Zobrazeno 1 - 10
of 2 909
pro vyhledávání: '"Ramoni A"'
Publikováno v:
IEEE Open Journal of the Communications Society, Vol 5, Pp 3120-3135 (2024)
Transmit power control (PC) will become increasingly crucial in alleviating interference as the densification of the wireless networks continues towards 6G. However, the practicality of most PC methods suffers from high complexity, including the sens
Externí odkaz:
https://doaj.org/article/e92fab5f5b9e4dc1abdc3a01e260c198
Autor:
Jessica Mendoza, Istvan Z. Kovacs, Melisa Lopez, Troels B. Sorensen, Ramoni Adeogun, Isabel De-La-Bandera, Raquel Barco
Publikováno v:
IEEE Access, Vol 10, Pp 54149-54163 (2022)
5G communication systems are one of the major enabling technologies to meet the needs of Industry 4.0. This paper focuses on the use case of automated guided vehicles (AGVs) in an outdoor industrial scenario. To meet the communication requirements in
Externí odkaz:
https://doaj.org/article/50dbfad06edf407d817b9f4d778c3786
Publikováno v:
IEEE Access, Vol 10, Pp 45784-45798 (2022)
Short-range low-power 6th generation (6G) in-X subnetworks are proposed as a viable radio concept for supporting extreme communication requirements in emerging applications such as wireless control of robotic arms and control of critical on-body devi
Externí odkaz:
https://doaj.org/article/045fb1741ca8490eb8a4b6a352d01b9b
The forthcoming sixth-generation (6G) industrial Internet-of-Things (IIoT) subnetworks are expected to support ultra-fast control communication cycles for numerous IoT devices. However, meeting the stringent requirements for low latency and high reli
Externí odkaz:
http://arxiv.org/abs/2411.12557
Publikováno v:
IEEE Access, Vol 9, Pp 132675-132704 (2021)
The deployment of relays between Internet of Things (IoT) end devices and gateways can improve link quality. In cellular-based IoT, relays have the potential to reduce base station overload. The energy expended in single-hop long-range communication
Externí odkaz:
https://doaj.org/article/ba91c87116e247ac8ae76e6432576747
Autor:
Uyoata, Uyoata E., Adeogun, Ramoni O.
Deep learning has been used to tackle problems in wireless communication including signal detection, channel estimation, traffic prediction, and demapping. Achieving reasonable results with deep learning typically requires large datasets which may be
Externí odkaz:
http://arxiv.org/abs/2408.16401
Autor:
Uyoata, Uyoata E., Akinsolu, Mobayode O., Obayiuwana, Enoruwa, Sangodoyin, Abimbola, Adeogun, Ramoni
The use of Intelligent Reflecting Surfaces (IRSs) is considered a potential enabling technology for enhancing the spectral and energy efficiency of beyond 5G communication systems. In this paper, a joint relay and intelligent reflecting surface (IRS)
Externí odkaz:
http://arxiv.org/abs/2408.16399
Autor:
Ramoni Adeogun, Gilberto Berardinelli, Preben E. Mogensen, Ignacio Rodriguez, Mohammad Razzaghpour
Publikováno v:
IEEE Access, Vol 8, Pp 110172-110188 (2020)
The continuous proliferation of applications requiring wireless connectivity will eventually result in latency and reliability requirements beyond what is achievable with current technologies. Such applications can for example include industrial cont
Externí odkaz:
https://doaj.org/article/b1a7b05c4ca84210a9855be43019b80d
Publikováno v:
IEEE Open Journal of Antennas and Propagation, Vol 1, Pp 175-188 (2020)
Estimating parameters of stochastic radio channel models based on new measurement data is an arduous task usually involving multiple steps such as multipath extraction and clustering. We propose two different machine learning methods, one based on ap
Externí odkaz:
https://doaj.org/article/d53313e73be745348c083f8f988825c5
Autor:
Ramoni Adeogun, Gilberto Berardinelli
Publikováno v:
Sensors, Vol 22, Iss 13, p 5062 (2022)
In this paper, we investigate dynamic resource selection in dense deployments of the recent 6G mobile in-X subnetworks (inXSs). We cast resource selection in inXSs as a multi-objective optimization problem involving maximization of the minimum capaci
Externí odkaz:
https://doaj.org/article/0bb6749013a143e683c5f16a545d257a