Energy Efficient Intelligent Reflecting Surface Assisted Terahertz Communications

Wu, Qirui; Zhang, Yirun; Huang, Chongwen; Yuen, Chau; Yang, Zhaohui; Shikh-Bahaei, Mohammad

doi:10.1109/iccworkshops50388.2021.9473736

Cited by 17 publications

(12 citation statements)

References 16 publications

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“…The results clearly depict the superiority of the proposed approach over the benchmark schemes. Furthermore, in [22], the authors formulated an energy efficiency maximization problem for the RIS‐assisted THz communication system and employed the covariance matrix adaptation evolution strategy (CMA‐ES) and Dinkelbach's method based algorithm to optimize the transmit beamforming, RIS phase‐shifts, and power allocation.…”

Section: Emerging Applications Of Rismentioning

confidence: 99%

“…As illustrated in Figure 1, RIS can also be integrated with the emerging communication technologies, such as non-orthogonal multiple access (NOMA), thus improving connectivity and coverage range [14][15][16][17]. RIS can mitigate the signal blockage in mmWave and terahertz (THz) communications [18][19][20][21][22]. In mobile edge computing (MEC) networks, integration of RIS technology can improve the latency performance [23][24][25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

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Exploring reconfigurable intelligent surfaces for 6G: State‐of‐the‐art and the road ahead

et al. 2022

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Reconfigurable intelligent surfaces (RISs) are envisioned to transform the propagation space into a smart radio environment (SRE) to realize the diverse applications of sixthgeneration (6G) wireless communication. By smartly tuning the massive number of elements via controller, an RIS can passively phase-shift the electromagnetic (EM) waves to enhance the system performance. The absence of radio-frequency (RF) chains makes RIS an energy-efficient and cost-effective solution for future wireless networks. In this paper, the state-of-the-art research on different aspects of RIS-assisted communication is explored. Specifically, the fundamentals of RIS are first introduced, including the RIS's structure, operating principle, and deployment strategies. The emerging applications of RISs are then comprehensively discussed for 6G wireless networks. In addition, the crucial challenges for RIS-assisted networks are elaborated, namely, RIS channel state information (CSI) acquisition and passive beamforming optimization. Furthermore, the recent research contributions leveraging the artificial intelligence (AI) based techniques for channel estimation, phase-shift optimization, and resource allocation in RIS-assisted networks are presented. Finally, to provide effective guidance for future research, important research directions for realizing RIS-assisted network are highlighted.

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Section: Emerging Applications Of Rismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Exploring reconfigurable intelligent surfaces for 6G: State‐of‐the‐art and the road ahead

et al. 2022

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“…Similarly, joint symbol level precoding and PSM were optimized to minimize the total power usage in a RIS-aided multi-user network in [17]. In [18], joint optimization of the transmit power and PSM was performed to maximize the EE of a RIS-aided multi-user MISO network for Terahertz communication using covariance matrix adaptation evolution strategy and Dinkelbach's method.…”

Section: A Related Literature and Motivationmentioning

confidence: 99%

A Pricing-Based Approach for Energy-Efficiency Maximization in RIS-Aided Multi-User MIMO SWIPT-Enabled Wireless Networks

