159 research outputs found
Caching-Aided Collaborative D2D Operation for Predictive Data Dissemination in Industrial IoT
Industrial automation deployments constitute challenging environments where
moving IoT machines may produce high-definition video and other heavy sensor
data during surveying and inspection operations. Transporting massive contents
to the edge network infrastructure and then eventually to the remote human
operator requires reliable and high-rate radio links supported by intelligent
data caching and delivery mechanisms. In this work, we address the challenges
of contents dissemination in characteristic factory automation scenarios by
proposing to engage moving industrial machines as device-to-device (D2D)
caching helpers. With the goal to improve reliability of high-rate
millimeter-wave (mmWave) data connections, we introduce the alternative
contents dissemination modes and then construct a novel mobility-aware
methodology that helps develop predictive mode selection strategies based on
the anticipated radio link conditions. We also conduct a thorough system-level
evaluation of representative data dissemination strategies to confirm the
benefits of predictive solutions that employ D2D-enabled collaborative caching
at the wireless edge to lower contents delivery latency and improve data
acquisition reliability
On the Temporal Effects of Mobile Blockers in Urban Millimeter-Wave Cellular Scenarios
Millimeter-wave (mmWave) propagation is known to be severely affected by the
blockage of the line-of-sight (LoS) path. In contrast to microwave systems, at
shorter mmWave wavelengths such blockage can be caused by human bodies, where
their mobility within environment makes wireless channel alternate between the
blocked and non-blocked LoS states. Following the recent 3GPP requirements on
modeling the dynamic blockage as well as the temporal consistency of the
channel at mmWave frequencies, in this paper a new model for predicting the
state of a user in the presence of mobile blockers for representative 3GPP
scenarios is developed: urban micro cell (UMi) street canyon and
park/stadium/square. It is demonstrated that the blockage effects produce an
