Predictive Strategy for Energy Harvesting MIMO Systems With Finite Alphabet Inputs and Limited Feedback

Due to the lack of explicit expression of instantaneous mutual information (IMI), online throughput optimization for energy harvesting (EH) MIMO systems with finite alphabet inputs suffers from heavy computation burden and large feedback overhead. We propose a two-layer predictive strategy with the...

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Published in:	IEEE transactions on communications Vol. 67; no. 11; pp. 7540 - 7557
Main Authors:	Ke, Feng, Zeng, Weiliang, Xiao, Chengshan
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.11.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Computer simulation Energy harvesting Feedback finite alphabet inputs limited feedback MIMO MIMO (control systems) MIMO communication Optimization Precoding predictive Receivers Strategy Throughput Topology Transmitters Wireless communication
ISSN:	0090-6778, 1558-0857
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Abstract	Due to the lack of explicit expression of instantaneous mutual information (IMI), online throughput optimization for energy harvesting (EH) MIMO systems with finite alphabet inputs suffers from heavy computation burden and large feedback overhead. We propose a two-layer predictive strategy with the aid of context information, by constructing a precoding codebook offline at both the transmitter and receiver, to relax the time pressure for the online strategy. Two schemes were provided to accelerate the codebook construction. One is to estimate IMI during optimization by using a reference function. The other is by starting the optimization of each precoder from an adjacent precoder. With the channel distribution information (CDI), the feedback overhead is reduced by minimizing the codebook size. By the methods above, the building speed of the precoding codebook can be up to two orders of magnitude faster than no accelerating strategies. Meanwhile, through topology analysis, we propose a higher layer predictive strategy, which determines the index of allocated energy at each slot within a frame. The higher layer strategy takes full advantage of the constructed precoding codebook and context information when estimating the future throughput. Simulation results demonstrate the effectiveness and practicality of the proposed two-layer strategy.
AbstractList	Due to the lack of explicit expression of instantaneous mutual information (IMI), online throughput optimization for energy harvesting (EH) MIMO systems with finite alphabet inputs suffers from heavy computation burden and large feedback overhead. We propose a two-layer predictive strategy with the aid of context information, by constructing a precoding codebook offline at both the transmitter and receiver, to relax the time pressure for the online strategy. Two schemes were provided to accelerate the codebook construction. One is to estimate IMI during optimization by using a reference function. The other is by starting the optimization of each precoder from an adjacent precoder. With the channel distribution information (CDI), the feedback overhead is reduced by minimizing the codebook size. By the methods above, the building speed of the precoding codebook can be up to two orders of magnitude faster than no accelerating strategies. Meanwhile, through topology analysis, we propose a higher layer predictive strategy, which determines the index of allocated energy at each slot within a frame. The higher layer strategy takes full advantage of the constructed precoding codebook and context information when estimating the future throughput. Simulation results demonstrate the effectiveness and practicality of the proposed two-layer strategy.
Author	Xiao, Chengshan Ke, Feng Zeng, Weiliang
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SubjectTerms	Computer simulation Energy harvesting Feedback finite alphabet inputs limited feedback MIMO MIMO (control systems) MIMO communication Optimization Precoding predictive Receivers Strategy Throughput Topology Transmitters Wireless communication
Title	Predictive Strategy for Energy Harvesting MIMO Systems With Finite Alphabet Inputs and Limited Feedback
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