Abstract
Wireless communication systems with energy harvester are attracting much attention due to their ability to improve the system operation time. Extensive research has been carried out on how to maximize the sum of mutual information over multiple time slots (i.e. the throughput). However, most research focuses on the energy harvesting transmitter with Gaussian inputs and single antenna. This study considers the throughput maximization problem for an energy harvesting transmitter with causal energy constraint over multiple-input multiple-output (MIMO) channels. Different from existing works, the authors consider the MIMO system with finite-alphabet inputs and partial instantaneous channel state information (CSI). Specifically, the transmitter knows the statistical CSI of the entire channel frame as well as the instantaneous CSI of current time slot. The precoder design in this scenario is an intractable optimization problem with respect to multiple precoding matrices. The authors' analysis shows that this difficult precoding problem can be equivalently transformed into a set of scalar optimization subproblems with respect to the transmit power. To solve these subproblems, an efficient algorithm based on the dynamic programming is proposed. The authors analyze the performance of the proposed algorithm, and simulation results validate its effectiveness.
Recommended Citation
X. Zhu et al., "Throughput Optimisation for Energy Harvesting Transmitter with Partial Instantaneous Channel State Information and Finite-alphabet Inputs," IET Communications, vol. 10, no. 4, pp. 443 - 544, Wiley Open Access; Institution of Engineering and Technology (IET), Mar 2016.
The definitive version is available at https://doi.org/10.1049/iet-com.2015.0532
Department(s)
Electrical and Computer Engineering
Publication Status
Open Access
International Standard Serial Number (ISSN)
1751-8628
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2024 The Authors, All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution 4.0 License.
Publication Date
03 Mar 2016