alternating renewal process with exponentially distributed non-blocked
intervals, and blocked durations that follow the general distribution. The
following metrics are derived (i) the mean and the fraction of time spent in
blocked/non-blocked state, (ii) the residual blocked/non-blocked time, and
(iii) the time-dependent conditional probability of having blockage/no blockage
at time t1 given that there was blockage/no blockage at time t0. The latter is
a function of the arrival rate (intensity), width, and height of moving
blockers, distance to the mmWave access point (AP), as well as the heights of
the AP and the user device. The proposed model can be used for system-level
characterization of mmWave cellular communication systems. For example, the
optimal height and the maximum coverage radius of the mmWave APs are derived,
while satisfying the required mean data rate constraint. The system-level
simulations corroborate that the use of the proposed method considerably
reduces the modeling complexity.Comment: Accepted, IEEE Transactions on Vehicular Technolog
Objective Function Development Based on the Parameters of a Regional Terminal Network
The article is devoted to the development of the objective function for calculating the parameters of a terminal network in a region. The key parameters of a terminal network, as logistic system, are described. The factors influencing the parameters of a terminal network are analyzed, and the objective function, concerning the value and qualitative aspects for determining the transport and spatial/quantitative parameters proposed. The level of service quality for the clients is also taken into account. The heuristic solution method, and the results of the approach are demonstrated with the help of an application example concerning different combinations of distribution.Der Artikel widmet sich der Modellierung eines Transportnetzes, das, unter Beachtung der Lage der Distributionszentren und der Lage der Kunden, minimale Gesamtlogistikkosten ergibt. Diese setzen sich wiederum aus den Transport- und aus den Standortkosten hinsichtlich der Lager- und Betriebskosten der Distributionszentren zusammen. DarΓΌber hinaus wird auch die Maximierung beziehungsweise das Niveau der ServicequalitΓ€t fΓΌr die Kunden einbezogen. Das heuristische LΓΆsungsverfahren wird anschlieΓend an einem Anwendungsbeispiel hinsichtlich unterschiedlicher Kombinationen von Distributionszentren erprobt und hinsichtlich der Zielerreichung dokumentiert