et al. 2022

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In this work, we investigate the performance of a reconfigurable intelligent surface (RIS)aided multi-user simultaneous wireless information and power transfer (SWIPT) network, where a multipleinput multiple-output (MIMO) base station (BS) serves multiple MIMO information receivers (IRs) while ensuring a minimum harvested power at multiple MIMO energy receivers (ERs). In order to improve the energy efficiency (EE) of the network, we consider a pricing-based performance metric called network utility. We then establish an optimization framework to jointly optimize the transmit precoding matrix (TPM) and phase shift matrix (PSM) to maximize the network utility function with constraints on the available transmit power at BS, minimum harvested power required at each ER, and unit modulus phase shift condition at RIS. Due to the non-convex nature of this problem, we divide it into two sub-problems where a sub-optimal solution of TPM and PSM are obtained separately using successive convex approximation and bisection search-based algorithms. Further, we propose an EE maximization (EEM) algorithm based on the block coordinate descent method to achieve the optimal solution of the master problem by iteratively obtaining the sub-optimal TPM, PSM, and network price using their respective algorithms. Moreover, we also prove that the solution obtained for each problem using their respective algorithm converges to the Karush-Kuhn-Tucker (KKT) optimum point of that problem. We also show the efficacy of the proposed algorithm using simulation results. In particular, we highlight the importance of using RIS in a multi-user MIMO SWIPT network and demonstrate the effect of various parameters on the network's EE performance.INDEX TERMS Reconfigurable intelligent surfaces (RIS), multiple-input and multiple-output (MIMO), multi-user, simultaneous wireless information and power transfer (SWIPT), energy efficiency (EE). I. INTRODUCTIONThe impending deployment of fifth-generation (5G) mobile communications around the world will be a major factor in driving productivity and will be the key enabler for longenvisaged verticals including personalised healthcare, manufacturing, smart energy grids, smart cities, finance, and transportation. However, realizing the ever-growing demand for a better communication network with improved quality of service (QoS) requirements such as lower power consumption, very high energy efficiency (EE), better spectral efficiency (SE), etc., researchers have already started to explore the evolution of 5G, commonly referred to as 5G and beyond (5GB) and sixth generation (6G) communications [1]. In this context, to enhance the experience of relaying in wireless communication networks, the idea of multiple passive reflecting surfaces/elements made of meta-materials has been floated to assist the communication between multiple devices such as base station (BS), users etc., [2]-[4]. This set of discrete reflecting elements is termed as reconfigurable intelligent surface (RIS) or intelligent reflecting surface (IRS),

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“…1, RIS can also be integrated with the emerging communication technologies, such as non-orthogonal multiple access (NOMA), thus improving connectivity and coverage range [14], [15], [16], [17]. RIS can mitigate the signal blockage in mmWave and terahertz (THz) communications [18], [19], [20], [21], [22]. In mobile edge computing (MEC) networks, integration of RIS technology can improve the latency performance [23], [24], [25], [26], [27], [28].…”

Section: Introductionmentioning

confidence: 99%

Exploring Reconfigurable Intelligent Surfaces for 6G: State-of-the-Art and the Road Ahead

Basharat¹,

Khan²,

Iqbal³

et al. 2022

Preprint

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Reconfigurable intelligent surfaces (RISs) are envisioned to transform the propagation space into a smart radio environment (SRE) to realize the diverse applications of sixth-generation (6G) wireless communication. By smartly tuning the massive number of elements via controller, an RIS can passively phase-shift the electromagnetic (EM) waves to enhance the system performance. The absence of radio-frequency (RF) chains makes RIS an energy-efficient and cost-effective solution for future wireless networks. In this paper, we explore the state-of-the-art research on different aspects of RIS-assisted communication. Specifically, we first introduce the fundamentals of RIS, including the RIS's structure, operating principle, and deployment strategies. We then comprehensively discuss the emerging applications of RISs for 6G wireless networks. In addition, we elaborate on the crucial challenges for RIS-assisted networks, namely, RIS channel state information (CSI) acquisition and passive beamforming optimization. Furthermore, we present the recent research contributions leveraging the artificial intelligence (AI) based techniques for channel estimation, phase-shift optimization, and resource allocation in RIS-assisted networks. Finally, to provide effective guidance for future research, we highlight important research directions for realizing RIS-assisted network.

show abstract

Energy Efficient Intelligent Reflecting Surface Assisted Terahertz Communications

Cited by 17 publications

References 16 publications

Exploring reconfigurable intelligent surfaces for 6G: State‐of‐the‐art and the road ahead

Exploring reconfigurable intelligent surfaces for 6G: State‐of‐the‐art and the road ahead

A Pricing-Based Approach for Energy-Efficiency Maximization in RIS-Aided Multi-User MIMO SWIPT-Enabled Wireless Networks

Exploring Reconfigurable Intelligent Surfaces for 6G: State-of-the-Art and the Road Ahead

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