Resource Management and Trajectory Optimization for UAV-IRS Assisted Maritime Edge Computing Networks

Chaoyue Zhang^¹, Bin Lin^¹(), Xu Hu^¹, Shuang Qi^¹, Liping Qian^², Yuan Wu^³

1Information Science and Technology College, Dalian Maritime University, Dalian 116026, China

2College of Information Engineering, Zhejiang University of Technology, Hangzhou 310014, China

3State Key Laboratory of Internet of Things for Smart City, University of Macau, Macau 999078, China

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Abstract

With the exponential growth of maritime wireless devices and the rapid development of maritime applications, traditional maritime communication networks encounter communication and computation limitations in supporting computation-intensive and latency-critical tasks. Edge computing and Intelligent Reflecting Surface (IRS) have emerged as promising techniques to improve communication and computation services for maritime devices with limited computation capabilities and battery capacity. This paper studies an IRS Mounted on Unmanned Aerial Vehicle (UIRS) assisted maritime edge computing network, in which the UIRS is deployed to assist the transmission from Unmanned Surface Vehicles (USVs) to the edge server via Non-Orthogonal Multiple Access (NOMA) protocol. We propose a resource management and trajectory optimization scheme by jointly optimizing subslot duration, offloading ratios, transmit power, edge computation capability allocation, UIRS phase shifts and UIRS trajectory, aiming at minimizing the overall energy consumption. Since the non-convex nature of the optimization problem, we propose a two-layered method by decomposing the original problem into two subproblems. The top-layered subproblem is solved by the Semi-Definite Relaxation (SDR) method and the underlying-layered subproblem is solved by the Deep Deterministic Policy Gradient (DDPG) algorithm. Numerical results demonstrate that our proposed scheme can effectively and efficiently reduce overall energy consumption.

Keywords

maritime edge computing networks IRS mounted on Unmanned Aerial Vehicles (UIRS)overall energy consumption minimization Semi-Definite Relaxation (SDR) scheme Deep Deterministic Policy Gradient (DDPG)

References

[1]

L. Lyu, Z. Chu, B. Lin, Y. Dai, and N. Cheng, Fast trajectory planning for UAV-enabled maritime IoT systems: A Fermat-point based approach, IEEE Wirel. Commun. Lett., vol. 11, no. 2, pp. 328–332, 2022.

Notation	Description
$U$	The number of USVs
$K$	The number of time slots
$N$	The number of UIRS reflection elements
$Δ_{k}^{f l y}$	Flying subslot in time slot $k$
$Δ_{k}^{h o v}$	Hovering subslot in time slot $k$
$l_{u}$	Coordinates of the USV $u$
$l_{m B S}$	Coordinates of the mBS
$l_{u i r s, k}$	Coordinates of UIRS in time slot $k$
$v_{k}^{h o r}$	Horizontal flying speed of UIRS in time slot $k$
$v_{k}^{v e r}$	Vertical flying speed of UIRS in time slot $k$
$φ_{n, k}$	Phase shift of $n$ th reflection element in time slot $k$
$h_{u, k}^{U I}$	Channel gain between USV $u$ and the UIRS in time slot $k$
$h_{k}^{I B}$	Channel gain between UIRS and mBS in time slot $k$
$h_{u}^{U B}$	Channel gain between USV $u$ and mBS
$d_{u, k}^{U I}$	Distance between USV $u$ and UIRS in time slot $k$
$d_{k}^{I B}$	Distance between UIRS and mBS in time slot $k$
$d_{u}^{U B}$	Distance between USV $u$ and mBS
$H_{u}$	Antenna height of USV $u$
$H_{m B S}$	Antenna height of mBS
$R_{u, k}$	The data rate of USV $u$ in time slot $k$
$W$	Channel bandwidth
$p_{u, k}$	Transmit power of USV $u$ in time slot $k$
$p_{h o v}$	Hovering power of UIRS
$α_{u, k}$	Offloading ratio of USV $u$ in time slot $k$
$C_{u}^{l o c}$	Required CPU cycles per bit at USV $u$
$C_{e s}$	Required CPU cycles per bit at edge server
$b_{u, k}$	Number of bits generated by USV $u$ in time slot $k$
$F_{u}$	Computation capability of USV $u$
$F_{u, k}^{e s}$	Computation capability of edge server allocated to serve USV $u$ in time slot $k$

Parameter	Value
Number of USVs $U$	$4 \sim 10$
Number of time slots $K$	$60$
Time span $τ$	$300$ s
Maximum horizontal flying speed $v_{m a x}^{h o r}$	$30$ m/s
Maximum vertical flying speed $v_{m a x}^{v e r}$	$8$ m/s
Hovering power of UIRS $h_{h o v}$	$43$ dBm
Reference channel gain $ρ_{0}$	$- 80$ dB
Channel bandwidth $W$	$1$ MHz
Gaussian noise power $σ^{2}$	$- 114$ dBm
Wavelength $λ$	$0.05$ m
Antenna height of USV $H_{u}$	$1.9$ m
Antenna height of mBS $H_{m B S}$	$5.45$ m
Maximum transmit power of USV $P_{u}^{m a x}$	$44.7$ dBm
Computation tasks $b_{u, k}$	$[1, 10]$ Mbit
Required CPU cycles per bit at USV $C_{u}^{l o c}$	$800$ cycles/bit
Required CPU cycles per bit at edge server $C_{e s}$	$1000$ cycles/bit
Computation capability of USV $F_{u}$	$0.1 \sim 0.2$ GHz
Computation capability of edge server $F_{e s}^{m a x}$	$4$ GHz
Switched coefficient for USV $κ_{u}^{l o c}$	$10^{- 27}$
Switched coefficient for edge server $κ_{e s}$	$10^{- 28}$
Tip velocity of the rotor blade $U_{t i p}$	$120$ m/s
Air density $ρ$	$1.225$ kg/m $^{3}$
Rotor disc area $A_{0}$	$0.503$ m $^{2}$
Rotor induced velocity in hovering $v^{'}$	$4.03$ m/s
Rotor solidity $S_{0}$	$0.05$
Fuselage drag ratio $ξ$	$0.6$

Algorithm 2　RMTO-Top Optimization
Input: Flying subslot $Δ_{k}^{f l y}$ , hovering subslot $Δ_{k}^{h o v}$ , offloading ratios of USVs $α_{k}$ , computation capability allocation $F_{k}$ , UIRS trajectory $L_{k}$ in the $k$ th step.
Output: UIRS phase shifts $φ_{k}^{}$ , transmit power $p_{k}^{}$ .
1 Obtain $W_{k}^{*}$ by solving Problem P4.
2 Obtain $w_{k}^{}$ according to $W_{k}^{}$ .
3 Obtain $φ_{n, k}^{}$ according to $w_{n, k}^{} = e^{j φ_{n, k}}$ .
4 Compute $p_{u, k}^{*}$ based on the Eq. (23).
5 Return $φ_{k}^{}, p_{k}^{}$ .