Π‘ΠΈΡΡΠ΅ΠΌΡ M|G|1 Ρ Π³ΡΡΠΏΠΏΠΎΠ²ΡΠΌ ΠΎΠ±ΡΠ»ΡΠΆΠΈΠ²Π°Π½ΠΈΠ΅ΠΌ ΠΈ ΠΈΡ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ ΠΊ Π°Π½Π°Π»ΠΈΠ·Ρ ΠΌΠΎΠ΄Π΅Π»ΠΈ ΠΏΡΠΎΡΠΎΠΊΠΎΠ»Π° ΡΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ ΠΏΠΎΡΠΎΠΊΠΎΠ²ΠΎΠΉ ΠΏΠ΅ΡΠ΅Π΄Π°ΡΠ΅ΠΉ. Π§Π°ΡΡΡ II
In this paper we introduce an analytical model of the stream control transmission protocol - a queuing system with batch service and job bundling timeout. The applicability of the analytical models in which jobs are served in batches of fixed and variable size [1] is assessed based on numerical results. An algorithm for the calculation of key performance measures is obtained. We also consider the process of signaling messages transmission over IP-network and analyze protocol parameters to provide quality of service in mobile networks.Π ΡΡΠ°ΡΡΠ΅ ΠΈΠ·Π»ΠΎΠΆΠ΅Π½Ρ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΊΠΈ ΠΌΠΎΠ΄Π΅Π»ΠΈ ΡΡΠ½ΠΊΡΠΈΠΎΠ½ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΏΡΠΎΡΠΎΠΊΠΎΠ»Π° ΡΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ ΠΏΠΎΡΠΎΠΊΠΎΠ²ΠΎΠΉ ΠΏΠ΅ΡΠ΅Π΄Π°ΡΠ΅ΠΉ (Stream Control Transmission Protocol, SCTP) Π² Π²ΠΈΠ΄Π΅ ΡΠΈΡΡΠ΅ΠΌΡ Ρ Π³ΡΡΠΏΠΏΠΎΠ²ΡΠΌ ΠΎΠ±ΡΠ»ΡΠΆΠΈΠ²Π°Π½ΠΈΠ΅ΠΌ Π·Π°ΡΠ²ΠΎΠΊ. ΠΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½Π° ΡΡΠ΅ΠΏΠ΅Π½Ρ Π°Π΄Π΅ΠΊΠ²Π°ΡΠ½ΠΎΡΡΠΈ ΠΊΠ»Π°ΡΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ ΡΠΈΠΏΠ° ||1 Ρ ΠΎΠ±ΡΠ»ΡΠΆΠΈΠ²Π°Π½ΠΈΠ΅ΠΌ Π³ΡΡΠΏΠΏΠ°ΠΌΠΈ ΠΏΠ΅ΡΠ΅ΠΌΠ΅Π½Π½ΠΎΠΉ ΠΈ ΡΠΈΠΊΡΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠΉ Π΄Π»ΠΈΠ½Ρ [1] ΠΏΡΠΈΠΌΠ΅Π½ΠΈΡΠ΅Π»ΡΠ½ΠΎ ΠΊ Π°Π½Π°Π»ΠΈΠ·Ρ ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»Π΅ΠΉ ΠΊΠ°ΡΠ΅ΡΡΠ²Π° ΠΎΠ±ΡΠ»ΡΠΆΠΈΠ²Π°Π½ΠΈΡ ΠΏΡΠΎΡΠΎΠΊΠΎΠ»Π° SCTP. ΠΡΠΎΠ²Π΅Π΄ΡΠ½ ΡΠΈΡΠ»Π΅Π½Π½ΡΠΉ Π°Π½Π°Π»ΠΈΠ· ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠΎΠ² ΠΊΠ°ΡΠ΅ΡΡΠ²Π° ΡΡΠ½ΠΊΡΠΈΠΎΠ½ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΏΡΠΎΡΠΎΠΊΠΎΠ»Π° Π΄Π»Ρ ΡΠ»ΡΡΠ°Ρ ΠΏΠ΅ΡΠ΅Π΄Π°ΡΠΈ ΡΠΈΠ³Π½Π°Π»ΡΠ½ΠΎΠ³ΠΎ ΡΡΠ°ΡΠΈΠΊΠ° ΡΠ΅ΡΠΈ ΡΠΎΡΠΎΠ²ΠΎΠΉ ΠΏΠΎΠ΄Π²ΠΈΠΆΠ½ΠΎΠΉ ΡΠ²ΡΠ·ΠΈ
