Multi-cell Systems Research Articles

System-Level Dynamic Model of Redox Flow Batteries (RFBs) for Energy Losses Analysis

This paper presents a zero-dimensional dynamic model of redox flow batteries (RFBs) for the system-level analysis of energy loss. The model is used to simulate multi-cell systems considering the effect of design and operational parameters on energy loss and overall performance. The effect and contribution of stack losses (e.g., overpotential and crossover losses) and system losses (e.g., shunt currents and pumps) to total energy loss are examined. The model is tested by using literature data from a vanadium RFB energy storage. The results show that four parameters mainly affect RFB system performance: manifold diameter, stack current, cell standard potential, and internal resistance. A reduction in manifold diameter from 60 mm to 20 mm reduced shunt current loss by a factor of four without significantly increasing pumping loss, thus boosting round-trip efficiency (RTE) by 10%. The increase in stack current at a low flow rate increases power, while the cell standard potential and internal resistance play a crucial role in influencing both power and energy output. In summary, the modeling activities enabled the understanding of critical aspects of RFB systems, thereby serving as tools for system design and operation awareness.

Right ventricular outflow tract cardiomyocytes possess distinct calcium handling and contractile properties

Abstract Funding Acknowledgements Type of funding sources: Other. Main funding source(s): Rembrandt Research Grant 2021 Background The right ventricular outflow tract (RVOT) is the predominant site of origin of idiopathic ventricular tachycardia. Idiopathic ventricular tachycardias are typically sensitive to adenosine, consistent with triggered activity caused by delayed afterdepolarizations due to intracellular Ca2+ overload. The distinct properties of RVOT myocytes explaining increased susceptibility to Ca2+ overload and triggered activity are unknown. Purpose To identify distinct RVOT electrophysiological properties including Ca2+ handling and its molecular underpinnings. Methods and Results Experiments were performed using freshly isolated cardiomyocytes from the RVOT and right ventricular (RV) apex of adult rabbit hearts. To identify distinct RVOT electrophysiological properties, we employed the perforated patch clamp methodology and measured action potentials and L-type Ca2+ current. Action potentials of RVOT myocytes were significantly shorter compared to RV myocytes, particularly at lower pacing frequency. L-type Ca2+ current peak density was significantly lower in RVOT myocytes compared to RV myocytes (34.0 ± 9.1% at 0 mV). However, voltage-dependencies of L-type Ca2+ current activation and inactivation were not statistically different. Next we measured intracellular Ca2+ transients using the fluorescent Ca2+ indicator indo-1 and found that Ca2+ transient amplitudes were significantly lower in RVOT myocytes compared to RV myocytes. To investigate a relationship between RVOT myocyte Ca2+ handling and contractility, we performed unloaded sarcomere shortening measurements using the Cytocypher Multicell system. Shortening of relaxation time following increased pacing frequency (1-2 Hz) was significantly larger for RVOT myocytes compared to RV myocytes. To identify molecular factors explaining the distinct properties of RVOT myocytes, we performed RNA-seq analysis of differential gene expression using the DESeq2 method. RVOT and RV transcriptomic profiles consisted of 30 differentially expressed genes (adjusted p ≤ 0.05). One of genes significantly higher expressed by the RV was synaptotagmin (SYT1), a Ca2+ sensor and positive regulator of the cardiac L-type Ca2+ channel through inhibition of syntaxin 1A [Ref. 1, 2]. Conclusion RVOT cardiomyocytes possess distinct electrophysiological and contractile properties, reflected by shorter action potentials, lower Ca2+ transient amplitudes, and larger shortening of relaxation time upon increasing pacing frequency. The RVOT and RV have similar but distinct gene expression signatures. Future experiments will focus on determining the role of SYT1 and other genes in regulation of the L-type Ca2+ current and Ca2+ handling in RVOT myocytes.Distinct properties of RVOT myocytes.Transcriptomic profile of the RVOT.

