Reconfigurable Intelligent Surface Assisted Mobile Edge Computing With Heterogeneous Learning Tasks

Huang, Shanfeng; Wang, Shuai; Wang, Rui; Wen, Miaowen; Huang, Kaibin

doi:10.1109/tccn.2021.3056707

Cited by 71 publications

(44 citation statements)

References 48 publications

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“…The authors in [71] present an infrastructure to perform ML tasks at a mobile edge computing (MEC) server with the assistance of an IRS in a multiuser MISO system. Therein, they aim at maximizing the learning performance.…”

Section: Single-user Siso Scenariomentioning

confidence: 99%

“…The use of IRSs is beneficial for MEC systems by enhancing both the EE and the SE. For example, in [71] the transmit power of the mobile users is minimized by considering an infrastructure to perform ML tasks in the MEC server. The use of IRSs is also interesting for unmanned aerial vehicle (UAV) networks, where the IRS is used to enhance the quality of the communication between the UAV and the ground users, thus being instrumental for the optimization of the UAV trajectory and the system performance (cf.…”

Section: Irs Applicationsmentioning

confidence: 99%

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Intelligent Reflective Surfaces for Wireless Networks: An Overview of Applications, Approached Issues, and Open Problems

et al. 2021

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An intelligent reflective surface (IRS) is a novel and revolutionizing communication technology destined to enable the control of the radio environment. An IRS is a real-time controllable reflectarray with a massive number of low-cost passive elements which introduce a phase shift to the incoming signals from the sources before the propagation towards the destination. This technology introduces the notion of a smart propagation environment with the aim of improving the system performance. In this paper, we provide a comprehensive literature overview on IRS technology, including its basic concepts and reconfiguration, as well as its design aspects and applications for wireless communication systems. We also study the performance metrics and the setups considered in recent publications related to IRS and provide suggestions of future research lines based on still unexplored use cases in the state-of-the-art.

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Section: Single-user Siso Scenariomentioning

confidence: 99%

Section: Irs Applicationsmentioning

confidence: 99%

Intelligent Reflective Surfaces for Wireless Networks: An Overview of Applications, Approached Issues, and Open Problems

et al. 2021

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“…Optimization Target [5] Vehicle-to-infrastructure (V2I) Computation offloading Lower bound of expected reliability [6] Vehicular network Mobile edge computing (MEC), cloud computing Offloading decisions [7] Internet of Vehicles (IoV) MEC Energy efficiency [8] Vehicular ad-hoc network (VANET) MEC Resource allocation [9] Internet of Things (IoT) Vehicular edge computing (VEC) Resource allocation [10] Vehicular network VEC, software-defined networking (SDN) Processing delay [11] Vehicle-to-vehicle (V2V) and V2I VEC, geolocation information Reliable data retrieval [12] IoV MEC, edge intelligence Total network delay [16] Cellular network MEC, unmanned aerial vehicle (UAV) Energy consumption [17] Computing system MEC, UAV Energy consumption [18] Computing system MEC, UAV Maximum Delay and trajectory [19] Computing system MEC, UAV Task completion time [20] IoT MEC, UAV Average latency [21] Computing system MEC, UAV Computation efficiency [22] Computing system MEC, UAV, wireless power transfer (WPT) Computation rate [23] IoT Centralized and distributed MEC, UAV Energy efficiency [24] IoT MEC, UAV Energy consumption [25] Social IoV (SIoV) MEC, UAV Resource allocation and trajectory [26] Vehicular network MEC, UAV, SDN Task execution time [27] Computing system MEC, UAV, non-orthogonal multiple access (NOMA) Bit allocation and trajectory [28] Computing system MEC, UAV, stochastic offloading Energy consumption [29] Vehicular network MEC, UAV, massive multiple-input multiple-output (MIMO) Energy consumption [32] Communication system UAV, reconfigurable intelligent surface (RIS) Achievable rate [33] Communication system UAV, RIS Sum-rate [34] IoT UAV, RIS Decoding error rate [35] Computing system MEC, RIS Latency [36] IoT MEC, RIS Sum computational bits [37] Computing system MEC, RIS, NOMA Delay [38] Computing…”

Section: References Network Type Key Technologiesmentioning

confidence: 99%

“…An RIS-aided MEC-enabled flexible time-sharing scheme that enables both NOMA and time-division multiple-access (TDMA) transmission via data division was proposed in [ 37 ] and the sum delay of the users was minimized under discrete-phase constraints of the RIS. Additionally, a RIS-assisted MEC system that can handle learning-driven tasks was presented in [ 38 ] and involved a multi-antenna intelligent edge server and multiple single-antenna users affiliated with machine learning (ML) tasks. In this system, the learning error was minimized by taking into account the transmit power constraints and the phase shifts of the RIS.…”

