A cross-correlation sub-sampling receiver for low power applications in a low SINR environment

Wireless sensor networks have recently emerged in a wide range of applications. Many attributes are essential for such networks such as: low cost, small form-factor, limited peak power consumption and the ability to operate in harsh interference scenarios. Most of these networks do not require high data-rates to operate. In this respect, sub-sampling receivers have shown promising results but suffer from noise folding and interference aliasing. In this paper, a sub-sampling receiver in combination with cross-correlation is used to enhance sensitivity and interference robustness while maintaining the sub-sampling advantages. An architecture which uses two different sampling frequencies is proposed. It shows ∼2dB SNR improvement compared to traditional architectures due to cross-correlation and an additional ∼2dB for each doubling of integrations. For a BER of 10– 3 , the required SIR is reduced with 4.5dB, 11.5dB and 14.5dB after using cross-correlation with the same, half and quarter data-rate used respectively. These improvements allow for a lower-power and lower-cost implementation

Compressive Sampling as an Enabling Solution for Energy-Efficient and Rapid Wideband RF Spectrum Sensing in Emerging Cognitive Radio Systems

Wireless systems have become an essential part of every sector of the national and global economy. In addition to existing commercial systems including GPS, mobile cellular, and WiFi communications, emerging systems like video over wireless, the Internet of Things, and machine-to-machine communications are expected to increase mobile wireless data traffic by several orders of magnitude over the coming decades, while natural resources like energy and radio spectrum remain scarce. The projected growth of the number of connected nodes into the trillions in the near term and increasing user demand for instantaneous, over-the-air access to large volumes of content will require a 1000-fold increase in network wireless data capacity by 2020. Spectrum is the lifeblood of these future wireless networks and the ’data storm’ driven by emerging technologies will lead to a pressing ’artificial’ spectrum scarcity. Cognitive radio is a paradigm proposed to overcome the existing challenge of underutilized spectrum. Emerging cognitive radio systems employing multi-tiered, shared-spectrum access are expected to deliver superior spectrum efficiency over existing scheduled-access systems; they have several device categories (3 or more tiers) with different access privileges. We focus on lower tiered ’smart’ devices that evaluate the spectrum dynamically and opportunistically use the underutilized spectrum. These ’smart’ devices require spectrum sensing for incumbent detection and interferer avoidance. Incumbent detection will rely on database lookup or narrowband high-sensitivity sensing. Integrated interferer detectors, on the other hand, need to be fast, wideband, and energy efficient, while requiring only moderate sensitivity. These future 'smart' devices operating in small cell environments will need to rapidly (in 10s of μs) detect a few (e.g. 3 to 6) strong interferers within roughly a 1GHz span and accordingly reconfigure their hardware resources or request adjustments to their wireless connection consisting of primary and secondary links in licensed and unlicensed spectrum. Compressive sampling (CS), an evolutionary sensing/sampling paradigm that changes the perception of sampling, has been extensively used for image reconstruction. It has been shown that a single pixel camera that exploits CS has the ability to obtain an image with a single detection element, while measuring the image fewer times than the number of pixels with the prior assumption of sparsity. We exploited CS in the presented works to take a ’snapshot’ of the spectrum with low energy consumption and high frequency resolutions. Compressive sampling is applied to break the fixed trade-off between scan time, resolution bandwidth, hardware complexity, and energy consumption. This contrasts with traditional spectrum scanning solutions, which have constant energy consumption in all architectures to first order and a fixed trade-off between scan time and resolution bandwidth. Compressive sampling enables energy-efficient, rapid, and wideband spectrum sensing with high frequency resolutions at the expense of degraded instantaneous dynamic range due to the noise folding. We have developed a quadrature analog-to-information converter (QAIC), a novel CS rapid spectrum sensing technique for band-pass signals. Our first wideband, energy-efficient, and rapid interferer detector end-to-end system with a QAIC senses a wideband 1GHz span with a 20MHz resolution bandwidth and successfully detects up to 3 interferers in 4.4μs. The QAIC offers 50x faster scan time compared to traditional sweeping spectrum scanners and 6.3x the compressed aggregate sampling rate of traditional concurrent Nyquist-rate approaches. The QAIC is estimated to be two orders of magnitude more energy efficient than traditional spectrum scanners/sensors and one order of magnitude more energy efficient than existing low-pass CS spectrum sensors. We implemented a CS time-segmented quadrature analog-to-information converter (TS-QAIC) that extends the physical hardware through time segmentation (e.g. 8 physical I/Q branches to 16 I/Q through time segmentation) and employs adaptive thresholding to react to the signal conditions without additional silicon cost and complexity. The TS-QAIC rapidly detects up to 6 interferers in the PCAST spectrum between 2.7 and 3.7GHz with a 10.4μs sensing time for a 20MHz RBW with only 8 physical I/Q branches while consuming 81.2mW from a 1.2V supply. The presented rapid sensing approaches enable system scaling in multiple dimensions such as ADC bits, the number of samples, and the number of branches to meet user performance goals (e.g. the number of detectable interferers, energy consumption, sensitivity and scan time). We envision that compressive sampling opens promising avenues towards energy-efficient and rapid sensing architectures for future cognitive radio systems utilizing multi-tiered, shared spectrum access

