393 research outputs found
Pushing AI to Wireless Network Edge: An Overview on Integrated Sensing, Communication, and Computation towards 6G
Pushing artificial intelligence (AI) from central cloud to network edge has
reached board consensus in both industry and academia for materializing the
vision of artificial intelligence of things (AIoT) in the sixth-generation (6G)
era. This gives rise to an emerging research area known as edge intelligence,
which concerns the distillation of human-like intelligence from the huge amount
of data scattered at wireless network edge. In general, realizing edge
intelligence corresponds to the process of sensing, communication, and
computation, which are coupled ingredients for data generation, exchanging, and
processing, respectively. However, conventional wireless networks design the
sensing, communication, and computation separately in a task-agnostic manner,
which encounters difficulties in accommodating the stringent demands of
ultra-low latency, ultra-high reliability, and high capacity in emerging AI
applications such as auto-driving. This thus prompts a new design paradigm of
seamless integrated sensing, communication, and computation (ISCC) in a
task-oriented manner, which comprehensively accounts for the use of the data in
the downstream AI applications. In view of its growing interest, this article
provides a timely overview of ISCC for edge intelligence by introducing its
basic concept, design challenges, and enabling techniques, surveying the
state-of-the-art development, and shedding light on the road ahead
Over-the-Air Split Machine Learning in Wireless MIMO Networks
In split machine learning (ML), different partitions of a neural network (NN) are executed by different computing nodes, requiring a large amount of communication cost. As over-the-air computation (OAC) can efficiently implement all or part of the computation at the same time of communication, thus by substituting the wireless transmission in the traditional split ML framework with OAC, the communication load can be eased. In this paper, we propose to deploy split ML in a wireless multiple-input multiple-output (MIMO) communication network utilizing the intricate interplay between MIMO-based OAC and NN. The basic procedure of OAC split ML system is first provided, and we show that the inter-layer connection in a NN of any size can be mathematically decomposed into a set of linear precoding and combining transformations over a MIMO channel carrying out multi-stream analog communication. The precoding and combining matrices which are regarded as trainable parameters, and the MIMO channel matrix which are regarded as unknown (implicit) parameters, jointly serve as a fully connected layer of the NN. Most interestingly, the channel estimation procedure can be eliminated by exploiting the MIMO channel reciprocity of the forward and backward propagation, thus greatly saving the system costs and/or further improving its overall efficiency. The generalization of the proposed scheme to the conventional NNs is also introduced, i.e., the widely used convolutional neural networks. We demonstrate its effectiveness under both the static and quasi-static memory channel conditions with comprehensive simulations
A Comprehensive Overview on 5G-and-Beyond Networks with UAVs: From Communications to Sensing and Intelligence
Due to the advancements in cellular technologies and the dense deployment of
cellular infrastructure, integrating unmanned aerial vehicles (UAVs) into the
fifth-generation (5G) and beyond cellular networks is a promising solution to
achieve safe UAV operation as well as enabling diversified applications with
mission-specific payload data delivery. In particular, 5G networks need to
support three typical usage scenarios, namely, enhanced mobile broadband
(eMBB), ultra-reliable low-latency communications (URLLC), and massive
machine-type communications (mMTC). On the one hand, UAVs can be leveraged as
cost-effective aerial platforms to provide ground users with enhanced
communication services by exploiting their high cruising altitude and
controllable maneuverability in three-dimensional (3D) space. On the other
hand, providing such communication services simultaneously for both UAV and
ground users poses new challenges due to the need for ubiquitous 3D signal
coverage as well as the strong air-ground network interference. Besides the
requirement of high-performance wireless communications, the ability to support
effective and efficient sensing as well as network intelligence is also
essential for 5G-and-beyond 3D heterogeneous wireless networks with coexisting
aerial and ground users. In this paper, we provide a comprehensive overview of
the latest research efforts on integrating UAVs into cellular networks, with an
emphasis on how to exploit advanced techniques (e.g., intelligent reflecting
surface, short packet transmission, energy harvesting, joint communication and
radar sensing, and edge intelligence) to meet the diversified service
requirements of next-generation wireless systems. Moreover, we highlight
important directions for further investigation in future work.Comment: Accepted by IEEE JSA
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