Compressive Sensing of Multiband Spectrum towards Real-World Wideband Applications.

PhD Theses.Spectrum scarcity is a major challenge in wireless communication systems with their rapid evolutions towards more capacity and bandwidth. The fact that the real-world spectrum, as a nite resource, is sparsely utilized in certain bands spurs the proposal of spectrum sharing. In wideband scenarios, accurate real-time spectrum sensing, as an enabler of spectrum sharing, can become ine cient as it naturally requires the sampling rate of the analog-to-digital conversion to exceed the Nyquist rate, which is resourcecostly and energy-consuming. Compressive sensing techniques have been applied in wideband spectrum sensing to achieve sub-Nyquist-rate sampling of frequency sparse signals to alleviate such burdens. A major challenge of compressive spectrum sensing (CSS) is the complexity of the sparse recovery algorithm. Greedy algorithms achieve sparse recovery with low complexity but the required prior knowledge of the signal sparsity. A practical spectrum sparsity estimation scheme is proposed. Furthermore, the dimension of the sparse recovery problem is proposed to be reduced, which further reduces the complexity and achieves signal denoising that promotes recovery delity. The robust detection of incumbent radio is also a fundamental problem of CSS. To address the energy detection problem in CSS, the spectrum statistics of the recovered signals are investigated and a practical threshold adaption scheme for energy detection is proposed. Moreover, it is of particular interest to seek the challenges and opportunities to implement real-world CSS for systems with large bandwidth. Initial research on the practical issues towards the real-world realization of wideband CSS system based on the multicoset sampler architecture is presented. In all, this thesis provides insights into two critical challenges - low-complexity sparse recovery and robust energy detection - in the general CSS context, while also looks into some particular issues towards the real-world CSS implementation based on the i multicoset sampler

Compressive Spectrum Sensing in Cognitive IoT

PhDWith the rising of new paradigms in wireless communications such as Internet of things (IoT), current static frequency allocation policy faces a primary challenge of spectrum scarcity, and thus encourages the IoT devices to have cognitive capabilities to access the underutilised spectrum in the temporal and spatial dimensions. Wideband spectrum sensing is one of the key functions to enable dynamic spectrum access, but entails a major implementation challenge in terms of sampling rate and computation cost since the sampling rate of analog-to-digital converters (ADCs) should be higher than twice of the spectrum bandwidth based on the Nyquist-Shannon sampling theorem. By exploiting the sparse nature of wideband spectrum, sub-Nyquist sampling and sparse signal recovery have shown potential capabilities in handling these problems, which are directly related to compressive sensing (CS) from the viewpoint of its origin. To invoke sub-Nyquist wideband spectrum sensing in IoT, blind signal acquisition with low-complexity sparse recovery is desirable on compact IoT devices. Moreover, with cooperation among distributed IoT devices, the complexity of sampling and reconstruc- tion can be further reduced with performance guarantee. Specifically, an adaptively- regularized iterative reweighted least squares (AR-IRLS) reconstruction algorithm is proposed to speed up the convergence of reconstruction with less number of iterations. Furthermore, a low-complexity compressive spectrum sensing algorithm is proposed to reduce computation complexity in each iteration of IRLS-based reconstruction algorithm, from cubic time to linear time. Besides, to transfer computation burden from the IoT devices to the core network, a joint iterative reweighted sparse recovery scheme with geo-location database is proposed to adopt the occupied channel information from geo- location database to reduce the complexity in the signal reconstruction. Since numerous IoT devices access or release the spectrum randomly, the sparsity levels of wideband spec-trum signals are varying and unknown. A blind CS-based sensing algorithm is proposed to enable the local secondary users (SUs) to adaptively adjust the sensing time or sam- pling rate without knowledge of spectral sparsity. Apart from the signal reconstruction at the back-end, a distributed sub-Nyquist sensing scheme is proposed by utilizing the surrounding IoT devices to jointly sample the spectrum based on the multi-coset sam- pling theory, in which only the minimum number of low-rate ADCs on the IoT devices are required to form coset samplers without the prior knowledge of the number of occu- pied channels and signal-to-noise ratios. The models of the proposed algorithms are derived and verified by numerical analyses and tested on both real-world and simulated TV white space signals

Asynchronous device detection for cognitive device-to-device communications

Dynamic spectrum sharing will facilitate the interference coordination in device-to-device (D2D) communications. In the absence of network level coordination, the timing synchronization among D2D users will be unavailable, leading to inaccurate channel state estimation and device detection, especially in time-varying fading environments. In this study, we design an asynchronous device detection/discovery framework for cognitive-D2D applications, which acquires timing drifts and dynamical fading channels when directly detecting the existence of a proximity D2D device (e.g. or primary user). To model and analyze this, a new dynamical system model is established, where the unknown timing deviation follows a random process, while the fading channel is governed by a discrete state Markov chain. To cope with the mixed estimation and detection (MED) problem, a novel sequential estimation scheme is proposed, using the conceptions of statistic Bayesian inference and random finite set. By tracking the unknown states (i.e. varying time deviations and fading gains) and suppressing the link uncertainty, the proposed scheme can effectively enhance the detection performance. The general framework, as a complimentary to a network-aided case with the coordinated signaling, provides the foundation for development of flexible D2D communications along with proximity-based spectrum sharing

