Joint Resource Allocation in SWIPT-Based Multiantenna Decode-and-Forward Relay Networks

In this paper, we consider relay-assisted simultaneous wireless information and power transfer (SWIPT) for two-hop cooperative transmission, where a half-duplex multiantenna relay adopts decode-and-forward relaying strategy for information forwarding. The relay is assumed to be energy-free and needs...

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Bibliographic Details
Published in:IEEE transactions on vehicular technology 2017-10, Vol.66 (10), p.9192-9200
Main Author: Liu, Yuan
Format: Article
Language:English
Subjects:
Antennas
Clustering
Complexity
Computer simulation
Decode-and-forward (DF) relaying
Decoding
Energy harvesting
Greedy algorithms
multi-antenna relay
Optimization
Power management
Power transfer
Receiving antennas
Relay networks
Relaying
Relays
Resource allocation
Resource management
simultaneous wireless information and power transfer (SWIPT)
Wireless networks
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Summary:In this paper, we consider relay-assisted simultaneous wireless information and power transfer (SWIPT) for two-hop cooperative transmission, where a half-duplex multiantenna relay adopts decode-and-forward relaying strategy for information forwarding. The relay is assumed to be energy-free and needs to harvest energy from the source node. By embedding power splitting (PS) at each relay antenna to coordinate the received energy and information, joint problem of determining PS ratios and power allocation at the multiantenna relay node is formulated to maximize the end-to-end achievable rate. We show that the multiantenna relay is equivalent to a virtual single-antenna relay in such a SWIPT system, and the problem is optimally solved with closed form. To reduce the hardware cost of the PS scheme, we further propose the antenna clustering scheme, where the multiple antennas at the relay are partitioned into two disjoint groups, which are exclusively used for information decoding and energy harvesting, respectively. Optimal clustering algorithm is first proposed but with exponential complexity. Then, a greedy clustering algorithm is introduced with linear complexity and approaches to the optimal performance. Several valuable insights are provided via theoretical analysis and simulation results.
ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2017.2717018