A novel comprehensive classification system is proposed for recognizing the orthogonal frequency division multiplexing (OFDM) signal and extracting its parameters. An empirical distribution function-based Gaussianity test technique is first applied to distinguish OFDM from single carrier modulations. Cyclostationarity and correlation tests are then applied to estimate OFDM symbol duration and cyclic prefix duration. A bank of fast Fourier transform (FFT) bank filter is devised to detect the number of subcarriers. Numerical results are presented to verify the efficiency of the proposed scheme

In this paper we have shown that by taking advantage of the acoustic particle velocity, in addition to the acoustic pressure, multichannel reception can be accomplished in underwater channels. Theoretical formulation and Monte Carlo simulations are provided for a vector sensor equalizer that measures the pressure and the velocity at a single point in space. These results demonstrate the usefulness of a compact vector sensor as a multichannel equalizer.

In this study a new computational method is developed to quantify decision making errors in cells, caused by noise and signaling failures. Analysis of tumor necrosis factor (TNF) signaling pathway which regulates the transcription factor Nuclear Factor κB (NF-κB) using this method identifies two types of incorrect cell decisions called false alarm and miss. These two events represent, respectively, declaring a signal which is not present and missing a signal that does exist. Using single cell experimental data and the developed method, we compute false alarm and miss error probabilities in wild-type cells and provide a formulation which shows how these metrics depend on the signal transduction noise level. We also show that in the presence of abnormalities in a cell, decision making processes can be significantly affected, compared to a wild-type cell, and the method is able to model and measure such effects. In the TNF—NF-κB pathway, the method computes and reveals changes in false alarm and miss probabilities in A20-deficient cells, caused by cell’s inability to inhibit TNF-induced NF-κB response. In biological terms, a higher false alarm metric in this abnormal TNF signaling system indicates perceiving more cytokine signals which in fact do not exist at the system input, whereas a higher miss metric indicates that it is highly likely to miss signals that actually exist. Overall, this study demonstrates the ability of the developed method for modeling cell decision making errors under normal and abnormal conditions, and in the presence of transduction noise uncertainty. Compared to the previously reported pathway capacity metric, our results suggest that the introduced decision error metrics characterize signaling failures more accurately. This is mainly because while capacity is a useful metric to study information transmission in signaling pathways, it does not capture the overlap between TNF-induced noisy response curves. Cell continuously receives signals from the surrounding environment and is supposed to make correct decisions, i.e., respond properly to various signals and initiate certain cellular functions. Modeling and quantification of decision making processes in a cell have emerged as important areas of research in recent years. Due to signal transduction noise, cells respond differently to similar inputs, which may result in incorrect cell decisions. Here we develop a novel method for characterization of decision making processes in cells, using statistical signal processing and decision theory concepts. To demonstrate the utility of the method, we apply it to an important signaling pathway that regulates molecules which play key roles in cell survival. Our method reveals that cells can make two types of incorrect decisions, namely, false alarm and miss events. We measure the likelihood of these decisions using single cell experimental data, and demonstrate how these incorrect decisions are related to the signal transduction noise or absence of certain molecular functions. Using our method, decision making errors in other molecular systems can be modeled. Such models are useful for understanding and developing treatments for pathological processes such as inflammation, various cancers and autoimmune diseases.

Intracellular signaling networks transmit signals from the cell membrane to the nucleus, via biochemical interactions. The goal is to regulate some target molecules, to properly control the cell function. Regulation of the target molecules occurs through the communication of several intermediate molecules that convey specific signals originated from the cell membrane to the specific target outputs.

We introduce a comprehensive statistical characterization of the multipath wireless channel built as a superposition of scattered waves with random phases. We consider an arbitrary number $N$ of specular (dominant) components plus an undetermined number of other weak diffusely propagating waves. Our approach covers the cases in which the specular components have constant amplitudes, as well as when these components experience random fluctuations. We show that this class of fading models can be expressed in terms of a continuous mixture of an underlying Rician (or Rician shadowed) fading model, averaged over the phase distributions of the specular waves. The proposed model parameters can be adjusted to tailor the statistical distribution of the received radio signal power to a wide variety of wireless scenarios, some of which are not covered by other state-of-the-art stochastic wireless channel models. In this regard, we verify that the proposed models accurately fit experimental measurements for which their multi-modality cannot be properly captured by other current stochastic models. It is shown that the fluctuations of the specular components have a detrimental impact on performance, and it is formally demonstrated that the lower error rate is obtained when the signal power is concentrated on a single specular component, regardless of whether it fluctuates or not.

Accurate characterization of multiple-input multiple-output (MIMO) fading channels is an important prerequisite for the design of multiantenna wireless-communication systems. In this paper, a single-bounce two-ring statistical model for the time-varying MIMO flat fading channels is proposed. In the model, both the base and mobile stations are surrounded by their own ring of scatterers. For the proposed model, a closed-form expression for the spatio-temporal cross-correlational function between any two subchannels is derived, assuming single-bounce scattering. The new analytical expression includes several key physical parameters of interest such as the mean angle-of-departure, the mean angle-of-arrival, the associated angle spreads, and the Doppler spread in a compact form. The model includes many existing correlation models as special cases. Its utility is demonstrated by a comparison with collected MIMO data in terms of the spatio-temporal correlations, level crossing rate, average fade duration, and the instantaneous mutual information.