ΠΠΎΠ΄Π΅Π»Ρ Π²ΡΠ΄Π΅Π»Π΅Π½ΠΈΡ ΡΠ΅ΡΡΡΡΠΎΠ² Π±Π΅ΡΠΏΡΠΎΠ²ΠΎΠ΄Π½ΠΎΠΉ ΡΠ΅ΡΠΈ ΠΎΠ±ΡΡΠΌΠ°ΠΌΠΈ ΡΠ»ΡΡΠ°ΠΉΠ½ΠΎΠΉ Π²Π΅Π»ΠΈΡΠΈΠ½Ρ
The objective of this paper is the construction and analysis of the model of the wireless LTE network (Long Term Evolution) as a multiserver queuing system where losses are caused by a lack of resources required to service requests. Adopted by the service application takes a random amount of resources given to several types of distribution functions. Random vectors describing the requirements of applications to resources, processes do not depend on input ο¬ow and service distribution are jointly independent and identically distributed. L independent Poisson ο¬ows of requests enter the system, and there are N identical devices. Service times are distributed exponentially. The functioning of the system is described by the semi-Markov process, which takes into account the number of serviced requests, their types and amounts of resources they occupy. Explicit expressions for the stationary distribution of the semi-Markov process, and the theorem on product form solution are main results of the paper. Further studies suggest checking the hypothesis of invariance with respect to the form of the stationary distribution of the distribution of the service time and the development of numerical methods for the analysis of probability measures of the system.ΠΠ°Π΄Π°ΡΠ΅ΠΉ Π΄Π°Π½Π½ΠΎΠΉ ΡΡΠ°ΡΡΠΈ ΡΠ²Π»ΡΠ΅ΡΡΡ ΠΏΠΎΡΡΡΠΎΠ΅Π½ΠΈΠ΅ ΠΈ Π°Π½Π°Π»ΠΈΠ· ΠΌΠΎΠ΄Π΅Π»ΠΈ ΡΠΎΡΡ Π±Π΅ΡΠΏΡΠΎΠ²ΠΎΠ΄Π½ΠΎΠΉ ΡΠ΅ΡΠΈ LTE (Long Term Evolution) Π² Π²ΠΈΠ΄Π΅ ΠΌΠ½ΠΎΠ³ΠΎΠ»ΠΈΠ½Π΅ΠΉΠ½ΠΎΠΉ ΡΠΈΡΡΠ΅ΠΌΡ ΠΌΠ°ΡΡΠΎΠ²ΠΎΠ³ΠΎ ΠΎΠ±ΡΠ»ΡΠΆΠΈΠ²Π°Π½ΠΈΡ (Π‘ΠΠ) Ρ ΠΏΠΎΡΠ΅ΡΡΠΌΠΈ, Π²ΡΠ·Π²Π°Π½Π½ΡΠΌΠΈ Π½Π΅Ρ
Π²Π°ΡΠΊΠΎΠΉ ΡΠ΅ΡΡΡΡΠΎΠ², Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΡΡ
Π΄Π»Ρ ΠΎΠ±ΡΠ»ΡΠΆΠΈΠ²Π°Π½ΠΈΡ Π·Π°ΡΠ²ΠΎΠΊ. ΠΡΠΈΠ½ΡΡΠ°Ρ Π½Π° ΠΎΠ±ΡΠ»ΡΠΆΠΈΠ²Π°Π½ΠΈΠ΅ Π·Π°ΡΠ²ΠΊΠ° Π·Π°Π½ΠΈΠΌΠ°Π΅Ρ ΡΠ»ΡΡΠ°ΠΉΠ½ΡΠΉ ΠΎΠ±ΡΠ΅ΠΌ ΡΠ΅ΡΡΡΡΠΎΠ² Π½Π΅ΡΠΊΠΎΠ»ΡΠΊΠΈΡ
ΡΠΈΠΏΠΎΠ² Ρ Π·Π°Π΄Π°Π½Π½ΡΠΌΠΈ ΡΡΠ½ΠΊΡΠΈΡΠΌΠΈ ΡΠ°ΡΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΡ. Π‘Π»ΡΡΠ°ΠΉΠ½ΡΠ΅ Π²Π΅ΠΊΡΠΎΡΡ, ΠΎΠΏΠΈΡΡΠ²Π°ΡΡΠΈΠ΅ ΡΡΠ΅Π±ΠΎΠ²Π°Π½ΠΈΡ Π·Π°ΡΠ²ΠΎΠΊ ΠΊ ΡΠ΅ΡΡΡΡΠ°ΠΌ, Π½Π΅ Π·Π°Π²ΠΈΡΡΡ ΠΎΡ ΠΏΡΠΎΡΠ΅ΡΡΠΎΠ² ΠΏΠΎΡΡΡΠΏΠ»Π΅Π½ΠΈΡ ΠΈ ΠΎΠ±ΡΠ»ΡΠΆΠΈΠ²Π°Π½ΠΈΡ Π·Π°ΡΠ²ΠΎΠΊ, Π½Π΅Π·Π°Π²ΠΈΡΠΈΠΌΡ Π² ΡΠΎΠ²ΠΎΠΊΡΠΏΠ½ΠΎΡΡΠΈ ΠΈ ΠΎΠ΄ΠΈΠ½Π°ΠΊΠΎΠ²ΠΎ ΡΠ°ΡΠΏΡΠ΅Π΄Π΅Π»Π΅Π½Ρ. ΠΠ° ΡΠΈΡΡΠ΅ΠΌΡ ΠΏΠΎΡΡΡΠΏΠ°Π΅Ρ L Π½Π΅Π·Π°Π²ΠΈΡΠΈΠΌΡΡ
