The ricean K factor: Estimation and performance analysis

Abstract

In wireless communications, the relative strength of the direct and scattered components of the received signal, as expressed by the Ricean factor, provides an indication of link quality. Accordingly, efficient and accurate methods for estimating are of considerable interest. In this paper, we propose a general class of moment-based estimators which use the signal envelope. This class of estimators unifies many of the previous estimators, and introduces new ones. We derive, for the first time, the asymptotic variance (AsV) of these estimators and compare them with the Cramér-Rao bound (CRB). We then tackle the problem of estimating from the in-phase and quadrature-phase (I/Q) components of the received signal and illustrate the improvement in performance as compared with the envelope-based estimators. We derive the CRBs for the I/Q data model, which, unlike the envelope CRB, is tractable for correlated samples. Furthermore, we introduce a novel estimator that relies on the I/Q components, and derive its AsV even when the channel samples are correlated. We corroborate our analytical findings by simulations. Index Terms-Detection and estimation, propagation and channel characterization.

Ricean parameter estimation using phase information in low SNR environments

“…Estimators of K based solely on the envelope of X such as MOME have fundamentally poor variance performance at low values of K [4]. This problem can be overcome by also using the phase information in X when estimating K. The CRB for estimating K from X strictly dominates the CRB of purely Ricean data, especially at low values of SNR [1], [4].…”

“…This problem can be overcome by also using the phase information in X when estimating K. The CRB for estimating K from X strictly dominates the CRB of purely Ricean data, especially at low values of SNR [1], [4]. A technique that uses phase information was also put forward by [4] and estimates K using estimates of Ω and s…”

“…It has been studied in many papers [1]- [4], often in the context of wireless communications, as we do here. If we model a communication system as a transmitter sending a single tone to a receiver, which is also exposed to additive uncorrelated Gaussian noise, the complex version of the received signal can then be represented as: …”

The Influence of the User in Body-Centric Antennas and Propagation at 3–6 GHz—A Rician $K$-Factor Approach

“…Recent literature provides examples of the use of Rician Kfactor as a metric of the environment, showing it can be used to characterize fading from multiple human pedestrians [8], provide an indication of channel status [9], determine optimal Rayleigh stirring conditions for reverberation chambers [10] and to specify a radio channel's frequency-selective nature [11].…”

BER of IEEE 802.11ad OFDM radios vs. carrier frequency in real 60 GHz indoor channels

“…This factor characterizes statistically the distribution of the received signal amplitude envelope and it is a measure of the relative strength of the LoS signal power compared to scattered MPCs. In other words, representing the CIR h(t) as [10], [12]:…”