A Bayesian approach to estimate and model SINR in wireless networks

Abstract

Summary In wireless communications, the signal‐to‐interference‐plus‐noise ratio (SINR) is important in spectrum management and link scheduling. In cognitive radio and ad hoc networks, where the spectrum is shared between nodes, the SINR is required to measure the outage probability and the level of accumulated interference on a specific node from other nodes sharing the same band. Several techniques have been proposed to estimate and statistically model the SINR. However, most of these techniques do not account for uncertainty in factors such as the number of nodes and their locations, the distance between nodes, the transmission powers, and the frequencies. In addition, these methods are not able to learn from and adapt to the changes of the network. Therefore, there is a need for models able to dynamically deal with the uncertainty affecting the SINR and provide a modular framework for its estimation. In this article, a Bayesian model is proposed to probabilistically model the SINR and describe how variables affect its probability distribution. Simulation results confirm the validity and robustness of the proposed method.

Multi-Attributes, Utility-Based, Channel Quality Ranking Mechanism for Cognitive Radio Networks

“…The signal-to-interference-plus-noise-ratio (SINR) is another crucial parameter in channel quality ranking, especially in cognitive radio networks [35], since interference is one of the critical factors that affects the performance of communication in these networks. The SINR is the relative ratio between the power of the transmitted signal and the interference power added to the noise power.…”

Channel quality estimation metrics in cognitive radio networks: a survey

“…Unlike other techniques for estimating SINR, Riahi Manesh et al [16] proposed a Bayesian model to handle uncertainty that involves several parameters affecting SINR, including the received signal power, additive white Gaussian noise, and aggregate interference power. This model infers the probability distributions of variables that affect SINR as shown in Fig.…”

An improved ensemble machine learning classifier for efficient spectrum sensing in cognitive radio networks

“…Bayesian‐based models 8 have been effectively addressed in literature to keep the signal‐to‐interference‐to‐noise ratio (SINR) values 9 at control to reduce interference and reduce the false alarm rate in detection model. Bayesian‐based methods are best suited for spectrum sensing applications as their very principle is defined by the occurrence of an event based on a given condition, which quite suits the PU detection process.…”