arXiv subjects

Zdenek Becvar

Publications and source records attributed to Zdenek Becvar.

11 recordsLinked to original sources

arXiv2025

Real-World Modeling of Computation Offloading for Neural Networks with Early Exits and Splits

We focus on computation offloading of applications based on convolutional neural network (CNN) from moving devices, such as mobile robots or autonomous vehicles, to MultiAccess Edge Computing (MEC) servers via a mobile network. In order to reduce overall CNN inference time, we design and implement CNN with early exits and splits, allowing a flexible partial or full offloading of CNN inference. Through real-world experiments, we analyze an impact of the CNN inference offloading on the total CNN processing delay, energy consumption, and classification accuracy in a practical road sign recognition task. The results confirm that offloading of CNN with early exits and splits can significantly reduce both total processing delay and energy consumption compared to full local processing while not impairing classification accuracy. Based on the results of real-world experiments, we derive practical models for energy consumption and total processing delay related to offloading of CNN with early exits and splits.

cs.NI↗

arXiv2024

Multi-hop Relaying with Mixed Half and Full Duplex Relays for Offloading to MEC

In this paper, we focus on offloading a computing task from a user equipment (UE) to a multi-access edge computing (MEC) server via multi-hop relaying. We assume a general relaying case where relays are energy-constrained devices, such as other UEs, internet of things (IoT) devices, or unmanned aerial vehicles. To this end, we formulate the problem as a minimization of the sum energy consumed by the energy-constrained devices under the constraint on the maximum requested time of the task processing. Then, we propose a multi-hop relaying combining half and full duplexes at each individual relay involved in the offloading. We proof that the proposed multi-hop relaying is convex, thus it can be optimized by conventional convex optimization methods. We show our proposal outperforms existing multi-hop relaying schemes in terms of probability that tasks are processed within required time by up to 38\% and, at the same time, decreases energy consumption by up to 28%.

cs.IT↗

arXiv2023

Channel Reuse for Backhaul in UAV Mobile Networks with User QoS Guarantee

In mobile networks, unmanned aerial vehicles (UAVs) acting as flying base stations (FlyBSs) can effectively improve performance. Nevertheless, such potential improvement requires an efficient positioning of the FlyBS. In this paper, we study the problem of sum downlink capacity maximization in FlyBS-assisted networks with mobile users and with a consideration of wireless backhaul with channel reuse while a minimum required capacity to every user is guaranteed. The problem is formulated under constraints on the FlyBS's flying speed, propulsion power consumption, and transmission power for both of flying and ground base stations. None of the existing solutions maximizing the sum capacity can be applied due to the combination of these practical constraints. This paper pioneers in an inclusion of all these constraints together with backhaul to derive the optimal 3D positions of the FlyBS and to optimize the transmission power allocation for the channels at both backhaul and access links as the users move over time. The proposed solution is geometrical based, and it shows via simulations a significant increase in the sum capacity (up by 19%-47%) compared with baseline schemes where one or more of the aspects of backhaul communication, transmission power allocation, and FlyBS's positioning are not taken into account.

eess.SY↗

arXiv2022

QoS-Aware Sum Capacity Maximization for Mobile Internet of Things Devices Served by UAVs

The use of unmanned aerial vehicles (UAVs) acting as flying base stations (FlyBSs) is considered as an effective tool to improve performance of the mobile networks. Nevertheless, such potential improvement requires an efficient positioning of the FlyBS. In this paper, we maximize the sum downlink capacity of the mobile Internet of Things devices (IoTD) served by the FlyBSs while a minimum required capacity to every device is guaranteed. To this end, we propose a geometrical approach allowing to derive the 3D positions of the FlyBS over time as the IoTDs move and we determine the transmission power allocation for the IoTDs. The problem is formulated and solved under practical constraints on the FlyBS's transmission and propulsion power consumption as well as on flying speed. The proposed solution is of a low complexity and increases the sum capacity by 15%-46% comparing to state-of-the-art works.