ΠΏΡΠ°ΡΡΠΎΠ½ΠΎΠ²ΡΠΊΠΈΡ
ΠΏΠΎΡΠΎΠΊΠΎΠ² Π·Π°ΡΠ²ΠΎΠΊ, Π° Π΄Π»Ρ ΠΈΡ
ΠΎΠ±ΡΠ»ΡΠΆΠΈΠ²Π°Π½ΠΈΡ ΠΈΠΌΠ΅Π΅ΡΡΡ N ΠΈΠ΄Π΅Π½ΡΠΈΡΠ½ΡΡ
ΠΏΡΠΈΠ±ΠΎΡΠΎΠ². ΠΠ»ΠΈΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ ΠΎΠ±ΡΠ»ΡΠΆΠΈΠ²Π°Π½ΠΈΡ Π·Π°ΡΠ²ΠΎΠΊ ΡΠ°ΡΠΏΡΠ΅Π΄Π΅Π»Π΅Π½Ρ ΠΏΠΎ ΡΠΊΡΠΏΠΎΠ½Π΅Π½ΡΠΈΠ°Π»ΡΠ½ΠΎΠΌΡ Π·Π°ΠΊΠΎΠ½Ρ. Π€ΡΠ½ΠΊΡΠΈΠΎΠ½ΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ Π‘ΠΠ ΠΎΠΏΠΈΡΡΠ²Π°Π΅ΡΡΡ ΠΏΠΎΠ»ΡΠΌΠ°ΡΠΊΠΎΠ²ΡΠΊΠΈΠΌ ΠΏΡΠΎΡΠ΅ΡΡΠΎΠΌ, ΠΊΠΎΡΠΎΡΡΠΉ ΡΡΠΈΡΡΠ²Π°Π΅Ρ ΡΠΈΡΠ»ΠΎ Π½Π°Ρ
ΠΎΠ΄ΡΡΠΈΡ
ΡΡ Π½Π° ΠΎΠ±ΡΠ»ΡΠΆΠΈΠ²Π°Π½ΠΈΠΈ Π·Π°ΡΠ²ΠΎΠΊ, ΠΈΡ
ΡΠΈΠΏΡ ΠΈ ΠΎΠ±ΡΡΠΌΡ Π·Π°Π½ΠΈΠΌΠ°Π΅ΠΌΡΡ
ΠΈΠΌΠΈ ΡΠ΅ΡΡΡΡΠΎΠ². ΠΠΎΠ»ΡΡΠ΅Π½Ρ ΡΠ²Π½ΡΠ΅ Π²ΡΡΠ°ΠΆΠ΅Π½ΠΈΡ Π΄Π»Ρ ΡΡΠ°ΡΠΈΠΎΠ½Π°ΡΠ½ΠΎΠ³ΠΎ ΡΠ°ΡΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΡ ΠΏΠΎΠ»ΡΠΌΠ°ΡΠΊΠΎΠ²ΡΠΊΠΎΠ³ΠΎ ΠΏΡΠΎΡΠ΅ΡΡΠ°, Π° ΠΎΡΠ½ΠΎΠ²Π½ΡΠΌ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠΎΠΌ ΡΡΠ°ΡΡΠΈ ΡΠ²Π»ΡΠ΅ΡΡΡ ΡΠ΅ΠΎΡΠ΅ΠΌΠ° ΠΎ ΠΌΡΠ»ΡΡΠΈΠΏΠ»ΠΈΠΊΠ°ΡΠΈΠ²Π½ΠΎΡΡΠΈ ΠΏΠΎ ΡΠΈΡΠ»Ρ Π²Ρ
ΠΎΠ΄ΡΡΠΈΡ
ΠΏΠΎΡΠΎΠΊΠΎΠ² ΡΡΠ°ΡΠΈΠΎΠ½Π°ΡΠ½ΠΎΠ³ΠΎ ΡΠ°ΡΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΡ ΠΎΠ±ΡΡΠΌΠΎΠ² Π·Π°Π½ΡΡΡΡ
ΡΠ΅ΡΡΡΡΠΎΠ². ΠΠ°Π»ΡΠ½Π΅ΠΉΡΠΈΠ΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΠΏΡΠ΅Π΄ΠΏΠΎΠ»Π°Π³Π°ΡΡ ΠΏΡΠΎΠ²Π΅ΡΠΊΡ Π³ΠΈΠΏΠΎΡΠ΅Π·Ρ ΠΎΠ± ΠΈΠ½Π²Π°ΡΠΈΠ°Π½ΡΠ½ΠΎΡΡΠΈ Π²ΠΈΠ΄Π° ΡΡΠ°ΡΠΈΠΎΠ½Π°ΡΠ½ΠΎΠ³ΠΎ ΡΠ°ΡΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΡ ΠΎΡΠ½ΠΎΡΠΈΡΠ΅Π»ΡΠ½ΠΎ Π·Π°ΠΊΠΎΠ½Π° ΡΠ°ΡΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΡ Π΄Π»ΠΈΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ ΠΎΠ±ΡΠ»ΡΠΆΠΈΠ²Π°Π½ΠΈΡ, Π° ΡΠ°ΠΊΠΆΠ΅ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΊΡ ΡΠΈΡΠ»Π΅Π½Π½ΡΡ
ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² Π΄Π»Ρ Π°Π½Π°Π»ΠΈΠ·Π° Π²Π΅ΡΠΎΡΡΠ½ΠΎΡΡΠ½ΠΎ-Π²ΡΠ΅ΠΌΠ΅Π½Π½ΡΡ
Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊ ΡΠΈΡΡΠ΅ΠΌΡ
ΠΠΎΡΡΡΠΎΠ΅Π½ΠΈΠ΅ ΠΈ Π°Π½Π°Π»ΠΈΠ· ΠΌΠΎΠ΄Π΅Π»ΠΈ Π²Ρ ΠΎΠ΄Π½ΠΎΠ³ΠΎ ΠΊΠΎΠΌΠΌΡΡΠ°ΡΠΎΡΠ° Π² ΡΠ΅ΡΠΈ Ρ ΠΎΠΏΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΊΠΎΠΌΠΌΡΡΠ°ΡΠΈΠ΅ΠΉ