Section: Introductionmentioning

confidence: 99%

“…As the UAVs fly in a three-dimensional (3-D) space and above rooftops, especial geometrical and mobility characteristics are introduced. Table 1 provides a brief description of the key elements of the aforementioned previous works, which give emphasis either on MEC IoV architectures, where the vehicles’ computing tasks are completed without cooperation of UAVs [ 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 ]; or UAV-enabled MEC network architectures that do not include RIS units [ 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ]; or RIS-assisted UAV networks without MEC capabilities [ 32 , 33 , 34 ]; or RIS-assisted MEC network architectures with only ground-based nodes [ 35 , 36 , 37 , 38 ].…”

Section: Introductionmentioning

confidence: 99%

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Energy Optimization in Dual-RIS UAV-Aided MEC-Enabled Internet of Vehicles

Michailidis

Miridakis

Michalas

et al. 2021

Sensors

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Mobile edge computing (MEC) represents an enabling technology for prospective Internet of Vehicles (IoV) networks. However, the complex vehicular propagation environment may hinder computation offloading. To this end, this paper proposes a novel computation offloading framework for IoV and presents an unmanned aerial vehicle (UAV)-aided network architecture. It is considered that the connected vehicles in a IoV ecosystem should fully offload latency-critical computation-intensive tasks to road side units (RSUs) that integrate MEC functionalities. In this regard, a UAV is deployed to serve as an aerial RSU (ARSU) and also operate as an aerial relay to offload part of the tasks to a ground RSU (GRSU). In order to further enhance the end-to-end communication during data offloading, the proposed architecture relies on reconfigurable intelligent surface (RIS) units consisting of arrays of reflecting elements. In particular, a dual-RIS configuration is presented, where each RIS unit serves its nearby network nodes. Since perfect phase estimation or high-precision configuration of the reflection phases is impractical in highly mobile IoV environments, data offloading via RIS units with phase errors is considered. As the efficient energy management of resource-constrained electric vehicles and battery-enabled RSUs is of outmost importance, this paper proposes an optimization approach that intends to minimize the weighted total energy consumption (WTEC) of the vehicles and ARSU subject to transmit power constraints, timeslot scheduling, and task allocation. Extensive numerical calculations are carried out to verify the efficacy of the optimized dual-RIS-assisted wireless transmission.

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Controlled Signal Technique in VL‐NOMA Communication Under Interference‐Controlled Environment With Intelligent Reflecting Surfaces

Ngene,

Thakur,

Singh

2024

Engineering Reports

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This paper proposes a controlled signal technique for visible light non‐orthogonal multiple access (VL‐NOMA) communication in an interference‐controlled environment with intelligent reflecting surfaces (IRS) for beyond 5G (B5G) and 6G communication networks. The light‐emitting diode (LED) is used for carrier signal generation to transmit signals to the two users (photodiodes, PDs) due to its advantages, such as its programmable nature and flexibility. The potential challenge is how the signals could be controlled with an IRS approach, which prompted this research. We have used IRS, which is a cutting‐edge enabling technology that modifies the signal's reflection by utilizing numerous inexpensive passive reflecting elements to improve the signal's performance. Furthermore, deep reinforcement learning (DRL) is deployed to control the reflected signals, simulate, make decisions, and link LED‐IRS‐PDs, redirecting the signals. The entire system is successfully synchronized, and then the bit error rate (BER), line of sight (LOS), and non‐line of sight (NLOS) performances are investigated. Furthermore, we place a blocker at the center of the model as a NLOS to check how the transmitted signals will perform. We observed that the propagated signal improved the BER as per LOS, hence, the NLOS blocker reduced the signal's performance. Furthermore, we optimized the signals to investigate BER, LOS, and NLOS signal performance. We observed that LOS signals performed better than NLOS signals.

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Reconfigurable Intelligent Surface Assisted Mobile Edge Computing With Heterogeneous Learning Tasks

Cited by 71 publications

References 48 publications

Intelligent Reflective Surfaces for Wireless Networks: An Overview of Applications, Approached Issues, and Open Problems

Intelligent Reflective Surfaces for Wireless Networks: An Overview of Applications, Approached Issues, and Open Problems

Energy Optimization in Dual-RIS UAV-Aided MEC-Enabled Internet of Vehicles

Controlled Signal Technique in VL‐NOMA Communication Under Interference‐Controlled Environment With Intelligent Reflecting Surfaces

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