Synchronization for Impulse-Radio UWB With Energy-Detection and Multi-User Interference: Algorithms and Application to IEEE 802.15.4a

Energy-detection (ED) receivers can take advantage of the ranging and multipath resistance capabilities of impulse-radio ultra-wideband (IR-UWB) physical layers at a much lower complexity than coherent receivers. However, ED receivers are extremely vulnerable to multi-user interference (MUI). Therefore, the design of IR-UWB ED architectures must take MUI into account. In this paper, we present the design and evaluation of two complementary algorithms for reliable and robust synchronization of IR-UWB ED receivers in the presence of MUI: 1) power-independent detection and preamble code interference cancellation (PICNIC) and 2) detection of start-frame-delimiter through sequential ratio tests (DESSERT). PICNIC addresses packet detection and timing acquisition while DESSERT focuses on start-frame-delimiter (SFD) detection. Both algorithms are evaluated with the IEEE 802.15.4a IR-UWB physical layer, standardized for low data-rate networks. The performance evaluation with extensive simulations show that our algorithms outperform nonrobust synchronization algorithms by up to two orders of magnitude in the presence of MUI

Novel Approaches in RF/Analog CMOS Spectrum Sensing and Its Applications

Real time spectrum sensing refers to searching for possible signals at a specific time and location, which is applicable to cognitive radio (CR) for primary signal detection and ultra-wideband (UWB) radio for interferer detection. There are several approaches for spectrum sensing. Choosing a proper method for spectrum sensing necessitates evaluating several trade-offs among sensing time, accuracy, power consumption and simplicity of implementation. In this dissertation several approaches for spectrum sensing along with the applications to CR and UWB receivers are presented. A novel simple spectrum sensing technique for detecting weak primary signals with negative signal-to-noise ratio (SNR) is proposed, which is called quasi-cyclostationary feature detection (QCFD) technique. Moreover, a simple, reliable, and fast real-time spectrum sensing technique based on phasers, which are dispersive delay structures (DDSs), is proposed. Lastly, a UWB receiver robust to the narrowband (NB) blockers, in the vicinity of UWB frequency, is presented. To increase the robustness of the UWB receiver towards interferers, a dynamic blocker detector, utilizing a phaser-based real time spectrum sensing technique, is employed. The proposed spectrum sensing methods provide the best solutions for the intended applications, considering the trade-offs, compared to the state-of-the-art CMOS spectrum sensors

Radar Interference Mitigation for Automated Driving: Exploring Proactive Strategies

Autonomous driving relies on a variety of sensors, especially on radars, which have unique robustness under heavy rain/fog/snow and poor light conditions. With the rapid increase of the amount of radars used on modern vehicles, where most radars operate in the same frequency band, the risk of radar interference becomes a compelling issue. This article analyses automotive radar interference and proposes several new approaches, which combine industrial and academic expertise, toward the path of interference-free autonomous driving