24 research outputs found
Reasoning over the Air: A Reasoning-based Implicit Semantic-Aware Communication Framework
Semantic-aware communication is a novel paradigm that draws inspiration from
human communication focusing on the delivery of the meaning of messages. It has
attracted significant interest recently due to its potential to improve the
efficiency and reliability of communication and enhance users' QoE. Most
existing works focus on transmitting and delivering the explicit semantic
meaning that can be directly identified from the source signal. This paper
investigates the implicit semantic-aware communication in which the hidden
information that cannot be directly observed from the source signal must be
recognized and interpreted by the intended users. To this end, a novel implicit
semantic-aware communication (iSAC) architecture is proposed for representing,
communicating, and interpreting the implicit semantic meaning between source
and destination users. A projection-based semantic encoder is proposed to
convert the high-dimensional graphical representation of explicit semantics
into a low-dimensional semantic constellation space for efficient physical
channel transmission. To enable the destination user to learn and imitate the
implicit semantic reasoning process of source user, a generative adversarial
imitation learning-based solution, called G-RML, is proposed. Different from
existing communication solutions, the source user in G-RML does not focus only
on sending as much of the useful messages as possible; but, instead, it tries
to guide the destination user to learn a reasoning mechanism to map any
observed explicit semantics to the corresponding implicit semantics that are
most relevant to the semantic meaning. Compared to the existing solutions, our
proposed G-RML requires much less communication and computational resources and
scales well to the scenarios involving the communication of rich semantic
meanings consisting of a large number of concepts and relations.Comment: accepted at IEEE Transactions on Wireless Communication
Developing a geocomputational workflow to check the consistency of volunteered geographic information
Revisiting the extended spring indices using gridded weather data machine learning : abstract
Phenological modelling using volunteered observations and machine learning methods : abstract
Ultrareliable and low-latency wireless communication:tail, risk, and scale
Abstract
Ensuring ultrareliable and low-latency communication (URLLC) for 5G wireless networks and beyond is of capital importance and is currently receiving tremendous attention in academia and industry. At its core, URLLC mandates a departure from expected utility-based network design approaches, in which relying on average quantities (e.g., average throughput, average delay, and average response time) is no longer an option but a necessity. Instead, a principled and scalable framework which takes into account delay, reliability, packet size, network architecture and topology (across access, edge, and core), and decision-making under uncertainty is sorely lacking. The overarching goal of this paper is a first step to filling this void. Towards this vision, after providing definitions of latency and reliability, we closely examine various enablers of URLLC and their inherent tradeoffs. Subsequently, we focus our attention on a wide variety of techniques and methodologies pertaining to the requirements of URLLC, as well as their applications through selected use cases. These results provide crisp insights for the design of low-latency and high-reliability wireless networks
Fronthaul-Aware Software-Defined Wireless Networks: Resource Allocation and User Scheduling
Software-defined networking (SDN) provides an agile and programmable way to
optimize radio access networks via a control-data plane separation.
Nevertheless, reaping the benefits of wireless SDN hinges on making optimal use
of the limited wireless fronthaul capacity. In this work, the problem of
fronthaul-aware resource allocation and user scheduling is studied. To this
end, a two-timescale fronthaul-aware SDN control mechanism is proposed in which
the controller maximizes the time-averaged network throughput by enforcing a
coarse correlated equilibrium in the long timescale. Subsequently, leveraging
the controller's recommendations, each base station schedules its users using
Lyapunov stochastic optimization in the short timescale, i.e., at each time
slot. Simulation results show that significant network throughput enhancements
and up to 40% latency reduction are achieved with the aid of the SDN
controller. Moreover, the gains are more pronounced for denser network
deployments.Comment: Accepted in IEEE Transactions on Wireless Communication
Context-Aware Small Cell Networks: How Social Metrics Improve Wireless Resource Allocation
In this paper, a novel approach for optimizing and managing resource
allocation in wireless small cell networks (SCNs) with device-to-device (D2D)
communication is proposed. The proposed approach allows to jointly exploit both
the wireless and social context of wireless users for optimizing the overall
allocation of resources and improving traffic offload in SCNs. This
context-aware resource allocation problem is formulated as a matching game in
which user equipments (UEs) and resource blocks (RBs) rank one another, based
on utility functions that capture both wireless and social metrics. Due to
social interrelations, this game is shown to belong to a class of matching
games with peer effects. To solve this game, a novel, selforganizing algorithm
is proposed, using which UEs and RBs can interact to decide on their desired
allocation. The proposed algorithm is then proven to converge to a two-sided
stable matching between UEs and RBs. The properties of the resulting stable
outcome are then studied and assessed. Simulation results using real social
data show that clustering of socially connected users allows to offload a
substantially larger amount of traffic than the conventional context-unaware
approach. These results show that exploiting social context has high practical
relevance in saving resources on the wireless links and on the backhaul.Comment: Submitted to the IEEE Transaction on Wireless Communication