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Adaptive Compressed Wideband Spectrum Sensing Based on Radio Environment Map Dedicated for Space Information Networks
Abstract
Spectrum sensing is the basis of dynamic spectrum access and sharing for space information networks consisting of various satellite and terrestrial networks. The traditional spectrum sensing method, guided by the Nyquist-Shannon sampling theorem, might not be suitable for the emerging communication systems such as the fifth-generation mobile communications (5G) and space information networks utilizing spectrum from sub-6 GHz up to 100 GHz to offer ubiquitous broadband applications. In contrast, compressed spectrum sensing can not only relax the requirements on hardware and software, but also reduce the energy consumption and processing latency. As for the compressed measurement (low-speed sampling) process of the existing compressed spectrum sensing algorithms, the compression ratio is usually set to a fixed value, which limits their adaptability to the dynamically changing radio environment with different sparseness. In this paper, an adaptive compressed spectrum sensing algorithm based on radio environment map (REM) dedicated for space information networks is proposed to address this problem. Simulations show that the proposed algorithm has better adaptability to the varying environment than the existing compressed spectrum sensing algorithms.
... Indeed,in [21], REMs were used to locate relevant PUs in a geographic region of interest, characterizing their positions, directivities, powers, and modulation types. Likewise, in [22], REMs were used to sense the spectrum based on an adaptive compressed spectrum-sensing algorithm, contributing spatial information to the network capable of adapting to the radio environment. REMs are very flexible tools, as shown in [23], where they are combined with ML to determine the effective coverage area perceived by a cognitive sensor network, correctly estimating it at around 92%. ...
Cogitive radio networks (CRNs) require high capacity and accuracy to detect the presence of licensed or primary users (PUs) in the sensed spectrum. In addition, they must correctly locate the spectral opportunities (holes) in order to be available to nonlicensed or secondary users (SUs). In this research, a centralized network of cognitive radios for monitoring a multiband spectrum in real time is proposed and implemented in a real wireless communication environment through generic communication devices such as software-defined radios (SDRs). Locally, each SU uses a monitoring technique based on sample entropy to determine spectrum occupancy. The determined features (power, bandwidth, and central frequency) of detected PUs are uploaded to a database. The uploaded data are then processed by a central entity. The objective of this work was to determine the number of PUs, their carrier frequency, bandwidth, and the spectral gaps in the sensed spectrum in a specific area through the construction of radioelectric environment maps (REMs). To this end, we compared the results of classical digital signal processing methods and neural networks performed by the central entity. Results show that both proposed cognitive networks (one working with a central entity using typical signal processing and one performing with neural networks) accurately locate PUs and give information to SUs to transmit, avoiding the hidden terminal problem. However, the best-performing cognitive radio network was the one working with neural networks to accurately detect PUs on both carrier frequency and bandwidth.
Spectrum sensing aims to sense the potential spectrum resources available in the cognitive radio environment. It is also the premise of spectrum management and spectrum sharing in cognitive radio systems. To perceive the primary user’s activity and make full use of spectrum holes, rapid detection of a broad frequency span is an essential part of cognitive radio technology. Reducing the observation time for the data collection, the data storage requirements, and hardware/software computational complexity are urgent and challenging issues in wideband spectrum sensing. High accuracy power spectral density estimation is not the primary requirement; of course, the accuracy must be controlled within the appropriate range and can support the primary user activity’s determination. This paper proposes a sub-Nyquist wideband spectrum sensing method based on compressive covariance sensing for the rapid wideband spectrum sensing. Compared with the traditional Nyquist-rate method, this method can use low-speed ADC to detect wideband signals and effectively control the observation time and computational complexity. This paper’s main contributions include: (1) developing a sub-Nyquist sampling structure based on the multi-coset sampling banks, (2) proposing a coarse-grained power spectral density estimation method for wideband spectrum sensing with short observation time and low complexity. Simulations show that the proposed method exhibits this method is suitable for fast spectral detection. At the same time, the error of spectrum analysis is basically within the acceptable range.
