Multi-Cell LTE-U/Wi-Fi Coexistence Evaluation Using a Reinforcement Learning Framework

José M. de C. Neto; Sildolfo F. G. Neto; Pedro M. de Santana; Vicente A. de Sousa

doi:10.3390/s20071855

Multi-Cell LTE-U/Wi-Fi Coexistence Evaluation Using a Reinforcement Learning Framework

Sensors ◽

10.3390/s20071855 ◽

2020 ◽

Vol 20 (7) ◽

pp. 1855 ◽

Cited By ~ 3

Author(s):

José M. de C. Neto ◽

Sildolfo F. G. Neto ◽

Pedro M. de Santana ◽

Vicente A. de Sousa

Keyword(s):

Reinforcement Learning ◽

Mobile Networks ◽

New Technologies ◽

Data Rate ◽

Added Value ◽

Broadband Internet ◽

Learning Framework ◽

Data Rates ◽

User Data ◽

5 Ghz

Cellular broadband Internet of Things (IoT) applications are expected to keep growing year-by-year, generating demands from high throughput services. Since some of these applications are deployed over licensed mobile networks, as long term evolution (LTE), one already common problem is faced: the scarcity of licensed spectrum to cope with the increasing demand for data rate. The LTE-Unlicensed (LTE-U) forum, aiming to tackle this problem, proposed LTE-U to operate in the 5 GHz unlicensed spectrum. However, Wi-Fi is already the consolidated technology operating in this portion of the spectrum, besides the fact that new technologies for unlicensed band need mechanisms to promote fair coexistence with the legacy ones. In this work, we extend the literature by analyzing a multi-cell LTE-U/Wi-Fi coexistence scenario, with a high interference profile and data rates targeting a cellular broadband IoT deployment. Then, we propose a centralized, coordinated reinforcement learning framework to improve LTE-U/Wi-Fi aggregate data rates. The added value of the proposed solution is assessed by a ns-3 simulator, showing improvements not only in the overall system data rate but also in average user data rate, even with the high interference of a multi-cell environment.

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Reinforcement Learning Aided UAV Base Station Location Optimization for Rate Maximization

Electronics ◽

10.3390/electronics10232953 ◽

2021 ◽

Vol 10 (23) ◽

pp. 2953

Author(s):

Sudheesh Puthenveettil Gopi ◽

Maurizio Magarini

Keyword(s):

Reinforcement Learning ◽

Clustering Algorithm ◽

Base Station ◽

Data Rate ◽

Cellular System ◽

Q Learning ◽

Data Rates ◽

In The Beginning ◽

Exploratory Movements ◽

Station Location

The application of unmanned aerial vehicles (UAV) as base station (BS) is gaining popularity. In this paper, we consider maximization of the overall data rate by intelligent deployment of UAV BS in the downlink of a cellular system. We investigate a reinforcement learning (RL)-aided approach to optimize the position of flying BSs mounted on board UAVs to support a macro BS (MBS). We propose an algorithm to avoid collision between multiple UAVs undergoing exploratory movements and to restrict UAV BSs movement within a predefined area. Q-learning technique is used to optimize UAV BS position, where the reward is equal to sum of user equipment (UE) data rates. We consider a framework where the UAV BSs carry out exploratory movements in the beginning and exploitary movements in later stages to maximize the overall data rate. Our results show that a cellular system with three UAV BSs and one MBS serving 72 UE reaches 69.2% of the best possible data rate, which is identified by brute force search. Finally, the RL algorithm is compared with a K-means algorithm to study the need of accurate UE locations. Our results show that the RL algorithm outperforms the K-means clustering algorithm when the measure of imperfection is higher. The proposed algorithm can be made use of by a practical MBS–UAV BSs–UEs system to provide protection to UAV BSs while maximizing data rate.

