Performance bounds of MIMO receivers in the presence of radio frequency interference

Chopra, Aditya; Gulati, Kapil; Evans, Brian L.; Tinsley, Keith R.; Sreerama, Chaitanya

doi:10.1109/icassp.2009.4960209

Cited by 15 publications

(10 citation statements)

References 9 publications

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“…Finally, by substituting (15) and (16) into (13), we obtain the closed-form approximation of the information rate achieved by a Gaussian distributed input:…”

Section: Lemmamentioning

confidence: 99%

“…Up to date, most information-theoretic studies on impulsive noise focused on static channels and the estimation of capacities/information rates was mostly based on Monte Carlo simulations [9], [11], [14], [15]. For instance, over a static impulsive noise channel, it was shown in [14], [15] that a Gaussian input distribution can be considered as a good choice for impulsive noise channels.…”

Section: Introductionmentioning

confidence: 99%

“…For instance, over a static impulsive noise channel, it was shown in [14], [15] that a Gaussian input distribution can be considered as a good choice for impulsive noise channels. In [16], Herath et al further investigated the optimal signalling scheme and Shannon capacity limit of the static BG impulsive noise channel through lower and upper bounds using a raw approximation of the BG noise entropy.…”

Section: Introductionmentioning

confidence: 99%

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Estimating information rates of Bernoulli-Gaussian impulsive noise channels in Rayleigh fading

Tran

Nguyen

et al. 2014

2014 IEEE International Conference on Communications (ICC)

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This paper presents simple methods to tightly estimate the information rate achieved by a Gaussian input and the constrained capacity of a finite-alphabet input of a Bernoulli-Gaussian (BG) impulsive noise channel in Rayleigh fading. Specifically, under the assumption of a Gaussian input, we propose a novel approach to calculate the achievable rate by examining the instantaneous output entropy in two regions of channel gains. In the high-gain region, the rate is evaluated via an upper bound obtained under the Gaussian output assumption. In the other region, we apply the piecewise-linear curve fitting (PWLCF) method to estimate the rate. It is then demonstrated that the information rate achieved by Gaussian inputs can be effectively calculated with a pre-determined accuracy. For a finitealphabet input, we detail a PWLCF-based method to estimate the constrained capacity. In particular, we first propose a numerical technique to calculate the instantaneous output entropy using 2-dimensional Gauss-Hermite quadrature formulas. The average output entropy is then obtained using PWLCF. Combined with the closed-form expression the entropy of the BG impulse noise, an accurate estimation of the constrained capacity is finally established.

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“…Finally, by substituting (15) and (16) into (13), we obtain the closed-form approximation of the information rate achieved by a Gaussian distributed input:…”

Section: Lemmamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Estimating information rates of Bernoulli-Gaussian impulsive noise channels in Rayleigh fading

Tran

Nguyen

et al. 2014

2014 IEEE International Conference on Communications (ICC)

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“…In [10], the authors analyze performance bounds for optimum and sub-optimum receivers in the presence of Middleton Class A impulsive noise over non-fading channels. In [11], the authors evaluate performance bounds of 2 × 2 MIMO communication with Alamouti codes using a generalized statistical-physical interference model from [12]. In [13] the performance of maximum ratio combining techniques was investigated in multi-user environments and in presence of receiver channel estimation error.…”

Section: Prior Workmentioning

confidence: 99%

Outage Probability for Diversity Combining in Interference-Limited Channels

Chopra

Evans

2013

IEEE Trans. Wireless Commun.

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Many wireless data communication systems such as LTE, and Wi-Fi, are increasingly facing interference that is much stronger than thermal noise. Interference may arise due to dense spatial reuse of spectrum intended to increase user data rates, or from other devices emitting radiation in the same spectrum, or from electronic circuitry within the communication platform. For a multi-antenna receiver operating in an interference-limited channel, we evaluate four diversity combining algorithms in terms of outage probability in the low-outage regime. The contributions of this paper are (1) derivation of closed-form expressions for the output signalto-interference ratio (SIR) statistics of fixed weight, maximal ratio, selection and post-detection combining; (2) comparison of the relative outage performance of these algorithms; and (3) proposed diversity combining algorithms to reduce outage probability. Our results can be applied in analyzing the outage performance and throughput capacity of both centralized and decentralized interference-limited wireless networks.Index Terms-Co-channel interference, symmetric alpha stable, Poisson point processes, impulsive noise, diversity reception, outage probability, spatial dependence.

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“…Such an expression has not yet been published. In [4], an upper bound of the channel capacity is calculated for bivariate Middleton class A interference, which is an extension of the original Middleton's class A interference. However, that work is based on a different approach, which results in an upper bound of the capacity.…”

mentioning

confidence: 99%

Channel capacity of Middleton's class A interference channel

2009

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Middleton's class A interference model has properties that make it possible to represent a wide class of interference signals. By choice of model parameters, interference signals ranging from pure Gaussian distributed to pure impulsive interference can be modelled. These properties make the model very useful for a large variety of applications. However, an expression for the channel capacity of the class A interference channel has not yet been published. The channel capacity of this model is derived, and numerical examples are given for some useful sets of model parameters.Introduction: The performance of communication systems is impaired by interference, or noise, from both external and internal sources. A common approach in the analysis of interference impact is to model the interference as having a Gaussian amplitude distribution with flat power spectral density (additive white Gaussian noise, AWGN). The advantage of the AWGN model is its simplicity for analysis and that a large amount of results can be found in the literature, most notably Shannon's channel capacity results for the AWGN channel [1]. However, the AWGN model does not cover the behaviour of a large class of commonly occurring interference signals, especially impulsive interference. A more accurate interference model is the Middleton's class A interference model [2]. This model has the advantage that it can represent a number of interference signals with arbitrary impulsiveness content. By choice of model parameters, we can model a large class of interference ranging from purely Gaussian distributed noise to highly impulsive interference. A communication channel experiencing class A interference is referred to as an additive white class-A noise (AWCN) channel [3]. The drawback is that the analysis of the AWCN channel is not as simple as in the AWGN case. This is probably the reason why the model has not gained as much popularity as the simpler AWGN model. However, the interest for the class A model is increasing and it is more and more often seen in interference analyses.Areas where the AWCN channel model is applicable include dynamic spectrum access (DSA) for cognitive radios (CRs) where other communication systems act as impulsive interferers and IEEE 802.11 g, an orthogonal frequency division multiplexing (OFDM) system operating in the 2.4 GHz ISM band where microwave ovens can create impulsive interference. To maximise the spectrum utilisation in, for example, an OFDM system, waterfilling can be used to efficiently distribute signals over parallel channels. However, waterfilling requires that the channel capacity is known. Such an expression has not yet been published. In [4], an upper bound of the channel capacity is calculated for bivariate Middleton class A interference, which is an extension of the original Middleton's class A interference. However, that work is based on a different approach, which results in an upper bound of the capacity. Our present work is based on the fact that Middleton's class A model can be viewed as a Markov chai...

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Performance bounds of MIMO receivers in the presence of radio frequency interference

Cited by 15 publications

References 9 publications

Estimating information rates of Bernoulli-Gaussian impulsive noise channels in Rayleigh fading

Estimating information rates of Bernoulli-Gaussian impulsive noise channels in Rayleigh fading

Outage Probability for Diversity Combining in Interference-Limited Channels

Channel capacity of Middleton's class A interference channel

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