Keith R. Tinsley

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 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.

Identification of high speed jittered digital interconnects using bicoherence spectra

Interference due to narrowband and broadband sources in mobile computing platforms have the capability to degrade overall wireless performance. Identification of these radio frequency interference (RFI) sources allows for mitigation and improved wireless performance. While shielding gives some immunity, interference sources operating within the same platform as sensitive radio receivers presents challenges far and above traditional electromagnetic compatibility (EMC) of device integration. In this paper, bicoherence, a signal processing analysis algorithm, is used to identify the presence of high-speed interconnects in radiated platform emissions. This paper uses a practical example of RFI found in current wireless platforms and shows that detection is feasible in Gaussian and non-Gaussian additive noise environments.

Methodology for RFI Immune Wireless Platforms: RFI Predictive Modeling

In this paper, a systemic approach is presented to evaluate wireless platform performance early in the design cycle given the anticipated presence of radio frequency interference (RFI). By applying this methodology, sensitivity due to architecture choices such as: platform materials, component layout, and RFI reduction techniques can be evaluated and thereby lead to innovative platform designs.