Latest regulations on TV white space communications and emerging trends of spectrum access through geolocation databases relax the regulatory constraints on Cognitive Radios (CRs). Geolocation databases are designed to store information related to incumbents, and CRs are envisioned to consult this database before spectrum access. Spectrum occupancy and related environment information can be constructed using these geolocation databases. In that regard, Radio Environment Map (REM) is a promising tool that provides a practical means for the realization of cognitive radio networks (CRNs). It constructs a comprehensive map of the CRN by utilizing multi-domain information from geolocation databases, characteristics of spectrum use, geographical terrain models, propagation environment, and regulations. REMs contribute to cognition engines by building long-term knowledge via processing spectrum measurements collected from sensors to estimate the state of locations where there is no measurement data. In addition, REM utilizes feedback from the CRs and can apply various learning tools. The vision is to design CRNs such that CRs, though being simple devices without advanced cognitive functionalities, can become cognitive via REMs and operate in an efficient manner. In this paper, an overview of the REM concept is presented in various dimensions ranging from its architecture and stored information to REM construction techniques as well as REM quality metrics.
Recently, there is an increasing yet challenging demand on broadband mobile communications for high-speed trains. In this article, cognitive Doppler spread compensation algorithms are proposed for high-speed rail broadband mobile communications, which make use of the dedicated radio environment map (REM) for railway to compensate the time-varying Doppler spread. The dedicated REM for high-speed rail can be viewed as a spatial-temporal database consisting of the radio channel parameters along a given railway. The performance of the proposed Doppler spread compensation algorithms are evaluated with a typical OFDM-based broadband mobile system. Simulation results show that the link-level performance of high-speed rail broadband mobile communications can be improved significantly due to the REM-enabled radio channel condition awareness and the cognitive Doppler spread compensation algorithms. The REM-based cognitive radio approach presents a new paradigm for systems design of high-speed rail broadband mobile communications.
The growing demand for Unmanned Aircraft System (UAS) Intelligence, Surveillance, and Reconnaissance (ISR) missions has created a strain on current RF bandwidth utilization and video/data transmission capabilities. Solving this problem is critical to deploying UASs in large numbers. In this paper, we introduce a theoretical ISR mission using multiple UASs communications with multiple ground terminals and dismounted soldiers. Then we describe the use of Dynamic Spectrum Access (DSA) concepts to dynamically select radio operating frequency for the downlink (UAV-to-ground) and for the uplink (ground-toUAV). DSA radios monitor and characterize the Radio Frequency (RF) spectrum that is in use in a local area; identify available spectrum for reliable communications; and dynamically allocate spectrum usage to networking and legacy waveforms based upon a preprogrammed rule-set. We provide a detailed parametric analysis of the number of UAS links that can be supported with DSA versus the UAS mission area and the target Interference-to-Noise power Ratio (INR) in various scenarios. We determine the benefits of DSA by contrasting DSA-enabled mission versus the traditional frequency planning approach. We describe the DSA software architecture, how it is integrated into existing radios, and its application to typical ISR waveforms such as the Common Data Link (CDL).
Given a dictionary D = {d(k)} of vectors d(k), we seek to represent a signal S as a linear combination S = summation operator(k) gamma(k)d(k), with scalar coefficients gamma(k). In particular, we aim for the sparsest representation possible. In general, this requires a combinatorial optimization process. Previous work considered the special case where D is an overcomplete system consisting of exactly two orthobases and has shown that, under a condition of mutual incoherence of the two bases, and assuming that S has a sufficiently sparse representation, this representation is unique and can be found by solving a convex optimization problem: specifically, minimizing the l(1) norm of the coefficients gamma. In this article, we obtain parallel results in a more general setting, where the dictionary D can arise from two or several bases, frames, or even less structured systems. We sketch three applications: separating linear features from planar ones in 3D data, noncooperative multiuser encoding, and identification of over-complete independent component models.
