Analog-to-Digital Cognitive Radio: Sampling, Detection, and Hardware

Cohen, Dan; Tsiper, Shahar; Eldar, Yonina C.

doi:10.1109/msp.2017.2740966

Cited by 73 publications

(41 citation statements)

References 103 publications

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“…Note that differently from Lemma B.1, here we did not need an assumption on the variance to show uniform convergence in k ∈ N , as here we consider convergence in distribution (of the CDFs) while in Lemma B.1 we considered convergence of the PDFs. 5 The proof of Lemma B.1 shows when the variances of a Gaussian random vector with a non-singular diagonal covariance matrix converge uniformly, then it converges in distribution. Here, σ 2…”

Section: Xn Lmentioning

confidence: 99%

“…a growing interest in interference-limited communications due to the transition from orthogonal architectures, which have dominated wireless communication standards to date, to nonorthogonal schemes [1]. Among the important examples of non-orthogonal communications is non-orthogonal multiple access (NOMA), which is becoming a major paradigm for 5G communications [2]; DSL communications, which is limited by crosstalk [3]; and cognitive radio networks, in which the primary user are the dominant source of interference for the secondary user [4], [5]. As digital communication signals are generated by a random procedure which repeats with each transmitted symbol and frame [6,Sec.…”

Section: Introductionmentioning

confidence: 99%

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The Capacity of Memoryless Channels With Sampled Cyclostationary Gaussian Noise

Shlezinger

Abakasanga

Dabora

et al. 2020

IEEE Trans. Commun.

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Non-orthogonal communications play an important role in future digital communication architectures. In such scenarios, the received signal is corrupted by an interfering communications signal, which is much stronger than the thermal noise, and is often modeled as a cyclostationary process in continuoustime. To facilitate digital processing, the receiver typically samples the received signal synchronously with the symbol rate of the information signal. If the period of the statistics of the interference is synchronized with that of the information signal, then the sampled interference is modeled as a discretetime (DT) cyclostationary random process. However, in the common interference scenario, the period of the statistics of the interference is not necessarily synchronized with that of the information signal. In such cases, the DT interference may be modeled as an almost cyclostationary random process. In this work we characterize the capacity of DT memoryless additive noise channels in which the noise arises from a sampled cyclostationary Gaussian process. For the case of synchronous sampling, capacity can be obtained in closed form. When sampling is not synchronized with the symbol rate of the interference, the resulting channel is not information stable, thus classic information-theoretic tools are not applicable.Using information spectrum methods, we prove that capacity can be obtained as the limit of a sequence of capacities of channels with additive cyclostationary Gaussian noise. Our results allow to characterize the effects of changes in the sampling rate and sampling time offset on the capacity of the resulting DT channel. In particular, it is demonstrated that minor variations in the sampling period, such that the resulting noise switches from being synchronously-sampled to being asynchronously-sampled, can substantially change the capacity.

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Section: Xn Lmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Capacity of Memoryless Channels With Sampled Cyclostationary Gaussian Noise

Shlezinger

Abakasanga

Dabora

et al. 2020

IEEE Trans. Commun.

Self Cite

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“…When N = 50, the sparsity level 50 ≤ K ≤ 100, which indicates a maximum 50% occupancy of the entire wideband. This number exceeds the occupancy ratio summarized in [27]. A spectral diagram of the wideband signal with 50 bands is illustrated in Fig.…”

Section: Simulation Resultsmentioning

confidence: 77%

“…Several alternative CS-based methods were proposed, aiming to reduce the complexity without operating the reconstruction process [25], [26]. However, these representative methods are yet hard to be implemented in practice due to their strict requirement on synchronization among multi-channels [27]. In addition to that, in [28], [29], Cao and Basaran proposed to detect the signals based on Bayesian method respectively under the constraints of the prior probability knowledge of the original signal, which is hard to obtain in practical dynamic UAV communication environments.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Wideband Spectrum Sensing Algorithm for Unmanned Aerial Vehicle Communication Networks

Shu

Yan

et al. 2019

IEEE Internet Things J.

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With increasingly smaller size, more powerful sensing capabilities and higher level of autonomy, multiple unmanned aerial vehicles (UAVs) can form UAV networks to collaboratively complete missions more reliably, efficiently and economically. While UAV networks are promising for many applications, there are many outstanding issues to be resolved before large scale UAV networks are practically used. In this paper we study the application of cognitive radio technology for UAV communication networks, to provide high capacity and reliable communication with opportunistic and timely spectrum access. Compressive sensing is applied in the cognitive radio to boost the performance of spectrum sensing. However, the performance of existing compressive spectrum sensing schemes is constrained with non-strictly sparse spectrum. In addition, the reconstruction process applied in existing schemes has unnecessarily high computational complexity and low energy efficiency. We proposed a new compressive signal processing algorithm, called Iterative Compressive Filtering, to improve the UAV network communication performance. The key idea is using orthogonal projection as a bandstop filter in compressive domain. The components of primary users (PUs) in the recognized subchannels are adaptively eliminated in compressive domain, which can directly update the measurement for further detection of other active users. Experiment results showed increased efficiency of the proposed algorithm over existing compressive spectrum sensing algorithms. The proposed algorithm achieved higher detection probability in identifying the occupied subchannels under the condition of non-strictly sparse spectrum with large computational complexity reduction, which can provide strong support of reliable and timely communication for UAV networks.

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“…Toeplitz covariance estimation arises in a range of applications, including direction of arrival (DOA) estimation [1][2][3], spectrum-sensing for cognitive radio [4,5], medical and radar imaging [6][7][8], [9][10][11] and Gaussian process regression (kriging) and kernel machine learning [12,13]. We focus on estimation methods with low sample complexity, can be measured in two ways [14]:…”

Section: Introductionmentioning

confidence: 99%

Low-Rank Toeplitz Matrix Estimation Via Random Ultra-Sparse Rulers

Lawrence

Musco

et al. 2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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We study how to estimate a nearly low-rank Toeplitz covariance matrix T from compressed measurements. Recent work of Qiao and Pal addresses this problem by combining sparse rulers (sparse linear arrays) with frequency finding (sparse Fourier transform) algorithms applied to the Vandermonde decomposition of T . Analytical bounds on the sample complexity are shown, under the assumption of sufficiently large gaps between the frequencies in this decomposition.In this work, we introduce random ultra-sparse rulers and propose an improved approach based on these objects. Our random rulers effectively apply a random permutation to the frequencies in T 's Vandermonde decomposition, letting us avoid frequency gap assumptions and leading to improved sample complexity bounds. In the special case when T is circulant, we theoretically analyze the performance of our method when combined with sparse Fourier transform algorithms based on random hashing. We also show experimentally that our ultra-sparse rulers give significantly more robust and sample efficient estimation then baseline methods.

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Analog-to-Digital Cognitive Radio: Sampling, Detection, and Hardware

Cited by 73 publications

References 103 publications

The Capacity of Memoryless Channels With Sampled Cyclostationary Gaussian Noise

The Capacity of Memoryless Channels With Sampled Cyclostationary Gaussian Noise

An Efficient Wideband Spectrum Sensing Algorithm for Unmanned Aerial Vehicle Communication Networks

Low-Rank Toeplitz Matrix Estimation Via Random Ultra-Sparse Rulers

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