Cognitive GFDM Full-Duplex Radios With RF Impairments and ACI Constraints

Mohammadian, Amirhossein; Tellambura, Chintha

doi:10.1109/ojcoms.2020.2993611

Cited by 7 publications

(7 citation statements)

References 48 publications

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“…First, optimal pilot designs that use the MSE or a similar measure can be developed. Second, other RF impairments such as CFO and IQ imbalance can be incorporated with our proposed algorithms, and further afield, they may also be compensated for full-duplex transceivers [14], [21], [69].…”

Section: Discussionmentioning

confidence: 99%

“…Phase noise is shown to exacerbate ICI and ISI terms and degrade the system's SIR performance. Moreover, GFDM full-duplex transceivers and cognitive radio networks with phase noise, CFO and IQ imbalances are studied in [13], [14], [21], [45]. Therefore, phase noise estimation and compensation are necessary to prevent the degradation of GFDM performance.…”

Section: A Related Workmentioning

confidence: 99%

“…Based on (14), we have S = ′ XF ℎ̄ . Moreover, based on (18) and (31), we have S = T ′ F ′ ̄ ′ , where T ′ is a circulant matrix formed by elements of ′ x.…”

Section: Appendix F S ∕mentioning

confidence: 99%

“…dards. This potential has motivated extensive studies of GFDM for cognitive radio networks [11]- [14], space-time codes [15], filter designs [16], [17], multiple-input and multiple-output communications [18] Internet-of-Things [19], and optical networks [20]. This list is not exhaustive.…”

Section: Introductionmentioning

confidence: 99%

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Joint Channel and Phase Noise Estimation and Data Detection for GFDM

Mohammadian

Tellambura

2021

IEEE Open J. Commun. Soc.

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Generalized frequency division multiplexing (GFDM), an enabler of beyond-5G wireless networks, can be critically impaired due to radio frequency (RF) phase noise. However, joint channel estimation and phase noise compensation for GFDM systems have not been addressed before. Hence, we tackle this problem. To this end, we propose an iterative algorithm for joint channel and phase noise estimation and two algorithms for joint data detection and phase noise compensation. These algorithms use linear and non-linear least-squares (NLS) methods and employ block-type and comb-type pilots. The complexity of these algorithms is also analyzed. Moreover, to reduce their complexity, interpolation techniques are deployed to decrease the number of unknowns. We also analyze the signal-to-interference-plus noise ratio (SINR) and sum-rate of GFDM contaminated with phase noise. Furthermore, the accuracy of the channel and phase noise estimates is established via Cramér-Rao lower bounds (CRLBs). The simulation results illustrate that the mean square error (MSE) performance of the proposed joint channel and phase noise estimator reaches the CRLB. Moreover, the proposed joint data symbol detection and phase noise compensation algorithms nearly eliminate the impacts of phase noise in GFDM systems.

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Section: Discussionmentioning

confidence: 99%

Section: A Related Workmentioning

confidence: 99%

“…Based on (14), we have S = ′ XF ℎ̄ . Moreover, based on (18) and (31), we have S = T ′ F ′ ̄ ′ , where T ′ is a circulant matrix formed by elements of ′ x.…”

Section: Appendix F S ∕mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Joint Channel and Phase Noise Estimation and Data Detection for GFDM

Mohammadian

Tellambura

2021

IEEE Open J. Commun. Soc.

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“…To suppress their effect, the optimal filter is designed, which maximizes the SIR of the system. Furthermore, studies in [570], [571] investigate impacts of RF impairments including PN, CFO, IQ imbalance, and non-linear power amplifier on the performance of cognitive GFDM full-duplex secondary link and solve the power allocation problem to maximize the sum rate of the system.…”

Section: B Non-orthogonal Multicarrier Systemsmentioning

confidence: 99%

RF Impairments in Wireless Transceivers: Phase Noise, CFO, and IQ Imbalance – A Survey

Mohammadian

Tellambura

2021

IEEE Access

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Wireless transceivers for mass-market applications must be cost effective. We may achieve this goal by deploying non-ideal low-cost radio frequency (RF) analog components. However, their imperfections may result in RF impairments, including phase noise (PN), carrier frequency offset (CFO), and in-phase (I) and quadrature-phase (Q) imbalance. These impairments introduce in-band and out-of-band interference terms and degrade the performance of wireless systems. In this survey, we present RF-impairment signal models and discuss their impacts. Moreover, we review RF-impairment estimation and compensation in singlecarrier (SC) and multicarrier systems, especially orthogonal frequency division multiplexing (OFDM). Furthermore, we discuss the effects of the RF impairments in already-established wireless technologies, e.g., multiple-input multiple-output (MIMO), massive MIMO, full-duplex, and millimeter-wave communications and review existing estimation and compensation algorithms. Finally, future research directions investigate the RF impairments in emerging technologies, including cell-free massive MIMO communications, nonorthogonal multicarrier systems, non-orthogonal multiple access (NOMA), ambient backscatter communications, and intelligent reflecting surface (IRS)-assisted communications. Furthermore, we discuss artificial intelligence (AI) approaches for developing estimation and compensation algorithms for RF impairments.

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