Maximizing profitability through cloud-enabled Reinforcement Learning for UAV coverage in real-time e-business applications

Real-time e-business applications are vital for operational efficiency, but connectivity challenges persist, particularly in remote or crowded areas. Drone Base Station (DBS) architecture, proposed for Beyond fifth Generation (B5G) and Sixth Generation (6G) multi-cell networks, offers on-demand hotspot coverage, addressing connectivity gaps in remote or crowded environments. DBSs provide a promising solution to meet the demanding requirements of high data rates, real-time responsiveness, low latency, and extended network coverage, particularly for real-time e-business applications. A critical challenge in this context involves efficiently allocating the needed number of DBSs to the different hotspot service areas, referred to as cells, to optimize the operator’s total profit under unpredictable user demands, varying area-specific service costs, and price dependence real-time e-service. The objective is to achieve the highest total revenue while minimizing the cost (cost savings) throughout the multi-cell system. This challenge is formulated as a profit-maximization discount return problem, integrating the coverage constraint, the variable cell-dependent operational cost, the e-service-based price and the uncertain demands of users across cells. Traditional optimization methods fail due to environmental uncertainty, which leads to the need to reformulate the problem as a Markov Decision Problem (MDP). We introduce a cloud-based Reinforcement Learning (RL) algorithm for DBS dispatch to address the MDP formulation. This algorithm dynamically adjusts to uncertain per-cell user distributions, considering variable operating costs and service-dependent prices across cells. Through extensive evaluation, the RL-based dispatch approach is compared with reference drone dispatch algorithms, demonstrating superior performance in maximizing operator profit through cost savings by optimizing DBS dispatch decisions based on learned user behaviors, variable operational costs, and e-service types.

Multi-cluster MIMO non-orthogonal multiple access for multi-cell systems

Multi-cluster MIMO non-orthogonal multiple access for multi-cell systems

Pilot contamination mitigation by pilot assignment and adaptive linear precoding for massive MIMO multi-cell systems

Pilot contamination mitigation by pilot assignment and adaptive linear precoding for massive MIMO multi-cell systems

Multi-Agent Reinforcement Learning Based Distributed Multi-User Scheduling and Beamforming Design in Multi-Cell Systems

Multi-Agent Reinforcement Learning Based Distributed Multi-User Scheduling and Beamforming Design in Multi-Cell Systems

Experimental and Theoretical Investigations of Shunt Currents between Alkaline Water Electrolyzers

