Kapil Gulati

Statistics of Co-Channel Interference in a Field of Poisson and Poisson-Poisson Clustered Interferers

With increasing spatial reuse of radio spectrum, co-channel interference is becoming a dominant noise source and may severely degrade the communication performance of wireless transceivers. In this paper, we consider the problem of statistical-physical modeling of co-channel interference from an annular field of Poisson or Poisson-Poisson cluster distributed interferers. Poisson and Poisson-Poisson cluster processes are commonly used to model interferer distributions in large wireless networks without and with interferer clustering, respectively. Further, by considering the interferers distributed over a parametric annular region, we derive interference statistics for finite- and infinite- area interference region with and without a guard zone around the receiver. Statistical modeling of interference is a useful tool to analyze outage probabilities in wireless networks and design interference-aware transceivers. Our contributions include: 1) developing a unified framework for deriving interference models for various wireless network environments; 2) demonstrating the applicability of the symmetric alpha stable and Gaussian mixture (with Middleton Class A as a particular form) distributions in modeling co-channel interference; and 3) deriving analytical conditions on the system model parameters for which these distributions accurately model the statistical properties of the interference. Applications include co-channel interference modeling for various wireless networks, including wireless ad hoc, cellular, local area, and femtocell networks.

Mitigating Near-field Interference in Laptop Embedded Wireless Transceivers

In laptop and desktop computers, clocks and busses generate significant radio frequency interference (RFI) for the embedded wireless data transceivers. RFI is well modeled using non-Gaussian impulsive statistics. Data communication transceivers, however, are typically designed under the assumption of additive Gaussian noise and exhibit degradation in communication performance in the presence of RFI. When detecting a signal in additive impulsive noise, Spaulding and Middleton showed a potential improvement in detection of 25 dB at a bit error rate of 10 − 5 when using a Bayesian detector instead of a standard correlation receiver. In this paper, we model RFI using Middleton Class A and Symmetric Alpha Stable (SαS) models. The contributions of this paper are to evaluate (1) the performance vs. complexity of parameter estimation algorithms, (2) the closeness of fit of RFI models to the measured interference data from a computer platform, (3) the communication performance vs. computational complexity tradeoffs in receivers designed to mitigate RFI modeled as Class A interference, (4) the communication performance vs. computational complexity tradeoffs in filtering and detections methods to combat RFI modeled as SαS interference, and (5) the approximations to filtering and detection methods developed to mitigate RFI for a computationally efficient implementation.

Statistical Modeling of Asynchronous Impulsive Noise in Powerline Communication Networks

Powerline distribution networks are increasingly being employed to support smart grid communication infrastructure and in-home LAN connectivity. However, their primary function of power distribution results in a hostile environment for communication systems. In particular, asynchronous impulsive noise, with levels as high as 50dB above thermal noise, causes significant degradation in communication performance. Much of the prior work uses limited empirical measurements to propose a statistical model for instantaneous statistics of asynchronous noise. In this paper, we (i) derive a canonical statistical-physical model of the instantaneous statistics of asynchronous noise based on the physical properties of the PLC network, and (ii) validate the distribution using simulated and measured PLC noise data. The results of this paper can be used to analyze, simulate, and mitigate the effect of the asynchronous noise on PLC systems.

Statistical Modeling of Co-Channel Interference

With increasing spatial reuse of the radio spectrum, co-channel interference is becoming the dominant noise source and may severely degrade the communication performance of wireless transceivers. In this paper, we consider the problem of statistical-physical modeling of co-channel interference. Statistical modeling of interference is a useful tool to analyze the outage probabilities in wireless networks and to design interference-aware transceivers. Our contributions include (1) developing a unified framework to derive interference models for various wireless network environments, (2) demonstrating the applicability of the symmetric alpha stable and Middleton Class A distributions in modeling co-channel interference in ad-hoc and cellular network environments, and (3) deriving analytical conditions on the system model parameters for which these distributions accurately model the statistical properties of the interference. Simulation results allow us to compare the key properties of empirical co-channel interference and their statistical models under different wireless network environments.