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A BLOCK COPROCESSOR FOR USER DATA RATE IMPROVEMENTS TO GPRS CODING SCHEME 4

Journal of Circuits System and Computers ◽

10.1142/s0218126607003848 ◽

2007 ◽

Vol 16 (04) ◽

pp. 541-551 ◽

Cited By ~ 1

Author(s):

R. SIMON SHERRATT ◽

KAI ZHANG ◽

OWEN J. WILKES

Keyword(s):

Error Detection ◽

Data Rate ◽

Mobile Internet ◽

Full Duplex ◽

Coding Scheme ◽

Coding Schemes ◽

Data Rates ◽

Error Detection And Correction ◽

Field Programmable ◽

User Data

The general packet radio service (GPRS) has been developed to allow packet data to be transported efficiently over an existing circuit-switched radio network, such as GSM. The main application of GPRS are in transporting Internet protocol (IP) datagrams from web servers (for telemetry or for mobile Internet browsers). Four GPRS baseband coding schemes are defined to offer a trade-off in requested data rates versus propagation channel conditions. However, data rates in the order of > 100 kbits/s are only achievable if the simplest coding scheme is used (CS-4) which offers little error detection and correction (EDC) (requiring excellent SNR) and the receiver hardware is capable of full duplex which is not currently available in the consumer market. A simple EDC scheme to improve the GPRS block error rate (BLER) performance is presented, particularly for CS-4, however gains in other coding schemes are seen. For every GPRS radio block that is corrected by the EDC scheme, the block does not need to be retransmitted releasing bandwidth in the channel and improving the user's application data rate. As GPRS requires intensive processing in the baseband, a viable field programmable gate array (FPGA) solution is presented in this paper.

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ТЕЛЕВІЗІЙНА КОМУНІКАЦІЯ В КОНТЕКСТІ РОЗВИТКУ СУСПІЛЬСТВА

State and Regions. Series: Social Communications ◽

10.32840/cpu2219-8741/2020.1(41).10 ◽

2020 ◽

pp. 68

Author(s):

O. Yatchuk ◽

N. Kodatska

Keyword(s):

Public Opinion ◽

Mobile Communications ◽

Mass Communication ◽

New Technologies ◽

Modern World ◽

Digital Television ◽

Broadband Internet ◽

Television Content ◽

The Usa ◽

Traditional Technologies

<div>The article presents the dynamics of the main factors for the development of modern television, the transition from traditional technologies of television broadcasting to the latest technologies. The process of developing digital television and broadband has been technically determined to address the challenge of integrating new technologies into traditional TV content. Ways of expanding the possibilities of interaction between the viewer and the television producer in the context of overcoming the crisis of traditional technologies are analyzed. It is determined that integration, development of mobile communications and Internet technologies are a hallmark of the modern world media space and have a significant influence on the formation of public opinion. The analysis of actual media researches concerning problems of the theory of mass communication is carried out. The phenomenon of «social television», which combines watching TV with simultaneous communication in social networks, is considered. The author draws attention to the disclosure of the determining factors of media communication, the study of the process of feedback from viewers on television. It is stated that the development of modern technologies, namely digital broadcasting and introduction of broadband Internet access to create a multimedia platform that combines Internet communication services and television content, contributes to improving the mechanism of interaction between the TV and the viewer. The theory of the conceptualization of journalism based on identity construction is examined: journalists understand their audience and, as members of that audience, connect with their communities. An analysis of integrated journalistic activity is conducted, which consists of three stages: gathering, editing and disseminating information. It is determined that the influence of the Internet allows the consumer to play his or her role at each stage, thus helping the journalist determine the degree of relevance of the story to himself and to the potential audience. The common features of modern telecontent were singled out and a comparative analysis of the trend of development of interactive TV programs of the USA, UK and Ukraine was made.</div>Key words: public opinion, two-vector communication, feedback, communication methods, television audience, country telephony.

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Hierarchical Reinforcement Learning Framework for Secure UAV Communication in the Presence of Multiple UAV Adaptive Eavesdroppers

2020 IEEE 6th International Conference on Computer and Communications (ICCC) ◽

10.1109/iccc51575.2020.9344970 ◽

2020 ◽

Author(s):

Liu Jue ◽

Yang Weiwei

Keyword(s):

Reinforcement Learning ◽

Hierarchical Reinforcement Learning ◽

Learning Framework

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New EAF Slag Characterization Methodology for Strategic Metal Recovery

Materials ◽

10.3390/ma14061513 ◽

2021 ◽

Vol 14 (6) ◽

pp. 1513

Author(s):

Nour-Eddine Menad ◽

Nassima Kana ◽

Alain Seron ◽

Ndue Kanari

Keyword(s):

