Joint Resource Allocation and Phase Shift Optimization for RIS-Aided eMBB/URLLC Traffic Multiplexing

Almekhlafi, Mohammed; Arfaoui, Mohamed Amine; Elhattab, Mohamed; Assi, Chadi; Ghrayeb, Ali

doi:10.1109/tcomm.2021.3127265

Cited by 56 publications

(47 citation statements)

References 38 publications

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“…Similar to the approach given in [17], we introduce an auxiliary variable ζ to solve phase-shift optimization problem. The resulting problem can be then re-written as…”

Section: A Solution Approachmentioning

confidence: 99%

“…After applying the proposed transformations, the resulted optimization problem turns out to be a convex optimization problem that can be solved by any convex optimization solver such as CVX [16], [17]. Note that, if the obtained phase-shift matrix does not satisfy the rank-one constraint, the Gaussian randomization (GR) method can be applied to construct a rankone solution [16].…”

Section: By Applying the Change Of Variables H W→r (T)φ(t)hmentioning

confidence: 99%

See 1 more Smart Citation

Optimizing Information Freshness in RIS-assisted NOMA-based IoT Networks

Ali¹,

Elhattab²,

Arfaoui³

et al. 2022

Preprint

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This paper investigates the benefits of integrating reconfigurable intelligent surface (RIS) on minimizing the average sum age of information (AoI) in uplink non-orthogonal multiple access-based Internet-of-Things (IoT) networks. In this setup, an optimization problem is formulated to optimize the RIS configuration, the transmit power per IoT device and the clustering policy of IoT devices. The formulated problem is a mixedinteger non-convex one, and in order to solve it we obtain first the RIS configuration by adopting a semi-definite relaxation (SDR) approach. Afterwards, the joint power allocation and user-clustering problem is solved using the concept of bi-level optimization and is decomposed into an outer user clustering problem and an inner power allocation problem. Optimal closedform expressions are derived for the inner problem and the Hungarian method is employed to solve the outer one. Numerical results demonstrate the performance superiority of our approach.

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“…Similar to the approach given in [17], we introduce an auxiliary variable ζ to solve phase-shift optimization problem. The resulting problem can be then re-written as…”

Section: A Solution Approachmentioning

confidence: 99%

Section: By Applying the Change Of Variables H W→r (T)φ(t)hmentioning

confidence: 99%

Optimizing Information Freshness in RIS-assisted NOMA-based IoT Networks

Ali¹,

Elhattab²,

Arfaoui³

et al. 2022

Preprint

Self Cite

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show abstract

“…Among them belong the adoption of RISs for extending signal coverage [54]- [61], enabling or boosting accurate localization [62]- [73], and enabling physical-layer security [74]- [84]. In [85], the coexistence of enhanced Mobile Broadband (eMBB) and URLLC services in a cellular network that is assisted by an RIS was studied. A passive beamformer that can achieve the asymptotic optimal performance by controlling the properties of the incident wave at the RIS was designed in [86], under a limited RIS control link and practical reflection coefficients.…”

Section: Introductionmentioning

confidence: 99%

Pervasive Machine Learning for Smart Radio Environments Enabled by Reconfigurable Intelligent Surfaces

Alexandropoulos¹,

Stylianopoulos²,

Huang³

et al. 2022

Preprint

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The emerging technology of Reconfigurable Intelligent Surfaces (RISs) is provisioned as an enabler of smart wireless environments, offering a highly scalable, low-cost, hardwareefficient, and almost energy-neutral solution for dynamic control of the propagation of electromagnetic signals over the wireless medium, ultimately providing increased environmental intelligence for diverse operation objectives. One of the major challenges with the envisioned dense deployment of RISs in such reconfigurable radio environments is the efficient configuration of multiple metasurfaces with limited, or even the absence of, computing hardware. In this paper, we consider multi-user and multi-RIS-empowered wireless systems, and present a thorough survey of the online machine learning approaches for the orchestration of their various tunable components. Focusing on the sum-rate maximization as a representative design objective, we present a comprehensive problem formulation based on Deep Reinforcement Learning (DRL). We detail the correspondences among the parameters of the wireless system and the DRL terminology, and devise generic algorithmic steps for the artificial neural network training and deployment, while discussing their implementation details. Further practical considerations for multi-RIS-empowered wireless communications in the sixth Generation (6G) era are presented along with some key open research challenges. Differently from the DRL-based status quo, we leverage the independence between the configuration of the system design parameters and the future states of the wireless environment, and present efficient multi-armed bandits approaches, whose resulting sum-rate performances are numerically shown to outperform random configurations, while being sufficiently close to the conventional Deep Q-Network (DQN) algorithm, but with lower implementation complexity.

