On secondary network interference alignment in cognitive radio

Zhou, Haichuan; Ratnarajah, Tharmalingam; Liang, Ying-Chang

doi:10.1109/dyspan.2011.5936260

Cited by 19 publications

(16 citation statements)

References 8 publications

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“…When the strength of secondary interference to the primary is comparable to the desired signal, treating as noise is not an option because of interference constraints while decoding and canceling requires complex primary receivers. In this context, interference alignment (IA) as an interference mitigation tool has received important attention recently in the cognitive radio research community [6,7]. The concept behind IA is that signals can be designed in such a way that they cast overlapping shadows at the receivers where they constitute interference and remain distinguishable at the receivers where they are desired.…”

Section: Introductionmentioning

confidence: 99%

Interference alignment for spectral coexistence of heterogeneous networks

Sharma

Chatzinotas

Ottersten

2013

J Wireless Com Network

101

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The coexistence of heterogeneous networks within the same spectrum for enhancing the spectrum efficiency has attracted large interest lately in the research community. Furthermore, the research interest towards the deployment of small cells and multibeam satellites is increasing due to high capacity, easier deployment and higher energy efficiency. However, due to the scarcity of available spectrum and the requirement of additional spectrum for these systems, small cells need to coexist with macrocells and multibeam satellites need to coexist with monobeam satellites within the same spectrum. In this context, this contribution investigates an underlay spectral coexistence mechanism which exploits an interference alignment (IA) technique in order to mitigate the interference of cognitive transmitters towards the primary receivers in a normal uplink mode. More specifically, three types of IA techniques, namely static, uncoordinated and coordinated are investigated. The performance of the IA technique is evaluated and compared with primary only, resource division and no-mitigation techniques in terms of sum-rate capacity, primary to secondary rate ratio and primary rate protection ratio. It is shown that the coordinated IA technique perfectly protects the primary rate in both terrestrial and satellite coexistence scenarios.

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Section: Introductionmentioning

confidence: 99%

Interference alignment for spectral coexistence of heterogeneous networks

Sharma

Chatzinotas

Ottersten

2013

J Wireless Com Network

101

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“…The exact number of needed dimensions and the precoding vectors to achieve IA are rather cumbersome to compute, but a number of approaches have been presented in the literature towards this end [13]- [15]. In the context of cognitive communications, the IA in an underlay mode has received important attention recently in the cognitive radio research community [16] [17]. The fundamental assumptions in this technique are that there are multiple available dimensions (space, frequency, time or code) and that the Secondary Transmitter (ST) is aware of the Channel State Information (CSI) towards the Primary Receivers (PR).…”

Section: Introductionmentioning

confidence: 99%

Multiantenna signal processing for cognitive communications

Chatzinotas

Sharma

Ottersten

2013

2013 IEEE China Summit and International Conference on Signal and Information Processing

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Abstract-Cognitive communications has attracted a large interest during the last decade due to spectrum scarcity. In combination with multiantenna techniques, cognitive communications have the ability to increase spectral efficiency by enabling the coexistence of a primary and secondary systems. In this paper, we focus in two specific cognitive approaches: a) Multiantenna Interference Alignment (IA) and b) Multiantenna Spectrum Sensing (SS). In the first case, we investigate how IA over multiple spatial dimensions can be exploited in order to lower harmful interference towards the primary system into acceptable levels. In the second case, we compare the sensing performance of different eigenvalue-based blind SS techniques. This paper concludes by presenting some interesting open problems in this area.

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“…So far, many works have studied applying IA to the CR networks [13][14][15][16][17][18][19][20][21][22][23]. In [13], the author characterized the achievable degree of freedom (DoF) for the secondary network.…”

Section: Introductionmentioning

confidence: 99%

“…In [13], the author characterized the achievable degree of freedom (DoF) for the secondary network. In [14], Zhou et al optimized both the precoding vectors and power allocation to enhance the rates of secondary users, where a gradient method was used. In [15], under the presumption that the interference imposed by PU at each SU can be neglected, an IA scheme minimizing the distance between the interfering subspace and the received subspace was proposed.…”

Section: Introductionmentioning

confidence: 99%

Interference Alignment Based on Rank Constraint in MIMO Cognitive Radio Networks

Diao

Dong

et al. 2017

Symmetry

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Abstract:In this paper, we focus on the interference management in the cognitive radio (CR) network comprised of multiple primary users (PUs) and multiple secondary users (SUs). Firstly, two interference alignment (IA) schemes are proposed to mitigate the interference among PUs. The first one is an interference rank minimization (IRM) scheme, which aims to minimize the rank of the joint interference matrix via alternating between the forward and reverse communication links. Considering the overhead of information exchanged between the transmitters and receivers in the IRM scheme, we further develop an interference subspace distance minimization (ISDM) scheme which runs at the transmitters only. The ISDM scheme focuses on aligning the subspaces spanned by interference with an aligned subspace introduced in this paper. For the secondary network, though IRM and ISDM mitigate the received interference at secondary receivers, they make no attempt to eliminate the interference from SUs to PUs. To address this, we improve the IRM and ISDM schemes by putting a rank constraint into their optimizations, where the rank constraint forces the ranks of the interference matrices from SUs to PUs to be zero. Simulation results validate the effectiveness of the proposed schemes in terms of the average sum rate.

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On secondary network interference alignment in cognitive radio

Cited by 19 publications

References 8 publications

Interference alignment for spectral coexistence of heterogeneous networks

Interference alignment for spectral coexistence of heterogeneous networks

Multiantenna signal processing for cognitive communications

Interference Alignment Based on Rank Constraint in MIMO Cognitive Radio Networks

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