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javan erfanian

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Papers by javan erfanian

Research paper thumbnail of 6G Vision & Analysis of Potential Use Cases

IEEE Communications Magazine

FIGURE 1. Generic use cases grouped into four use case classes,

Research paper thumbnail of Reduced complexity symbol detectors with parallel structure for ISI channels

IEEE Transactions on Communications, 1994

The problem of practical realization of the optimal fixed-delay symbol-by-symbol detection algori... more The problem of practical realization of the optimal fixed-delay symbol-by-symbol detection algorithm, which is optimum in the sense of minimizing the symbol error probability, given a delay constraint D, is investigated. A fully-parallel structure is developed, and through systematic reformulations of the algorithm, the computational requirements are reduced considerably. In addition, the problems associated with a large dynamic range such as overflow (or underffow) are (practically) removed. A number of approximations are applied to this simplified parallel symbol (SPS) detector that lead to the derivation of suboptimal detectors. One such suboptimal detector is shown to be the same as the minimum-metric Viterbi detector. A brief comparison of the SPS detector and the Viterbi detector shows that the former has a slightly better performance at low values of signalto-noise ratio (SNR) and the latter performs a smaller number of computations (particularly) at higher values of SNR; otherwise, the two detectors are comparable in performance and complexity.

Research paper thumbnail of Deploying Artificial Intelligence in the Wireless Infrastructure: the Challenges Ahead

2019 IEEE 2nd 5G World Forum (5GWF), 2019

The adoption of artificial intelligence (AI) techniques entails a substantial change in the wirel... more The adoption of artificial intelligence (AI) techniques entails a substantial change in the wireless ecosystem where data as well as their owners become crucial. As a result, the roll out of AI techniques in wireless systems raises a plethora of questions. In this context, we describe the challenges observed by the wireless stakeholders when deploying AI. Furthermore, we introduce the recent discussion in field of ethics that appear when managing wireless communications data.

Research paper thumbnail of Low-complexity parallel-structure symbol-by-symbol detection for ISI channels

Low-complexity parallel-structure symbol-by-symbol detection for ISI channels

Conference Proceeding IEEE Pacific Rim Conference on Communications, Computers and Signal Processing, 1989

The problem of practical realization of the optimal fixed-delay symbol-by-symbol detection algori... more The problem of practical realization of the optimal fixed-delay symbol-by-symbol detection algorithm is investigated. A new structure is developed which is fully parallel with a speedup factor of 2D+1 (compared to serial implementation), where D is the delay constraint. Through systematic reformulations of the algorithm, a number of simplifications are performed that avoid the complex and slow computations of exponentials

Research paper thumbnail of 6G Vision & Analysis of Potential Use Cases

IEEE Communications Magazine

FIGURE 1. Generic use cases grouped into four use case classes,

Research paper thumbnail of Reduced complexity symbol detectors with parallel structure for ISI channels

IEEE Transactions on Communications, 1994

The problem of practical realization of the optimal fixed-delay symbol-by-symbol detection algori... more The problem of practical realization of the optimal fixed-delay symbol-by-symbol detection algorithm, which is optimum in the sense of minimizing the symbol error probability, given a delay constraint D, is investigated. A fully-parallel structure is developed, and through systematic reformulations of the algorithm, the computational requirements are reduced considerably. In addition, the problems associated with a large dynamic range such as overflow (or underffow) are (practically) removed. A number of approximations are applied to this simplified parallel symbol (SPS) detector that lead to the derivation of suboptimal detectors. One such suboptimal detector is shown to be the same as the minimum-metric Viterbi detector. A brief comparison of the SPS detector and the Viterbi detector shows that the former has a slightly better performance at low values of signalto-noise ratio (SNR) and the latter performs a smaller number of computations (particularly) at higher values of SNR; otherwise, the two detectors are comparable in performance and complexity.

Research paper thumbnail of Deploying Artificial Intelligence in the Wireless Infrastructure: the Challenges Ahead

2019 IEEE 2nd 5G World Forum (5GWF), 2019

The adoption of artificial intelligence (AI) techniques entails a substantial change in the wirel... more The adoption of artificial intelligence (AI) techniques entails a substantial change in the wireless ecosystem where data as well as their owners become crucial. As a result, the roll out of AI techniques in wireless systems raises a plethora of questions. In this context, we describe the challenges observed by the wireless stakeholders when deploying AI. Furthermore, we introduce the recent discussion in field of ethics that appear when managing wireless communications data.

Research paper thumbnail of Low-complexity parallel-structure symbol-by-symbol detection for ISI channels

Low-complexity parallel-structure symbol-by-symbol detection for ISI channels

Conference Proceeding IEEE Pacific Rim Conference on Communications, Computers and Signal Processing, 1989

The problem of practical realization of the optimal fixed-delay symbol-by-symbol detection algori... more The problem of practical realization of the optimal fixed-delay symbol-by-symbol detection algorithm is investigated. A new structure is developed which is fully parallel with a speedup factor of 2D+1 (compared to serial implementation), where D is the delay constraint. Through systematic reformulations of the algorithm, a number of simplifications are performed that avoid the complex and slow computations of exponentials

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