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“…In [6], the BS serves only URLLC users. In contrast, in [7], the coexistence of eMBB and URLLC users is studied for a single-antenna BS. However, modern communication systems are expected to employ multiple-antenna BSs.…”

Section: Introductionmentioning

confidence: 99%

“…However, modern communication systems are expected to employ multiple-antenna BSs. Moreover, the IRS phase shifts in [7] are updated in each mini-slot which introduces additional delays for the URLLC users due to the required additional signalling overhead between BS and IRS. Furthermore, the algorithms developed in [3]- [7] aim to optimize all IRS elements individually which leads to high complexity and a huge signalling overhead particularly when the IRS is large [8].…”

Section: Introductionmentioning

confidence: 99%

Codebook Based Two-Time Scale Resource Allocation Design for IRS-Assisted eMBB-URLLC Systems

Ghanem¹,

Jamali²,

Schellmann³

et al. 2022

Preprint

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This paper investigates the resource allocation algorithm design for wireless systems assisted by large intelligent reflecting surfaces (IRSs) with coexisting enhanced mobile broadband (eMBB) and ultra reliable low-latency communication (URLLC) users. We consider a two-time scale resource allocation scheme, whereby the base station's precoders are optimized in each mini-slot to adapt to newly arriving URLLC traffic, whereas the IRS phase shifts are reconfigured only in each time slot to avoid excessive base station-IRS signaling. To facilitate efficient resource allocation design for large IRSs, we employ a codebook-based optimization framework, where the IRS is divided into several tiles and the phase-shift elements of each tile are selected from a pre-defined codebook. The resource allocation algorithm design is formulated as an optimization problem for the maximization of the average sum data rate of the eMBB users over a time slot while guaranteeing the quality-of-service (QoS) of each URLLC user in each mini-slot. An iterative algorithm based on alternating optimization (AO) is proposed to find a high-quality suboptimal solution. As a case study, the proposed algorithm is applied in an industrial indoor environment modelled via the Quadriga channel simulator. Our simulation results show that the proposed algorithm design enables the coexistence of eMBB and URLLC users and yields large performance gains compared to three baseline schemes. Furthermore, our simulation results reveal that the proposed two-time scale resource allocation design incurs only a small performance loss compared to the case when the IRSs are optimized in each mini-slot.• We propose a novel resource allocation algorithm design for an eMBB-URLLC system assisted by large IRSs. The proposed resource allocation algorithm is formulated as a two-time scale resource allocation problem, whereby the precoders at the BS are optimized in each mini-slot to adapt to the newly arriving URLLC traffic, whereas the IRS phase shifts are reconfigured only in each time slot to avoid excessive BS-IRSs signaling. Then, the resource allocation algorithm design is formulated as a non-convex

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Joint Resource Allocation and Phase Shift Optimization for RIS-Aided eMBB/URLLC Traffic Multiplexing

Cited by 56 publications

References 38 publications

Optimizing Information Freshness in RIS-assisted NOMA-based IoT Networks

Optimizing Information Freshness in RIS-assisted NOMA-based IoT Networks

Pervasive Machine Learning for Smart Radio Environments Enabled by Reconfigurable Intelligent Surfaces

Codebook Based Two-Time Scale Resource Allocation Design for IRS-Assisted eMBB-URLLC Systems

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