Analog to Digital Cognitive Radio: Sampling, Detection and Hardware

The proliferation of wireless communications has recently created a bottleneck in terms of spectrum availability. Motivated by the observation that the root of the spectrum scarcity is not a lack of resources but an inefficient managing that can be solved, dynamic opportunistic exploitation of spectral bands has been considered, under the name of Cognitive Radio (CR). This technology allows secondary users to access currently idle spectral bands by detecting and tracking the spectrum occupancy. The CR application revisits this traditional task with specific and severe requirements in terms of spectrum sensing and detection performance, real-time processing, robustness to noise and more. Unfortunately, conventional methods do not satisfy these demands for typical signals, that often have very high Nyquist rates. Recently, several sampling methods have been proposed that exploit signals' a priori known structure to sample them below the Nyquist rate. Here, we review some of these techniques and tie them to the task of spectrum sensing in the context of CR. We then show how issues related to spectrum sensing can be tackled in the sub-Nyquist regime. First, to cope with low signal to noise ratios, we propose to recover second-order statistics from the low rate samples, rather than the signal itself. In particular, we consider cyclostationary based detection, and investigate CR networks that perform collaborative spectrum sensing to overcome channel effects. To enhance the efficiency of the available spectral bands detection, we present joint spectrum sensing and direction of arrival estimation methods. Throughout this work, we highlight the relation between theoretical algorithms and their practical implementation. We show hardware simulations performed on a prototype we built, demonstrating the feasibility of sub-Nyquist spectrum sensing in the context of CR.Comment: Submitted to IEEE Signal Processing Magazin

Algorithmic Framework and Implementation of Spectrum Holes Detection for Cognitive Radios

The ability to dynamically discover portions of unused radio spectrum (spectrum holes) is an important ability of cognitive radio systems. Spectrum holes present a potential opportunity for wireless communication. Detection of holes and signals allows cognitive radios to dynamically access and share the spectrum with minimal interference. This work steps through the design, implementation, and analysis of a spectrum holes detector for cognitive radios. Energy detection and cyclostationary detection algorithms for detecting spectrum holes are compared through computer simulations. Ultimately an energy detection algorithm is proposed which performs better than the cyclostationary detection algorithm and requires no a-priori knowledge of noise power. The energy detection algorithm is implemented on the bladeRF x115 software-defined radio for wideband detection, leveraging on-board FPGA hardware and field-programmable analog hardware to scan a gigahertz-order range of frequencies and discover spectrum holes in real time. Resource utilization and requirements of the implementation are analyzed, and a utilization of 8.8% of the FPGA\u27s logic resources is reported. Experiments are performed on the implementation to measure its detection performance and demonstrate its ability to detect holes over a wide bandwidth with reasonable latency

Spectrum control and iterative coding for high capacity multiband OFDM

The emergence of Multiband Orthogonal Frequency Division Modulation (MB-OFDM) as an ultra-wideband (UWB) technology injected new optimism in the market through realistic commercial implementation, while keeping promise of high data rates intact. However, it has also brought with it host of issues, some of which are addressed in this thesis. The thesis primarily focuses on the two issues of spectrum control and user capacity for the system currently proposed by the Multiband OFDM Alliance (MBOA). By showing that line spectra are still an issue for new modulation scheme (MB-OFDM), it proposes a mechanism of scrambling the data with an increased length linear feedback shift register (compared to the current proposal), a new set of seeds, and random phase reversion for the removal of line spectra. Following this, the thesis considers a technique for increasing the user capacity of the current MB-OFDM system to meet the needs of future wireless systems, through an adaptive multiuser synchronous coded transmission scheme. This involves real time iterative generation of user codes, which are generated over time and frequency leading to increased capacity. With the assumption of complete channel state information (CSI) at the receiver, an iterative MMSE algorithm is used which involves replacement of each users s signature with its normalized MMSE filter function allowing the overall Total Squared Correlation (TSC) of the system to decrease until the algorithm converges to a fixed set of signature vectors. This allows the system to be overloaded and user\u27s codes to be quasi-orthogonal. Simulation results show that for code of length nine (spread over three frequency bands and three time slots), ten users can be accommodated for a given QoS and with addition of single frequency sub-band which allows the code length to increase from nine to twelve (four frequency sub-bands and three time slots), fourteen users with nearly same QoS can be accommodated in the system. This communication is overlooked by a central controller with necessary functionalities to facilitate the process. The thesis essentially considers the uplink from transmitting devices to this central controller. Furthermore, analysis of this coded transmission in presence of interference is carried to display the robustness of this scheme through its adaptation by incorporating knowledge of existing Narrowband (NB) Interference for computing the codes. This allows operation of sub-band coexisting with NB interference without substantial degradation given reasonable interference energy (SIR=-l0dB and -5dB considered). Finally, the thesis looks at design implementation and convergence issues related to code vector generation whereby, use of Lanczos algorithm is considered for simpler design and faster convergence. The algorithm can be either used to simplify design implementation by providing simplified solution to Weiner Hopf equation (without requiring inverse of correlation matrix) over Krylov subspace or can be used to expedite convergence by updating the signature sequence with eigenvector corresponding to the least eigenvalue of the signature correlation matrix through reduced rank eigen subspace search