eess.SP↗

arXiv2022

Sum Capacity Maximization in Multi-Hop Mobile Networks with Flying Base Stations

Deployment of multi-hop network of unmanned aerial vehicles (UAVs) acting as flying base stations (FlyBSs) presents a remarkable potential to effectively enhance the performance of wireless networks. Such potential enhancement, however, relies on an efficient positioning of the FlyBSs as well as a management of resources. In this paper, we study the problem of sum capacity maximization in an extended model for mobile networks where multiple FlyBSs are deployed between the ground base station and the users. Due to an inclusion of multiple hops, the existing solutions for two-hop networks cannot be applied due to the incurred backhaul constraints for each hop. To this end, we propose an analytical approach based on an alternating optimization of the FlyBSs' 3D positions as well as the association of the users to the FlyBSs over time. The proposed optimization is provided under practical constraints on the FlyBS's flying speed and altitude as well as the constraints on the achievable capacity at the backhaul link. The proposed solution is of a low complexity and extends the sum capacity by 23%-38% comparing to state-of-the-art solutions.

eess.SY↗

arXiv2020

6G in the Sky: On-Demand Intelligence at the Edge of 3D Networks

6G will exploit satellite, aerial and terrestrial platforms jointly to improve radio access capability and to unlock the support of on-demand edge cloud services in the three dimensional space (3D) by incorporating Mobile Edge Computing (MEC) functionalities on aerial platforms and low orbit satellites. This will extend the MEC support to devices and network elements in the sky and will forge a space borne MEC enabling intelligent personalized and distributed on demand services. 3D end users will experience the impression of being surrounded by a distributed computer fulfilling their requests in apparently zero latency. In this paper, we consider an architecture providing communication, computation, and caching (C3) services on demand, anytime and everywhere in 3D space, building on the integration of conventional ground (terrestrial) base stations and flying (non-terrestrial) nodes. Given the complexity of the overall network, the C3 resources and the management of the aerial devices need to be jointly orchestrated via AI-based algorithms, exploiting virtualized networks functions dynamically deployed in a distributed manner across terrestrial and non-terrestrial nodes.

cs.NI↗

arXiv2019

Predicting Device-to-Device Channels from Cellular Channel Measurements: A Learning Approach

Device-to-device (D2D) communication, which enables a direct connection between users while bypassing the cellular channels to base stations (BSs), is a promising way to offload the traffic from conventional cellular networks. In D2D communication, one recurring problem is that, in order to optimally allocate resources across D2D and cellular users, the knowledge of D2D channel gains is needed. However, such knowledge is hard to obtain at reasonable signaling costs. In this paper, we show this problem can be circumvented by tapping into the information provided by the estimation of the cellular channels between the users and surrounding BSs as this estimation is done anyway for a normal operation of the network. While the cellular and D2D channel gains exhibit independent fast fading behavior, we show that average gains of the cellular and D2D channels share a non-explicit correlation structure, which is rooted into the network topology, terrain, and buildings setup. We propose a machine (deep) learning approach capable of predicting the D2D channel gains from seemingly independent cellular channels. Our results show a high degree of convergence between true and predicted D2D channel gains. The predicted gains allow to reach a near-optimal communication capacity in many radio resource management algorithms.

cs.IT↗

arXiv2018

Energy Efficiency of Opportunistic Device-to-Device Relaying Under Lognormal Shadowing

Energy consumption is a major limitation of low power and mobile devices. Efficient transmission protocols are required to minimize an energy consumption of the mobile devices for ubiquitous connectivity in the next generation wireless networks. Opportunistic schemes select a single relay using the criteria of the best channel and achieve a near-optimal diversity performance in a cooperative wireless system. In this paper, we study the energy efficiency of the opportunistic schemes for device-to-device communication. In the opportunistic approach, an energy consumed by devices is minimized by selecting a single neighboring device as a relay using the criteria of minimum consumed energy in each transmission in the uplink of a wireless network. We derive analytical bounds and scaling laws on the expected energy consumption when the devices experience log-normal shadowing with respect to a base station considering both the transmission as well as circuit energy consumptions. We show that the protocol improves the energy efficiency of the network comparing to the direct transmission even if only a few devices are considered for relaying. We also demonstrate the effectiveness of the protocol by means of simulations in realistic scenarios of the wireless network.