A pivotal component of national climate strategies is the transition of the energy sector from fossil fuels to green energy. In this context, electrochemical processes will play a significant role in the future, requiring electrochemical systems with high efficiency and maximum system life cycles.1,2 For industrial electrochemical systems capital and operating costs are often reduced by a system design with a shared and circulating electrolyte supply providing ionically conductive cell-to-cell pathways. Under such conditions, parasitic ion migration occurs between adjacent cells, known as shunt currents.3,4 Shunt currents can have severe implications, such as decrease in faraday efficiency, material corrosion or interferences with instrumentation 3,5,6.To ensure high efficiency and maximum system life cycle, shunt current estimation is of high importance for the design of electrochemical multi-cell systems. A widely used approach in the literature is the theoretical, model-based approach using equivalent circuit models for shunt current determination7–9. Only a few publications demonstrate experimental determination methods 10,11. Furthermore, most shunt current studies in the field of electrochemistry focus on redox flow batteries.This work shows an innovative experimental approach for direct shunt current measurements between two alkaline water electrolysis cells with shared and circulating electrolyte feed under various conditions. The flow field design enables the insertion of reference electrodes into the flow cells for direct measurement of the potential differences between adjacent electrodes. Combined with the experimental data of the ionic tube resistances, the cell-to-cell shunt currents were accurately determined. Furthermore, an equivalent circuit model was created, fed with experimental data and validated with measured results. After successful validation the model was extended to electrolysis systems with more than two cells.Experimental data and simulations are in good agreement. The conducted experiments show the impact of temperature, cell voltage and tube manifold geometry on shunt current formation between alkaline water electrolyzers. Simulations performed are carried out to calculate shunt currents as a function of these parameters in large multi-cell systems. Furthermore, efficiency losses and corrosion processes as a result of shunt currents are estimated based on the results of this work.Literature Baños R, Manzano-Agugliaro F, Montoya FG, Gil C, Alcayde A, Gómez J. Optimization methods applied to renewable and sustainable energy: A review. Renew Sustain Energy Rev. 2011;15(4):1753-1766. doi:10.1016/j.rser.2010.12.008Yan Z, Hitt JL, Turner JA, Mallouk TE. Renewable electricity storage using electrolysis. Proc Natl Acad Sci U S A. 2020;117(23):12558-12563. doi:10.1073/pnas.1821686116Delgado NM, Monteiro R, Cruz J, Bentien A, Mendes A. Shunt currents in vanadium redox flow batteries – a parametric and optimization study. Electrochim Acta. 2022;403:139667. doi:10.1016/j.electacta.2021.139667Kaminski EA, Savinell RF. A Technique for Calculating Shunt Leakage and Cell Currents in Bipolar Stacks Having Divided or Undivided Cells. J Electrochem Soc. 1983;130(5):1103-1107. doi:10.1149/1.2119891Yin C, Guo S, Fang H, Liu J, Li Y, Tang H. Numerical and experimental studies of stack shunt current for vanadium redox flow battery. Appl Energy. 2015;151:237-248. doi:10.1016/j.apenergy.2015.04.080Pletcher D, Walsh FC. Industrial Electrochemistry. Springer Science & Business Media; 2012.Schaeffer JA, Chen L Der, Seaba JP. Shunt current calculation of fuel cell stack using Simulink®. J Power Sources. 2008;182(2):599-602. doi:10.1016/j.jpowsour.2008.04.014Wandschneider FT, Röhm S, Fischer P, Pinkwart K, Tübke J, Nirschl H. A multi-stack simulation of shunt currents in vanadium redox flow batteries. J Power Sources. 2014;261:64-74. doi:10.1016/j.jpowsour.2014.03.054Ye Q, Hu J, Cheng P, Ma Z. Design trade-offs among shunt current, pumping loss and compactness in the piping system of a multi-stack vanadium flow battery. J Power Sources. 2015;296:352-364. doi:10.1016/j.jpowsour.2015.06.138Rous̆ar I, Cezner V. Experimental Determination and Calculation of Parasitic Currents in Bipolar Electrolyzers with Application to Chlorate Electrolyzer. J Electrochem Soc. 1974;121(5):648. doi:10.1149/1.2401878Fink H, Remy M. Shunt currents in vanadium flow batteries: Measurement, modelling and implications for efficiency. J Power Sources. 2015;284:547-553. doi:10.1016/j.jpowsour.2015.03.057

단일 클러스터 다중안테나 비직교 다중접속 전송

To improve spectral efficiency in multi-cell systems, we propose a multiple-input multiple-output (MIMO) non-orthogonal multiple access technique that simultaneously transmits multiple data streams to a cluster consisting of one cell-center user and one cell-edge user. Based on the MIMO channel state of the cell-center user, the proposed technique for single-cell systems finds the receive beamforming matrix for the user and performs power allocation to its data streams. Considering the MIMO channel state of the cell-edge user and interference from the cell-center user, the technique designs the receive beamforming matrix for the cell-edge user and allocates the power for the user to its data streams. By extending this technique to multi-cell systems, the proposed technique finds the receive beamforming matrix for the cell-center user and performs power allocation to its data streams according to its MIMO channel state and interference from neighboring cells. The receive beamforming matrix design and power allocation for the cell-edge user is performed based on its MIMO channel state, interference from the cell-center user, and interference from neighboring cells. Simulation results demonstrate that the proposed technique achieves better performance than the existing techniques in terms of data rates and outage probabilities for the users.

A Novel Linear Precoder Design for Reliable UL/DL Detection in TDD Cellular Networks

The ever-growing demands on wireless connectivity, especially with the emergence of various data-intensive low-latency applications, require novel multiplexing solutions capable of reliably supporting high rates at low latency. Non-orthogonal time division duplex (TDD) coupled with multiuser detection can meet these emerging needs, provided that accurate channel state information is available. This paper proposes a new pilot-free TDD frame structure that allows designing highly effective multiuser decoders and precoders for uplink and downlink multicell systems, in an unsupervised manner. The key idea is that each user repeats and permutes its uplink data using a pre-assigned permutation code. Invoking canonical correlation analysis (CCA) at the serving BS on the two deinterleaved uplink blocks yields high quality CCA-based beamformers capable of both recovering the uplink and precoding the downlink user signals in a way that effectively mitigates interference. The paper includes a pilotless synchronization framework that leverages CCA to recover the timing and frequency offsets in an asynchronous multiuser MIMO setup, without using pilots. Simulations are used to study the performance of the proposed approach on a large-scale network with multiple users and cells, while laboratory experiments with a small-scale network of software radios are used to demonstrate that the approach works well in practice under common hardware imperfections.

