Energy-Efficient URLLC Service Provision via a Near-Space Information Network

The integration of a near-space information network (NSIN) with the reconfigurable intelligent surface (RIS) is envisioned to significantly enhance the communication performance of future wireless communication systems by proactively altering wireless channels. This paper investigates the problem of...

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Vydáno v:IEEE transactions on wireless communications Ročník 23; číslo 8; s. 9839 - 9853
Hlavní autoři: An, Puguang, Yang, Peng, Cao, Xianbin, Guo, Kun, Gao, Yue, Quek, Tony Q. S.
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.08.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Autonomous aerial vehicles
Channel estimation
Decoding
Design optimization
electromagnetic channel estimation
energy efficiency
Error analysis
Message passing
Near-space information network
Network latency
Optimization
reconfigurable intelligent surface
Reconfigurable intelligent surfaces
Reliability
Resource allocation
Throughput
Ultra reliable low latency communication
URLLC
Wireless communication
Wireless communication systems
ISSN:1536-1276, 1558-2248
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Shrnutí:The integration of a near-space information network (NSIN) with the reconfigurable intelligent surface (RIS) is envisioned to significantly enhance the communication performance of future wireless communication systems by proactively altering wireless channels. This paper investigates the problem of deploying a RIS-integrated NSIN to provide energy-efficient, ultra-reliable and low-latency communications (URLLC) services. We mathematically formulate this problem as a resource optimization problem, aiming to maximize the effective throughput and minimize the system power consumption, subject to URLLC and physical resource constraints. The formulated problem is challenging in terms of accurate channel estimation, RIS phase alignment, and effective solution design. We propose a joint resource allocation algorithm to handle these challenges. In this algorithm, we develop an accurate channel estimation approach by exploring message passing and optimize phase shifts of RIS reflecting elements to further increase the channel gain. Besides, we derive an analysis-friendly expression of decoding error probability and decompose the problem into two-layered optimization problems by analyzing the monotonicity, which makes the formulated problem analytically tractable. Extensive simulations have been conducted to verify the performance of the proposed algorithm. Simulation results show that the proposed algorithm can achieve outstanding channel estimation performance and is more energy-efficient than diverse benchmark algorithms.
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ISSN:1536-1276
1558-2248
DOI:10.1109/TWC.2024.3366705