Suppose x is an unknown vector in Ropfm (a digital image or signal); we plan to measure n general linear functionals of x and then reconstruct. If x is known to be compressible by transform coding with a known transform, and we reconstruct via the nonlinear procedure defined here, the number of measurements n can be dramatically smaller than the size m. Thus, certain natural classes of images with m pixels need only n=O(m1/4log5/2(m)) nonadaptive nonpixel samples for faithful recovery, as opposed to the usual m pixel samples. More specifically, suppose x has a sparse representation in some orthonormal basis (e.g., wavelet, Fourier) or tight frame (e.g., curvelet, Gabor)-so the coefficients belong to an lscrp ball for 0<ples1. The N most important coefficients in that expansion allow reconstruction with lscr2 error O(N1/2-1p/). It is possible to design n=O(Nlog(m)) nonadaptive measurements allowing reconstruction with accuracy comparable to that attainable with direct knowledge of the N most important coefficients. Moreover, a good approximation to those N important coefficients is extracted from the n measurements by solving a linear program-Basis Pursuit in signal processing. The nonadaptive measurements have the character of "random" linear combinations of basis/frame elements. Our results use the notions of optimal recovery, of n-widths, and information-based complexity. We estimate the Gel'fand n-widths of lscrp balls in high-dimensional Euclidean space in the case 0<ples1, and give a criterion identifying near- optimal subspaces for Gel'fand n-widths. We show that "most" subspaces are near-optimal, and show that convex optimization (Basis Pursuit) is a near-optimal way to extract information derived from these near-optimal subspaces
Coexistence of satellite and terrestrial wireless communication systems in the same frequency band is quite promising for addressing the challenge of spectrum scarcity. To cope with the inevitable inter-system interference, radio resource allocation at both sides should be carefully re-optimized. In this paper, we focus on a scenario where a satellite communication system and a terrestrial distributed antenna system (DAS) coexist via spectrum sharing. We particularly utilize the radio map (RM) to reduce the system overhead for channel acquisition. Based on the large-scale channel state information at the transmitter (CSIT), which is derived from the RM, we propose an optimized power allocation scheme to improve the achievable sum rate of the terrestrial system. For the satellite side, an opportunistic user scheduling scheme is presented, to reduce the harmful leakage interference to the terrestrial mobile users. Simulation results demonstrate that the proposed RM-based coordination scheme can significantly promote the performance of satellite terrestrial coexistence, although the small-scale channel fading has been ignored in the formulated optimization.
Focusing on its main requirements and challenges and by analyzing the characteristics of different space platforms, an overall architecture for space information networks is proposed based on national strategic planning and the present development status of associated technologies. Furthermore, the core scientific problems that need to be solved are expounded. In addition, the primary considerations and a preliminary integrated demonstration environment for verification of key technologies are presented.
Cognitive radio technology is a smarter, faster, and more efficient way to transmit information to and from fixed, mobile, other wireless communication devices. Cognitive radio builds upon software-defined radio technology. A cognitive radio system is 'aware' of its operating environment and automatically adjusts itself to maintain desired communicationsits like having a trained operator inside the radio making constant adjustments for maximum performance. Operating frequency, power output, antenna orientation/beamwidth, modulation, and transmitter bandwidth are just a few of the operating parameters that can automatically be adjusted on the fly in a cognitive radio system.Fette has constructed a cutting-edge volume that hits all of the important issues including research, management, and support. Cognitive techniques will be discussed such as position and network awareness, infrastructure and physical and link layer concerns. Though still a nascent technology, cognitive radio is being pushed by the US military and for mission-critical civilian communications (such as emergency and public safety services).
This chapter discusses the motivation and the important role of network support in cognitive radios. It introduces the radio environment map (REM) and explains how the REM can provide network support to cognitive radios for various applications. The chapter addresses the important role of network support in developing cognitive radios for various application scenarios, including infrastructure-based as well as infrastructure-less ad hoc networks. As a vehicle to providing network support to cognitive radios, the REM is proposed to be an integrated database that consists of multi-domain information, such as geographical features, available services, spectral regulations, locations and activities of radios, policies of the user and/or service provider, and past experience. A cognitive engine to enhance or achieve most cognitive functionalities, such as reasoning, learning, planning, and decision support, can exploit REM. Leveraging both internal and external network support through global and local REMs presents a sensible approach to implement cognitive radios in a reliable, flexible, and cost-effective way. The REM presents a smooth evolutionary path from the legacy radio to the cognitive radio. The REM can be viewed as a natural, but major evolution of radio resource management used in today's commercial wireless networks.
