On Modeling Coverage and Rate of Random Cellular Networks under Generic Channel Fading

In this paper we provide an analytic framework for computing the expected downlink coverage probability, and the associated data rate of cellular networks, where base stations are distributed in a random manner. The provided expressions are in computable integral forms that accommodate generic channel fading conditions. We develop these expressions by modelling the cellular interference using stochastic geometry analysis, then we employ them for comparing the coverage resulting from various channel fading conditions namely Rayleigh and Rician fading, in addition to the fading-less channel. Furthermore, we expand the work to accommodate the effects of random frequency reuse on the cellular coverage and rate. Monte-Carlo simulations are conducted to validate the theoretical analysis, where the results show a very close match

Channel Quantization Based on the Statistical Characterization of Spatially Correlated Fading

Multiuser multiple-input-multiple-output (MUMIMO) techniques, such as scheduling and precoding, have shown to improve the spectral efficiency of wireless communication systems. However, these techniques require an accurate knowledge of the channel of the different users at the transmitter. In frequency-division duplex (FDD) systems, this information has to be provided by the different users, motivating the research of efficient limited feedback schemes. This paper presents a novel statistical characterization of the spatial multiple-input-single-output (MISO) channel. In this characterization, one antenna is selected as the reference, and the channel fading experienced from this antenna is also considered to be the reference. The conditional probability density functions (CPDFs) of the envelope and phase of the channel fading coefficients from the rest of the antennas (denoted as nonreference channel fading and nonreference antennas) are obtained given the reference one. Based on this statistical characterization, this paper proposes a channel quantization scheme that individually quantizes the channel fading coefficient of each transmit antenna that is seen by each user. The envelope and phase of the reference channel fading are quantized considering a Rayleigh distribution and a uniform distribution, respectively. The nonreference channel fading coefficients are quantized according to their respective CPDFs, which in turn depend on the spatial correlation between each channel fading and the reference channel fading. Numerical simulations have been carried out to compare the performance of the proposed conditional quantization (CQ) scheme with a polar quantization (PQ) and with a quantization based on the Karhunen-Loève (KL) transform. PQ does not consider spatial correlation, CQ needs one spatial correlation coefficient per nonreference antenna, and the KL scheme makes use of the full spatial correlation matrix. The results show that CQ achieves a lower quantization mean square error (MSE) than the other two schemes in highly and moderately correlated environments. When the spatial channel model (SCM) is considered, the proposed scheme allows the spatial correlation to be successfully exploited in arrays with N=hbox4 and N=hbox8 transmit antennas for antenna separations that are lower than d=hbox1.3lambda and d=hbox0.75lambda, respectivelyThis work was supported by European Union ERDF and Spanish Government through Project TEC2012-38142-C04 and Generalitat Valenciana through Project PROME-TEOII/2014/003.Domene Oltra, F.; Piñero Sipán, MG.; Diego Antón, MD.; Gonzalez, A. (2015). Channel Quantization Based on the Statistical Characterization of Spatially Correlated Fading. IEEE Transactions on Vehicular Technology. 64(9):3931-3943. https://doi.org/10.1109/TVT.2014.2363170S3931394364

Worse-than-Rayleigh Fading: Experimental Results and Theoretical Models

This article is motivated by the recent recognition that channel fading for new wireless applications is not always well described by traditional models used for mobile communication systems. In particular, fading data collected for vehicleto- vehicle and wireless sensor network applications has motivated new models for conditions in which channel fading statistics can be worse than Rayleigh. We review the use of statistical channel models, describe our example applications, and provide both measured and modeling results for these severe fading conditions

State Predictor of Classification Cognitive Engine Applied to Channel Fading

This study presents the application of machine learning (ML) to a space-to-ground communication link, showing how ML can be used to detect the presence of detrimental channel fading. Using this channel state information, the communication link can be used more efficiently by reducing the amount of lost data during fading. The motivation for this work is based on channel fading observed during on-orbit operations with NASA's Space Communication and Navigation (SCaN) testbed on the International Space Station (ISS). This paper presents the process to extract a target concept (fading and not-fading) from the raw data. The pre-processing and data exploration effort is explained in detail, with a list of assumptions made for parsing and labelling the dataset. The model selection process is explained, specifically emphasizing the benefits of using an ensemble of algorithms with majority voting for binary classification of the channel state. Experimental results are shown, highlighting how an end-to-end communication system can utilize knowledge of the channel fading status to identity fading and take appropriate action. With a laboratory testbed to emulate channel fading, the overall performance is compared to standard adaptive methods without fading knowledge, such as adaptive coding and modulation