An Overview of Generalized Frequency Division Multiplexing (GFDM)

Tai, Ching-Lun; Wang, Tzu-Han; Huang, Yuhua

doi:10.36227/techrxiv.16432992

Cited by 2 publications

(3 citation statements)

References 55 publications

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“…Due to the prevailing OFDM channel estimation techniques during the research, this section presents the OFDM fundamentals to provide a look inside this modulation technique and insights into the following sections. Regardless, we recommend looking inside the content in [2,3,9,11,12,52] and the references therein for those also interested in reviewing the fundamentals of the FBMC, GFDM, and UFMC modulations.…”

Section: Ofdm Principlesmentioning

confidence: 99%

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Artificial intelligence for channel estimation in multicarrier systems for B5G/6G communications: a survey

Boas

Silva

Figueiredo

et al. 2022

J Wireless Com Network

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Multicarrier modulation allows for deploying wideband systems resilient to multipath fading channels, impulsive noise, and intersymbol interference compared to single-carrier systems. Despite this, multicarrier signals suffer from different types of distortion, including channel noise sources and long- and short-term fading. Consequently, the receiver must estimate the channel features and compensate it for data recovery based on channel estimation techniques, such as non-blind, blind, and semi-blind approaches. These techniques are model-based and designed with accurate mathematical channel models encompassing their features. Nevertheless, complex environments challenge accurate mathematical channel estimation modeling, which might neither be accurate nor correspond to reality. This impairment decreases the system performance due to the channel estimation accuracy loss. Fortunately, (AI) algorithms can learn the relationship among different system variables using a model-driven or model-free approach. Thereby, AI algorithms are used for channel estimation by exploiting its complexity without unrealistic assumptions, following a better performance than conventional techniques under the same channel. Hence, this paper comprehensively surveys AI-based channel estimation for multicarrier systems. First, we provide essential background on conventional channel estimation techniques in the context of multicarrier systems. Second, the AI-aided channel estimation strategies are investigated using the following approaches: classical learning, neural networks, and reinforcement learning. Lastly, we discuss current challenges and point out future research directions based on recent findings.

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Section: Ofdm Principlesmentioning

confidence: 99%

“…GFDM and UFMC are seen as a variation of OFDM. The GFDM is a generalized conventional OFDM that maps different services into flexible subcarriers and CP by deploying different filters [3,12]. GFDM is also robust to frequency offset and has low PAPR with a high ICI sensibility.…”

mentioning

confidence: 99%

Artificial intelligence for channel estimation in multicarrier systems for B5G/6G communications: a survey

Boas

Silva

Figueiredo

et al. 2022

J Wireless Com Network

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“…In general, there are several 5G communication contenders. Some of these include generalized frequency division multiplexing (GFDM), filtered orthogonal frequency division multiplexing (f‐OFDM), non‐orthogonal multiple access (NOMA), universal filtered multicarrier (UFMC), and filter bank multiple carriers (FBMC) [6–8]. Among these, GFDM is deliberated as an encouraging non‐orthogonal candidate waveform technology that effectively resolves the OFDM flaws for networks beyond 5G [9].…”

Section: Introductionmentioning

confidence: 99%

Advanced lifting wavelet transform and V‐degree polynomial‐based channel estimation in MIMO‐GFDM

Ray,

Oza

2023

Int J Communication

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SummaryThe future generation of wireless communications systems uses the generalized frequency division multiplexing (GFDM) due to its viable candidate waveform to perform multiple user scheduling. It is a non‐orthogonal waveform susceptible to intercarrier and intersymbol interference (ISI); still, it offers flexible pulse shaping that enhances the efficiency of user scheduling. Here, to accomplish spatial diversity, the multiple‐input multiple‐output (MIMO) is incorporated with GFDM to enhance the performance; there is also an extra inter‐antenna interference problem that limits the model's performance. The issue concerning inter‐antenna interference can be resolved by adopting pilot‐based information transfer. The detection of a signal at the receiver based on the minimum mean square error has the issue of computational complexity while enhancing the estimation quality of a detector. Hence, a low‐complexity channel estimation technique is proposed in this research using the V‐degree polynomial‐based channel estimation technique, wherein the cubic order computation is reduced to square order. Also, the proposed adaptive pulse shaping technique, wherein the filter coefficient is optimized using the gazelle optimization algorithm (GOA) to provide optimal pulse shaping filter parameters by employing bit error rate (BER) as the objective function. The performance of a proposed V‐degree polynomial‐based channel estimation is analyzed based on various assessment measures like BER and MSE and acquired the minimal values of 5.75E‐05 and 2.07E‐05, respectively.

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An Overview of Generalized Frequency Division Multiplexing (GFDM)

Cited by 2 publications

References 55 publications

Artificial intelligence for channel estimation in multicarrier systems for B5G/6G communications: a survey

Artificial intelligence for channel estimation in multicarrier systems for B5G/6G communications: a survey

Advanced lifting wavelet transform and V‐degree polynomial‐based channel estimation in MIMO‐GFDM

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