Interference alignment in cognitive radio networks

Rezaei, Fatemeh; Tadaion, Aliakbar

doi:10.1049/iet-com.2013.0731

Cited by 23 publications

(30 citation statements)

References 27 publications

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“…The most commonly used scheme to protect PUs from the interference imposed by SUs is by removing some antennas from the SUs' transmitters [22]. However, such an approach demands SUs to be equipped with additional antennas when the number of primary users increases, as shown in Equation (27).…”

Section: Rank Constraint Schemementioning

confidence: 99%

“…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%

“…The authors of [21] put forward a blind opportunistic IA method with which the SU is able to compute blindly the required channel state information (CSI) without interaction with the primary network. In contrast to the above algorithms concentrating on the CR network with one PU and multiple SUs, the authors of [22,23] investigated the interference mitigation for the CR network with multiple PUs and multiple SUs. In [22], the authors presented an improved version of a minimum weighted leakage interference (IMWLI) algorithm with a much faster convergence rate.…”

Section: Introductionmentioning

confidence: 99%

“…In contrast to the above algorithms concentrating on the CR network with one PU and multiple SUs, the authors of [22,23] investigated the interference mitigation for the CR network with multiple PUs and multiple SUs. In [22], the authors presented an improved version of a minimum weighted leakage interference (IMWLI) algorithm with a much faster convergence rate. Authors of [23] developed an optimal interference elimination solution when there are sufficient antennas in the secondary links.…”

Section: Introductionmentioning

confidence: 99%

“…Compared to the system in which a PU coexists with multiple SUs, the multi-PU CR system needs to deal with more serious interference, in particular the interference between PUs and SUs. The most commonly-used scheme to eliminate the mutual interference between PUs and SUs is by removing some antennas from the SUs' transmitters and receivers, as proposed in [22,23]. Although such an approach allows SUs to transmit their information without causing/receiving any interference to/from the primary links, it demands them to be equipped with more antennas when the number of PUs increases.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Rank Constraint Schemementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Interference Alignment Based on Rank Constraint in MIMO Cognitive Radio Networks

Diao

Dong

et al. 2017

Symmetry

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Interference‐resilient spectrum sharing with collision detection and data recovery for cognitive radio networks

Imran

Loan

Mahmood

2020

Int J Communication

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Summary Data recovery under collision of primary/secondary users in cognitive radio networks is largely an unaddressed problem. Existing approaches require either (a) retransmissions, (b) interference‐free alignment of transmission parameters, or (c) prior knowledge for data recovery. In contrast to the existing approaches, the paper proposes estimation of channel state information based on received collided frames and recommends techniques for spectrum sharing, collision detection, and data recovery. Specifically, based on estimated channel vector, interference‐resilient spectrum‐sharing protocol and techniques are proposed for (a) identification of primary user (PU) activity before (or during) secondary user's (SU's) transmission, (b) MIMO channel estimation under collision, (c) optimal pilot permutation, and (d) interference cancellation (with/without receiver diversity). Results are reported for different baseband modulation techniques under frequency selective/flat fading channel scenarios that demonstrate the efficacy of a proposed work.

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Distributed interference alignment algorithm in downlink multi-user cooperative networks

Sun

2018

J Supercomput

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Interference alignment in cognitive radio networks

Cited by 23 publications

References 27 publications

Interference Alignment Based on Rank Constraint in MIMO Cognitive Radio Networks

Interference Alignment Based on Rank Constraint in MIMO Cognitive Radio Networks

Interference‐resilient spectrum sharing with collision detection and data recovery for cognitive radio networks

Distributed interference alignment algorithm in downlink multi-user cooperative networks

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