DocumentCode :
1787754
Title :
Self-learning MIMO-RF receiver systems: Process resilient real-time adaptation to channel conditions for low power operation
Author :
Banerjee, Debashis ; Muldrey, Barry ; Sen, Satyaki ; Xian Wang ; Chatterjee, Avhishek
Author_Institution :
Dept. of ECE, Georgia Inst. of Technol., Atlanta, GA, USA
fYear :
2014
fDate :
2-6 Nov. 2014
Firstpage :
710
Lastpage :
717
Abstract :
Prior research has established that dynamically trading-off the performance of the 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, 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 complicates the problem of designing the feedback control system that guarantees the best control strategy for minimizing power consumption across all channel conditions and process corners. Since this problem is largely intractable due to the complexity of simulation across all channel conditions and process corners, we propose a self-learning strategy for adaptive MIMO-RF systems. In this approach, RF devices learn their own performance vs. power consumption vs. tuning knob relationships "on-the-fly" and formulate the optimum reconfiguration strategy using neural-network based learning techniques during real-time operation. The methodology is demonstrated for a MIMO-RF receiver front-end and is supported by hardware validation leading to 2.5X power savings in minimal learning time.
Keywords :
MIMO communication; learning (artificial intelligence); radio receivers; RF devices; feedback control system; low power operation; neural-network based learning techniques; optimal power control strategy; reduced power consumption; resilient real-time adaptation; self-learning MIMO-RF receiver systems; MIMO; Optimized production technology; Power demand; Radio frequency; Real-time systems; Receivers; Tuning; Adaptation; Artificial neural network; LNA; Low Power; MIMO; Mixer; OFDM; Receiver. Radio-Frequency; Self-learning;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer-Aided Design (ICCAD), 2014 IEEE/ACM International Conference on
Conference_Location :
San Jose, CA
Type :
conf
DOI :
10.1109/ICCAD.2014.7001430
Filename :
7001430
Link To Document :
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