Mitigating near-field interference in laptop embedded wireless transceivers

In laptop and desktop computers, clocks and busses generate significant radio frequency interference (RFI) for the embedded wireless data transceivers. RFI is impulsive in nature. When detecting a signal in additive impulsive noise, Spaulding and Middleton showed a potential improvement in detection of 25 dB at a bit error rate of 10-5 when using a Bayesian detector instead of a standard correlation receiver. In this paper, we model impulsive noise using Middleton class A and symmetric alpha stable (SaS) models. The contributions of this paper are to evaluate (1) the performance vs. complexity of parameter estimation algorithms, (2) the closeness of fit of parameter estimation algorithms to measured RFI data from the computer platform, (3) the communication performance vs. computational complexity tradeoffs for the correlation receiver, Wiener filter, and Bayesian detector, and (4) the performance of myriad filtering in combating RFI interference modeled as SaS interference.

MIMO Receiver Design in the Presence of Radio Frequency Interference

Multi-input multi-output (MIMO) receivers have been designed and their communication performance analyzed under the assumption of additive Gaussian noise. Wireless transceivers, however, may be affected by radio frequency interference (RFI) that is well modeled using non-Gaussian impulsive statistics. In this paper, we consider the problem of receiver design for a two transmit, two receive antenna MIMO system in the presence of RFI. First, we show that RFI is well modeled using a bivariate Middleton Class A model and validate the model with measured data. Using this RFI model, we demonstrate that conventional MIMO receivers experience significant degradation in communication performance. Then we derive the maximum likelihood (ML) receiver assuming bivariate Middleton Class A noise. Furthermore, we develop a parameter estimation method for this noise model and propose two sub-optimal ML receivers with reduced computational complexity. Simulations show significant improvement in symbol error rate performance of the proposed techniques over receivers designed assuming additive Gaussian noise.

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

Multi-input multi-output (MIMO) receivers have generally been designed and their communication performance analyzed under the assumption of additive Gaussian noise. Wireless transceivers, however, may also be affected by radio frequency interference (RFI) that is well modeled using non-Gaussian impulsive statistics. In this paper, we derive bounds on the communication performance for a two transmit, two receive antenna MIMO system in the presence of RFI. Our contributions include derivation of (1) channel capacity in the presence of RFI, (2) probability of symbol error for uncoded transmissions, and (3) Chernoff bound on the pairwise error probability and cutoff rate as a measure of the throughput performance for coded transmissions. Comparison with the communication performance bounds for receivers designed assuming additive Gaussian noise demonstrates degradation in communication performance in the presence of RFI.

Statistical modeling of co-channel interference in a field of Poisson distributed interferers

With increasing spatial reuse of the radio spectrum, co-channel interference is becoming a dominant noise source and may severely degrade the communication performance of wireless transceivers. In this paper, we consider the problem of statistical-physical modeling of co-channel interference from an annulus field of Poisson interferers. Our contributions include (1) demonstrating the applicability of the symmetric alpha stable and Middleton Class A distributions in modeling co-channel interference in various topologies of interferers, and (2) deriving analytical conditions on the system parameters for which these distributions accurately model the interference statistics. Through simulation, we compare the decay rate of tail probabilities of the empirical co-channel interference and the symmetric alpha stable, Middleton Class A, and Gaussian models for different topologies of interferers. Practical applications include co-channel interference modeling for various wireless network environments, including ad hoc and cellular networks.

Joint Temporal Statistics of Interference in Decentralized Wireless Networks

Characterizing interference statistics is central to the design and analysis of both physical layer and medium access control layer techniques to mitigate interference in a wireless network. The applicability of interference statistics, however, is limited by the assumptions adopted to derive the statistics in closed-form. Common assumptions for a decentralized wireless network include temporally independent user locations and an unbounded pathloss function. In this correspondence, we derive the joint temporal statistics of interference that capture the temporal correlation in the network along with the realistic assumption of a bounded pathloss function. The closed-form statistics are asymptotically exact for low tail probabilities, and match closely in simulations even when the tail probability is fairly high. The primary contributions are to i) show that joint interference statistics follow a multivariate Gaussian mixture distribution under the assumption of a bounded pathloss function, and ii) characterize the joint tail probability decay behavior for both bounded and unbounded pathloss functions.

Estimating parameters of two-dimensional damped exponential mixtures

In this paper a novel approach for estimating the parameters of multiple damped exponentials from a two-dimensional mixture is proposed. The algorithm exploits the internal Vandermonde structure of the two-dimensional data block to estimate the signal parameters from the common factor of two matrix polynomials. A new estimation method for common factor extraction of two matrix polynomials is developed that accounts for the perturbations in the polynomial coefficients that usually are inevitable in real applications.