Compact Digital Predistortion for Multi-band and Wide-band RF Transmitters

This thesis is focusing on developing a compact digital predistortion (DPD) system which costs less DPD added power consumptions. It explores a new theory and techniques to relieve the requirement of the number of training samples and the sampling-rate of feedback ADCs in DPD systems. A new theory about the information carried by training samples is introduced. It connects the generalized error of the DPD estimation algorithm with the statistical properties of modulated signals. Secondly, based on the proposed theory, this work introduces a compressed sample selection method to reduce the number of training samples by only selecting the minimal samples which satisfy the foreknown probability information. The number of training samples and complex multiplication operations required for coefficients estimation can be reduced by more than ten times without additional calculation resource. Thirdly, based on the proposed theory, this thesis proves that theoretically a DPD system using memory polynomial based behavioural modes and least-square (LS) based algorithms can be performed with any sampling-rate of feedback samples. The principle, implementation and practical concerns of the undersampling DPD which uses lower sampling-rate ADC are then introduced. Finally, the observation bandwidth of DPD systems can be extended by the proposed multi-rate track-and-hold circuits with the associated algorithm. By addressing several parameters of ADC and corresponding DPD algorithm, multi-GHz observation bandwidth using only a 61.44MHz ADC is achieved, and demonstrated the satisfactory linearization performance of multi-band and continued wideband RF transmitter applications via extensive experimental tests

CaSCADE: Compressed Carrier and DOA Estimation

Spectrum sensing and direction of arrival (DOA) estimation have been thoroughly investigated, both separately and as a joint task. Estimating the support of a set of signals and their DOAs is crucial to many signal processing applications, such as Cognitive Radio (CR). A challenging scenario, faced by CRs, is that of multiband signals, composed of several narrowband transmissions spread over a wide spectrum each with unknown carrier frequencies and DOAs. The Nyquist rate of such signals is high and constitutes a bottleneck both in the analog and digital domains. To alleviate the sampling rate issue, several sub-Nyquist sampling methods, such as multicoset sampling or the modulated wideband converter (MWC), have been proposed in the context of spectrum sensing. In this work, we first suggest an alternative sub-Nyquist sampling and signal reconstruction method to the MWC, based on a uniform linear array (ULA). We then extend our approach to joint spectrum sensing and DOA estimation and propose the CompreSsed CArrier and DOA Estimation (CaSCADE) system, composed of an L-shaped array with two ULAs. In both cases, we derive perfect recovery conditions of the signal parameters (carrier frequencies and DOAs if relevant) and the signal itself and provide two reconstruction algorithms, one based on the ESPRIT method and the second on compressed sensing techniques. Both our joint carriers and DOAs recovery algorithms overcome the well-known pairing issue between the two parameters. Simulations demonstrate that our alternative spectrum sensing system outperforms the MWC in terms of recovery error and design complexity and show joint carrier frequencies and DOAs from our CaSCADE system's sub-Nyquist samples

Spatial-Spectral Sensing using the Shrink & Match Algorithm in Asynchronous MIMO OFDM Signals

Spectrum sensing (SS) in cognitive radio (CR) systems is of paramount importance to approach the capacity limits for the Secondary Users (SU), while ensuring the undisturbed transmission of Primary Users (PU). In this paper, we formulate a cognitive radio (CR)systems spectrum sensing (SS) problem in which Secondary Users (SU), with multiple receive antennae, sense a channel shared among multiple asynchronous Primary Users (PU) transmitting Multiple Input Multiple Output (MIMO) Orthogonal Frequency Division Multiplexing (OFDM) signals. The method we propose to estimate the opportunities available to the SUs combines advances in array processing and compressed channel sensing, and leverages on both the so called "shrinkage method" as well as on an over-complete basis expansion of the PUs interference covariance matrix to detect the occupied and idle angles of arrivals and subcarriers. The covariance "shrinkage" step and the sparse modeling step that follows, allow to resolve ambiguities that arise when the observations are scarce, reducing the sensing cost for the SU, thereby increasing its spectrum exploitation capabilities compared to competing sensing methods. Simulations corroborate that exploiting the sparse representation of the covariance matrix in CR sensing resolves the spatial and frequency spectrum of the sources.Comment: Submitted to Globecom 2013; 11 pages, 4 figure