New Technologies ◽

Slag Sample ◽

Steel Slag ◽

Analytical Techniques ◽

Added Value ◽

Metal Extraction ◽

Magnetic Fraction ◽

Magnetic Components ◽

Physico Chemical ◽

Hematite Fe2o3

The grown demand of current and future development of new technologies for high added value and strategic metals, such as molybdenum, vanadium, and chromium, and facing to the depletion of basic primary resources of these metals, the metal extraction and recovery from industrial by-products and wastes is a promising choice. Slag from the steelmaking sector contains a significant amount of metals; therefore, it must be considered to be an abundant secondary resource for several strategic materials, especially chromium. In this work, the generated slag from electric arc furnace (EAF) provided by the French steel industry was characterized by using multitude analytical techniques in order to determine the physico-chemical characteristics of the targeted slag. The revealed main crystallized phases are larnite (Ca2SiO4), magnetite (Fe3O4), srebrodolskite (Ca2Fe2O5), wüstite (FeO), maghemite (Fe2.6O3), hematite (Fe2O3), chromite [(Fe,Mg)Cr2O4], and quartz (SiO2). The collected slag sample contains about 34.1% iron (48.5% Fe2O3) and 3.5% chromium, whilst the vanadium contents is around 1500 ppm. The Mössbauer spectroscopy suggested that the non-magnetic fraction represents 42 wt% of the slag, while the remainder (58 wt%) is composed of magnetic components. The thermal treatment of steel slag up to 900 °C indicated that this solid is almost stable and few contained phases change their structures.

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Technologies and Extraction Methods of Polyphenolic Compounds Derived from Pomegranate (Punica granatum) Peels. A Mini Review

Processes ◽

10.3390/pr9020236 ◽

2021 ◽

Vol 9 (2) ◽

pp. 236

Author(s):

Dimitrios Lampakis ◽

Prodromos Skenderidis ◽

Stefanos Leontopoulos

Keyword(s):

Electric Fields ◽

Bioactive Compounds ◽

New Technologies ◽

Ohmic Heating ◽

Punica Granatum ◽

Extraction Methods ◽

Added Value ◽

Green Solvents ◽

Ultrasound Assisted Extraction ◽

Functional Product

The interest in using plant by-product extracts as functional ingredients is continuously rising due to environmental and financial prospects. The development of new technologies has led to the achievement of aqueous extracts with high bioactivity that is preferable due to organic solvents nonuse. Recently, widely applied and emerging technologies, such as Simple Stirring, Pressure-Applied Extraction, Enzymatic Extraction, Ultrasound-Assisted Extraction, Pulsed Electric Fields, High Hydrostatic Pressure, Ohmic Heating, Microwave Assistant Extraction and the use of “green” solvents such as the deep eutectic solvents, have been investigated in order to contribute to the minimization of disadvantages on the extraction of bioactive compounds. This review is focused on bioactive compounds derived from pomegranate (Punica granatum) peels and highlighted the most attractive extraction methods. It is believed that these findings could be a useful tool for the pomegranate juices industry to apply an effective and economically viable extraction process, transforming a by-product to a high added value functional product.

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A Reinforcement Learning Framework for Spiking Networks with Dynamic Synapses

Computational Intelligence and Neuroscience ◽

10.1155/2011/869348 ◽

2011 ◽

Vol 2011 ◽

pp. 1-12 ◽

Cited By ~ 3

Author(s):

Karim El-Laithy ◽

Martin Bogdan

Keyword(s):

Reinforcement Learning ◽

Spike Timing ◽

Neural Representation ◽

Model Parameters ◽

Learning Framework ◽

Reference Target ◽

Wide Range ◽

Spiking Network ◽

Dynamic Synapses ◽

Exclusive Or

An integration of both the Hebbian-based and reinforcement learning (RL) rules is presented for dynamic synapses. The proposed framework permits the Hebbian rule to update the hidden synaptic model parameters regulating the synaptic response rather than the synaptic weights. This is performed using both the value and the sign of the temporal difference in the reward signal after each trial. Applying this framework, a spiking network with spike-timing-dependent synapses is tested to learn the exclusive-OR computation on a temporally coded basis. Reward values are calculated with the distance between the output spike train of the network and a reference target one. Results show that the network is able to capture the required dynamics and that the proposed framework can reveal indeed an integrated version of Hebbian and RL. The proposed framework is tractable and less computationally expensive. The framework is applicable to a wide class of synaptic models and is not restricted to the used neural representation. This generality, along with the reported results, supports adopting the introduced approach to benefit from the biologically plausible synaptic models in a wide range of intuitive signal processing.

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