Currently, there are two generally recognized principles of switching of information signals in high-speed networks: networks with wave routing, and networks with the principle of optical packet switching. In networks with wave routing it is not required to produce opto-electrical and electro-optical conversions and to create a buffer, but with this switching principle the working range of wavelengths is not efficiently used. In networks with optical packet switching the traffic is transmitted in packets, which consist of a header and an information part of a consistent size. In this case, using of the frequency range is the most complete, but there is a need of optical-electronic conversions. In an effort to combine the advantages of two optical switching technologies, a new combined switching principle was proposed, called optical switching bursts. In this technology there are not buffering and electronic processing in intermediate nodes, there is a reservation of the channel for a limited time. For the effective implementation of such a network connection, we must calculate its probability characteristics. To assess probabilistic characteristics of the network the methods of theory of mass service are widely used. The input switch is one of the key devices on the network. The article describes the input switch of the network with the optical switching of bursts, calculates the probable characteristics of the network using analytical and simulation models. Examples of the calculation of the probability of blocking of packets flowing in the input switch are presented.Π Π½Π°ΡΡΠΎΡΡΠ΅Π΅ Π²ΡΠ΅ΠΌΡ ΡΡΡΠ΅ΡΡΠ²ΡΠ΅Ρ Π΄Π²Π° ΠΎΠ±ΡΠ΅ΠΏΡΠΈΠ·Π½Π°Π½Π½ΡΡ
ΠΏΡΠΈΠ½ΡΠΈΠΏΠ° ΠΊΠΎΠΌΠΌΡΡΠ°ΡΠΈΠΈ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΎΠ½Π½ΡΡ
ΡΠΈΠ³Π½Π°Π»ΠΎΠ² Π² Π²ΡΡΠΎΠΊΠΎΡΠΊΠΎΡΠΎΡΡΠ½ΡΡ
ΡΠ΅ΡΡΡ
: ΡΠ΅ΡΠΈ Ρ Π²ΠΎΠ»Π½ΠΎΠ²ΠΎΠΉ ΠΌΠ°ΡΡΡΡΡΠΈΠ·Π°ΡΠΈΠ΅ΠΉ ΠΈ ΡΠ΅ΡΠΈ Ρ ΠΏΡΠΈΠ½ΡΠΈΠΏΠΎΠΌ ΠΎΠΏΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΊΠΎΠΌΠΌΡΡΠ°ΡΠΈΠ΅ΠΉ ΠΏΠ°ΠΊΠ΅ΡΠΎΠ². Π ΡΠ΅ΡΡΡ
Ρ Π²ΠΎΠ»Π½ΠΎΠ²ΠΎΠΉ ΠΌΠ°ΡΡΡΡΡΠΈΠ·Π°ΡΠΈΠ΅ΠΉ Π½Π΅ ΡΡΠ΅Π±ΡΠ΅ΡΡΡ ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄ΠΈΡΡ ΠΎΠΏΡΠΈΠΊΠΎ-ΡΠ»Π΅ΠΊΡΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΈ ΡΠ»Π΅ΠΊΡΡΠΎ-ΠΎΠΏΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΏΡΠ΅ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΠΉ ΠΈ ΡΠΎΠ·Π΄Π°Π²Π°ΡΡ Π±ΡΡΠ΅Ρ, Π½ΠΎ ΠΏΡΠΈ Π΄Π°Π½Π½ΠΎΠΌ ΠΏΡΠΈΠ½ΡΠΈΠΏΠ΅ ΠΊΠΎΠΌΠΌΡΡΠ°ΡΠΈΠΈ Π½Π΅ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎ ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΠ΅ΡΡΡ ΡΠ°Π±ΠΎΡΠΈΠΉ Π΄ΠΈΠ°ΠΏΠ°Π·ΠΎΠ½ Π΄Π»ΠΈΠ½ Π²ΠΎΠ»Π½. Π ΡΠ΅ΡΡΡ
