Performance analysis of cellular mobile systems with successive co-channel interference cancellation

Hasna, Mazen O.; Alouini, Mohamed‐Slim; Bastami, Ali H.; Ebbini, E.S.

doi:10.1109/twc.2002.806359

Cited by 79 publications

(57 citation statements)

References 21 publications

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“…, which is reflected through the parameter τ being the excess time delay, and according to [24], and [27], the distribution of γ k,I,tot can be expressed as…”

Section: A Dominant Ic Algorithmmentioning

confidence: 99%

“…This interference cancellation mechanism can have real applications in multihop CRNs as the mitigation of interference on the secondary network can be of prime importance when primary PUs access the shared spectrum resources heavily. Two interference cancellation algorithms were proposed; namely dominant receive interference cancellation (DRIC) [27], [28], and adaptive receive interference cancellation (ARIC) [29]. The first algorithm requires perfect predication and statistical ordering of interference powers, whereas the latter does not need prior knowledge of the statistical properties of interference sources and can efficiently utilize the available receive antennas.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Underlay Cognitive Multihop MIMO Networks With and Without Receive Interference Cancellation

Al-Qahtani

Radaydeh

Hessien

et al. 2017

IEEE Trans. Commun.

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“…, which is reflected through the parameter τ being the excess time delay, and according to [24], and [27], the distribution of γ k,I,tot can be expressed as…”

Section: A Dominant Ic Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Underlay Cognitive Multihop MIMO Networks With and Without Receive Interference Cancellation

Al-Qahtani

Radaydeh

Hessien

et al. 2017

IEEE Trans. Commun.

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“…Radio resource management [4] and multiantenna techniques such as beamforming [5], combining [6] or interference nulling [7] are popular choices for controlling and mitigating interference.…”

Section: Introductionmentioning

confidence: 99%

Interference mitigation in an LTE femtocell base station using uplink antenna selection

Soler-Garrido

Sandell

Chin

2012

J Wireless Com Network

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Antenna selection represents an attractive technique that allows to improve performance of wireless systems due to its simplicity and ease of implementation. Antenna selection is able to provide diversity through the use of multiple antennas while keeping low cost and energy consumption by selecting only a subset of the available elements at any given time, hence requiring a smaller number of RF transmitter and receiver chains. This makes it especially suitable for femtocell or small-cell base stations, typically employed by cellular operators to provide indoor radio coverage in customer premises. This article presents an analysis and validation of antenna selection using a LTE-FDD femtocell prototype. The effects on antenna selection performance of different antenna training schedules and channel variation rates are investigated under realistic propagation conditions. It is observed that antenna selection is able to provide substantial gains in terms of channel quality and reliability in interference-limited scenarios, attesting its suitability for large-scale femtocell deployments.

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“…Each entry of the MCM represents the MC coefficient between two related array sensors and can be estimated from collected measurement data [11,12,14,15]. We first investigate the performance of the LCMV beamformers which are widely applied for achieving beamforming with various goals [16][17][18][19][20][21]. The ESB beamformers find their optimal weight vector from that of the LCMV beamformers by discarding the weight vector component contributed by the noise subspace.…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Antenna Array Beamformers With Mutual Coupling Effects

Lee

Chen²

2011

PIER B

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Abstract-Antenna array beamformers suffer from performance deterioration in the presence of mutual coupling (MC) between array sensors. In this paper, we present a theoretical analysis in terms of the output signal-to-interference-plus-noise ratio (SINR) for the performance of antenna array beamformers under MC effects. Based on the model of a distortion matrix to encapsulate the MC effects, a closed-form expression for the SINR is derived that is shown to accurately predict the SINR obtained in simulations. This theoretical formula is valid for any distortion matrix estimated from collected measurement data. The SINR formulas provide insights into the influence of the MC effects on the performances of the linearly constrained minimum variance (LCMV) beamformer and the eigenspace-based (ESB) beamformer. It is shown that the ESB beamformer outperforms the LCMV beamformer under MC effects. Moreover, we derive the formulas for computing the eigenvalues of signal correlation matrix under MC effects. Simulation results are presented for confirming the validity of the theoretical results.

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Performance analysis of cellular mobile systems with successive co-channel interference cancellation

Cited by 79 publications

References 21 publications

Underlay Cognitive Multihop MIMO Networks With and Without Receive Interference Cancellation

Underlay Cognitive Multihop MIMO Networks With and Without Receive Interference Cancellation

Interference mitigation in an LTE femtocell base station using uplink antenna selection

Performance Analysis of Antenna Array Beamformers With Mutual Coupling Effects

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