A NLLS Based Sub-Nyquist Rate Spectrum Sensing for Wideband Cognitive Radio

For systems and devices, such as cognitive radio and networks, that need to be aware of available frequency bands, spectrum sensing has an important role. A major challenge in this area is the requirement of a high sampling rate in the sensing of a wideband signal. In this paper a wideband spectrum sensing method is presented that utilizes a sub-Nyquist sampling scheme to bring substantial savings in terms of the sampling rate. The correlation matrix of a finite number of noisy samples is computed and used by a non-linear least square (NLLS) estimator to detect the occupied and vacant channels of the spectrum. We provide an expression for the detection threshold as a function of sampling parameters and noise power. Also, a sequential forward selection algorithm is presented to find the occupied channels with low complexity. The method can be applied to both correlated and uncorrelated wideband multichannel signals. A comparison with conventional energy detection using Nyquist-rate sampling shows that the proposed scheme can yield similar performance for SNR above 4 dB with a factor of 3 smaller sampling rate.Comment: IEEE Dyspan 2011. arXiv admin note: substantial text overlap with arXiv:1010.215

Sub-Nyquist Radar: Principles and Prototypes

In the past few years, new approaches to radar signal processing have been introduced which allow the radar to perform signal detection and parameter estimation from much fewer measurements than that required by Nyquist sampling. These systems - referred to as sub-Nyquist radars - model the received signal as having finite rate of innovation and employ the Xampling framework to obtain low-rate samples of the signal. Sub-Nyquist radars exploit the fact that the target scene is sparse facilitating the use of compressed sensing (CS) methods in signal recovery. In this chapter, we review several pulse-Doppler radar systems based on these principles. Contrary to other CS-based designs, our formulations directly address the reduced-rate analog sampling in space and time, avoid a prohibitive dictionary size, and are robust to noise and clutter. We begin by introducing temporal sub-Nyquist processing for estimating the target locations using less bandwidth than conventional systems. This paves the way to cognitive radars which share their transmit spectrum with other communication services, thereby providing a robust solution for coexistence in spectrally crowded environments. Next, without impairing Doppler resolution, we reduce the dwell time by transmitting interleaved radar pulses in a scarce manner within a coherent processing interval or "slow time". Then, we consider multiple-input-multiple-output array radars and demonstrate spatial sub-Nyquist processing which allows the use of few antenna elements without degradation in angular resolution. Finally, we demonstrate application of sub-Nyquist and cognitive radars to imaging systems such as synthetic aperture radar. For each setting, we present a state-of-the-art hardware prototype designed to demonstrate the real-time feasibility of sub-Nyquist radars.Comment: 51 pages, 26 figures, 2 tables, Book chapter. arXiv admin note: text overlap with arXiv:1611.0644

Non-uniform sampling and reconstruction of multi-band signals and its application in wideband spectrum sensing of cognitive radio

Sampling theories lie at the heart of signal processing devices and communication systems. To accommodate high operating rates while retaining low computational cost, efficient analog-to digital (ADC) converters must be developed. Many of limitations encountered in current converters are due to a traditional assumption that the sampling state needs to acquire the data at the Nyquist rate, corresponding to twice the signal bandwidth. In this thesis a method of sampling far below the Nyquist rate for sparse spectrum multiband signals is investigated. The method is called periodic non-uniform sampling, and it is useful in a variety of applications such as data converters, sensor array imaging and image compression. Firstly, a model for the sampling system in the frequency domain is prepared. It relates the Fourier transform of observed compressed samples with the unknown spectrum of the signal. Next, the reconstruction process based on the topic of compressed sensing is provided. We show that the sampling parameters play an important role on the average sample ratio and the quality of the reconstructed signal. The concept of condition number and its effect on the reconstructed signal in the presence of noise is introduced, and a feasible approach for choosing a sample pattern with a low condition number is given. We distinguish between the cases of known spectrum and unknown spectrum signals respectively. One of the model parameters is determined by the signal band locations that in case of unknown spectrum signals should be estimated from sampled data. Therefore, we applied both subspace methods and non-linear least square methods for estimation of this parameter. We also used the information theoretic criteria (Akaike and MDL) and the exponential fitting test techniques for model order selection in this case

Spectrum Sensing Techniques For Cognitive Radio Networks

In this chapter, we present the state of the art of the spectrum sensing techniques for cognitive radio networks as well and their comparisons. The rest of the chapter is organized as below: Section I.1, Section I.2, and Section I.3 present the spectrum management problem and the cognitive radio cycle as well as the compressive sensing solution; Section II.1 describes the spectrum sensing model; Section II.2 presents the existing spectrum sensing techniques, including energy, autocorrelation, Euclidian distance, wavelet, and matched filter based sensing. Finally, a conclusion is given at the end of the chapter