Interference shaping constraints for underlay MIMO interference channels

Lameiro, Christian; Santamarı́a, Ignacio; Utschick, Wolfgang

doi:10.1109/icassp.2014.6855020

Cited by 5 publications

(9 citation statements)

References 32 publications

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“…is carried out under interference temperature constraints to protect the PUs. On the other hand, other works have considered a rate constraint at the PU [9], or use spatial shaping constraints when the SU is equipped with multiple antennas [10].…”

Section: Introductionmentioning

confidence: 99%

Benefits of Improper Signaling for Underlay Cognitive Radio

Lameiro

Santamarı́a

Schreier

2015

IEEE Wireless Commun. Lett.

115

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In this letter we study the potential benefits of improper signaling for a secondary user (SU) in underlay cognitive radio networks. We consider a basic yet illustrative scenario in which the primary user (PU) always transmit proper Gaussian signals and has a minimum rate constraint. After parameterizing the SU transmit signal in terms of its power and circularity coefficient (which measures the degree of impropriety), we prove that the SU improves its rate by transmitting improper signals only when the ratio of the squared modulus between the SU-PU interference link and the SU direct link exceeds a given threshold. As a by-product of this analysis, we obtain the optimal circularity coefficient that must be used by the SU depending on its power budget. Some simulation results show that the SU benefits from the transmission of improper signals especially when the PU is not highly loaded.Index Terms-Cognitive radio, interference channel, improper signaling, asymmetric complex signaling.

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Section: Introductionmentioning

confidence: 99%

Benefits of Improper Signaling for Underlay Cognitive Radio

Lameiro

Santamarı́a

Schreier

2015

IEEE Wireless Commun. Lett.

115

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“…Spatial shaping constraints considering the structure of interference was proposed by Lameiro et al 21,22 Under the assumption that the interference imposed by PU at each SU could be neglected, Lameiro et al 21 extended the interference leakage minimization algorithm to incorporate the spatial interference shaping constraints. Lameiro et al 21,22 improved and presented a more general optimization framework to design the spatial shaping constraints. Guo et al 23 proposed an eigenmodes constraint with which the number of eigenmodes used by the PU was adaptively adjusted by the rate requirement.…”

Section: Introductionmentioning

confidence: 99%

Interference alignment based on adaptive eigenmodes in a multiple-input, multiple-output cognitive radio network

Diao

Dong

et al. 2018

International Journal of Distributed Sensor Networks

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This article aims to optimize the information rate of a cognitive radio network with multiple secondary users. A primary user rate optimization approach based on dichotomy of the degree of freedom is proposed, where the primary users' eigenmodes are adjusted according to its rate requirement. In order to provide a higher sum rate of secondary users, two interference alignment schemes are presented. The first one is an interference sub-space alignment scheme, which aims to align the sub-spaces spanned by interference from other secondary users with the sub-space spanned by interference from primary user. However, interference sub-space alignment may not be favorable in low signal-to-interference ratio region due to the negligence of the influence of noise. Thus, an iterative interference alignment scheme which maximizes the secondary system sum rate based on Grassmann manifold is developed. To accelerate the convergence speed, the objective function in Grassmann manifold is transformed into two parts without the inversion operation using the extensions of the Minkowski inequality. Simulation results show that interference sub-space alignment is more effective than Grassmann manifold to mitigate interference in the system with more secondary users. We further validate the effectiveness of Grassmann manifold and interference sub-space alignment in comparison with the existing schemes employing a water filling algorithm.

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“…Nevertheless, such an approach requires additional cross-information between primary and secondary networks, which reduces the actual net throughput and increases the overall system complexity. Linear matrix inequality constraints (similar as the one considered in this paper) have been studied in our previous works [23,24] and in [25], where authors consider the robust optimization of the PU covariance matrix under different interference constraints. Similar interference constraints have also been considered in the context of cellular networks in [26], where shaping constraints are used to upper-bound the worst-case interference covariance matrix when the interference channels are unknown; and in [27,28] to efficiently coordinate interference with an underlaying device-to-device communication network.…”

Section: Introductionmentioning

confidence: 99%

Spatial interference shaping for underlay MIMO cognitive networks

2017

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Interference temperature (IT) is a widely-used approach for protecting primary users (PUs) from the secondary users (SUs) in underlay cognitive radio. However, when multiple antennas are available at the transmitters and receivers, the spatial structure of the interference comes into play, strongly affecting the performance of the primary network. In this work, we propose interference shaping constraints as an alternative to IT-based approaches. Spatial shaping constraints take account of the structure of interference and exploit it in benefit of the secondary network. Moreover, they can be designed dynamically based on the channel conditions and performance requirements of the PUs. We first show that spatial shaping constraints generalize IT, in that the latter can be expressed as a set of isotropic shaping constraints on each interference dimension. Then, we exemplary consider a PU that has a rate requirement, and propose an algorithm for obtaining suitable shaping matrices, which can be easily modified to include primary transmitter cooperation. This algorithm is performed at the primary receiver using only local channel state information. Afterwards, we address the transceiver optimization of the SU, modeled as a multiple-input multiple-output point-to-point link, and provide optimal and suboptimal transmit covariance designs under the proposed shaping constraints.

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Interference shaping constraints for underlay MIMO interference channels

Cited by 5 publications

References 32 publications

Benefits of Improper Signaling for Underlay Cognitive Radio

Benefits of Improper Signaling for Underlay Cognitive Radio

Interference alignment based on adaptive eigenmodes in a multiple-input, multiple-output cognitive radio network

Spatial interference shaping for underlay MIMO cognitive networks

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