Ρ ΠΎΠΏΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΊΠΎΠΌΠΌΡΡΠ°ΡΠΈΠ΅ΠΉ ΠΏΠ°ΠΊΠ΅ΡΠΎΠ² ΡΡΠ°ΡΠΈΠΊ ΠΏΠ΅ΡΠ΅Π΄Π°ΡΡΡΡ Π² Π²ΠΈΠ΄Π΅ ΠΏΠ°ΠΊΠ΅ΡΠΎΠ², ΠΊΠΎΡΠΎΡΡΠ΅ ΡΠΎΡΡΠΎΡΡ ΠΈΠ· Π·Π°Π³ΠΎΠ»ΠΎΠ²ΠΊΠ° ΠΈ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΎΠ½Π½ΠΎΠΉ ΡΠ°ΡΡΠΈ ΠΏΠΎΡΡΠΎΡΠ½Π½ΠΎΠ³ΠΎ ΡΠ°Π·ΠΌΠ΅ΡΠ°. Π Π΄Π°Π½Π½ΠΎΠΌ ΡΠ»ΡΡΠ°Π΅ ΡΠ°ΡΡΠΎΡΠ½ΡΠΉ Π΄ΠΈΠ°ΠΏΠ°Π·ΠΎΠ½ ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΠ΅ΡΡΡ Π½Π°ΠΈΠ±ΠΎΠ»Π΅Π΅ ΠΏΠΎΠ»Π½ΠΎ, Π½ΠΎ ΠΏΠΎΡΠ²Π»ΡΠ΅ΡΡΡ Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΠΎΡΡΡ ΠΎΠΏΡΠΈΠΊΠΎ-ΡΠ»Π΅ΠΊΡΡΠΎΠ½Π½ΡΡ
ΠΏΡΠ΅ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΠΉ. Π‘ΡΡΠ΅ΠΌΡΡΡ ΡΠΎΠ΅Π΄ΠΈΠ½ΠΈΡΡ ΠΏΡΠ΅ΠΈΠΌΡΡΠ΅ΡΡΠ²Π° Π΄Π²ΡΡ
ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΉ ΠΎΠΏΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΊΠΎΠΌΠΌΡΡΠ°ΡΠΈΠΈ, Π±ΡΠ» ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½ Π½ΠΎΠ²ΡΠΉ ΠΊΠΎΠΌΠ±ΠΈΠ½ΠΈΡΠΎΠ²Π°Π½Π½ΡΠΉ ΠΏΡΠΈΠ½ΡΠΈΠΏ ΠΊΠΎΠΌΠΌΡΡΠ°ΡΠΈΠΈ, ΠΏΠΎΠ»ΡΡΠΈΠ²ΡΠΈΠΉ Π½Π°Π·Π²Π°Π½ΠΈΠ΅ ΠΎΠΏΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΊΠΎΠΌΠΌΡΡΠ°ΡΠΈΠΈ ΠΏΠ°ΡΠ΅ΠΊ. Π Π΄Π°Π½Π½ΠΎΠΉ ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΈ Π½Π΅Ρ Π±ΡΡΠ΅ΡΠΈΠ·Π°ΡΠΈΠΈ ΠΈ ΡΠ»Π΅ΠΊΡΡΠΎΠ½Π½ΠΎΠΉ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈ Π΄Π°Π½Π½ΡΡ
Π² ΠΏΡΠΎΠΌΠ΅ΠΆΡΡΠΎΡΠ½ΡΡ
ΡΠ·Π»Π°Ρ
, ΠΏΡΠΈΡΡΡΡΡΠ²ΡΠ΅Ρ ΡΠ΅Π·Π΅ΡΠ²ΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ ΠΊΠ°Π½Π°Π»Π° Π½Π° ΠΎΠ³ΡΠ°Π½ΠΈΡΠ΅Π½Π½ΠΎΠ΅ Π²ΡΠ΅ΠΌΡ. ΠΠ»Ρ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΠ³ΠΎ Π²Π½Π΅Π΄ΡΠ΅Π½ΠΈΡ ΡΠ°ΠΊΠΎΠΉ ΡΠ΅ΡΠΈ ΡΠ²ΡΠ·ΠΈ Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΠΎ ΡΠ°ΡΡΡΠΈΡΠ°ΡΡ Π΅Ρ Π²Π΅ΡΠΎΡΡΠ½ΠΎΡΡΠ½ΡΠ΅ Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊΠΈ. ΠΠ»Ρ ΠΎΡΠ΅Π½ΠΊΠΈ Π²Π΅ΡΠΎΡΡΠ½ΠΎΡΡΠ½ΡΡ
Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊ ΡΠ΅ΡΠΈ ΡΠΈΡΠΎΠΊΠΎ ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΡΡΡΡ ΠΌΠ΅ΡΠΎΠ΄Ρ ΡΠ΅ΠΎΡΠΈΠΈ ΠΌΠ°ΡΡΠΎΠ²ΠΎΠ³ΠΎ ΠΎΠ±ΡΠ»ΡΠΆΠΈΠ²Π°Π½ΠΈΡ. ΠΡ
ΠΎΠ΄Π½ΠΎΠΉ ΠΊΠΎΠΌΠΌΡΡΠ°ΡΠΎΡ ΡΠ²Π»ΡΠ΅ΡΡΡ ΠΎΠ΄Π½ΠΈΠΌ ΠΈΠ· ΠΊΠ»ΡΡΠ΅Π²ΡΡ
