10 research outputs found
Wireless Image Transmission Using Deep Source Channel Coding With Attention Modules
Recent research on joint source channel coding (JSCC) for wireless
communications has achieved great success owing to the employment of deep
learning (DL). However, the existing work on DL based JSCC usually trains the
designed network to operate under a specific signal-to-noise ratio (SNR)
regime, without taking into account that the SNR level during the deployment
stage may differ from that during the training stage. A number of networks are
required to cover the scenario with a broad range of SNRs, which is
computational inefficiency (in the training stage) and requires large storage.
To overcome these drawbacks our paper proposes a novel method called Attention
DL based JSCC (ADJSCC) that can successfully operate with different SNR levels
during transmission. This design is inspired by the resource assignment
strategy in traditional JSCC, which dynamically adjusts the compression ratio
in source coding and the channel coding rate according to the channel SNR. This
is achieved by resorting to attention mechanisms because these are able to
allocate computing resources to more critical tasks. Instead of applying the
resource allocation strategy in traditional JSCC, the ADJSCC uses the
channel-wise soft attention to scaling features according to SNR conditions. We
compare the ADJSCC method with the state-of-the-art DL based JSCC method
through extensive experiments to demonstrate its adaptability, robustness and
versatility. Compared with the existing methods, the proposed method takes less
storage and is more robust in the presence of channel mismatch.Comment: 13 pages, 13 figures, journal pape
Wireless Image Transmission Using Deep Source Channel Coding With Attention Modules
Recent research on joint source channel coding (JSCC) for wireless communications has achieved great success owing to the employment of deep learning (DL). However, the existing work on DL based JSCC usually trains the designed network to operate under a specific signal-to-noise ratio (SNR) regime, without taking into account that the SNR level during the deployment stage may differ from that during the training stage. A number of networks are required to cover the scenario with a broad range of SNRs, which is computational inefficiency (in the training stage) and requires large storage. To overcome these drawbacks our paper proposes a novel method called Attention DL based JSCC (ADJSCC) that can successfully operate with different SNR levels during transmission. This design is inspired by the resource assignment strategy in traditional JSCC, which dynamically adjusts the compression ratio in source coding and the channel coding rate according to the channel SNR. This is achieved by resorting to attention mechanisms because these are able to allocate computing resources to more critical tasks. Instead of applying the resource allocation strategy in traditional JSCC, the ADJSCC uses the channel-wise soft attention to scaling features according to SNR conditions. We compare the ADJSCC method with the state-of-the-art DL based JSCC method through extensive experiments to demonstrate its adaptability, robustness and versatility. Compared with the existing methods, the proposed method takes less storage and is more robust in the presence of channel mismatch
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
Semantics-Empowered Communication: A Tutorial-cum-Survey
Along with the springing up of the semantics-empowered communication (SemCom)
research, it is now witnessing an unprecedentedly growing interest towards a
wide range of aspects (e.g., theories, applications, metrics and
implementations) in both academia and industry. In this work, we primarily aim
to provide a comprehensive survey on both the background and research taxonomy,
as well as a detailed technical tutorial. Specifically, we start by reviewing
the literature and answering the "what" and "why" questions in semantic
transmissions. Afterwards, we present the ecosystems of SemCom, including
history, theories, metrics, datasets and toolkits, on top of which the taxonomy
for research directions is presented. Furthermore, we propose to categorize the
critical enabling techniques by explicit and implicit reasoning-based methods,
and elaborate on how they evolve and contribute to modern content & channel
semantics-empowered communications. Besides reviewing and summarizing the
latest efforts in SemCom, we discuss the relations with other communication
levels (e.g., conventional communications) from a holistic and unified
viewpoint. Subsequently, in order to facilitate future developments and
industrial applications, we also highlight advanced practical techniques for
boosting semantic accuracy, robustness, and large-scale scalability, just to
mention a few. Finally, we discuss the technical challenges that shed light on
future research opportunities.Comment: Submitted to an IEEE journal. Copyright might be transferred without
further notic
Deep Joint Source-Channel Coding for Wireless Image Retrieval
Motivated by surveillance applications with wireless cameras or drones, we consider the problem of image retrieval over a wireless channel. Conventional systems apply lossy compression on query images to reduce the data that must be transmitted over a bandwidth and power limited wireless link. We first note that reconstructing the original image is not needed for retrieval tasks; hence, we introduce a deep neutral network (DNN) based compression scheme targeting the retrieval task. Then, we completely remove the compression step, and propose another DNN-based communication scheme that directly maps the feature vectors to channel inputs. This joint source-channel coding (JSCC) approach not only improves the end-to-end accuracy, but also simplifies and speeds up the encoding operation which is highly beneficial for power and latency constrained IoT applications