The Experts below are selected from a list of 4365 Experts worldwide ranked by ideXlab platform
M. Fujii - One of the best experts on this subject based on the ideXlab platform.
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Path diversity reception using SMI steering vector arrays and multi-trellis Viterbi equalizer
ICUPC '98. IEEE 1998 International Conference on Universal Personal Communications. Conference Proceedings (Cat. No.98TH8384), 1998Co-Authors: M. FujiiAbstract:In this paper, path diversity reception using sample matrix inversion (SMI) steering vector arrays and a multi-trellis Viterbi equalizer is proposed to cope with frequency-selective fading. The proposed scheme estimates the channel impulse response and selects the delayed path that provides maximum path power. It then calculates two sets of array weight and output response vectors to extract the direct and selected paths and to estimate the array output response with the single constraint SMI algorithm. The branch-metric-combining multi-trellis Viterbi algorithm is used to estimate the most likely Transmitted Sequence from the array output signals. Simulation results show that the proposed scheme is superior to the previously proposed scheme in frequency-selective fading channels.
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Fractionally-spaced path diversity reception with arrays and LSSE for wideband wireless TDMA systems
IEEE GLOBECOM 1998 (Cat. NO. 98CH36250), 1998Co-Authors: M. FujiiAbstract:In this paper, fractionally-spaced path diversity reception that employs sample matrix inversion (SMI) steering vector arrays and limited-state Sequence estimation (LSSE) is proposed to cope with severe intersymbol interference. The proposed scheme estimates the fractionally-spaced channel impulse response and selects the sample timing phases of the direct and the delayed paths having maximum path power. It then calculates two sets of array weight and output response vectors to extract the direct path and the selected delayed path. LSSE is then used to estimate the most likely Transmitted Sequence from the array output signals. Simulation results show that this scheme can efficiently capture fractionally-spaced arrival paths and outperform symbol-spaced path diversity reception in frequency-selective fading channels.
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Joint processing of an adaptive array and an MLSE for frequency-selective fading channels
Proceedings of ICC'97 - International Conference on Communications, 1997Co-Authors: M. FujiiAbstract:In this paper, the joint processing of an adaptive array and a maximum likelihood Sequence estimator (MLSE) is proposed to cope with frequency-selective fading. The proposed scheme generates replicas of multiple desired paths with different delays received at all antennas. The errors between the array outputs and the replicas are branch metric combined in the Viterbi algorithm to estimate the most likely Transmitted Sequence. The replicas are also used as reference signals for controlling array weights to suppress long delay paths. Simulation results show that the proposed scheme can provide a better bit error rate performance than a conventional adaptive array or a conventional MLSE in frequency-selective fading channels.
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Path diversity reception using steering vector arrays and MLSE for frequency-selective fading channels
Proceedings of ICUPC 97 - 6th International Conference on Universal Personal Communications, 1997Co-Authors: M. FujiiAbstract:In this paper, the path diversity reception using steering vector arrays and a maximum likelihood Sequence estimator is proposed to cope with frequency-selective fading. The proposed scheme extracts the direct path and the 1-symbol delayed path separately using the two sets of the single constraint least mean square steering vector array. It combines the array output signals with the branch metric combining Viterbi algorithm to estimate the most likely Transmitted Sequence. Simulation results show that the proposed scheme can provide a better bit error rate performance than the 1-path reception in frequency-selective fading channels.
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Joint processing of an adaptive array and an MLSE for multipath channels
Proceedings of GLOBECOM'96. 1996 IEEE Global Telecommunications Conference, 1996Co-Authors: M. FujiiAbstract:Joint processing of an adaptive array and a maximum likelihood Sequence estimator (MLSE) is proposed to cope with multipath channels. The proposed scheme generates replicas of multiple desired paths with different delays received at all antennas. The errors between the array outputs and the replicas are branch metric combined in the Viterbi algorithm to estimate the most likely Transmitted Sequence. The replicas are also used as reference signals for controlling the array weights to suppress undesired paths. Simulation results show that the proposed scheme can provide a better bit error rate (BER) performance than a conventional adaptive array antenna or a conventional MLSE in multipath channels.
U. Mitra - One of the best experts on this subject based on the ideXlab platform.
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Sphere-constrained ML detection for frequency-selective channels
IEEE Transactions on Communications, 2006Co-Authors: H. Vikalo, B. Hassibi, U. MitraAbstract:The maximum-likelihood (ML) Sequence detection problem for channels with memory is investigated. The Viterbi algorithm (VA) provides an exact solution. Its computational complexity is linear in the length of the Transmitted Sequence, but exponential in the channel memory length. On the other hand, the sphere decoding (SD) algorithm also solves the ML detection problem exactly, and has expected complexity which is a low-degree polynomial (often cubic) in the length of the Transmitted Sequence over a wide range of signal-to-noise ratios. We combine the sphere-constrained search strategy of SD with the dynamic programming principles of the VA. The resulting algorithm has the worst-case complexity determined by the VA, but often significantly lower expected complexity
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Sphere-constrained ML detection for channels with memory
The Thrity-Seventh Asilomar Conference on Signals Systems & Computers 2003, 2003Co-Authors: H. Vikalo, B. Hassibi, U. MitraAbstract:The maximum-likelihood (ML) detection problem for channels with memory is investigated. The Viterbi algorithm (VA) provides an exact solution. Its computational complexity is linear in the length of the Transmitted Sequence but exponential in the channel memory length. Hence, the VA can be computationally inefficient when employed for detection on long channels. On the other hand, the sphere decoding (SD) algorithm also solves the ML detection problem exactly and has expected complexity which is polynomial (often cubic) in the length of the Transmitted Sequence over a wide range of signal-to-noise ratios (SNR). We combine the sphere-constrained search strategy of SD with the dynamic programming principles of the VA. The resulting algorithm has the worst-case complexity of the VA, but often significantly lower expected complexity.
