18,227 research outputs found
On the hopping pattern design for D2D discovery with invariant
In this paper, we focus on the hopping pattern design for device-to-device
(D2D) discovery. The requirements of hopping pattern is discussed, where the
impact of specific system constraints, e.g., frequency hopping, is also taken
into consideration. Specifically speaking, we discover and utilize the novel
feature of resource hopping, i.e., "hopping invariant" to design four new
hopping patterns and analyze their performance. The hopping invariant can be
used to deliver information for specific users without extra radio resources,
and due to the connection between hopping invariant and resource location,
receiver complexity can be significantly reduced. Furthermore, our schemes are
designed to be independent of discovery frame number, which makes them more
suitable to be implemented in practical systems
Implementation of CAVENET and its usage for performance evaluation of AODV, OLSR and DYMO protocols in vehicular networks
Vehicle Ad-hoc Network (VANET) is a kind of Mobile Ad-hoc Network (MANET) that establishes wireless connection between cars. In VANETs and MANETs, the topology of the network changes very often, therefore implementation of efficient routing protocols is very important problem. In MANETs, the Random Waypoint (RW) model is used as a simulation model for generating node mobility pattern. On the other hand, in VANETs, the mobility patterns of nodes is restricted along the roads, and is affected by the movement of neighbour nodes. In this paper, we present a simulation system for VANET called CAVENET (Cellular Automaton based VEhicular NETwork). In CAVENET, the mobility patterns of nodes are generated by an 1-dimensional cellular automata. We improved CAVENET and implemented some routing protocols. We investigated the performance of the implemented routing protocols by CAVENET. The simulation results have shown that DYMO protocol has better performance than AODV and OLSR protocols.Peer ReviewedPostprint (published version
Transient handover blocking probabilities in road covering cellular mobile networks
This paper investigates handover and fresh call blocking probabilities for subscribers moving along a road in a traffic jam passing through consecutive cells of a wireless network. It is observed and theoretically motivated that the handover blocking probabilities show a sharp peak in the initial part of a traffic jam roughly at the moment when the traffic jam starts covering a new cell. The theoretical motivation relates handover blocking probabilities to blocking probabilities in the M/D/C/C queue with time-varying arrival rates. We provide a numerically efficient recursion for these blocking probabilities. \u
Deterministic characterization of stochastic genetic circuits
For cellular biochemical reaction systems where the numbers of molecules is
small, significant noise is associated with chemical reaction events. This
molecular noise can give rise to behavior that is very different from the
predictions of deterministic rate equation models. Unfortunately, there are few
analytic methods for examining the qualitative behavior of stochastic systems.
Here we describe such a method that extends deterministic analysis to include
leading-order corrections due to the molecular noise. The method allows the
steady-state behavior of the stochastic model to be easily computed,
facilitates the mapping of stability phase diagrams that include stochastic
effects and reveals how model parameters affect noise susceptibility, in a
manner not accessible to numerical simulation. By way of illustration we
consider two genetic circuits: a bistable positive-feedback loop and a
negative-feedback oscillator. We find in the positive feedback circuit that
translational activation leads to a far more stable system than transcriptional
control. Conversely, in a negative-feedback loop triggered by a
positive-feedback switch, the stochasticity of transcriptional control is
harnessed to generate reproducible oscillations.Comment: 6 pages (Supplementary Information is appended
Culture and Cancer
Genetic mechanisms, since they broadly involve information
transmission, should be translatable into information dynamics formalism. From this perspective we reconsider the adaptive mutator, one possible means of 'second order selection' by which a highly structured 'language' of environment and development writes itself onto the variation upon which evolutionary selection and tumorigenesis operate. Our approach uses recent results in the spirit of the Large Deviations Program of applied probability that permit transfer of phase transition approaches from statistical mechanics to information theory, generating evolutionary and developmental punctuation in what we claim to be a highly natural manner
Toward Cultural Oncology: The Evolutionary Information Dynamics of Cancer
'Racial' disparities among cancers, particularly of the breast and prostate, are something of a mystery. For the US, in the face of slavery and its sequelae, centuries of interbreeding have greatly leavened genetic differences between 'Blacks' and 'whites', but marked contrasts in disease prevalence and progression persist. 'Adjustment' for socioeconomic status and lifestyle, while statistically accounting for much of the variance in breast cancer, only begs the question of ultimate causality. Here we propose a more basic biological explanation that extends the theory of immune cognition to include elaborate tumor control mechanisms constituting the principal selection pressure acting on pathologically mutating cell clones. The interplay between them occurs in the context of an embedding, highly structured, system of culturally specific psychosocial stress which we find is able to literally write an image of itself onto disease progression. The dynamics are analogous to punctuated equilibrium in simple evolutionary proces
Towards Designing Artificial Universes for Artificial Agents under Interaction Closure
We are interested in designing artificial universes for artificial agents. We view artificial agents as networks of highlevel processes on top of of a low-level detailed-description system. We require that the high-level processes have some intrinsic explanatory power and we introduce an extension of informational closure namely interaction closure to capture this. Then we derive a method to design artificial universes in the form of finite Markov chains which exhibit high-level processes that satisfy the property of interaction closure. We also investigate control or information transfer which we see as an building block for networks representing artificial agent
- âŚ