13 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
Network-Independent and User-Controlled RIS: An Experimental Perspective
The march towards 6G is accelerating and future wireless network
architectures require enhanced performance along with significant coverage
especially, to combat impairments on account of the wireless channel.
Reconfigurable intelligent surface (RIS) technology is a promising solution,
that has recently been considered as a research topic in standards, to help
manipulate the channel in favor of users needs. Generally, in experimental RIS
systems, the RIS is either connected to the transmitter (Tx) or receiver (Rx)
through a physical backhaul link and it is controlled by the network and
requires significant computation at the RIS for codebook (CB) designs. In this
paper, we propose a practical user-controlled RIS system that is isolated from
the network to enhance communication performance and provide coverage to the
user based on its location and preference. Furthermore, a low-complexity
algorithm is proposed to aid in CB selection for the user, which is performed
through the wireless cloud to enable a passive and energy efficient RIS.
Extensive experimental test-bed measurements demonstrate the enhanced
performance of the proposed system while both results match and validate each
other.Comment: Conference, 6 pages, 5 figures, 1 algorith
Reconfigurable Intelligent Surfaces for Wireless Communications: Principles, Challenges, and Opportunities
Recently there has been a flurry of research on the use of reconfigurable
intelligent surfaces (RIS) in wireless networks to create smart radio
environments. In a smart radio environment, surfaces are capable of
manipulating the propagation of incident electromagnetic waves in a
programmable manner to actively alter the channel realization, which turns the
wireless channel into a controllable system block that can be optimized to
improve overall system performance. In this article, we provide a tutorial
overview of reconfigurable intelligent surfaces (RIS) for wireless
communications. We describe the working principles of reconfigurable
intelligent surfaces (RIS) and elaborate on different candidate implementations
using metasurfaces and reflectarrays. We discuss the channel models suitable
for both implementations and examine the feasibility of obtaining accurate
channel estimates. Furthermore, we discuss the aspects that differentiate RIS
optimization from precoding for traditional MIMO arrays highlighting both the
arising challenges and the potential opportunities associated with this
emerging technology. Finally, we present numerical results to illustrate the
power of an RIS in shaping the key properties of a MIMO channel.Comment: to appear in the IEEE Transactions on Cognitive Communications and
Networking (TCCN