Joint Communication and Computation Design in Transmissive RMS Transceiver Enabled Multi-Tier Computing Networks

Li, Zhendong; Liu, Ziwei; Tang, Hongying; Lu, Jianmin

doi:10.1109/jsac.2022.3228553

Cited by 7 publications

(2 citation statements)

References 41 publications

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“…4 We consider that the transmit antenna and transmissive elements of each BS and ITS are arranged in uniform linear array (ULA) [19]. The channel of the l-th BS-ITS link S l is considered as a near-field channel [20], where a three-dimensional Thus, the line-of-sight (LoS) near-field channel coefficient between the BS l and ITS l can be modeled as…”

Section: All Of Its-assisted Bss Are Coordinated By a Central Process...mentioning

confidence: 99%

Joint ITS- and IRS-Assisted Cell-Free Networks

Chu,

Xiao,

Foh

et al. 2024

IEEE Wireless Commun. Lett.

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This paper investigates a joint intelligent transmissive surface (ITS) and intelligent reflecting surface (IRS)-assisted cell-free network. Specifically, various ITS-assisted base stations (BSs), handled via a central processing unit (CPU), broadcast information signals to multiple IoT devices, carried by active transmit beamforming and transmissive reflecting phase shifts. Meanwhile, an IRS passively reflect signals from the ITS-assisted BS to the IoT devices. To examine this network performance, a sum rate is maximized among all users to jointly optimize the active beamforming, ITS and IRS passive beampatterns. These coupled variables leads to the non-convexity of this formulated optimization problem, which cannot be directly solved. To deal with this issue, we begin with applying the Lagrange dual transformation (LDT) and quadratic transformation (QT) to recast the sum of multiple logarithmically fractional objectives to the subtractive form, and further to quadratic form. Next, an alternating optimization (AO) algorithm is presented to separately the active beamforming, ITS and IRS passive beampatterns in an iterative fashion. Each sub-optimal solution of these variables can be iteratively derived, in a closed-form, by solving the quadratic objective function with a convex constraint or a unitmodulus constraint via the dual method with bisection search or the Alternating Direction Method of Multipliers (ADMM) algorithm. Finally, simulation results are provided to confirm the performance of the proposed algorithm compared to several benchmark schemes. IndexTerms-Cell-free networks, intelligent reflecting/transmissive surface (IRS/ITS), Lagrange dual transformation (LDT), quadratic transformation (QT), and alternating direction method of multipliers (ADMM).

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Section: All Of Its-assisted Bss Are Coordinated By a Central Process...mentioning

confidence: 99%

Joint ITS- and IRS-Assisted Cell-Free Networks

Chu,

Xiao,

Foh

et al. 2024

IEEE Wireless Commun. Lett.

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“…Notably, DMA requires the same number of RF chains as microstrips, distinguishing it from RISs, which lack RF chains. In particular, the authors of [25] reported a new transmissive RIS enabled multitier computing framework, in which the joint beamforming design was explored with the aim of minimizing the overall system energy consumption. In another work [26], the authors further studied the architecture of a downlink communication transceiver for simultaneous wireless information and power transfer (SWIPT) networks.…”

Section: A Prior Workmentioning

confidence: 99%

User Selection for Simple Passive Beamforming in Multi-RIS-Aided Multi-User Communications

Jiang¹

2023

Preprint

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<p>This paper focuses on multi-user downlink signal transmission in a wireless system aided by multiple reconfigurable intelligent surfaces (RISs). In such a multi-RIS, multi-user, multi-antenna scenario, determining a set of RIS phase shifts to maximize the sum throughput becomes intractable. Hence, we propose a novel scheme that can substantially simplify the optimization of passive beamforming. By opportunistically selecting a user with the best channel condition as the only active transmitter in the system, it degrades to single-user passive beamforming, where two methods, i.e., joint optimization based on the semidefinite relaxation approach and alternating optimization, are applicable. The superiority of the proposed scheme is demonstrated through Monte-Carlo simulations. </p>

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