This chapter discusses the strategy of exploiting network support in cognitive radio (CR) systems architectures introducing the radio environment map (REM) as an innovative vehicle of providing network support to CRs. As a systematic top-down approach to providing network support to CRs, the radio environment map is proposed as an integrated database consisting of multi domain information such as geographical features, available services, spectral regulations, locations and activities of radios, policies of the user and/or service provider, and past experience. An radio environment map (REM) can be exploited by a CE to enhance or achieve most of cognitive functionalities such as SA, reasoning, learning, planning, and decision support. Leveraging both internal and external network support through global and local REMs presents a sensible approach to implementing CRs in a reliable, flexible, and cost effective way. Network support can dramatically relax the requirements on a CR device as well as improve the performance of the whole CR network. Considering the dynamic nature of spectral regulation and operation policy, the REM-based CR is flexible and future proof in the sense that it allows regulators or service providers to modify or change their rules or policies simply by updating REMs accordingly.
In the emerging paradigm of open spectrum access, cognitive radios dynamically sense the radio-spectrum environment and must rapidly tune their transmitter parameters to efficiently utilize the available spectrum. The unprecedented radio agility envisioned, calls for fast and accurate spectrum sensing over a wide bandwidth, which challenges traditional spectral estimation methods typically operating at or above Nyquist rates. Capitalizing on the sparseness of the signal spectrum in open-access networks, this paper develops compressed sensing techniques tailored for the coarse sensing task of spectrum hole identification. Sub-Nyquist rate samples are utilized to detect and classify frequency bands via a wavelet-based edge detector. Because spectrum location estimation takes priority over fine-scale signal reconstruction, the proposed novel sensing algorithms are robust to noise and can afford reduced sampling rates.
This paper presents a novel iterative greedy reconstruction algorithm for practical compressed sensing (CS), called the sparsity adaptive matching pursuit (SAMP). Compared with other state-of-the-art greedy algorithms, the most innovative feature of the SAMP is its capability of signal reconstruction without prior information of the sparsity. This makes it a promising candidate for many practical applications when the number of non-zero (significant) coefficients of a signal is not available. The proposed algorithm adopts a similar flavor of the EM algorithm, which alternatively estimates the sparsity and the true support set of the target signals. In fact, SAMP provides a generalized greedy reconstruction framework in which the orthogonal matching pursuit and the subspace pursuit can be viewed as its special cases. Such a connection also gives us an intuitive justification of trade-offs between computational complexity and reconstruction performance. While the SAMP offers a comparably theoretical guarantees as the best optimization-based approach, simulation results show that it outperforms many existing iterative algorithms, especially for compressible signals.
We consider the orthogonal matching pursuit (OMP) algorithm for the recovery of a high-dimensional sparse signal based on a small number of noisy linear measurements. OMP is an iterative greedy algorithm that selects at each step the column, which is most correlated with the current residuals. In this paper, we present a fully data driven OMP algorithm with explicit stopping rules. It is shown that under conditions on the mutual incoherence and the minimum magnitude of the nonzero components of the signal, the support of the signal can be recovered exactly by the OMP algorithm with high probability. In addition, we also consider the problem of identifying significant components in the case where some of the nonzero components are possibly small. It is shown that in this case the OMP algorithm will still select all the significant components before possibly selecting incorrect ones. Moreover, with modified stopping rules, the OMP algorithm can ensure that no zero components are selected.
We present a compressive wide-band spectrum sensing scheme for cognitive radios. The received analog signal at the cognitive ra- dio sensing receiver is transformed in to a digital signal using an analog-to-information converter. The autocorrelation of this com- pressed signal is then used to reconstruct an estimate of the signal spectrum. We evaluate the performance of this scheme in terms of the mean squared error of the power spectrum density estimate and the probability of detecting signal occupancy.
It is now well-known that one can reconstruct sparse or compressible signals accurately from a very limited number of measurements, possibly contaminated with noise. This technique known as “compressed sensing” or “compressive sampling” relies on properties of the sensing matrix such as the restricted isometry property. In this Note, we establish new results about the accuracy of the reconstruction from undersampled measurements which improve on earlier estimates, and have the advantage of being more elegant. To cite this article: E.J. Candès, C. R. Acad. Sci. Paris, Ser. I 346 (2008).
