The Prospect of Reconfigurable Intelligent Surfaces in Integrated Access and Backhaul Networks

Diamanti, Maria; Charatsaris, Panagiotis; Tsiropoulou, Eirini Eleni; Papavassiliou, Symeon

doi:10.1109/tgcn.2021.3126784

Cited by 41 publications

(25 citation statements)

References 33 publications

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“…Both the usersto-edge server and edge-to-fog server transmissions are performed wirelessly, while the corresponding bandwidth in the two transmission levels is defined as W e = 5 MHz and W f = 1 MHz, accordingly. Other communications-related parameters are set as: p max n = 24 dBm, T off ,max n = 0.05 s, p e = 24 dBm, a e = 3.5, a f = 2, E e = 200 J, ρ = −20 dB, I 0 = −174 dBm/Hz, p tol = dBm [44]. Considering the computing-related parameters, we consider F n = 10 9 CPU cycle/s and σ n = 10 −27 J/CPU cycle for each user, and F e = 5×10 11 CPU cycles/s and σ e = 10 −29 J/CPU cycle for the edge server [24].…”

Section: Evaluation and Resultsmentioning

confidence: 99%

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Incentive Mechanism and Resource Allocation for Edge-Fog Networks Driven by Multi-Dimensional Contract and Game Theories

Diamanti

Charatsaris

Tsiropoulou

et al. 2022

IEEE Open J. Commun. Soc.

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The edge computing paradigm has become extremely popular over the past years, as a means of offloading computationally intensive tasks by users of resource and battery-constrained devices. Nevertheless, the edge networks' overexploitation by the ever-increasing number of task-offloading users, gradually leads to their performance degradation. In this paper, we leverage on the different levels of available computing capabilities across the network, and we design an incentive mechanism that aims to shift the selfish users' preference from the edge to the upper fog computing layer, accounting for their level of delay tolerance. To deal with the users' heterogeneity in terms of their applications' multidimensional distinctive features (including their delay tolerance/sensitivity), a multi-dimensional contract theory modeling is adopted, according to which the edge server determines the bundles of the users' provided efforts and corresponding offered rewards. In this respect, each user's effort represents the amount of its initially offloaded task at the edge that is allowed to be further forwarded and processed at the fog. Considering that the users-to-edge server offloading is performed under Non-Orthogonal Multiple Access (NOMA), the problem of joint computation task offloading and uplink transmission power allocation is subsequently addressed via a Stackelberg game, where the edge server and the users are treated as leader and followers, respectively. The aim of the game is to minimize the end-to-end network's energy consumption and increase its resource utilization efficiency. The incentive mechanism and resource allocation framework is evaluated via modeling and simulation regarding its operation and efficiency under different scenarios.

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Section: Evaluation and Resultsmentioning

confidence: 99%

“…(36a)-(36d). known that bears the properties that are summarized in Theorem 3 [42], [43], while an extensive proof of Theorem 3 can be found in our prior work in [44]. Given that each follower's, i.e., user's, distributed optimization problem in Eq.…”

mentioning

confidence: 99%

Incentive Mechanism and Resource Allocation for Edge-Fog Networks Driven by Multi-Dimensional Contract and Game Theories

Diamanti

Charatsaris

Tsiropoulou

et al. 2022

IEEE Open J. Commun. Soc.

Self Cite

View full text Add to dashboard Cite

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“…Similarly, a matching random azimuth was generated for each source information, which was denoted by θ. Then, the delay in the signal arriving at each antenna was calculated using equation (5). Finally, the signals received from each antenna were sampled and sent to the neural network.…”

Section: Simulationmentioning

confidence: 99%

“…Therefore, largescale antennas have become an important technology in modern and future communication systems. Large-scale antennas can also be deployed in ultra-dense cells and hotspots to effectively reduce path loss, delay, and interference to achieve a reduction in power consumption [5]. Some studies [6][7][8] have investigated large-scale antenna techniques that simulate stationary beamforming and channel state precoding.…”

Section: Introductionmentioning

confidence: 99%

Technique for Large-Scale Antenna Beamforming Based on Neural Network

Xie

Guo

JianZhou

2022

Wireless Communications and Mobile Computing

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The commercialization of the fifth-generation technology and the rapid development of Internet of Things technology have made mobile communication networks increasingly complex. Simultaneously, the massive terminal connections have caused serious interference between networks. Large-scale array antennas have become a hot topic of recent studies to improve the wireless transmission characteristics and spectrum resource utilization efficiency. This study was aimed at exploring using large-scale array antennas combined with neural networks in ultradense cells to improve the wireless signal transmission quality. The simulation results showed that the scheme realized not only the wireless signal transmission with a neural network but also the effective recovery of the source signal. Similarly, the application of this scheme effectively reduced the power consumption during signal transmission, the delay, and the interference.

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“…Figure 1 presents an implementable RIS-assisted wireless communication system. Here, signals are reflected from the RIS to facilitate effective communication between the base station and the mobile terminal [20,21]. However, real-time control of the reflection amplitude and phase shift of RIS pose enormous computational complexity [22].…”

Section: Introductionmentioning

confidence: 99%

Atmospheric Propagation Modelling for Terrestrial Radio Frequency Communication Links in a Tropical Wet and Dry Savanna Climate

Isabona

Imoize

Ojo³

et al. 2022

Information

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Atmospheric impairment-induced attenuation is the prominent source of signal degradation in radio wave communication channels. The computation-based modeling of radio wave attenuation over the atmosphere is the stepwise application of relevant radio propagation models, data, and procedures to effectively and prognostically estimate the losses of the propagated radio signals that have been induced by atmospheric constituents. This contribution aims to perform a detailed prognostic evaluation of radio wave propagation attenuation due to rain, free space, gases, and cloud over the atmosphere at the ultra-high frequency band. This aim has been achieved by employing relevant empirical atmospheric data and suitable propagation models for robust prognostic modeling using experimental measurements. Additionally, the extrapolative attenuation estimation results and the performance analysis were accomplished by engaging different stepwise propagation models and computation parameters often utilized in Earth–satellite and terrestrial communications. Results indicate that steady attenuation loss levels rise with increasing signal carrier frequency where free space is more dominant. The attenuation levels attained due to rain, cloud, atmospheric gases, and free space are also dependent on droplet depths, sizes, composition, and statistical distribution. While moderate and heavy rain depths achieved 3 dB and 4 dB attenuations, the attenuation due to light rainfall attained a 2.5 dB level. The results also revealed that attenuation intensity levels induced by atmospheric gases and cloud effects are less than that of rain. The prognostic-based empirical attenuation modeling results can provide first-hand information to radio transmission engineers on link budgets concerning various atmospheric impairment effects during radio frequency network design, deployment, and management, essentially at the ultra-high frequency band.

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The Prospect of Reconfigurable Intelligent Surfaces in Integrated Access and Backhaul Networks

Cited by 41 publications

References 33 publications

Incentive Mechanism and Resource Allocation for Edge-Fog Networks Driven by Multi-Dimensional Contract and Game Theories

Incentive Mechanism and Resource Allocation for Edge-Fog Networks Driven by Multi-Dimensional Contract and Game Theories

Technique for Large-Scale Antenna Beamforming Based on Neural Network

Atmospheric Propagation Modelling for Terrestrial Radio Frequency Communication Links in a Tropical Wet and Dry Savanna Climate

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