Intelligent Reflecting Surface Aided Multi-Tier Hybrid Computing

The Digital twin edge network (DITEN) aims to integrate mobile edge computing (MEC) and digital twin (DT) to provide real-time system configuration and flexible resource allocation for the sixth-generation network. This paper investigates an intelligent reflecting surface (IRS)-aided multi-tier hybrid computing system that can achieve mutual benefits for DT and MEC in the DITEN. For the first time, this paper presents the opportunity to realize the network-wide convergence of DT and MEC. In the considered system, specifically, over-the-air computation (AirComp) is employed to monitor the status of the DT system, while MEC is performed with the assistance of DT to provide low-latency computing services. Besides, the IRS is utilized to enhance signal transmission and mitigate interference among heterogeneous nodes. We propose a framework for designing the hybrid computing system, aiming to maximize the sum computation rate under communication and computation resources constraints. To tackle the non-convex optimization problem, alternative optimization and successive convex approximation techniques are leveraged to decouple variables and then transform the problem into a more tractable form. Simulation results verify the effectiveness of the proposed algorithm and demonstrate the IRS can significantly improve the system performance with appropriate phase shift configurations. Moreover, the results indicate that the DT assisted MEC system can precisely achieve the balance between local computing and task offloading since real-time system status can be obtained with the help of DT. This paper proposes the network-wide integration of DT and MEC, then demonstrates the necessity of DT for achieving an optimal performance in DITEN systems through analysis and numerical results.

Intelligent Reflecting Surface Assisted Multi-Cluster AirComp via Dynamic Beamforming

This paper studies an multi-cluster over-the-air computation (AirComp) system, where an intelligent reflecting surface (IRS) assists the signal transmission from devices to an access point (AP). The clusters are activated to compute heterogeneous functions in a time-division manner. Specifically, two types of IRS beamforming (BF) schemes are proposed to reveal the performancecost tradeoff. One is the cluster-adaptive BF scheme, where each BF pattern is dedicated to one cluster, and the other is the dynamic BF scheme, which is applied to any number of IRS BF patterns. By deeply exploiting their inherent properties, both generic and lowcomplexity algorithms are proposed in which the IRS BF patterns, time and power resource allocation are jointly optimized. Numerical results show that IRS can significantly enhance the function computation performance, and demonstrate that the dynamic IRS BF scheme with half of the total IRS BF patterns can achieve near-optimal performance which can be deemed as a cost-efficient approach for IRS-aided multi-cluster AirComp systems.

Over-the-Air Computation: Foundations, Technologies, and Applications

The rapid advancement of artificial intelligence technologies has given rise to diversified intelligent services, which place unprecedented demands on massive connectivity and gigantic data aggregation. However, the scarce radio resources and stringent latency requirement make it challenging to meet these demands. To tackle these challenges, over-the-air computation (AirComp) emerges as a potential technology. Specifically, AirComp seamlessly integrates the communication and computation procedures through the superposition property of multiple-access channels, which yields a revolutionary multiple-access paradigm shift from "compute-after-communicate" to "compute-when-communicate". Meanwhile, low-latency and spectral-efficient wireless data aggregation can be achieved via AirComp by allowing multiple devices to access the wireless channels non-orthogonally. In this paper, we aim to present the recent advancement of AirComp in terms of foundations, technologies, and applications. The mathematical form and communication design are introduced as the foundations of AirComp, and the critical issues of AirComp over different network architectures are then discussed along with the review of existing literature. The technologies employed for the analysis and optimization on AirComp are reviewed from the information theory and signal processing perspectives. Moreover, we present the existing studies that tackle the practical implementation issues in AirComp systems, and elaborate the applications of AirComp in Internet of Things and edge intelligent networks. Finally, potential research directions are highlighted to motivate the future development of AirComp.

Performance-Oriented Design for Intelligent Reflecting Surface Assisted Federated Learning

To efficiently exploit the massive raw data that is pervading generated at mobile edge networks, federated learning (FL) has emerged as a promising distributed learning technique that was regarded as a substitute for centralized learning operations. By collaboratively training a shared learning model at edge devices, the raw data transmission and storage are bypassed via the local computed parameters/gradients exchange in FL. Hence, FL can overcome high communication latency and privacy issues. While the high dimensionality in iterative updates (millions of parameters/gradients may be included in the model training) still conflicts with the scarcity of communication resources. Over-the-air computation (AirComp) has come into the spotlight recently which profitably leverages the inherent superposition property of wireless channels to perform efficient model aggeration. However, the model aggregation accuracy is still severely damaged by the unfavorable wireless propagation channels. In this paper, we harness the intelligent reflecting surface (IRS) to program the wireless channel, thus acquiring a satisfying learning performance. Specifically, a performance-oriented design scheme that directly minimizes the optimality gap of the loss function is proposed to accelerate the convergence of AirComp based FL. Firstly, we analyze the convergence behavior of the FL procedure. Then, both offline and online design approaches are proposed based on the obtained optimality gap. We adopt the block coordinate descent (BCD) method to tackle the highly-intractable problem. Simulation results demonstrate that such a performance-oriented design strategy can achieve higher test accuracy than the conventional isolated mean square error (MSE) minimization approach in FL.