detection of faster-than-nyquist (ftn) signaling · ebrahim bedeer, mohamed h. ahmed, and halim...
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Ebrahim Bedeer*, Halim Yanikomeroglu**, Mohamed Hossam Ahmed***
*Ulster University, Belfast, UK **Carleton University, Ottawa, ON, Canada
***Memorial University, St. John’s, NL, Canada
April 15, 2019
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Agenda Introduction FTN Signaling System Model Our FTN Signaling Contributions Quasi-Optimal Detection (High SE) Symbol-by-Symbol Detection (Low SE) M-ary QAM Detection M-ary PSK Detection
Conclusions
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Introduction Orthogonality is an advantage and a constraint.
Nyquist limit is more of a guideline than a rule.
Nyquist limit simplifies receive design by avoiding ISI.
Faster-than-Nyquist (FTN signaling) intentionally
introduce ISI to improve SE.
Detection: Increased complexity
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Introduction FTN signaling concept exists at least since 1968 [Saltzberg-68].
FTN signaling term coined by Mazo in 1975 [Mazo-75].
Mazo Limit: FTN does not affect minimum distance of uncoded
sinc binary transmission up to a certain range.
Mazo Limit: 1/0.802 25% faster than Nyquist 25% in spectral efficiency.
Much faster Mazo limit: Possible, but with some SNR penalty. Saltzberg B. Intersymbol interference error bounds with application to ideal bandlimited signaling. IEEE Transactions on Information Theory. July 1968; 14(4):563-8. Mazo JE. Faster-than-Nyquist signaling. The Bell System Technical Journal. Oct. 1975; 54(8):1451-62.
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FTN Signaling Basic Idea
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FTN Signaling Basic Idea
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Extension of Mazo Limit Other pulse shapes (root-raised cosine, Gaussian, …)
Non-binary transmission
Frequency domain
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Our FTN Publications
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Ebrahim Bedeer, Halim Yanikomeroglu, and Mohamed H. Ahmed, “Quasi-optimal sequence estimation of binary faster-than-Nyquist signaling”, IEEE ICC 2017, Paris, France.
Ebrahim Bedeer, Mohamed H. Ahmed, and Halim Yanikomeroglu, “A very low complexity successive symbol-by-symbol sequence estimator for binary faster-than-Nyquist signaling”, IEEE Access, March 2017.
Ebrahim Bedeer, Mohamed H. Ahmed, and Halim Yanikomeroglu, “Low-complexity detection of high-order QAM faster-than-Nyquist signaling”, IEEE Access, July 2017.
Ebrahim Bedeer, Halim Yanikomeroglu, and Mohamed H. Ahmed, “Low-Complexity Detection of M-ary PSK Faster-than-Nyquist Signaling”, IEEE WCNC 2019 Workshops, Marrakech, Morocco.
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FTN Block Diagram
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Quasi-Optimal Detection (High SE)
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Ebrahim Bedeer, Halim Yanikomeroglu, and Mohamed H. Ahmed, “Quasi-optimal sequence estimation of binary faster-than-Nyquist signaling”, IEEE ICC 2017, Paris, France.
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Modified Sphere Decoding (MSD) Noise covariance matrix can be exploited to develop
MSD. Estimated data symbols can be found using MSD as
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Simulation Results
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Simulation Results
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Simulation Results
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Spectral Efficiency SE= log2(M) x [1/(1+β)] x (1/τ) bits/s/Hz
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Symbol-by-Symbol Detection (Low SE)
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Ebrahim Bedeer, Mohamed H. Ahmed, and Halim Yanikomeroglu, “A very low complexity successive symbol-by-symbol sequence estimator for binary faster-than-Nyquist signaling”, IEEE Access, March 2017.
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Successive Symbol-by-Symbol Sequence Estimation (SSSSE) Received sample
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Successive Symbol-by-Symbol Sequence Estimation (SSSSE) Received sample
Perfect estimation condition for QPSK FTN signaling
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Operating region of SSSSE
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Successive Symbol-by-Symbol Sequence Estimation (SSSSE) Received sample
Perfect estimation condition for QPSK FTN signaling
Estimated symbol
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Successive Symbol-by-Symbol with go-back-K Sequence Estimation (SSSgbKSE) Received sample
Estimated symbol
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Simulation Results
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Simulation Results
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M-ary PSK Detection
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Ebrahim Bedeer, Halim Yanikomeroglu, and Mohamed H. Ahmed, “Low-Complexity Detection of M-ary PSK Faster-than-Nyquist Signaling”, IEEE WCNC 2019 Workshops, Marrakech, Morocco.
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FTN Detection Problem
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Received sample
Received sampled signal in vector format
Received sampled signal after (optional) whitening filter
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FTN Detection Problem Received sampled signal
Maximum likelihood detection problem
Can be solved in polynomial time complexity using ideas from semidefinite relaxation and Gaussian randomization
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NP-hard
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Proposed FTN Detection Scheme
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Simulation Results
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8-PSK Roll-off factor: β = 0.3
Spectral Efficiency SE= log2(M) x [1/(1+β)] x (1/τ) bits/s/Hz
SE = 2.31 bits/s/Hz
Mazo limit: τ = 0.802
17% increase in SE excellent
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Simulation Results
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Roll-off factor: β = 0.5 Spectral Efficiency SE= log2(M) x [1/(1+β)] x (1/τ) bits/s/Hz
QPSK, SE = 2 bits/s/Hz
Nyquist signaling
Performance vs complexity tradeoff
J. B. Anderson and A. Prlja, “Turbo equalization and an M-BCJR algorithm for strongly narrowband intersymbol interference,” ISIT 2010.
QPSK 8-PSK
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Simulation Results
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Conclusions FTN signaling is promising to increase the SE.
Tradeoff between performance and complexity.
Gain of FTN increases at higher values of SE.
Channel coding?
AI / machine learning?
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