GFDM frame design for low-latency industrial networks

Ssimbwa, Julius; Lim, Byungju; Ko, Young-Chai

doi:10.23919/jcn.2021.000047

Cited by 8 publications

(2 citation statements)

References 25 publications

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“…Tsai et al, (2021) used the Time Reversal Zero Forcing algorithm to improve the performance and reduce the complexity of the GFDM transceiver (Tsai 2021). Ssimbwa et al, (2022), enhanced by reducing the BER of the GFDM system by using coded GFDM (Ssimbwa 2022).…”

Section: Introductionmentioning

confidence: 99%

GFDM Transceiver Based on Ann Channel Estimation

Ali¹,

Hreshee²

2023

Kufa J. Eng.

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Generalized frequency division multiplexing (GFDM) is one of the candidate waveforms beyond 5G for wireless communication. Channel estimation is challenging in wireless transmission because of inherent interference. It is based on a pilot signal that uses Least Square (LS) and Normalized Least Mean Square (NLMS) to perform the estimation. This paper used the Artificial Neural Network (ANN) as a channel estimation method, considered a novel estimation process for the GFDM transceiver. The channel estimation based on ANN depends on the data set generated from LS estimation, which considers the proposed method is optimized to LS based on the backpropagation neural network. The ANN algorithm considered a fitness function to estimate the channel. Levenberg-Marquardt backpropagation (LM), Bayesian Regularization backpropagation (BR), and Scaled Conjugate Gradient backpropagation (SCG) training methods training by using the Matlab toolbox used to perform the estimation. BR training method gives the best performance than LS and other training methods at 22 neurons in hidden layers, which at 20dB give BER = 0.0369 that enhanced over LS by 0.08.

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Section: Introductionmentioning

confidence: 99%

GFDM Transceiver Based on Ann Channel Estimation

Ali¹,

Hreshee²

2023

Kufa J. Eng.

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“…Future wireless communication networks are envisioned to play a major role towards enabling autonomous systems in the context of the Internet of Things (IoT), comprising connected vehicles, smart devices, or fully automated factories; see, for example, [2]- [5]. The data traffic produced from these wireless devices, referred to as machine-to-machine (M2M) communication, will significantly differ from the wireless traffic served by currently deployed wireless networks.…”

Section: Introductionmentioning

confidence: 99%

On the Interplay Between Deadline-Constrained Traffic and the Number of Allowed Retransmissions in Random Access Networks

Νομικός¹,

Themistoklis²,

Pignolet³

et al. 2022

Preprint

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In this paper, a network comprising wireless devices equipped with buffers transmitting deadline-constrained data packets over a slotted-ALOHA random-access channel is studied. Although communication protocols facilitating retransmissions increase reliability, packet transmission from the queue experiences delays and thus, packets with time constraints might be dropped before being successfully transmitted, while at the same time causing the queue size of the buffer to increase. Towards understanding the trade-off between reliability and delays that might lead to packet drops due to the deadline-constrained bursty traffic with retransmissions, a scenario of a wireless network utilizing a slotted-ALOHA random-access channel is investigated. The main focus is to reveal and investigate further the trade-off between the number of retransmissions and the packet deadline as a function of the arrival rate. Hence, we are able to determine numerically the optimal probability of transmissions and number of retransmissions, given the packet arrival rate and the packet deadline. The analysis of the system was done by means of discrete-time Markov chains. Two scenarios are studied: i) the collision channel model (in which a receiver can decode only when a single packet is transmitted), and ii) the case for which receivers have multi-packet reception capabilities. A performance evaluation for a user with different transmit probability and number of retransmissions is conducted, demonstrating their impact on the average drop rate and throughput, while at the same time showing that there exists a set of values, under which improved performance can be acquired.

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