Experimental Characterization and Modeling of Outdoor-to-Indoor and Indoor-to-Indoor Distributed Channels

Abstract

Abstract-We propose and parameterize an empirical model of the outdoor-to-indoor and indoor-to-indoor distributed (cooperative) radio channel, using experimental data in the 2.4 GHz band. In addition to the well-known physical effects of path loss, shadowing, and fading, we include several new aspects in our model that are specific to multi-user distributed channels: (i) correlated shadowing between different point to point links which has a strong impact on cooperative system performance, (ii) different types of indoor node mobility with respect to the transmitter and/or receiver nodes, implying a distinction between static and dynamic shadowing motivated by the measurement data, and (iii) a small-scale fading distribution that captures more severe fading than given by the Rayleigh distribution.

Channel Model Identification in Wireless Sensor Networks Using a Fully Distributed Quantized Consensus Algorithm

“…Such an approach will be considered as a reference for this work and referred hereafter as Centralized Channel Parameter Identification (CCPI). Similar techniques are reported also in Oestges et al (2010), for different scenarios, and in Bardella et al (2010), where the estimation of the channel parameters is obtained by applying statistical models to the data gathered through extensive experimental operation employing multichannel transmission.…”

Channel dynamics in body area networks: Recent results and challenges

“…In our previous works [17], [19], we showed that this type of fading could be described by a single distribution, consisting of a weighted combination of lineof-sight (LOS), Rayleigh and Double-Rayleigh contributions. Hence, any realization of the channel can be directly written as…”

“…Weights ω 0 , ω 1 and ω 2 describe the relative impacts of the LOS, Rayleigh and Double Rayleigh components, respectively. The probability density function of g = |h| is given, as shown in [19], [20], by the so called SOSF distribution:…”

Modeling Time-Variant Fast Fading Statistics of Mobile Peer-to-Peer Radio Channels

“…Following the concept of [7], the channel can be seen as a superposition of path loss, static shadowing, dynamic shadowing, and fast fading. To extract the fast fading from the channels, we first calculate the average power of the channel in its subbands and use a moving time window as…”

“…A first thorough investigation of peer-to-peer radio channels in an indoor office environment was carried out in [7], where it turned out that the statistics of small-scale fading may range from strongly Ricean channels (for static nodes) via Rayleigh channels (when only a single node of a link moves) to doubleRayleigh channels (when both nodes of a link move, and rich scattering is present around both nodes).…”