122 research outputs found
Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communications
Communication systems to date primarily aim at reliably communicating bit
sequences. Such an approach provides efficient engineering designs that are
agnostic to the meanings of the messages or to the goal that the message
exchange aims to achieve. Next generation systems, however, can be potentially
enriched by folding message semantics and goals of communication into their
design. Further, these systems can be made cognizant of the context in which
communication exchange takes place, providing avenues for novel design
insights. This tutorial summarizes the efforts to date, starting from its early
adaptations, semantic-aware and task-oriented communications, covering the
foundations, algorithms and potential implementations. The focus is on
approaches that utilize information theory to provide the foundations, as well
as the significant role of learning in semantics and task-aware communications.Comment: 28 pages, 14 figure
Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communications
Communication systems to date primarily aim at reliably communicating bit sequences. Such an approach provides efficient engineering designs that are agnostic to the meanings of the messages or to the goal that the message exchange aims to achieve. Next generation systems, however, can be potentially enriched by folding message semantics and goals of communication into their design. Further, these systems can be made cognizant of the context in which communication exchange takes place, thereby providing avenues for novel design insights. This tutorial summarizes the efforts to date, starting from its early adaptations, semantic-aware and task-oriented communications, covering the foundations, algorithms and potential implementations. The focus is on approaches that utilize information theory to provide the foundations, as well as the significant role of learning in semantics and task-aware communications
Multi-Receiver Task-Oriented Communications via Multi-Task Deep Learning
This paper studies task-oriented, otherwise known as goal-oriented,
communications, in a setting where a transmitter communicates with multiple
receivers, each with its own task to complete on a dataset, e.g., images,
available at the transmitter. A multi-task deep learning approach that involves
training a common encoder at the transmitter and individual decoders at the
receivers is presented for joint optimization of completing multiple tasks and
communicating with multiple receivers. By providing efficient resource
allocation at the edge of 6G networks, the proposed approach allows the
communications system to adapt to varying channel conditions and achieves
task-specific objectives while minimizing transmission overhead. Joint training
of the encoder and decoders using multi-task learning captures shared
information across tasks and optimizes the communication process accordingly.
By leveraging the broadcast nature of wireless communications, multi-receiver
task-oriented communications (MTOC) reduces the number of transmissions
required to complete tasks at different receivers. Performance evaluation
conducted on the MNIST, Fashion MNIST, and CIFAR-10 datasets (with image
classification considered for different tasks) demonstrates the effectiveness
of MTOC in terms of classification accuracy and resource utilization compared
to single-task-oriented communication systems
Consolidate Viability and Information Theories for Task-Oriented Communications: A Homeostasis Solution
The next generation of cellular networks, 6G, is expected to offer a range of
exciting applications and services, including holographic communication,
machine-to-machine communication, and data sensing from millions of devices.
There is an incremental exhaustion of the spectral resources. It is crucial to
efficiently manage these resources through value-driven approaches that
eliminate waste and continually enhance the communication process. These
management principles align with the Task-Oriented Communications (TOC)
philosophy. The aim is to allocate the minimum necessary communication resource
according to the receiver's objective and continuously improve the
communication process. However, it is currently unclear how to build knowledge
of the receiver's goal and operate accordingly for efficient-resource
utilization. Our management approach combines viability theory and transfer
entropy to ensure that the actor remains within a viable space as per their
goal and to gradually reduce the information exchange through knowledge
accumulation. We discuss these theories in the context of TOC and examine their
application in the plant process control case. Finally, we provide insights
into future research directions from computational, performance, and protocol
perspectives.Comment: 6 pages, 3 figure
LoRa-based Over-the-Air Computing for Sat-IoT
Satellite Internet of Things (Sat-IoT) is a novel framework in which
satellites integrate sensing, communication and computing capabilities to carry
out task-oriented communications. In this paper we propose to use the Long
Range (LoRa) modulation for the purpose of estimation in a Sat-IoT scenario.
Then we realize that the collisions generated by LoRa can be harnessed in an
Over-the-Air Computing (AirComp) framework. Specifically, we propose to use
LoRa for Type-based Multiple Access (TBMA), a semantic-aware scheme in which
communication resources are assigned to different parameters, not users. Our
experimental results show that LoRa-TBMA is suitable as a massive access
scheme, provides large gains in terms of mean squared error (MSE) and saves
scarce satellite communication resources (i.e., power, latency and bandwidth)
with respect to orthogonal multiple access schemes. We also analyze the
satellite scenarios that could take advantage of the LoRa-TBMA scheme. In
summary, that angular modulations, which are very useful in satellite
communications, can also benefit from AirComp.Comment: Paper accepted in 2023 European Signal Processing Conference
(EUSIPCO
Goal-oriented Communications for the IoT: System Design and Adaptive Resource Optimization
Internet of Things (IoT) applications combine sensing, wireless
communication, intelligence, and actuation, enabling the interaction among
heterogeneous devices that collect and process considerable amounts of data.
