Channel Capacity of Molecular Signaling via Diffusion in Confined Microenvironment

Aims: To model molecular signal propagation in confined environment. Background: Molecular communication (MC) is rooted in the concepts of understanding, modeling, and engineering information exchange among naturally and artificially synthesized nanosystems. To develop or analyze an MC system, there is the need to model the communication channel through which the molecular signal diffuse, from the transmitter to the receiver. Many models for the diffusion-based MC channel have been proposed in the literature for evaluating the performance of MC systems. Most of the contemporary works assume, and rightly so for some scenarios, that the MC channels under consideration have infinite boundaries. However, this assumption becomes invalid in bounded domains such as the interiors of natural cells and artificially synthesized nanosystems. Objective: In this paper, the model of molecular propagation in a confined. microenvironment is employ to explore the effect of such an environment on the MC system. Method: The mutual information of the channel and specifically the closed-form expression of the channel capacity of the molecular signaling in the confined geometry is derive. Result: Numerical results showing the variation in the channel capacity as the function of the channel dimension are presented. Conclusion: Results showed that the channel capacity increases with the decrease in the channel dimension. Subsequently, as the dimension of the channel tends to the nanoscale range typical of many artificially synthesized nanosystems, the effect of the channel width on the capacity and by induction on many other system metrics increases.

Stochastic Versus Ray Tracing Wireless Channel Modeling for 5G and V2X Applications: Opportunities and Challenges

Due to the rapid development of wireless communication applications, the study of Multiple Input Multiple Output (MIMO) communication systems has gained comprehensive research activities since it can significantly increase the channel capacity and link reliability without sacrificing bandwidth and/or transmitted power levels. Researchers tend to evaluate the performance of their MIMO antenna arrays using various channel modeling tools. These channel models are mainly categorized into either deterministic channels based on Ray Tracing (RT) tools or Stochastic Channel Models (SCM). In this chapter, we compare these two categories in terms of the MIMO channel capacity using a complete description of the antennas at the transmitting and receiving ends in terms of 3D polarimetric radiation patterns and scattering parameters. The performance is evaluated for 5G New Radio (NR) Enhanced Mobile Broadband (eMBB) and Ultra-Reliable Low-Latency Communication (URLLC) services and Vehicle-to-Everything (V2X) systems using state-of-the-art commercial SCM and RT tools to provide information regarding the capabilities and limitations of each approach under different channel environments and the Quality of Experience (QoE) for high data rate and low latency content delivery in the 5G NR sub-6GHz mid-band Frequency Range-1 (FR1) N77/N78 bands.

Radio Channel Capacity with Directivity Control of Antenna Beams in Multipath Propagation Environment

The basic technology that will determine the expansion of the technical capabilities of fifth generation cellular systems is a massive multiple-input-multiple-output. Therefore, assessing the influence of the antenna beam orientations on the radio channel capacity is very significant. In this case, the effects of mismatching the antenna beam directions are crucial. In this paper, the methodology for evaluating changes in the received signal power level due to beam misalignment for the transmitting and receiving antenna systems is presented. The quantitative assessment of this issue is presented based on simulation studies carried out for an exemplary propagation scenario. For non-line-of-sight (NLOS) conditions, it is shown that the optimal selection of the transmitting and receiving beam directions may ensure an increase in the level of the received signal by several decibels in relation to the coaxial position of the beams. The developed methodology makes it possible to analyze changes in the radio channel capacity versus the signal-to-noise ratio and distance between the transmitter and receiver at optimal and coaxial orientations of antenna beams for various propagation scenarios, considering NLOS conditions. In the paper, the influence of the directional antenna use and their direction choices on the channel capacity versus SNR and the distance between the transmitter and receiver is shown.

The channel capacity of multilevel linguistic features constrains speech comprehension

Humans are expert at processing speech but how this feat is accomplished remains a major question in cognitive neuroscience. Capitalizing on the concept of channel capacity, we developed a unified measurement framework to investigate the respective influence of seven acoustic and linguistic features on speech comprehension, encompassing acoustic, sub-lexical, lexical and supra-lexical levels of description. We show that comprehension is independently impacted by all these features, but at varying degrees and with a clear dominance of the syllabic rate. Comparing comprehension of French words and sentences further reveals that when supra-lexical contextual information is present, the impact of all other features is dramatically reduced. Finally, we estimated the channel capacity associated with each linguistic feature and compared them with their generic distribution in natural speech. Our data point towards supra-lexical contextual information as the feature limiting the flow of natural speech. Overall, this study reveals how multilevel linguistic features constrain speech comprehension.

Fast but Not Furious. When Sped Up Bit Rate of Information Drives Rule Induction

The language abilities of young and adult learners range from memorizing specific items to finding statistical regularities between them (item-bound generalization) and generalizing rules to novel instances (category-based generalization). Both external factors, such as input variability, and internal factors, such as cognitive limitations, have been shown to drive these abilities. However, the exact dynamics between these factors and circumstances under which rule induction emerges remain largely underspecified. Here, we extend our information-theoretic model (Radulescu et al., 2019), based on Shannon’s noisy-channel coding theory, which adds into the “formula” for rule induction the crucial dimension of time: the rate of encoding information by a time-sensitive mechanism. The goal of this study is to test the channel capacity-based hypothesis of our model: if the input entropy per second is higher than the maximum rate of information transmission (bits/second), which is determined by the channel capacity, the encoding method moves gradually from item-bound generalization to a more efficient category-based generalization, so as to avoid exceeding the channel capacity. We ran two artificial grammar experiments with adults, in which we sped up the bit rate of information transmission, crucially not by an arbitrary amount but by a factor calculated using the channel capacity formula on previous data. We found that increased bit rate of information transmission in a repetition-based XXY grammar drove the tendency of learners toward category-based generalization, as predicted by our model. Conversely, we found that increased bit rate of information transmission in complex non-adjacent dependency aXb grammar impeded the item-bound generalization of the specific a_b frames, and led to poorer learning, at least judging by our accuracy assessment method. This finding could show that, since increasing the bit rate of information precipitates a change from item-bound to category-based generalization, it impedes the item-bound generalization of the specific a_b frames, and that it facilitates category-based generalization both for the intervening Xs and possibly for a/b categories. Thus, sped up bit rate does not mean that an unrestrainedly increasing bit rate drives rule induction in any context, or grammar. Rather, it is the specific dynamics between the input entropy and the maximum rate of information transmission.