Joint Channel and Phase Noise Estimation and Data Detection for GFDM

Mohammadian, Amirhossein; Tellambura, Chintha

doi:10.1109/ojcoms.2021.3073348

Cited by 16 publications

(4 citation statements)

References 64 publications

(134 reference statements)

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“…For example, phase noise is a synchronization impairment that stems from local oscillators. Even though an improvement in hardware is an obvious solution, it is not sufficient because of the strong constraints on the radio frequency (RF) components [18].…”

Section: ) Harsh Environment and Extreme Conditionsmentioning

confidence: 99%

“…In NR Rel.15/16, PT-RS was introduced to combat the phase noise, which turned out to be very strong when the carrier frequency was increased to mmWaves. Phase noise is a synchronization impairment stemming from local oscillators [18], [141]. The DC components of the phase noise generate a common phase error (CPE) that is identical to all subcarriers and time instants in an OFDM symbol.…”

Section: Phase Tracking Reference Signals (Pt-rs)mentioning

confidence: 99%

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Physical Layer Enhancement for Next-Generation Railway Communication Systems

Sibel

Berbineau

et al. 2022

IEEE Access

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This paper presents an overview of the challenges and state-of-the-art physical layer enhancement designs for next-generation railway communication, also known as high-speed train (HST) communication. The physical layer design for the HST should adapt from its counterpart in the generalpurpose network because of the harsh propagation environment and extreme conditions, stringent latency and reliability requirements of dedicated railway applications, and frequency band scarcity caused by regulation. In this survey, we examine how conventional multiple-input-multiple-output (MIMO) family techniques such as beamforming, multi-cell MIMO, and relays can enhance the physical layer performance for HST. Physical layer enhancement assisted by novel reconfigurable intelligent surface (RIS) technology was also analyzed from different perspectives. Dedicated control channels, reference signals, waveforms, and numerology designs for train-to-infrastructure (T2I) and train-to-train (T2T) communication in sidelinks are also reviewed. Finally, a brief introduction to artificial intelligence (AI)/machine learning (ML)-aided HST physical layer design is provided. Several promising research avenues have also been suggested.

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Section: ) Harsh Environment and Extreme Conditionsmentioning

confidence: 99%

Section: Phase Tracking Reference Signals (Pt-rs)mentioning

confidence: 99%

Physical Layer Enhancement for Next-Generation Railway Communication Systems

Sibel

Berbineau

et al. 2022

IEEE Access

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“…GFDM is a multicarrier modulation technique based on blocks. Each block can easily change the number of subsymbols, subcarriers, and pulse-shaping filter types to satisfy various needs, including low out-of-band (OOB) radiation, high data speeds, and low latency [10][11][12]. By reducing its block size, GFDM can achieve the low latency required for the tactile Internet.…”

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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“…A system model is presented by the authors of [18] to develop an Einstein product of a tensor-based precoding scheme suitable for MIMO GFDM systems. In [19], a joint data symbol detection and phase noise compensation algorithm is proposed to reduce the effects of phase noise in GFDM systems. In this present study, a cooperative UAV-aided millimeter wave (mmWave) CP-free GFDM system is proposed considering data transmission for ground mMTC.…”

Section: Introductionmentioning

confidence: 99%

Gyre Precoding and T-Transformation-Based GFDM System for UAV-Aided mMTC Network

et al. 2021

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In this paper, an unmanned aerial vehicle (UAV)-aided multi-antenna configured downlink mmWave cooperative generalized frequency division multiplexing (GFDM) system is proposed. To provide physical layer security (PLS), a 3D controlled Lorenz mapping system is introduced. Furthermore, the combination of T-transformation spreading codes, walsh Hadamard transform, and discrete Fourier transform (DFT) techniques are integrated with a novel linear multi-user multiple-input multiple-output (MU-MIMO) gyre precoding (GP) for multi-user interference reduction. Furthermore, concatenated channel-coding with multi-user beamforming weighting-aided maximum-likelihood and zero forcing (ZF) signal detection schemes for an improved bit error rate (BER) are also used. The system is then simulated with a single base station (BS), eight massive machine-type communications (mMTC) users, and two UAV relay stations (RSs). Numerical results reveal the robustness of the proposed system in terms of PLS and an achievable ergodic rate with signal-to-interference-plus-noise ratio (SINR) under the implementation of T-transformation scheme. By incorporating the 3D mobility model, brownian perturbations of the UAVs are also analyzed. An out-of-band (OOB) reduction of 320 dB with an improved BER of 1×10−4 in 16-QAM for a signal-to-noise ratio, Eb/N0, of 20 dB is achieved.

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Joint Channel and Phase Noise Estimation and Data Detection for GFDM

Cited by 16 publications

References 64 publications

Physical Layer Enhancement for Next-Generation Railway Communication Systems

Physical Layer Enhancement for Next-Generation Railway Communication Systems

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

Gyre Precoding and T-Transformation-Based GFDM System for UAV-Aided mMTC Network

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