Analysis of power ramping schemes for UTRA-FDD random access channel

Yang, Yang; Yum, T.-S.P.

doi:10.1109/twc.2005.857993

Cited by 35 publications

(2 citation statements)

References 13 publications

(8 reference statements)

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“…If the initial PT fails, a higher target power level is used for retransmission. We assume that

μ L

is the power increment unit adopted at each of preamble retransmissions as in [22]. Let us denote the number of retransmissions by r , and let

m_{r} \overset{(def)}{=} r + 1

.…”

Section: System Modelmentioning

confidence: 99%

“…The probability can be obtained analogously by the method used in [22] according to the criteria (4) and (5), respectively.

\begin{matrix} u_{•} (r | n_{p}, k, n_{m}, n_{J}) = \{\begin{matrix} left \frac{prefixexp (() - \frac{β_{p} c}{μ})}{{(1 + β_{p})}^{b} (b - 1)!} \sum_{i = 0}^{a - 1} \sum_{j = 0}^{i} \frac{(b + j - 1)!}{j! (i - j)!} \frac{β_{p}^{i}}{{(1 + β_{p})}^{j}} {(\frac{c}{μ})}^{i - j}, 1 \leq k \leq n_{p} - 1, \\ left prefixexp (() - \frac{β_{p} c}{μ}) \sum_{i = 0}^{a - 1} \frac{1}{i!} {(\frac{β_{p} c}{μ})}^{i}, k = n_{p}, \end{matrix} \end{matrix}

where the three interim variables a , b and c for the power jamming (i.e.,

• = P

) are defined as:

\begin{matrix} ({, \begin{matrix} a = ⌊ (k - 1) \bar{m} L + m_{r} L ⌋, \\ b = ⌊ (n_{p} - k) \bar{m} L + n_{m} α \bar{m} L + n_{J} m_{J} L ⌋, \\ c = ⌊ η ⌋, \end{matrix}) \end{matrix}

and for the code jamming (i.e.,

• = C

…”

Section: Ra Performance Analysismentioning

confidence: 99%

See 1 more Smart Citation

Random Access Performance of Distributed Sensors Attacked by Unknown Jammers

Jeong

Wui

Kim

2017

Sensors

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In this paper, we model and investigate the random access (RA) performance of sensor nodes (SN) in a wireless sensor network (WSN). In the WSN, a central head sensor (HS) collects the information from distributed SNs, and jammers disturb the information transmission primarily by generating interference. In this paper, two jamming attacks are considered: power and code jamming. Power jammers (if they are friendly jammers) generate noises and, as a result, degrade the quality of the signal from SNs. Power jamming is equally harmful to all the SNs that are accessing HS and simply induces denial of service (DoS) without any need to hack HS or SNs. On the other hand, code jammers mimic legitimate SNs by sending fake signals and thus need to know certain system parameters that are used by the legitimate SNs. As a result of code jamming, HS falsely allocates radio resources to SNs. The code jamming hence increases the failure probability in sending the information messages, as well as misleads the usage of radio resources. In this paper, we present the probabilities of successful preamble transmission with power ramping according to the jammer types and provide the resulting throughput and delay of information transmission by SNs, respectively. The effect of two jamming attacks on the RA performances is compared with numerical investigation. The results show that, compared to RA without jammers, power and code jamming degrade the throughput by up to 30.3% and 40.5%, respectively, while the delay performance by up to 40.1% and 65.6%, respectively.

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“…If the initial PT fails, a higher target power level is used for retransmission. We assume that

μ L

is the power increment unit adopted at each of preamble retransmissions as in [22]. Let us denote the number of retransmissions by r , and let

m_{r} \overset{(def)}{=} r + 1

.…”

Section: System Modelmentioning

confidence: 99%

“…The probability can be obtained analogously by the method used in [22] according to the criteria (4) and (5), respectively.

