On Application of PHY Layer Abstraction Techniques for System Level Simulation and Adaptive Modulation in IEEE 802.11ac/ax Systems

Hoefel, Roger Pierre Fabris; Bejarano, Oscar

doi:10.14209/jcis.2016.18

Cited by 13 publications

(9 citation statements)

References 10 publications

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“…MATLAB R2019a simulation model of 5G(NR) PHY for mMTC node is comprise all the compulsory source obstructs as from the Table-1. The simulation model (as in Figure-2) incorporates three principle segments to be specific transmitter, channel and beneficiary [15]. Transmitter and beneficiary comprise of channel coding, adjustment and sub-segments though channel is tweaked on SUI Fading channel.…”

Section: G (Nr) Phymentioning

confidence: 99%

Robust Channel Encoding for MMTC Node in 5g (Nr)

Sharma¹,

Pandey²,

Patel³

et al. 2020

IJAR

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The paper is focused on robust channel encoding for Massive machine type communication (mMTC) communication in 5G (NR). The performance evaluation of channel encoding is obtained at 5G New Radio (NR) PHY. The results show that reliable bit error rate (BER) against the poor channel condition or random fluctuated channel applied. Channel encoding algorithm as a forward error correction code (FEC) is applied on packet to packet basis to improve the BER performance against inter symbol interference. The concept of adaptation of code rate is valuable to reduce the payload effect and provide optimum solution between BER and throughput. Adaptive code rate selection is based on impact of earlier transmitted packet bit using feedback indicator.

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Section: G (Nr) Phymentioning

confidence: 99%

Robust Channel Encoding for MMTC Node in 5g (Nr)

Sharma¹,

Pandey²,

Patel³

et al. 2020

IJAR

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“…Observe in (32) that, when no MCS achieves the desired PDR in a particular RU, this value is set to µ = 1 by default. The above adaptive MCS selection can be achieved based on channel conditions using the techniques outlined in [36]. This MCS selection subsequently then yields a corresponding There are a total of S k = 3 PPDUs, given n 1 = 9, n 2 = 3, n 3 = 2 RUs, respectively.…”

Section: Assignment-based Schedulingmentioning

confidence: 99%

“…The above adaptive MCS selection can be achieved based on channel conditions using the techniques outlined in [36]. This MCS selection subsequently then yields a corresponding…”

Section: Assignment-based Schedulingmentioning

confidence: 99%

Control Aware Radio Resource Allocation in Low Latency Wireless Control Systems

Eisen

Rashid

Gatsis

et al. 2019

IEEE Internet Things J.

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We consider the problem of allocating radio resources over wireless communication links to control a series of independent wireless control systems. Low-latency transmissions are necessary in enabling time-sensitive control systems to operate over wireless links with high reliability. Achieving fast data rates over wireless links thus comes at the cost of reliability in the form of high packet error rates compared to wired links due to channel noise and interference. However, the effect of the communication link errors on the control system performance depends dynamically on the control system state. We propose a novel control-communication co-design approach to the low-latency resource allocation problem. We incorporate control and channel state information to make scheduling decisions over time on frequency, bandwidth and data rates across the nextgeneration Wi-Fi based wireless communication links that close the control loops. Control systems that are closer to instability or further from a desired range in a given control cycle are given higher packet delivery rate targets to meet. Rather than a simple priority ranking, we derive precise packet error rate targets for each system needed to satisfy stability targets and make scheduling decisions to meet such targets while reducing total transmission time. The resulting Control-Aware Low Latency Scheduling (CALLS) method is tested in numerous simulation experiments that demonstrate its effectiveness in meeting controlbased goals under tight latency constraints relative to controlagnostic scheduling.

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“…To achieve PHY layer abstraction of current Wi-Fi systems which operate over wideband frequency-selective fading channels, link-to-system (L2S) mapping has been widely adopted [3]- [9]. L2S mapping function translates the post processing Signal-to-Interference-plus-Noise-Ratio (SINR) matrix into a single scalar metric called the effective SINR [10].…”

Section: Introductionmentioning

confidence: 99%

Efficient PHY Layer Abstraction under Imperfect Channel Estimation

Cao¹,

Zhang²,

Jin³

et al. 2022

Preprint

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As network simulator has been suffering from the large computational complexity in the physical (PHY) layer, a PHY layer abstraction model that efficiently and accurately characterizes the PHY layer performance from the system level simulations is well-needed. However, most existing work investigate the PHY layer abstraction under an assumption of perfect channel estimation, as a result, such a model may become unreliable if there exists channel estimation error in a real communication system. This work improves an efficient PHY layer method, EESM-log-SGN PHY layer abstraction, by considering the presence of channel estimation error. We develop two methods for implementing the EESM-log-SGN PHY abstraction under imperfect channel estimation. Meanwhile, we present the derivation of effective Signal-to-Interference-plus-Noise-Ratio (SINR) for minimum mean squared error (MMSE) receivers impacted by the channel estimation error. Via full PHY simulations, we verify that the effective SINR is not impacted by the channel estimation error under multiple-input and single-output (MISO)/single-input and single-output (SISO) configuration. Finally, the developed methods are validated under different orthogonal frequency division multiplexing (OFDM) scenarios.

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On Application of PHY Layer Abstraction Techniques for System Level Simulation and Adaptive Modulation in IEEE 802.11ac/ax Systems

Cited by 13 publications

References 10 publications

Robust Channel Encoding for MMTC Node in 5g (Nr)

Robust Channel Encoding for MMTC Node in 5g (Nr)

Control Aware Radio Resource Allocation in Low Latency Wireless Control Systems

Efficient PHY Layer Abstraction under Imperfect Channel Estimation

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