H. Vikalo - One of the best experts on this subject based on the ideXlab platform.
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Sphere-constrained ML detection for frequency-selective channels
IEEE Transactions on Communications, 2006Co-Authors: H. Vikalo, B. Hassibi, U. MitraAbstract:The maximum-likelihood (ML) Sequence detection problem for channels with memory is investigated. The Viterbi algorithm (VA) provides an exact solution. Its computational complexity is linear in the length of the Transmitted Sequence, but exponential in the channel memory length. On the other hand, the sphere decoding (SD) algorithm also solves the ML detection problem exactly, and has expected complexity which is a low-degree polynomial (often cubic) in the length of the Transmitted Sequence over a wide range of signal-to-noise ratios. We combine the sphere-constrained search strategy of SD with the dynamic programming principles of the VA. The resulting algorithm has the worst-case complexity determined by the VA, but often significantly lower expected complexity
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Sphere-constrained ML detection for channels with memory
The Thrity-Seventh Asilomar Conference on Signals Systems & Computers 2003, 2003Co-Authors: H. Vikalo, B. Hassibi, U. MitraAbstract:The maximum-likelihood (ML) detection problem for channels with memory is investigated. The Viterbi algorithm (VA) provides an exact solution. Its computational complexity is linear in the length of the Transmitted Sequence but exponential in the channel memory length. Hence, the VA can be computationally inefficient when employed for detection on long channels. On the other hand, the sphere decoding (SD) algorithm also solves the ML detection problem exactly and has expected complexity which is polynomial (often cubic) in the length of the Transmitted Sequence over a wide range of signal-to-noise ratios (SNR). We combine the sphere-constrained search strategy of SD with the dynamic programming principles of the VA. The resulting algorithm has the worst-case complexity of the VA, but often significantly lower expected complexity.
Gang Qiao - One of the best experts on this subject based on the ideXlab platform.
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A Blind Side Information Detection Method for Partial Transmitted Sequence Peak-to-Average Power Reduction Scheme in OFDM Underwater Acoustic Communication System
IEEE Access, 2018Co-Authors: Siyu Xing, Gang QiaoAbstract:In this paper, we propose a blind side information detection scheme for partial Transmitted Sequence (PTS) peak-to-average power ratio reduction (PAPR) method in underwater acoustic (UWA) orthogonal frequency division multiplexing (OFDM) communication systems. The proposed scheme employs a corresponding table between the distributions of pilot tones and the phase rotation candidates to obtain a pseudo-optimum PTS PAPR reduction performance and a dramatic reduction of the total calculation cost. Meanwhile, predefined distributions of pilot tones are used to identify the phase rotation factor combination that has been selected and used at the transmitter. The spectral efficiency and data rate remain unaffected as the proposed PTS technique requires no additional pilot tones except for the tones used for channel estimation. Due to the sparse characteristic of the underwater acoustic channel, compressed sensing is adopted to complete the channel estimation and the phase rotation detection. The basis pursuit denoising algorithm employed here reduces the number of pilot tones as compared with linear channel estimation method. Simulation results show that the proposed scheme has better PAPR reduction performance compared to the conventional PTS scheme and the performance gap increases with the number of subblocks. Simulation and field experimental results demonstrate that the proposed scheme can differentiate the phase rotation factor. Therefore, the quality of the UWA OFDM communication system is significantly enhanced.
B. Hassibi - One of the best experts on this subject based on the ideXlab platform.
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Sphere-constrained ML detection for frequency-selective channels
IEEE Transactions on Communications, 2006Co-Authors: H. Vikalo, B. Hassibi, U. MitraAbstract:The maximum-likelihood (ML) Sequence detection problem for channels with memory is investigated. The Viterbi algorithm (VA) provides an exact solution. Its computational complexity is linear in the length of the Transmitted Sequence, but exponential in the channel memory length. On the other hand, the sphere decoding (SD) algorithm also solves the ML detection problem exactly, and has expected complexity which is a low-degree polynomial (often cubic) in the length of the Transmitted Sequence over a wide range of signal-to-noise ratios. We combine the sphere-constrained search strategy of SD with the dynamic programming principles of the VA. The resulting algorithm has the worst-case complexity determined by the VA, but often significantly lower expected complexity
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Sphere-constrained ML detection for channels with memory
The Thrity-Seventh Asilomar Conference on Signals Systems & Computers 2003, 2003Co-Authors: H. Vikalo, B. Hassibi, U. MitraAbstract:The maximum-likelihood (ML) detection problem for channels with memory is investigated. The Viterbi algorithm (VA) provides an exact solution. Its computational complexity is linear in the length of the Transmitted Sequence but exponential in the channel memory length. Hence, the VA can be computationally inefficient when employed for detection on long channels. On the other hand, the sphere decoding (SD) algorithm also solves the ML detection problem exactly and has expected complexity which is polynomial (often cubic) in the length of the Transmitted Sequence over a wide range of signal-to-noise ratios (SNR). We combine the sphere-constrained search strategy of SD with the dynamic programming principles of the VA. The resulting algorithm has the worst-case complexity of the VA, but often significantly lower expected complexity.