However, the effectiveness of IoT applications needs to face the limitation of
available resources, including spectrum, energy, computing, learning and
inference capabilities. This paper challenges the prevailing approach to IoT
communication, which prioritizes the usage of resources in order to guarantee
perfect recovery, at the bit level, of the data transmitted by the sensors to
the central unit. We propose a novel approach, called goal-oriented (GO) IoT
system design, that transcends traditional bit-related metrics and focuses
directly on the fulfillment of the goal motivating the exchange of data. The
improvement is then achieved through a comprehensive system optimization,
integrating sensing, communication, computation, learning, and control. We
provide numerical results demonstrating the practical applications of our
methodology in compelling use cases such as edge inference, cooperative
sensing, and federated learning. These examples highlight the effectiveness and
real-world implications of our proposed approach, with the potential to
revolutionize IoT systems.Comment: Accepted for publication on IEEE Internet of Things Magazine, special
issue on "Task-Oriented Communications and Networking for the Internet of
Things
Semantic Communications for Image Recovery and Classification via Deep Joint Source and Channel Coding
With the recent advancements in edge artificial intelligence (AI), future
sixth-generation (6G) networks need to support new AI tasks such as
classification and clustering apart from data recovery. Motivated by the
success of deep learning, the semantic-aware and task-oriented communications
with deep joint source and channel coding (JSCC) have emerged as new paradigm
shifts in 6G from the conventional data-oriented communications with separate
source and channel coding (SSCC). However, most existing works focused on the
deep JSCC designs for one task of data recovery or AI task execution
independently, which cannot be transferred to other unintended tasks.
Differently, this paper investigates the JSCC semantic communications to
support multi-task services, by performing the image data recovery and
classification task execution simultaneously. First, we propose a new
end-to-end deep JSCC framework by unifying the coding rate reduction
maximization and the mean square error (MSE) minimization in the loss function.
Here, the coding rate reduction maximization facilitates the learning of
discriminative features for enabling to perform classification tasks directly
in the feature space, and the MSE minimization helps the learning of
informative features for high-quality image data recovery. Next, to further
improve the robustness against variational wireless channels, we propose a new
gated deep JSCC design, in which a gated net is incorporated for adaptively
pruning the output features to adjust their dimensions based on channel
conditions. Finally, we present extensive numerical experiments to validate the
performance of our proposed deep JSCC designs as compared to various benchmark
schemes
The relationship between organizational structure and the structure of organizational communications : an empirical study in an academic department
The objectives of this study were (1) to develop a model to explain the relationships between organizational structure and the structure of individual communication, and (2) to test this model empirically in an organizational setting. The model classifies the communications structure of individuals in organizations into three types of channels: formal channels, informal channels directed toward fulfilling organizational demands and informal communication directed toward individual social and psychological needs. The amount of formal communication varies with the cybernetic needs of the organization while the two types of informal communication vary with individual autonomy within the organization. A survey was conducted among junior and senior sociology majors and the faculty of the Sociology Department at the University of North Carolina at Greensboro. Respondents included 57 welfare students, 34 non-welfare students, and eleven faculty members
Multi-Device Task-Oriented Communication via Maximal Coding Rate Reduction
Task-oriented communication offers ample opportunities to alleviate the
communication burden in next-generation wireless networks. Most existing work
designed the physical-layer communication modules and learning-based codecs
with distinct objectives: learning is targeted at accurate execution of
specific tasks, while communication aims at optimizing conventional
communication metrics, such as throughput maximization, delay minimization, or
bit error rate minimization. The inconsistency between the design objectives
may hinder the exploitation of the full benefits of task-oriented
communications. In this paper, we consider a specific task-oriented
communication system for multi-device edge inference over a multiple-input
multiple-output (MIMO) multiple-access channel, where the learning (i.e.,
feature encoding and classification) and communication (i.e., precoding)
modules are designed with the same goal of inference accuracy maximization.
Instead of end-to-end learning which involves both the task dataset and
wireless channel during training, we advocate a separate design of learning and
communication to achieve the consistent goal. Specifically, we leverage the
maximal coding rate reduction (MCR2) objective as a surrogate to represent the
inference accuracy, which allows us to explicitly formulate the precoding
optimization problem. We cast valuable insights into this formulation and
develop a block coordinate descent (BCD) solution algorithm. Moreover, the MCR2
objective also serves the loss function of the feature encoding network, based
on which we characterize the received features as a Gaussian mixture (GM)
model, facilitating a maximum a posteriori (MAP) classifier to infer the
result. Simulation results on both the synthetic and real-world datasets
demonstrate the superior performance of the proposed method compared to various
baselines.Comment: submitted to IEEE for possible publicatio
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