The IEEE 802.22 wireless regional area network (WRAN) is the first worldwide commercial application of cognitive radio (CR) networks refarming the TV broadcast bands. According to US FCC's recent public notice, WRAN products are scheduled to be available for the market by February, 2009. This paper first presents a brief review of the IEEE 802.22 WRAN standardization, and then introduces the radio environment map (REM) as an innovative cost-efficient approach to developing and managing WRAN systems. REMs can provide powerful infrastructure support to the functionality of the WRAN cognitive engine (CE). The data model of the REM is presented together with an extensive discussion on how to exploit the REM for a variety of applications in WRAN systems, focusing on the REM-enabled case- and knowledge-based learning algorithms (REM-CKL) for WRAN CEs. Furthermore, REM-based radio scenario-driven testing (REM-SDT) is also presented as a viable approach to evaluating the performance of CEs. Future research topics are discussed in the final section.
Compressed sensing (CS) offers a joint compression and sensing processes, based on the existence of a sparse representation of the treated signal and a set of projected measurements. Work on CS thus far typically assumes that the projections are drawn at random. In this paper, we consider the optimization of these projections. Since such a direct optimization is prohibitive, we target an average measure of the mutual coherence of the effective dictionary, and demonstrate that this leads to better CS reconstruction performance. Both the basis pursuit (BP) and the orthogonal matching pursuit (OMP) are shown to benefit from the newly designed projections, with a reduction of the error rate by a factor of 10 and beyond.
Cognitive radio is viewed as a novel approach for improving the utilization of a precious natural resource: the radio electromagnetic spectrum. The cognitive radio, built on a software-defined radio, is defined as an intelligent wireless communication system that is aware of its environment and uses the methodology of understanding-by-building to learn from the environment and adapt to statistical variations in the input stimuli, with two primary objectives in mind: · highly reliable communication whenever and wherever needed; · efficient utilization of the radio spectrum. Following the discussion of interference temperature as a new metric for the quantification and management of interference, the paper addresses three fundamental cognitive tasks. 1) Radio-scene analysis. 2) Channel-state estimation and predictive modeling. 3) Transmit-power control and dynamic spectrum management. This work also discusses the emergent behavior of cognitive radio.
The Time-Frequency and Time-Scale communities have recently developed a large number of overcomplete waveform dictionaries --- stationary wavelets, wavelet packets, cosine packets, chirplets, and warplets, to name a few. Decomposition into overcomplete systems is not unique, and several methods for decomposition have been proposed, including the Method of Frames (MOF), Matching Pursuit (MP), and, for special dictionaries, the Best Orthogonal Basis (BOB). Basis Pursuit (BP) is a principle for decomposing a signal into an "optimal" superposition of dictionary elements, where optimal means having the smallest l 1 norm of coefficients among all such decompositions. We give examples exhibiting several advantages over MOF, MP! and BOB, including better sparsity, and super-resolution. BP has interesting relations to ideas in areas as diverse as ill-posed problems, in abstract harmonic analysis, total variation de-noising, and multi-scale edge de-noising. Basis Pursuit in highly ...
Suppose we wish to recover a vector x_0 Є R^m (e.g., a digital signal or image) from incomplete and contaminated observations y = Ax_0 + e; A is an n by m matrix with far fewer rows than columns (n « m) and e is an error term. Is it possible to recover x_0 accurately based on the data y?
To recover x_0, we consider the solution x^# to the ℓ_(1-)regularization problem min ‖x‖ℓ_1 subject to ‖Ax - y‖ℓ(2) ≤ Є, where Є is the size of the error term e. We show that if A obeys a uniform uncertainty principle (with unit-normed columns) and if the vector x_0 is sufficiently sparse, then the solution is within the noise level ‖x^# - x_0‖ℓ_2 ≤ C Є. As a first example, suppose that A is a Gaussian random matrix; then stable recovery occurs for almost all such A's provided that the number of nonzeros of x_0 is of about the same order as the number of observations. As a second instance, suppose one observes few Fourier samples of x_0; then stable recovery occurs for almost any set of n coefficients provided that the number of nonzeros is of the order of n/[log m]^6. In the case where the error term vanishes, the recovery is of course exact, and this work actually provides novel insights into the exact recovery phenomenon discussed in earlier papers. The methodology also explains why one can also very nearly recover approximately sparse signals.
Cognitive radio: making software radios more personal
- Iii Mitola
- J Maguire
- J Mitola III