ΡΡΡΡΠΎΠΉΡΡΠ² ΡΠ΅ΡΠΈ. Π ΡΡΠ°ΡΡΠ΅ ΠΎΠΏΠΈΡΡΠ²Π°Π΅ΡΡΡ ΡΠ°Π±ΠΎΡΠ° Π²Ρ
ΠΎΠ΄Π½ΠΎΠ³ΠΎ ΠΊΠΎΠΌΠΌΡΡΠ°ΡΠΎΡΠ° ΡΠ΅ΡΠΈ Ρ ΠΎΠΏΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΊΠΎΠΌΠΌΡΡΠ°ΡΠΈΠ΅ΠΉ ΠΏΠ°ΡΠ΅ΠΊ, ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄ΠΈΡΡΡ ΡΠ°ΡΡΡΡ Π²Π΅ΡΠΎΡΡΠ½ΡΡ
Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊ ΡΠ΅ΡΠΈ Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ Π°Π½Π°Π»ΠΈΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΈ ΠΈΠΌΠΈΡΠ°ΡΠΈΠΎΠ½Π½ΡΡ
ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ. ΠΡΠΈΠ²Π΅Π΄Π΅Π½Ρ ΠΏΡΠΈΠΌΠ΅ΡΡ ΡΠ°ΡΡΡΡΠ° Π²Π΅ΡΠΎΡΡΠ½ΠΎΡΡΠΈ Π±Π»ΠΎΠΊΠΈΡΠΎΠ²ΠΊΠΈ ΠΏΠ°ΠΊΠ΅ΡΠΎΠ², ΠΏΠΎΡΡΡΠΏΠ°ΡΡΠΈΡ
Π²ΠΎ Π²Ρ
ΠΎΠ΄Π½ΠΎΠΉ ΠΊΠΎΠΌΠΌΡΡΠ°ΡΠΎΡ
Evaluation ofΒ theΒ New andΒ Accepted Customers Blocking Probabilties inΒ aΒ Network ofΒ Resource Loss Systems
The paper considers a network of resource loss systems (ReLS) with random resource requirements and two types of nodes. Customers initially arrive to the first type of nodes, where they receive service for exponentially distributed time. The service of customers can be interrupted. In this case, they are rerouted to the second type of nodes, where they receive service for an exponentially distributed time. Once the service is completed, they return back to the original node and continue its service. Customers require a random volume of limited resources. If there are not enough of unoccupied resources upon the arrival of a customer, then it is considered lost. Similarly, if an accepted customer is rerouted to another node and finds that there are not enough of resources to meet its requirements, then it is also lost. In this paper, we provide an approach to analyze the stationary behavior of the considered system, as well as establish expressions for the new customer loss probability and the accepted customer loss probability. The developed model has a wide range of applications in performance evaluation of fifth generation (5G) New Radio (NR) access networks. To this aim, we investigate the response of the considered service system in detail by revealing critical dependencies and trade-offs between input system parameters and performance measures of interest.acceptedVersionPeer reviewe
- β¦