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Abstract
One of the key applications in the fifth generation (5G) communication systems is to support extremely high reliability (\sim 99.999%) and low latency (\lt 1 ms), namely ultra-reliable and low-latency communication. In this paper, we consider the problem of maximizing energy efficiency (EE) for downlink multi-user multiple-input single-output (MISO) networks under short packet transmission. An optimization problem is formulated to jointly optimize the precoders at the base station (BS) for serving multiple downlink users and the error probability with finite blocklength (FBL) codes, subject to the constraints on decoding error probability per URLLC user and on the BS transmit power. Since the formulated problem is non-convex, we convert this problem into a convex one by analyzing the structure of the EE objective. We then propose an algorithm to find a near-optimal solution for maximizing the EE. Simulation results validate the effectiveness of the proposed algorithm that supports energy-efficient URLLC.
Original language | English |
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Title of host publication | 2020 IEEE 91st Vehicular Technology Conference, VTC Spring 2020 - Proceedings |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Electronic) | 9781728152073 |
DOIs | |
State | Published - May 2020 |
Event | 91st IEEE Vehicular Technology Conference, VTC Spring 2020 - Antwerp, Belgium Duration: 25 May 2020 → 28 May 2020 |
Publication series
Name | IEEE Vehicular Technology Conference |
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Volume | 2020-May |
ISSN (Print) | 1550-2252 |
Conference
Conference | 91st IEEE Vehicular Technology Conference, VTC Spring 2020 |
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Country/Territory | Belgium |
City | Antwerp |
Period | 25/05/20 → 28/05/20 |
Keywords
- Precoder design
- finite blocklength (FBL) codes
- multi-user
- multiple-input single-output (MISO)
- optimization
- ultra-reliable low-latency communication (URLLC)
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Dive into the research topics of 'Energy-Efficient Precoder Design for URLLC-Enabled Downlink Multi-User MISO Networks Using Finite Blocklength Codes'. Together they form a unique fingerprint.Projects
- 1 Finished
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Deep Learning for Green Wireless Communications: Power Control and Optimization
Ku, M.-L. (PI)
1/08/19 → 31/07/20
Project: Research