Prediction of the SINR RMS in the IEEE 802.16 OFDMA System

Moiseev, S. N.; Kondakov, M.S.

doi:10.1109/tsp.2009.2018600

Cited by 7 publications

(5 citation statements)

References 5 publications

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“…The SINR in Figs 3-7 and 8 was controlled with the transmit power of users. The SINR at the femto BS is expressed as follows (similar to those of Refs [23,24]):…”

Section: Simulation Resultsmentioning

confidence: 99%

“…The SINR in Figs 3–7 and 8 was controlled with the transmit power of users. The SINR at the femto BS is expressed as follows (similar to those of Refs [23,24]):

SINR=\frac{\sum_{k=1}^K{P}_S^{(k)}\cdot {PL}_S^{(k)}}{\sum_{j=1}^J{P}_I^{(j)}\cdot {PL}_I^{(j)}+{N}_n}

where

{P}_S^{(k)}

denotes the transmit power of observed femto user,

{PL}_S^{(k)}

is the path loss from femto user to the observed femto BS, the superscript

{\left(\cdot \right)}^{(k)}

indicates the k th femto user,

{P}_I^{(j)}

is the transmit power as interference caused by the macro user,

{PL}_I^{(j)}

is the path loss caused by the distance between macro user to the femto BS, the superscript

{\left(\cdot \right)}^{(j)}

indicates the j th macro user, and N n is the noise power. The received interference power caused by the macro user can be calculated using

P_{italicRX_I} = \sum_{...}

…”

Section: Simulation Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive Carrier Frequency Offset Estimation in Interference Environments for OFDMA Uplink Systems

Cheng,

Chen,

Wang

et al. 2023

IEEJ Transactions Elec Engng

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In orthogonal frequency division multiple access (OFDMA) systems, carrier frequency offsets (CFOs) influence the different users. In this paper, we proposed a CFO estimation algorithm with strong interference resistant capability for OFDMA systems. The first part of the proposed algorithm processes the received signals so as to deal with the CFO estimation problem. And the second part of the proposed algorithm is to use the proposed adaptive process to obtain the updated CFO estimation and track the parameters' time‐variations. Simulations demonstrate the effectiveness of this method and show that its performance is to the Cramer‐Rao Bounds (CRB). © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

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“…The SINR in Figs 3-7 and 8 was controlled with the transmit power of users. The SINR at the femto BS is expressed as follows (similar to those of Refs [23,24]):…”

Section: Simulation Resultsmentioning

confidence: 99%

“…The SINR in Figs 3–7 and 8 was controlled with the transmit power of users. The SINR at the femto BS is expressed as follows (similar to those of Refs [23,24]):

SINR=\frac{\sum_{k=1}^K{P}_S^{(k)}\cdot {PL}_S^{(k)}}{\sum_{j=1}^J{P}_I^{(j)}\cdot {PL}_I^{(j)}+{N}_n}

where

{P}_S^{(k)}

denotes the transmit power of observed femto user,

{PL}_S^{(k)}

is the path loss from femto user to the observed femto BS, the superscript

{\left(\cdot \right)}^{(k)}

indicates the k th femto user,

{P}_I^{(j)}

is the transmit power as interference caused by the macro user,

{PL}_I^{(j)}

is the path loss caused by the distance between macro user to the femto BS, the superscript

{\left(\cdot \right)}^{(j)}

indicates the j th macro user, and N n is the noise power. The received interference power caused by the macro user can be calculated using

P_{italicRX_I} = \sum_{...}

…”

Section: Simulation Resultsmentioning

confidence: 99%

Adaptive Carrier Frequency Offset Estimation in Interference Environments for OFDMA Uplink Systems

Cheng,

Chen,

Wang

et al. 2023

IEEJ Transactions Elec Engng

View full text Add to dashboard Cite

show abstract

“…Some TTI metrics may be zero, due to eNodeB scheduler algorithm, in which case we use an interpolator to fill in missing data, and then smooth. We introduce (optionally) a non-linearity, which is 10log() in our case, in line with producing homoscedasticity, that is, to try to make variances homogeneous across metrics of interest (in time), if needed, as described in [14]. [14] uses a log non-linearity to produce homoscedasticity for heteroscedastic SINR RMS(Root Mean Square) value prediction for IEEE 802.…”

Section: Methodsmentioning

confidence: 99%

Throughput Prediction Across Heterogeneous Boundaries in Wireless Communications

Sayeed

Miller²,

Sayeed³

2016

JCSANDM

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In this paper we demonstrate how an estimated functional kernel-regression polynomial from a particular RF technology can be created by the mobiles being served by that technology. A 3rd order polynomial description of the regression can be used to predict future throughput by observing the “pilot” quality prior to handover. The UE may use it to predict the throughput it will get in the new technology 50 to 200 m-sec prior to handover. The prediction can inform the Transport Control Protocol (TCP) layer or the application layer of the upcoming handover and the throughput expected after handover so that the user application receives the best quality of service. This paper is an extended version of the paper presented at IEEE Sarnoff Symposium 2015 [1]. It extends the paper with expanded foundational knowledge and explanation of the results and their implications. In this paper we: • propose that there is a way to predict the unobservable quality metrics in the new cell prior to commencement of the handover. This is achieved by 1) a prediction mechanism and 2) a signaling mechanism. In this paper we focus on the prediction mechanism. • propose that the observable metric (“pilot” quality) is predicted with prediction error below 9% with prediction step sizes of 200 m-sec. • show that the throughput metric (we choose bits/physical-resourceblock = β) can be predicted with error below 8% with prediction horizon of 200 m-sec.

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Section: The Enodeb Obtains the Number Of Physical Resource Blocks (Pmentioning

confidence: 99%

“…We have used the poly-fit function over finite durations of 10 to 100 m-secs, where the process may be considered somewhat stationary. However, still variations of the central moments in the signal remain and we use the non-linearity of [14] to ensure we get a good prediction performance.…”

Section: Simulation Setupmentioning

confidence: 99%

Throughput Prediction Across Heterogeneous Boundaries in Wireless Communications

Sayeed

Miller²,

Sayeed³

2016

JCSM

View full text Add to dashboard Cite

In this paper we demonstrate how an estimated functional kernel-regression polynomial from a particular RF technology can be created by the mobiles being served by that technology. A 3rd order polynomial description of the regression can be used to predict future throughput by observing the "pilot" quality prior to handover. The UE may use it to predict the throughput it will get in the new technology 50 to 200 m-sec prior to handover. The prediction can inform the Transport Control Protocol (TCP) layer or the application layer of the upcoming handover and the throughput expected after handover so that the user application receives the best quality of service. This paper is an extended version of the paper presented at IEEE Sarnoff Symposium 2015 [1]. It extends the paper with expanded foundational knowledge and explanation of the results and their implications. In this paper we: • propose that there is a way to predict the unobservable quality metrics in the new cell prior to commencement of the handover. This is achieved by 1) a prediction mechanism and 2) a signaling mechanism. In this paper we focus on the prediction mechanism. • propose that the observable metric ("pilot" quality) is predicted with prediction error below 9% with prediction step sizes of 200 m-sec.

show abstract

Prediction of the SINR RMS in the IEEE 802.16 OFDMA System

Cited by 7 publications

References 5 publications

Adaptive Carrier Frequency Offset Estimation in Interference Environments for OFDMA Uplink Systems

Adaptive Carrier Frequency Offset Estimation in Interference Environments for OFDMA Uplink Systems

Throughput Prediction Across Heterogeneous Boundaries in Wireless Communications

Throughput Prediction Across Heterogeneous Boundaries in Wireless Communications

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