The Experts below are selected from a list of 45729 Experts worldwide ranked by ideXlab platform
Abhijit Chatterjee - One of the best experts on this subject based on the ideXlab platform.
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Self-Learning RF Receiver Systems: Process Aware Real-Time Adaptation to Channel Conditions for Low Power Operation
IEEE Transactions on Circuits and Systems I: Regular Papers, 2017Co-Authors: Debashis Banerjee, Barry Muldrey, Xian Wang, Abhijit ChatterjeeAbstract:Prior research has established that dynamically trading-off the performance of the radio-frequency (RF) front-end for reduced power consumption across changing channel conditions, using a feedback control system that modulates circuit and algorithmic level “tuning knobs” in real-time based on received signal quality, leads to significant power savings. It is also known that the optimal power control strategy depends on the Process conditions corresponding to the RF devices concerned. This leads to an explosion in the search space needed to find the best feedback control strategy, across all combinations of channel conditions and Receiver Process corners, making simulation driven optimal control law design impractical and computationally infeasible. Since this problem is largely intractable due to the above complexity of simulation, we propose a self-learning strategy for adaptive RF systems. In this approach, RF devices learn their own performance vs. power consumption vs. tuning knob relationships “on-the-fly” and formulate the most power-optimal control strategy for real-time adaptation of the RF system using neural-network based learning techniques during real-time operation. The methodology is demonstrated using SISO and MIMO RF Receiver front-ends as test vehicles and is supported by hardware validation leading to 2.5X-3X power savings with minimal overhead.
Debashis Banerjee - One of the best experts on this subject based on the ideXlab platform.
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Self-Learning RF Receiver Systems: Process Aware Real-Time Adaptation to Channel Conditions for Low Power Operation
IEEE Transactions on Circuits and Systems I: Regular Papers, 2017Co-Authors: Debashis Banerjee, Barry Muldrey, Xian Wang, Abhijit ChatterjeeAbstract:Prior research has established that dynamically trading-off the performance of the radio-frequency (RF) front-end for reduced power consumption across changing channel conditions, using a feedback control system that modulates circuit and algorithmic level “tuning knobs” in real-time based on received signal quality, leads to significant power savings. It is also known that the optimal power control strategy depends on the Process conditions corresponding to the RF devices concerned. This leads to an explosion in the search space needed to find the best feedback control strategy, across all combinations of channel conditions and Receiver Process corners, making simulation driven optimal control law design impractical and computationally infeasible. Since this problem is largely intractable due to the above complexity of simulation, we propose a self-learning strategy for adaptive RF systems. In this approach, RF devices learn their own performance vs. power consumption vs. tuning knob relationships “on-the-fly” and formulate the most power-optimal control strategy for real-time adaptation of the RF system using neural-network based learning techniques during real-time operation. The methodology is demonstrated using SISO and MIMO RF Receiver front-ends as test vehicles and is supported by hardware validation leading to 2.5X-3X power savings with minimal overhead.
Barry Muldrey - One of the best experts on this subject based on the ideXlab platform.
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Self-Learning RF Receiver Systems: Process Aware Real-Time Adaptation to Channel Conditions for Low Power Operation
IEEE Transactions on Circuits and Systems I: Regular Papers, 2017Co-Authors: Debashis Banerjee, Barry Muldrey, Xian Wang, Abhijit ChatterjeeAbstract:Prior research has established that dynamically trading-off the performance of the radio-frequency (RF) front-end for reduced power consumption across changing channel conditions, using a feedback control system that modulates circuit and algorithmic level “tuning knobs” in real-time based on received signal quality, leads to significant power savings. It is also known that the optimal power control strategy depends on the Process conditions corresponding to the RF devices concerned. This leads to an explosion in the search space needed to find the best feedback control strategy, across all combinations of channel conditions and Receiver Process corners, making simulation driven optimal control law design impractical and computationally infeasible. Since this problem is largely intractable due to the above complexity of simulation, we propose a self-learning strategy for adaptive RF systems. In this approach, RF devices learn their own performance vs. power consumption vs. tuning knob relationships “on-the-fly” and formulate the most power-optimal control strategy for real-time adaptation of the RF system using neural-network based learning techniques during real-time operation. The methodology is demonstrated using SISO and MIMO RF Receiver front-ends as test vehicles and is supported by hardware validation leading to 2.5X-3X power savings with minimal overhead.
Xian Wang - One of the best experts on this subject based on the ideXlab platform.
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Self-Learning RF Receiver Systems: Process Aware Real-Time Adaptation to Channel Conditions for Low Power Operation
IEEE Transactions on Circuits and Systems I: Regular Papers, 2017Co-Authors: Debashis Banerjee, Barry Muldrey, Xian Wang, Abhijit ChatterjeeAbstract:Prior research has established that dynamically trading-off the performance of the radio-frequency (RF) front-end for reduced power consumption across changing channel conditions, using a feedback control system that modulates circuit and algorithmic level “tuning knobs” in real-time based on received signal quality, leads to significant power savings. It is also known that the optimal power control strategy depends on the Process conditions corresponding to the RF devices concerned. This leads to an explosion in the search space needed to find the best feedback control strategy, across all combinations of channel conditions and Receiver Process corners, making simulation driven optimal control law design impractical and computationally infeasible. Since this problem is largely intractable due to the above complexity of simulation, we propose a self-learning strategy for adaptive RF systems. In this approach, RF devices learn their own performance vs. power consumption vs. tuning knob relationships “on-the-fly” and formulate the most power-optimal control strategy for real-time adaptation of the RF system using neural-network based learning techniques during real-time operation. The methodology is demonstrated using SISO and MIMO RF Receiver front-ends as test vehicles and is supported by hardware validation leading to 2.5X-3X power savings with minimal overhead.
Chris Steenhoek - One of the best experts on this subject based on the ideXlab platform.
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on the performance of variable fractional delay arbitrary sample rate conversion for digital signals
Military Communications Conference, 2014Co-Authors: John Eric Kleider, Chris SteenhoekAbstract:Arbitrary sample rate conversion (ASRC) is a critical digital signal Processing function for any signal that is received and down converted from analog RF to either digital-IF or-base band. There are numerous ASRC applications, including, but not limited to commercial and military communications, radar- and audio-signal Processing, and multi-function systems (such as coexistence of EW and communication systems). Furthermore, variable fractional delay (VFD) Processing is critical when synchronizing to any transmit signal, for precise timing in geolocation Processing or for any Receiver Process where accurate timing is required. There are many designs for VFD-ASRC which may operate across a wide range of parameters in a functional sense, but which do not perform equally across their operational range. Few methods exist in the literature for evaluating the performance of these designs in a generalized fashion for applicability to project specifications. This work proposes a method for evaluating the performance of any VFD-ASRC system. The generalized technique uses a high performance filter bank followed by frequency-domain magnitude- and group delay-error metrics to provide a measure of the quality of a VFD-ASRC design. The method can be applied to any single- and multiple-carrier modulation signal.