Novel evaluation framework for sensing spread spectrum in cognitive radio

The cognitive radio network is designed to cater to the optimization demands of restricted spectrum availability. A review of existing literature on spectrum sensing shows that there is still a broader scope for its improvement. Therefore, this paper introduces an efficient computational framework capable of evaluating the effectiveness of the spread spectrum concept in the context of cognitive radio network in a more scalable and granular way. The proposed method introduces a dual hypothesis using a different set of dependable parameters to emphasize the detection of optimal energy for a low signal quality state over the noise. The proposed evaluation framework is benchmarked using a statistical analysis method not present in any existing approaches toward spread spectrum sensing. The simulated outcome of the study exhibits that the proposed system offers a significantly better probability of detection than the current system using a simplified evaluation scheme with multiple test parameters

Array Architectures and Physical Layer Design for Millimeter-Wave Communications Beyond 5G

Ever increasing demands in mobile data rates have resulted in exploration of millimeter-wave (mmW) frequencies for the next generation (5G) wireless networks. Communications at mmW frequencies is presented with two keys challenges. Firstly, high propagation loss requires base stations (BSs) and user equipment (UEs) to use a large number of antennas and narrow beams to close the link with sufficient received signal power. Consequently, communications using narrow beams create a new challenge in channel estimation and link establishment based on fine angular probing. Current mmW system use analog phased arrays that can probe only one angle at the time which results in high latency during link establishment and channel tracking. It is desirable to design low latency beam training by exploring both physical layer designs and array architectures that could replace current 5G approaches and pave the way to the communications for frequency bands in higher mmW band and sub-THz region where larger antenna arrays and communications bandwidth can be exploited. To this end, we propose a novel signal processing techniques exploiting unique properties of mmW channel, and show both theoretically, in simulation and experiments its advantages over conventional approaches. Secondly, we explore different array architecture design and analyze their trade-offs between spectral efficiency and power consumption and area. For comprehensive comparison, we have developed a methodology for optimal design of system parameters for different array architecture candidates based on the spectral efficiency target, and use these parameters to estimate the array area and power consumption based on the circuits reported in the literature. We show that the hybrid analog and digital architectures have severe scalability concerns in radio frequency signal distribution with increased array size and spatial multiplexing levels, while the fully-digital array architectures have the best performance and power/area trade-offs.The developed approaches are based on a cross-disciplinary research that combines innovation in model based signal processing, machine learning, and radio hardware. This work is the first to apply compressive sensing (CS), a signal processing tool that exploits sparsity of mmW channel model, to accelerate beam training of mmW cellular system. The algorithm is designed to address practical issues including the requirement of cell discovery and synchronization that involves estimation of angular channel together with carrier frequency offset and timing offsets. We have analyzed the algorithm performance in the 5G compliant simulation and showed that an order of magnitude saving is achieved in initial access latency for the desired channel estimation accuracy. Moreover, we are the first to develop and implement a neural network assisted compressive beam alignment to deal with hardware impairments in mmW radios. We have used 60GHz mmW testbed to perform experiments and show that neural networks approach enhances alignment rate compared to CS. To further accelerate beam training, we proposed a novel frequency selective probing beams using the true-time-delay (TTD) analog array architecture. Our approach utilizes different subcarriers to scan different directions, and achieves a single-shot beam alignment, the fastest approach reported to date. Our comprehensive analysis of different array architectures and exploration of emerging architectures enabled us to develop an order of magnitude faster and energy efficient approaches for initial access and channel estimation in mmW systems

On Random Sampling for Compliance Monitoring in Opportunistic Spectrum Access Networks

In the expanding spectrum marketplace, there has been a long term evolution towards more market€“oriented mechanisms, such as Opportunistic Spectrum Access (OSA), enabled through Cognitive Radio (CR) technology. However, the potential of CR technologies to revolutionize wireless communications, also introduces challenges based upon the potentially non€“deterministic CR behaviour in the Electrospace. While establishing and enforcing compliance to spectrum etiquette rules are essential to realization of successful OSA networks in the future, there has only been recent increased research activity into enforcement. This dissertation presents novel work on the spectrum monitoring aspect, which is crucial to effective enforcement of OSA. An overview of the challenges faced by current compliance monitoring methods is first presented. A framework is then proposed for the use of random spectral sampling techniques to reduce data collection complexity in wideband sensing scenarios. This approach is recommended as an alternative to Compressed Sensing (CS) techniques for wideband spectral occupancy estimation, which may be difficult to utilize in many practical congested scenarios where compliance monitoring is required. Next, a low€“cost computational approach to online randomized temporal sensing deployment is presented for characterization of temporal spectrum occupancy in cognitive radio scenarios. The random sensing approach is demonstrated and its performance is compared to CS€“based approach for occupancy estimation. A novel frame€“based sampling inversion technique is then presented for cases when it is necessary to track the temporal behaviour of individual CRs or CR networks. Parameters from randomly sampled Physical Layer Convergence Protocol (PLCP) data frames are used to reconstruct occupancy statistics, taking account of missed frames due to sampling design, sensor limitations and frame errors. Finally, investigations into the use of distributed and mobile spectrum sensing to collect spatial diversity to improve the above techniques are presented, for several common monitoring tasks in spectrum enforcement. Specifically, focus is upon techniques for achieving consensus in dynamic topologies such as in mobile sensing scenarios

Wideband cyclostationary spectrum sensing and characterization for cognitive radios

Motivated by the spectrum scarcity problem, Cognitive Radios (CRs) have been proposed as a solution to opportunistically communicate over unused spectrum licensed to Primary users (PUs). In this context, the unlicensed Secondary users (SUs) sense the spectrum to detect the presence or absence of PUs, and use the unoccupied bands without causing interference to PUs. CRs are equipped with capabilities such as, learning, adaptability, and recongurability, and are spectrum aware. Spectrum awareness comes from spectrum sensing, and it can be performed using different techniques