Latency Minimization for Intelligent Reflecting Surface Aided Mobile Edge Computing

By Tong Bai, Cunhua Pan, Yansha Deng, Maged Elkashlan, Arumgam Nallanathan, Lajos Hanzo. IEEE Journal on Selected Areas in Communications, 2019

484 citations recorded by the source corpus.

Computation off-loading in mobile edge computing (MEC) systems constitutes an efficient paradigm of supporting resource-intensive applications on mobile devices. However, the benefit of MEC cannot be fully exploited, when the communications link used for off-loading computational tasks is hostile. Fortunately, the propagation-induced impairments may be mitigated by intelligent reflecting surfaces (IRS), which are capable of enhancing both the spectral- and energy-efficiency. Specifically, an IRS comprises an IRS controller and a large number of passive reflecting elements, each of which may impose a phase shift on the incident signal, thus collaboratively improving the propagation environment. In this paper, the beneficial role of IRSs is investigated in MEC systems, where single-antenna devices may opt for off-loading a fraction of their computational tasks to the edge computing node via a multi-antenna access point with the aid of an IRS. Pertinent latency-minimization problems are formulated for both single-device and multi-device scenarios, subject to practical constraints imposed on both the edge computing capability and the IRS phase shift design. To solve this problem, the block coordinate descent (BCD) technique is invoked to decouple the original problem into two subproblems, and then the computing and communications settings are alternatively optimized using low-complexity iterative algorithms. It is demonstrated that our IRS-aided MEC system is capable of significantly outperforming the conventional MEC system operating without IRSs. Quantitatively, about 20 % computational latency reduction is achieved over the conventional MEC system in a single cell of a 300 m radius and 5 active devices, relying on a 5-antenna access point.

Subfields: Electrical and Electronic Engineering, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/57c4f4f184f2e69306ed2cf48d4605c909af1b2d

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Citations in the corpus

Reconfigurable Intelligent Surfaces for 6G Systems: Principles, Applications, and Research Directions (2021, unknown): “The simulation results of [9] showed that the overall task latency can be reduced from 115 ms to 65ms, if a 100-element RIS is employed.”
Intelligent Reflecting Surface-Aided Wireless Communications: A Tutorial (2020, background): “Such computing performance gains are expected to be further enhanced if IRS is deployed for mobile edge computing (MEC) systems [140]–[142].”
Intelligent Reflecting Surface Aided Multigroup Multicast MISO Communication Systems (2019, methodology): “A deep reinforcement learning (DRL)-based algorithm [7] and a mobile edge computing-based algorithm [13] were proposed”
Physics-Based Modeling and Scalable Optimization of Large Intelligent Reflecting Surfaces (2020, background): “We note that the main reason why many existing works, e.g., [6], [11], [19], report performance gains for IRSs with much smaller numbers of unit cells (e.g., on the order of tens of unit cells) is the use of link models which do not include all the losses for far-ﬁeld scenarios and/or an…”
An Overview of Signal Processing Techniques for RIS/IRS-Aided Wireless Systems (2021, unknown): “…[149], simultaneous wireless information and power transfer (SWIPT) [99], [108], [113], [150]–[154], mobile edge computing networks [74], [111], [155]–[160], multicast networks [96], [161], cognitive radio networks [138], [162], [163], non-orthogonal multiple access [90], [92], [110], [112],…”
Two-Timescale Channel Estimation for Reconfigurable Intelligent Surface Aided Wireless Communications (2019, methodology): “However, some precoding schemes [10]–[12] require the individual CSI about both the BS-RIS channel and the RIS-UE channel, so the cascaded channel estimation methods cannot meet the requirements of these precoding schemes.”
Reconfigurable Intelligent Surfaces: Principles and Opportunities (2020, background): “Thus, the quality of service in heterogeneous networks and the latency performance in mobile edge computing (MEC) networks are improved [14], [15].”
Smart Radio Environments Empowered by Reconfigurable Intelligent Surfaces: How it Works, State of Research, and Road Ahead (2020, background): “In [235], the authors consider a scenario in which singleantenna devices may decide to offload part of their computational tasks to the edge computing node via an RISaided multi-antenna access point.”
Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications (2021, background): “The latency for local model updates of the active devices can be further reduced by establishing favorable propagation links via RIS, thereby mitigating stragglers [205].”
Smart Radio Environments Empowered by Reconfigurable Intelligent Surfaces: How It Works, State of Research, and The Road Ahead (2020, background): “In [236], the authors consider a scenario in which single-antenna devices may decide to offload part of their computational tasks to the edge computing node via an RIS-aided multi-antenna access point.”
Modeling and Architecture Design of Reconfigurable Intelligent Surfaces Using Scattering Parameter Network Analysis (2020, background): “…as secure wireless communication [15], backscatter communication [16], simultaneous wireless information and power transfer (SWIPT) [17], [18], spectrum sharing [19], cognitive radio [20], unmanned aerial vehicle (UAV) communication [21], millimeter wave [22], [23], and mobile edge computing [24].”