A computational modeling approach for predicting multicell spheroid patterns based on signaling-induced differential adhesion.

Physiological and pathological processes including embryogenesis and tumorigenesis rely on the ability of individual cells to work collectively to form multicell patterns. In these heterogeneous multicell systems, cell-cell signaling induces differential adhesion between cells that leads to tissue-level patterning. However, the sensitivity of pattern formation to changes in the strengths of signaling or cell adhesion processes is not well understood. Prior work has explored these issues using synthetically engineered heterogeneous multicell spheroid systems, in which cell subpopulations engage in bidirectional intercellular signaling to regulate the expression of different cadherins. While engineered cell systems provide excellent experimental tools to observe pattern formation in cell populations, computational models of these systems may be leveraged to explore more systematically how specific combinations of signaling and adhesion parameters can drive the emergence of unique patterns. We developed and validated two- and three-dimensional agent-based models (ABMs) of spheroid patterning for previously described cells engineered with a bidirectional signaling circuit that regulates N- and P-cadherin expression. Systematic exploration of model predictions, some of which were experimentally validated, revealed how cell seeding parameters, the order of signaling events, probabilities of induced cadherin expression, and homotypic adhesion strengths affect pattern formation. Unsupervised clustering was also used to map combinations of signaling and adhesion parameters to these unique spheroid patterns predicted by the ABM. Finally, we demonstrated how the model may be deployed to design new synthetic cell signaling circuits based on a desired final multicell pattern.

Toward Radio Access Network Slicing Enforcement in Multi-cell 5G System

The proliferation of sophisticated applications and services comes with diverse performance requirements. The 5G cellular network is advocated to support this diversity through an end-to-end network slicing. Even-though the slicing is not a novel concept, its implementation in the RAN still remains challenging. In this article, we aim to enforce the real time 5G slicing from radio resources perspective in a multi-cell system. For that, an exact optimization model is proposed. Due to its high convergence time, heuristics are developed and evaluated with regard to the optimal model. Results are promising, as two heuristics are highly enforcing the real time RAN slicing.

CoMP-Assisted NOMA and Cooperative NOMA in Indoor VLC Cellular Systems

In this paper, we investigate the dynamic power allocation for a visible light communication (VLC) cellular system consisting of two coordinating attocells, each equipped with one access-point (AP). The coordinated multipoint (CoMP) between the two cells is introduced to assist users experiencing high inter-cell-interference (ICI). Specifically, the coordinated zero-forcing (ZF) precoding is used to cancel the ICI at the users located near the centers of the cells, whereas the joint transmission (JT) is employed to eliminate the ICI at the users located at the edge of both cells and to improve their receptions as well. Furthermore, two multiple access techniques are invoked within each cell, namely, non-orthogonal-multiple-access (NOMA) and cooperative non-orthogonal-multiple-access (C-NOMA). Hence, two multiple access techniques are proposed for the considered multi-user multi-cell system, namely, the CoMP-assisted NOMA scheme and the CoMP-assisted C-NOMA scheme. For each scheme, two power allocation frameworks are formulated each as an optimization problem, where the objective of the former is maximizing the network sum data rate while guaranteeing a certain quality-of-service (QoS) for each user, whereas the goal of the latter is to maximize the minimum data rate among all coexisting users. The formulated optimization problems are not convex, and hence, difficult to be solved directly unless using heuristic methods, which comes at the expense of high computational complexity. To overcome this issue, optimal and low complexity power allocation schemes are derived. In the simulation results, the performance of the proposed CoMP-assisted NOMA and CoMP-assisted C-NOMA schemes are compared with those of the CoMP-assisted orthogonal-multiple-access (OMA) scheme, the C-NOMA scheme and the NOMA scheme, where the superiority of the proposed schemes are demonstrated. Finally, the performance of the proposed schemes and the considered baselines is evaluated while varying various system parameters.

