Future OFDM-based Communication Systems Towards 6G and Beyond: Machine Learning Approaches

Juwono, Filbert H.; Reine, Regina

doi:10.53623/gisa.v1i1.34

Cited by 10 publications

(3 citation statements)

References 26 publications

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“…Waveforms that can satisfy the above requirements and be very flexible, robust therefore need to be invented or adapted from the old ones. To this end, OFDM is proposed as the waveforms respectively usable in the RAN of 6G networks to support machine learning, deep learning [98] [99]. The OFDM Hybrid Number and Index Modulation (OFDM-HNIM) is suggested.…”

Section: G Waveformsmentioning

confidence: 99%

A Comprehensive Survey of Candidate Waveforms for 5G, beyond 5G and 6G Wireless Communication Systems

Adoum¹,

Zoukalne²,

Idriss³

et al. 2023

OJAppS

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The evolution of global mobile data over the past decades in broadcasting, Internet of Things (IoT), education, healthcare, commerce, and energy has put strong pressure on 3G/4G mobile networks to improve their service offerings. These generations of mobile networks were initially invented to meet the requirements of the above-mentioned applications. However, as the requirements in these applications continue to increase, new mobile technologies such as 5G (fifth generation), 5G and beyond (B5G, beyond fifth generation), and 6G (sixth generation) are still progressing and being experimented. These networks are very heterogeneous generations of mobile networks that will have to offer very high throughput per user, good energy efficiency, better traffic capacity per area, improved spectral efficiency, very low latency, and high mobility. To meet these requirements, the radio interface of future mobile networks will have to be flexible and rationalized the available frequency resources. Therefore, new modulation methods, access techniques and waveforms capable of supporting these technological changes are proposed. This review presents brief descriptions of the types of 5G, B5G, and 6G waveforms. The 5G consists of OFDM including its transmission techniques: generalized frequency division multiplexing (GFDM), filter bank based multi-carrier (FBMC), universal filtered multi-carrier (UFMC), and index modulation (IM). Meanwhile, the 6G covers orthogonal time frequency space (OTFS), orthogonal chirp division multiplexing (OCDM) and orthogonal time sequence multiplexing (OTSM). The networks' potentialities, advantages, disadvantages, and future directions are outlined.

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Section: G Waveformsmentioning

confidence: 99%

A Comprehensive Survey of Candidate Waveforms for 5G, beyond 5G and 6G Wireless Communication Systems

Adoum¹,

Zoukalne²,

Idriss³

et al. 2023

OJAppS

View full text Add to dashboard Cite

show abstract

“…Therefore, academia and business alike are speeding up the development of the sixth generation (6G). OFDM has been reintroduced as the modulation waveform for 6G and beyond networks due to its adaptability in merging with other technologies [7].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Channel Estimation and Signal Detection in OFDM-Based Communication Systems

Wong

Juwono

Reine³

2022

ELKHA

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The goal of 6G communication networks requires higher transmission speeds, tremendous data processing, and low-latency communication. Orthogonal frequency-division multiplexing (OFDM), which is widely utilized in 5G communication systems, may be a viable alternative for 6G. It significantly reduces inter symbol interference (ISI) in the frequency-selective fading environment. Channel estimation is critical in OFDM to optimize system performance. Deep learning has been employed as an appealing alternative for channel estimation and signal detection in OFDM-based communication systems due to its better potential for feature learning and representation. In this study, we examine the deep neural network (DNN) layers created from long-short term memory (LSTM) for detecting the signals by learning the received signal as well as channel information. We investigate the performance of the system under various conditions. The simulation results show that the signal bit error (SER) is equivalent to and better than that of the minimum mean squared error (MMSE) and least square (LS) methods.

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“…A multitude of state-of-the-art technologies have been proposed to fulfill the above requirements. Orthogonal frequency-division multiplexing (OFDM) is a modulation scheme for wideband systems, which is still regarded as a key physical layer technology in 6G communication systems [4][5][6]. OFDM can realize the parallel transmission of high speed data by frequency-division multiplexing and resist multi-path attenuation.…”

Section: Introductionmentioning

confidence: 99%

A Low Sampling Rate Receiver Design for Multi-Antenna Multi-User OFDM Systems

Liu

Yang

2022

Entropy

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The future 6G mobile communication network will support an unprecedented amount of Internet of Things (IoT) devices, which will boost the demand for low cost terminals under the principle of green communication. One of the critical issues for low cost terminals is the sampling rate of analog-to-digital converters (ADCs) at the receivers. A high sampling rate of the ADC gives rise to a high energy consumption and high hardware cost for the terminal. In the conventional multi-user OFDM systems, all users have to sample the received signal with a sampling rate that is larger than or equal to the Nyquist rate, despite only a small fraction of the bandwidth (number of subcarriers) is allocated to each user. This paper proposes a low sampling rate receiver design for multi-antenna multi-user OFDM systems. With the aid of zero-forcing precoding, the sampling rate of the receiver can be reduced to 1/K of the Nyquist rate, where K is the number of users. The simulation results show that with a significant reduction in sampling rate, performance loss is insignificant and acceptable in terms of bit error rate, mutual information and peak-to-average power ratio.

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Future OFDM-based Communication Systems Towards 6G and Beyond: Machine Learning Approaches

Cited by 10 publications

References 26 publications

A Comprehensive Survey of Candidate Waveforms for 5G, beyond 5G and 6G Wireless Communication Systems

A Comprehensive Survey of Candidate Waveforms for 5G, beyond 5G and 6G Wireless Communication Systems

Deep Learning for Channel Estimation and Signal Detection in OFDM-Based Communication Systems

A Low Sampling Rate Receiver Design for Multi-Antenna Multi-User OFDM Systems

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