Abstract
This paper studies power allocation for performance constrained dual-hop variable-gain amplify-and-forward (AF) relay networks in Nakagami- $m$ fading. In this context, the performance constraint is formulated as a constraint on the end-to-end signal-to-noise-ratio (SNR) and the overall power consumed is minimized while maintaining this constraint. This problem is considered under two different assumptions of the available channel state information (CSI) at the relays, namely full CSI at the relays and partial CSI at the relays. In addition to the power minimization problem, we also consider the end-to-end SNR maximization problem under a total power constraint for the partial CSI case. We provide closed-form solutions for all the problems which are easy to implement except in two cases, namely selective relaying with partial CSI for power minimization and SNR maximization, where we give the solution in the form of a one-variable equation which can be solved efficiently. Numerical results are then provided to characterize the performance of the proposed power allocation algorithms considering the effects of channel parameters and CSI availability. © 2014 IEEE.
Original language | English (US) |
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Pages (from-to) | 3616-3630 |
Number of pages | 15 |
Journal | IEEE Transactions on Signal Processing |
Volume | 62 |
Issue number | 14 |
DOIs | |
State | Published - Jul 2014 |
Bibliographical note
KAUST Repository Item: Exported on 2020-10-01Acknowledgements: The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Joakim Jalden. This work was supported by a Grant from King Abdulaziz City of Science and Technology (KACST). The work ofY. Chen was supported in part by the Open Research Project of the State Key Laboratory of Industrial Control Technology, Zhejiang University, China (Grant IC14T40). This work appeared in part in the Proceedings of the IEEE Global Communications Conference (GLOBECOM' 2013) Atlanta, GA, USA, Dec. 9-13, 2013.
ASJC Scopus subject areas
- Signal Processing
- Electrical and Electronic Engineering