Enhancing Small Cell Transmission Opportunity Through Passive Receiver Detection in Two-Tier Heterogeneous Networks

Li, Emma; Zhao, Guodong; Zhou, Xiangwei

doi:10.1109/tsp.2015.2428214

Cited by 10 publications

(21 citation statements)

References 29 publications

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“…where R0 and R1 are matrices with the block structure shown in (4) and (5), respectively. It is known that the GLRT is invariant to transformations for which the detection problem itself is invariant [21].…”

Section: Derivation Of the Glr Testmentioning

confidence: 99%

“…where G denotes the set of pd matrices with the required structure: arbitrary positive definite blocks along the main diagonal and rankone blocks along the antidiagonal, as in (5). We can now apply block diagonal transformations to S andR1 such that…”

Section: Theoremmentioning

confidence: 99%

“…The signal paths for the reference and the surveillance channels are typically separated by digital beamforming using directional antennas. In communications, passive detection might have application in cognitive radio [4], or to enhance the transmission opportunities of small cells in Heterogeneous Networks (HetNets) [5].…”

Section: Introductionmentioning

confidence: 99%

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Passive detection of rank-one signals with a multiantenna reference channel

Santamarı́a

Scharf

Cochran

et al. 2016

2016 24th European Signal Processing Conference (EUSIPCO)

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In this work we consider a two-channel passive detection problem, in which there is a surveillance array where the presence/absence of a target signal is to be detected, and a reference array that provides a noise-contaminated version of the target signal. We assume that the transmitted signal is an unknown rank-one signal, and that the noises are uncorrelated between the two channels, but each one having an unknown and arbitrary spatial covariance matrix. We show that the generalized likelihood ratio test (GLRT) for this problem rejects the null hypothesis when the largest canonical correlation of the sample coherence matrix between the surveillance and the reference channels exceeds a threshold. Further, based on recent results from random matrix theory, we provide an approximation for the null distribution of the test statistic.

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Section: Derivation Of the Glr Testmentioning

confidence: 99%

Section: Theoremmentioning

confidence: 99%

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Passive detection of rank-one signals with a multiantenna reference channel

Santamarı́a

Scharf

Cochran

et al. 2016

2016 24th European Signal Processing Conference (EUSIPCO)

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show abstract

“…Then, the C-SBS can determine whether to access a frequency band or not. There are many contributions on the HetNet with cognitive small cells, e.g., [15], [16], [17], [18], [19], [20], [21]. Specifically, [15], [16], [17] investigated the overlay HetNet and proposed a two-phase opportunistic spectrum access scheme.…”

Section: Introductionmentioning

confidence: 99%

Learning-Based Coexistence in Two-Tier Heterogeneous Networks with Cognitive Small Cells

Zhang,

Zhao,

Zhou

et al. 2016

Preprint

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We study the coexistence problem in a two-tier heterogeneous network (HetNet) with cognitive small cells. In particular, we consider an underlay HetNet, where the cognitive small base station (C-SBS) is allowed to use the frequency bands of the macro cell with an access probability (AP) as long as the C-SBS satisfies a preset interference probability (IP) constraint at macro users (MUs). To enhance the AP (or transmission opportunity) of the C-SBS, we propose a learning-based algorithm for the C-SBS and exploit the distance information between the macro base station (MBS) and MUs. Generally, the signal from the MBS to a specific MU contains the distance information between the MBS to the MU. We enable the C-SBS to analyze the MBS signal on a target frequency band, and learn the distance information between the MBS and the corresponding MU. With the learnt distance information, we calculate the upper bound of the probability that the C-SBS may interfere with the MU, and design an AP with a closed-form expression under the IP constraint. Numerical results indicate that the proposed algorithm outperforms the existing methods up to 60% AP (or transmission opportunity).

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“…This assumption has been used in the literatures on proactive estimation, e.g.,[4][5][6][7][8] and[16], since the CLPC has been widely adopted in modern wireless systems 3. Generally, depending on the system bandwidth and also the signal processing ability, the PR may only collect the received signals within a certain time duration, which is called the maximum allowable TDOA Tm.…”

mentioning

confidence: 99%

Non-cooperative cross-channel gain estimation using full-duplex amplify-and-forward relaying in cognitive radio networks

Huang

Zhao

et al. 2016

2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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In this paper, we propose a new estimation method to obtain the cross-channel gain, which avoids the severe interference to the primary receiver (PR) in existing relay-assisted estimation methods. In our method, we let the cognitive transmitter add a time delay when it conducts the full-duplex amplify-and-forward relaying. This forces the time-difference-of-arrival (TDOA) between the direct and relay signals to be large enough rather than randomly large or small. Then we develop our estimation method only in the large TDOA case and precisely control the interference to the PR. Simulation results indicate that the proposed method can significantly reduce the interference to the PR.

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Enhancing Small Cell Transmission Opportunity Through Passive Receiver Detection in Two-Tier Heterogeneous Networks

Cited by 10 publications

References 29 publications

Passive detection of rank-one signals with a multiantenna reference channel

Passive detection of rank-one signals with a multiantenna reference channel

Learning-Based Coexistence in Two-Tier Heterogeneous Networks with Cognitive Small Cells

Non-cooperative cross-channel gain estimation using full-duplex amplify-and-forward relaying in cognitive radio networks

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