Stochastic fluctuations promote ordered pattern formation of cells in the Notch-Delta signaling pathway.

The Notch-Delta signaling pathway mediates cell differentiation implicated in many regulatory processes including spatiotemporal patterning in tissues by promoting alternate cell fates between neighboring cells. At the multicellular level, this "lateral inhibition” principle leads to checkerboard patterns with alternation of Sender and Receiver cells. While it is well known that stochasticity modulates cell fate specification, little is known about how stochastic fluctuations at the cellular level propagate during multicell pattern formation. Here, we model stochastic fluctuations in the Notch-Delta pathway in the presence of two different noise types–shot and white–for a multicell system. Our results show that intermediate fluctuations reduce disorder and guide the multicell lattice toward checkerboard-like patterns. By further analyzing cell fate transition events, we demonstrate that intermediate noise amplitudes provide enough perturbation to facilitate “proofreading” of disordered patterns and cause cells to switch to the correct ordered state (Sender surrounded by Receivers, and vice versa). Conversely, high noise can override environmental signals coming from neighboring cells and lead to switching between ordered and disordered patterns. Therefore, in analogy with spin glass systems, intermediate noise levels allow the multicell Notch system to escape frustrated patterns and relax towards the lower energy checkerboard pattern while at large noise levels the system is unable to find this ordered base of attraction.

Performance Evaluation of Spectral Efficiency for Uplink and Downlink Multi-Cell Massive MIMO Systems

Massive multiple-input and multiple-output (MIMO) systems have become the most persuasive technology for 5G as it increased the energy efficiency gigantically as compared to other wireless communication systems. Being the most vibrant research technology in the communication sector, this research work is based on the optimal model development of energy-efficient massive MIMO systems. The proposed model is a realistic model that augmented the spectral efficiency (SE) of massive MIMO systems where a multi-cell model scenario is considered. Channel estimation is carried out at the base stations (BSs) based on uplink (UL) transmission while the minimum mean-squared error (MMSE), Element-wise MMSE, and Least-square (LS) estimators are used for the estimation. We analyze the achievable SE of the UL based on the MMSE channel estimator with different receive combining schemes. Moreover, the downlink (DL) transmission model is also modelled with different precoding schemes by taking the same vectors used in combining schemes. The simulation results show a significant improvement in spectral efficiency by developing UL and DL transmission models and also realized that the average sum of SE per cell can be improved by optimized MMSE channel estimation, installing multiple BS antennas, and serving multiple UEs per cell. The findings of this work specify that the massive MIMO system can be developed by optimizing the channel estimation for the augmentation of SE in UL and DL transmissions. Conclusively, it can be summarized that some complex computations of MMSE channel estimators can enhance the average sum of SE per cell as per the results verified in this model.

Hybrid Beamforming for Millimeter Wave Massive MIMO under Multicell Multiuser Environment

Objectives: Due to the high cost and power consumption of fully digital beamforming, the goal of this research is to thoroughly investigate Kalman-based beamformers in conjunction with existing beamformers for multicell multiuser scenarios. Methods: In this research work, Kalman based hybrid beamforming for millimeter wave massive MIMO along with existing linear beamformers has been investigated. Mathematical model for spectral efficiency has been developed for each linear beamformer under multicell multiuser scenario and the performance has been shown using MATLAB software. Findings: Simulation results show that all the beamformers perform better for multi-cell systems compared to single cell systems i.e. better spectral efficiency for a given signal to noise ratio has been achieved for multicell system. Simulation results also depict precoders spectral efficiency improved as the number of transmitting antennas increase and the number of users decrease for multicell multiuser scenario. Novelty: Expanding the kalman based beamformer that contains digital combiner for multicell multiuser scenario is the novelty of this research. Keywords: Hybrid Beamforming; Massive MIMO; Millimeter Wave; Multicell; Multiuser

On the Energy Efficiency of Multicell Massive MIMO with Antenna Selection and Power Allocation