cs.IT↗

arXiv2017

Combined shared/dedicated resource allocation for Device-to-Device Communication

Device-to-device (D2D) communication is an effective technology enhancing spectral efficiency and network throughput of contemporary cellular networks. Typically, the users exploiting D2D reuse the same radio resources as common cellular users (CUEs) that communicate through a base station. This mode is known as shared mode. Another option is to dedicate specific amount of resources exclusively for the D2D users in so-called a dedicated mode. In this paper, we propose novel combined share/dedicated resource allocation scheme enabling the D2D users to utilize the radio resources in both modes simultaneously. To that end, we propose a graph theory-based framework for efficient resource allocation. Within this framework, neighborhood relations between the cellular users and the D2D users and between the individual D2D users are derived to form graphs. Then, the graphs are decomposed into subgraphs to identify resources, which can be reused by other users so that capacity of the D2D users is maximized. The results show that the sum D2D capacity is increased from 1.67 and 2.5 times (depending on a density of D2D users) when compared to schemes selecting only between shared or dedicated modes.

cs.IT↗

arXiv2017

Combination of Visible Light and Radio Frequency Bands for Device-to-Device Communication

Future mobile networks are supposed to serve high data rates required by users. To accommodate the high data rates, a direct communication between nearby mobile terminals (MTs), known as Device-to-device (D2D) communication, can be exploited. Furthermore, a communication in high frequency bands, such as, visible light communication (VLC), is also foreseen as an enabler for the high data rates. In conventional D2D communication, pairs of the MTs should reuse the same frequencies to keep a high spectral efficiency of the system. However, this implies either interference among the D2D pairs or utilization of complex and advanced resource allocation algorithms. In this paper, we propose a new concept for D2D communication combining VLC and RF technologies in order to maximize capacity of the system. This paper provides an analysis of the operational limits for the proposed concept and investigates capacity gains introduced by the combined usage of RF and VLC bands for D2D. Moreover, we discuss several practical issues related to the proposed RF-VLC D2D concept. Performed analyses show that the RF--VLC D2D is able to improve the capacity in an indoor scenario by a factor of 4.1 and 1.5 when compared to stand-alone RF D2D and VLC D2D, respectively.

cs.IT↗

arXiv2017

Mobile Edge Computing: A Survey on Architecture and Computation Offloading

Technological evolution of mobile user equipments (UEs), such as smartphones or laptops, goes hand-in-hand with evolution of new mobile applications. However, running computationally demanding applications at the UEs is constrained by limited battery capacity and energy consumption of the UEs. Suitable solution extending the battery life-time of the UEs is to offload the applications demanding huge processing to a conventional centralized cloud (CC). Nevertheless, this option introduces significant execution delay consisting in delivery of the offloaded applications to the cloud and back plus time of the computation at the cloud. Such delay is inconvenient and make the offloading unsuitable for real-time applications. To cope with the delay problem, a new emerging concept, known as mobile edge computing (MEC), has been introduced. The MEC brings computation and storage resources to the edge of mobile network enabling to run the highly demanding applications at the UE while meeting strict delay requirements. The MEC computing resources can be exploited also by operators and third parties for specific purposes. In this paper, we first describe major use cases and reference scenarios where the MEC is applicable. After that we survey existing concepts integrating MEC functionalities to the mobile networks and discuss current advancement in standardization of the MEC. The core of this survey is, then, focused on user-oriented use case in the MEC, i.e., computation offloading. In this regard, we divide the research on computation offloading to three key areas: i) decision on computation offloading, ii) allocation of computing resource within the MEC, and iii) mobility management. Finally, we highlight lessons learned in area of the MEC and we discuss open research challenges yet to be addressed in order to fully enjoy potentials offered by the MEC.

cs.IT↗