67 research outputs found
Semantic Channel Equalizer: Modelling Language Mismatch in Multi-User Semantic Communications
We consider a multi-user semantic communications system in which agents
(transmitters and receivers) interact through the exchange of semantic messages
to convey meanings. In this context, languages are instrumental in structuring
the construction and consolidation of knowledge, influencing conceptual
representation and semantic extraction and interpretation. Yet, the crucial
role of languages in semantic communications is often overlooked. When this is
not the case, agent languages are assumed compatible and unambiguously
interoperable, ignoring practical limitations that may arise due to language
mismatching. This is the focus of this work. When agents use distinct
languages, message interpretation is prone to semantic noise resulting from
critical distortion introduced by semantic channels. To address this problem,
this paper proposes a new semantic channel equalizer to counteract and limit
the critical ambiguity in message interpretation. Our proposed solution models
the mismatch of languages with measurable transformations over semantic
representation spaces. We achieve this using optimal transport theory, where we
model such transformations as transportation maps. Then, to recover at the
receiver the meaning intended by the teacher we operate semantic equalization
to compensate for the transformation introduced by the semantic channel, either
before transmission and/or after the reception of semantic messages. We
implement the proposed approach as an operation over a codebook of
transformations specifically designed for successful communication. Numerical
results show that the proposed semantic channel equalizer outperforms
traditional approaches in terms of operational complexity and transmission
accuracy.Comment: This work has been accepted for publication in 2023 IEEE Global
Communications Conference (GLOBECOM) SAC: Machine Learning for Communication
Optimal Cross Slice Orchestration for 5G Mobile Services
5G mobile networks encompass the capabilities of hosting a variety of
services such as mobile social networks, multimedia delivery, healthcare,
transportation, and public safety. Therefore, the major challenge in designing
the 5G networks is how to support different types of users and applications
with different quality-of-service requirements under a single physical network
infrastructure. Recently, network slicing has been introduced as a promising
solution to address this challenge. Network slicing allows programmable network
instances which match the service requirements by using network virtualization
technologies. However, how to efficiently allocate resources across network
slices has not been well studied in the literature. Therefore, in this paper,
we first introduce a model for orchestrating network slices based on the
service requirements and available resources. Then, we propose a Markov
decision process framework to formulate and determine the optimal policy that
manages cross-slice admission control and resource allocation for the 5G
networks. Through simulation results, we show that the proposed framework and
solution are efficient not only in providing slice-as-a-service based on the
service requirements, but also in maximizing the provider's revenue.Comment: 6 pages, 6 figures, WCNC 2018 conferenc
Technical Report: Energy Evaluation of preamble Sampling MAC Protocols for Wireless Sensor Networks
The paper presents a simple probabilistic analysis of the energy consumption
in preamble sampling MAC protocols. We validate the analytical results with
simulations. We compare the classical MAC protocols (B-MAC and X-MAC) with
LAMAC, a method proposed in a companion paper. Our analysis highlights the
energy savings achievable with LA-MAC with respect to B-MAC and X-MAC. It also
shows that LA-MAC provides the best performance in the considered case of high
density networks under traffic congestion
Concurrent data transmissions in green wireless networks: When best send one's packets?
978-1-4673-0761-1International audienceIn this paper, we consider the scenario of a cellular network where base stations aim to transmit several data packets to a set of users in the downlink, within a predefined time, at minimal energy cost. The base stations are non-cooperating and the instantaneous transmission rate depends on the instantaneous SINR at the receiver. The purpose of this article is to highlight a power-efficient transmit policy. By assuming a large number of homogeneous users, we model the problem as a mean field game, with tractable equations, that allow us to bypass the complexity of analyzing a Nash equilibrium in a L-body dynamic game. The framework we propose yields a consistent analysis of the optimal transmit power strategy, that allows every base station to, selfishly but rationally, satisfy its transmission, at a minimal energy cost
Dynamic edge computing empowered by reconfigurable intelligent surfaces
In this paper, we propose a novel algorithm for energy-efficient low-latency dynamic mobile edge computing (MEC), in the context of beyond 5G networks endowed with reconfigurable intelligent surfaces (RISs). We consider a scenario where new computing requests are continuously generated by a set of devices and are handled through a dynamic queueing system. Building on stochastic optimization tools, we devise a dynamic learning algorithm that jointly optimizes the allocation of radio resources (i.e., power, transmission rates, sleep mode and duty cycle), computation resources (i.e., CPU cycles), and RIS reflectivity parameters (i.e., phase shifts), while guaranteeing a target performance in terms of average end-to-end delay. The proposed strategy enables dynamic control of the system, performing a low-complexity optimization on a per-slot basis while dealing with time-varying radio channels and task arrivals, whose statistics are unknown. The presence and optimization of RISs helps boosting the performance of dynamic MEC, thanks to the capability to shape and adapt the wireless propagation environment. Numerical results assess the performance in terms of service delay, learning, and adaptation capabilities of the proposed strategy for RIS-empowered MEC
- …