The energy consumption of massive multiple-input multiple-output (MIMO) systems increases with the number of antennas. Optimizing the energy efficiency (EE) of massive MIMO systems is one of the ways to achieve green communication. This paper proposes an EE optimization method that genetic algorithm-based antenna selection and power allocation (GA-AS+PA) for the downlink of a multicell massive MIMO system under the restriction of the users’ sum-rate. First, we use the genetic algorithm to determine the active transmitting antenna of each base station (BS). Then, the transmission power for each user is allocated using the convex optimization method. Finally, the EE of system is optimized under the achieved optimum BS’s transmit power and the number of active antennas. From the simulation results, the GA-AS+PA method can improve the EE of the system while meeting user sum-rate requirements, which achieves better performance compared with random antenna selection+equal power allocation method (RAS+EPA), random antenna selection+power allocation method (RAS+PA), the antenna selection method based on genetic algorithm+equal power allocation method (GA-AS+EPA), and equal power allocation (EPA) these four methods. The EE of the proposed GA-AS+PA method is improved by 33.3% compared to the EPA method.

Model-Driven Learning for Generic MIMO Downlink Beamforming With Uplink Channel Information

Accurate downlink channel information is crucial to the beamforming design, but it is difficult to obtain in practice. This paper investigates a deep learning-based optimization approach of the downlink beamforming to maximize the system sum rate, when only the uplink channel information is available. Our main contribution is to propose a model-driven learning technique that exploits the structure of the optimal downlink beamforming to design an effective hybrid learning strategy with the aim to maximize the sum rate performance. This is achieved by jointly considering the learning performance of the downlink channel, the power and the sum rate in the training stage. The proposed approach applies to generic cases in which the uplink channel information is available, but its relation to the downlink channel is unknown and does not require an explicit downlink channel estimation. We further extend the developed technique to massive multiple-input multiple-output scenarios and achieve a distributed learning strategy for multicell systems without an inter-cell signalling overhead. Simulation results verify that our proposed method provides the performance close to the state of the art numerical algorithms with perfect downlink channel information and significantly outperforms existing data-driven methods in terms of the sum rate.

Pilot Contamination Suppression Based Coordination in Multi-cell Massive MIMO Systems

Pilot Contamination Suppression Based Coordination in Multi-cell Massive MIMO Systems

Resource Optimization With Interference Coupling in Multi-RIS-Assisted Multi-Cell Systems

Deploying reconfigurable intelligent surface (RIS) to enhance wireless transmission is a promising approach. In this paper, we investigate large-scale multi-RIS-assisted multi-cell systems, where multiple RISs are deployed in each cell. Different from the full-buffer scenario, the mutual interference in our system is not known a priori, and for this reason we apply the load coupling model to analyze this system. The objective is to minimize the total resource consumption subject to user demand requirement by optimizing the reflection coefficients in the cells. The cells are highly coupled and the overall problem is non-convex. To tackle this, we first investigate the single-cell case with given interference, and propose a low-complexity algorithm based on the Majorization-Minimization method to obtain a locally optimal solution. Then, we embed this algorithm into an algorithmic framework for the overall multi-cell problem, and prove its feasibility and convergence to a solution that is at least locally optimal. Simulation results demonstrate the benefit of RIS in time-frequency resource utilization in the multi-cell system.

Ant Colony-based Optimization algorithm to overcome the pilot contamination issue within multi-cell Massive MIMO systems

the pilot contamination (PC) issue still faces and limits the promising massive MIMO (mMIMO) technology, to unleash the high benefits of mMIMO, we provide here a new decontaminating strategy, which exploits the large-scale fading coefficients' characteristics to construct a search space for the Ant colony-based optimization (ACO) algorithm. This algorithm is employed to find the best pattern in which each UE is linked to its most concurrent UEs of the adjacent cells; specifically, each UEs has an enemy UEs that if they are allocated with the same pilot, the severity of the PC upon the two UEs become subversive for the quality-of-service of the two UEs. Hence, the ACO algorithm finds for each UE its enemy UE, which leads to construct a Hamiltonian graph. This graph is exploited during the assignment of the pilot sequences to the overall UEs; specifically, the linked UEs are successively allocated with the available OPSs, which leads to address the PC problem within multi-cell mMIMO systems.