\begin{matrix} u_{•} (r | n_{p}, k, n_{m}, n_{J}) = \{\begin{matrix} left \frac{prefixexp (() - \frac{β_{p} c}{μ})}{{(1 + β_{p})}^{b} (b - 1)!} \sum_{i = 0}^{a - 1} \sum_{j = 0}^{i} \frac{(b + j - 1)!}{j! (i - j)!} \frac{β_{p}^{i}}{{(1 + β_{p})}^{j}} {(\frac{c}{μ})}^{i - j}, 1 \leq k \leq n_{p} - 1, \\ left prefixexp (() - \frac{β_{p} c}{μ}) \sum_{i = 0}^{a - 1} \frac{1}{i!} {(\frac{β_{p} c}{μ})}^{i}, k = n_{p}, \end{matrix} \end{matrix}

where the three interim variables a , b and c for the power jamming (i.e.,

• = P

) are defined as:

\begin{matrix} ({, \begin{matrix} a = ⌊ (k - 1) \bar{m} L + m_{r} L ⌋, \\ b = ⌊ (n_{p} - k) \bar{m} L + n_{m} α \bar{m} L + n_{J} m_{J} L ⌋, \\ c = ⌊ η ⌋, \end{matrix}) \end{matrix}

and for the code jamming (i.e.,

• = C

…”

Section: Ra Performance Analysismentioning

confidence: 99%

Random Access Performance of Distributed Sensors Attacked by Unknown Jammers

Jeong

Wui

Kim

2017

Sensors

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Impact of machine‐type communications on energy and delay performance of random access channel in LTE‐advanced

Gerasimenko

Petrov

Galinina

et al. 2013

Trans Emerging Tel Tech

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Machine‐type communications (MTC) are a rapidly growing technology, which is expected to generate significant revenues to mobile network operators. In particular, smart grid is predicted to become one of the key MTC use cases that involves unattended meters autonomously reporting information to a grid infrastructure. With this research, we consider a typical smart metering MTC application scenario in the context of 3GPP LTE‐advanced wireless cellular system featuring a large number of devices connecting to the network near‐simultaneously. The resulting overload of the random access channel requires a novel evaluation methodology based on comprehensive analysis and simulations. In this paper, we target to complement a validated evaluation framework fully compatible with the 3GPP test cases with a thorough analysis of random access channel performance in overloaded MTC scenarios. We also look at the regular MTC operation, when the devices are sending their data after initial network entry has been performed. By including energy consumption into our methodology together with the conventional performance metrics, we aim at providing a complete and unified insight into MTC device operation, including its energy efficiency. Copyright © 2013 John Wiley & Sons, Ltd.

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A multiple power level random access method for M2M communications in LTE‐A network

Alavikia

Ghasemi

2016

Trans. Emerging Tel. Tech.

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Access control is an effective way to protect the radio access part of Long‐Term Evolution–Advanced (LTE‐A) network from the overload caused by a huge number of Machine‐Type Communication Devices (MTCDs). A class of access control mechanisms is the Access Class Barring (ACB), which regulates the Machine‐to‐Machine (M2M) traffic in accordance with the available Random Access (RA) resources. In this paper, we extend the single power level ACB scheme to a multiple power level method to increase the number of successfully transmitted requests in the case of overload. Our analysis is based on the capture effect in the third step of RA procedure of the LTE‐A system in which one of the transmitted requests by 2 or more co‐tagged MTCDs, MTCDs which use the same preamble in the first step, can be decoded by the evolved–Node B (eNB). We first formulate the power level selection as an optimization problem assuming the perfect capture model without considering MTCDs' energy budget. Then, to take into account MTCDs' energy consumption, the scenario is extended for the signal‐to‐interference ratio–based capture model. In addition, we investigate the advantages of the proposed multiple power level RA method on discriminating the access of MTCDs with different priorities. The numerical results show that using the optimal parameters, the RA throughput can be improved in comparison with the single power level system at the cost of slightly increasing MTCDs' energy consumption and the complexity of RA procedure.

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Analysis of power ramping schemes for UTRA-FDD random access channel

Cited by 35 publications

References 13 publications

Random Access Performance of Distributed Sensors Attacked by Unknown Jammers

Random Access Performance of Distributed Sensors Attacked by Unknown Jammers

Impact of machine‐type communications on energy and delay performance of random access channel in LTE‐advanced

A multiple power level random access method for M2M communications in LTE‐A network

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