challenging problem of blind sub-Nyquist sampling of multiband signals, whose unknown frequency support occupies only a small portion of a wide spectrum. Our primary design goals are efficient hardware implementation and low computational load on the supporting digital processing. We propose a system, named the modulated wideband converter, which first multiplies the analog signal by a bank of periodic waveforms. The product is then low-pass filtered and sampled uniformly at a low rate, which is orders of magnitude smaller than Nyquist. Perfect recovery from the proposed samples is achieved under certain necessary and sufficient conditions. We also develop a digital architecture, which allows either reconstruction of the analog input, or processing of any band of interest at a low rate, that is, without interpolating to the high Nyquist rate. Numerical simulations demonstrate many engineering aspects: robustness to noise and mismodeling, potential hardware simplifications, real-time performance for signals with time-varying support and stability to quantization effects. We compare our system with two previous approaches: periodic nonuniform sampling, which is bandwidth limited by existing hardware devices, and the random demodulator, which is restricted to discrete multitone signals and has a high computational load. In the broader context of Nyquist sampling, our scheme has the potential to break through the bandwidth barrier of state-of-the-art analog conversion technologies such as interleaved converters. Index Terms—Analog-to-digital conversion (ADC), compressive sampling (CS), infinite measurement vectors (IMV), multiband

The recently introduced theory of compressive sensing enables the recovery of sparse or compressible signals from a small set of nonadaptive, linear measurements. If properly chosen, the number of measurements can be much smaller than the number of Nyquist-rate samples. Interestingly, it has been shown that random projections are a near-optimal measurement scheme. This has inspired the design of hardware systems that directly implement random measurement protocols. However, despite the intense focus of the community on signal recovery, many (if not most) signal processing problems do not require full signal recovery. In this paper, we take some first steps in the direction of solving inference problems—such as detection, classification, or estimation—and filtering problems using only compressive measurements and without ever reconstructing the signals involved. We provide theoretical bounds along with experimental results.

This dissertation presents the design, implementation and evaluation of a novel software radio architecture based on wideband digitization, a general purpose processor and application level software. The goal of a software radio is to create a communications system in which any aspect of the signal processing can be dynamically modified to adapt to changing environmental conditions, traffic constraints, user requirements and infrastructure limitations. The coupling of wideband digitization with application level software running on a general purpose processor allows for the modification of a greater range of functionality than any existing solution

Abstract—This tutorial paper examines architectural and circuit design techniques for a microsensor node operating at power levels low enough to enable the use of an energy harvesting source. These requirements place demands on all levels of the design. We propose an architecture for achieving the required ultra-low energy operation and discuss the circuit techniques necessary to implement the system. Dedicated hardware implementations improve the efficiency for specific functionality, and modular partitioning permits fine-grained optimization and power-gating. We describe modeling and operating at the minimum energy point in the subthreshold region for digital circuits. We also examine approaches for improving the energy efficiency of analog components like the transmitter and the ADC. A microsensor node using the techniques we describe can function in an energy-harvesting scenario. Index Terms—Integrated circuits, energy-aware systems, low-power design, wireless sensor networks. 1

In this paper, we provide a survey of dynamic spectrum access techniques. Various approaches envisioned for dynamic spectrum access are broadly categorized under three models: dynamic exclusive use model, open sharing model, and hierarchical access model. Based on this taxonomy, we provide an overview of the technical challenges and recent advances under each model. Index Terms: Dynamic spectrum access, spectrum property rights, spectrum commons, spectrum underlay, spectrum overlay, opportunistic spectrum access. 1.

Recent theoretical developments in the area of compressive sensing (CS) have the potential to significantly extend the capabilities of digital data acquisition systems such as analogto-digital converters and digital imagers in certain applications. A key hallmark of CS is that it enables sub-Nyquist sampling for signals, images, and other data. In this paper, we explore and exploit another heretofore relatively unexplored hallmark, the fact that certain CS measurement systems are democractic, which means that each measurement carries roughly the same amount of information about the signal being acquired. Using the democracy property, we re-think how to quantize the compressive measurements in practical CS systems. If we were to apply the conventional wisdom gained from conventional Shannon-Nyquist uniform sampling, then we would scale down the analog signal amplitude (and therefore increase the quantization error) to avoid the gross saturation errors that occur when the signal amplitude exceeds the quantizer’s dynamic range. In stark contrast, we demonstrate that a CS system achieves the best performance when it operates at a significantly nonzero saturation rate. We develop two methods to recover signals from saturated CS measurements. The first directly exploits the democracy property by simply discarding the saturated measurements. The second integrates saturated measurements as constraints into standard linear programming and greedy recovery techniques. Finally, we develop a simple automatic gain control system that uses the saturation rate to optimize the input gain.

This paper presents a switched-capacitor multibit ADC delta--sigma modulator for baseband demodulation integrated in a single-chip Bluetooth radio-modem transceiver that achieves 77 dB of signal-to-noise-plus-distortion ratio (SINAD) and 80 dB of dynamic range over a 500-kHz bandwidth with a 32-MHz sample rate. The 1-mm 2 circuit is implemented in a 0.35- m BiCMOS SOI process and consumes 4.4 mA of current from a 2.7-V supply.

Abstract- Cognitive radio systems offer the opportunity to improve spectrum utilization by detecting unoccupied spectrum bands and adapting the transmission to those bands while avoiding the interference to primary users. This novel approach to spectrum access introduces unique functions at the physical layer: reliable detection of primary users and adaptive transmission over a wide bandwidth. In this paper, we address design issues involved in an implementation of these functions that could limit their performance or even make them infeasible. The critical design problem at the receiver is to achieve stringent requirements on radio sensitivity and perform signal processing to detect weak signals received by a wideband RF front-end with limited dynamic range. At the transmitter, wideband modulation schemes require adaptation to different frequency bands and power levels without creating interference to active primary users. We introduce algorithms and techniques whose implementation could meet these challenging requirements. I.

Designing leading-edge systems (e.g., communications systems) requires knowledge about the technological limits. Jitter is the limiting effect in ADCs with a digitization bandwidth between 1 MHz and 1 GHz. The effect of aperture jitter and clock jitter have been investigated previously. However, some very important aspects are still missing, in particular investigations on the spectral distribution of the jitter induced error. This gap is filled by this paper.