RIS-Assisted Federated Learning in Multi-Cell Wireless Networks

Over-the-air computation(AirComp)based federated learning(FL)has been a promising technique for distilling artificial intelligence(AI)at the network edge.However,the performance of AirComp-based FL is decided by the device with the lowest channel gain due to the signal alignment property.More importantly,most existing work focuses on a single-cell scenario,where inter-cell interference is ignored.To overcome these shortages,a reconfigurable intelligent surface(RIS)-assisted AirComp-based FL system is proposed for multi-cell networks,where a RIS is used for enhancing the poor user signal caused by channel fading,especially for the device at the cell edge,and reducing inter-cell interference.The convergence of FL in the proposed system is first analyzed and the optimality gap for FL is derived.To minimize the optimality gap,we formulate a joint uplink and downlink optimization problem.The formulated problem is then divided into two separable nonconvex subproblems.Following the successive convex approximation(SCA)method,we first approximate the nonconvex term to a linear form,and then alternately optimize the beamforming vector and phase-shift matrix for each cell.Simulation results demonstrate the advantages of deploying a RIS in multi-cell networks and our proposed system significantly improves the performance of FL.

New Approach of Multi-Cell Stacked Cell Inverter for Solar Photovoltaic System

In this paper, a new inverter topology dedicated to isolated or grid-connected PV systems is proposed. This inverter is based on the structures of a stacked multi-cell converter (SMC) and an H-bridge. This new topology has allowed the voltage stresses of the converter to be distributed among several switching cells. Secondly, divide the input voltage into several fractions to reduce the number of power semiconductors to be switched. In this contribution, the general topology of this micro-inverter has been described and the simulation tests developed to validate its operation have been presented. Finally, we discussed the simulation results, the efficiency of this topology and the feasibility of its use in a grid-connected photovoltaic production system.

Evaluation of Linear Precoding Schemes for Cooperative Multi-Cell MU MIMO in Future Mobile Communication Systems

In Mobile Communication Systems, inter-cell interference becomes one of the challenges that degrade the system’s performance, especially in the region with massive mobile users. The linear precoding schemes were proposed to mitigate interferences between the base stations (inter-cell). These schemes are categorized into linear and non-linear;this study focused on linear precoding schemes, which are grounded into three types, namely Zero Forcing (ZF), Block Diagonalization (BD), and Signal Leakage Noise Ratio (SLNR). The study included the Cooperative Multi-cell Multi Input Multi Output (MIMO) System, whereby each Base Station serves more than one mobile station and all Base Stations on the system are assisted by each other by shared the Channel State Information (CSI). Based on the Multi-Cell Multiuser MIMO system, each Base Station on the cell is intended to maximize the data transmission rate by its mobile users by increasing the Signal Interference to Noise Ratio after the interference has been mitigated due to the usefully of linear precoding schemes on the transmitter. Moreover, these schemes used different approaches to mitigate interference. This study mainly concentrates on evaluating the performance of these schemes through the channel distribution models such as Ray-leigh and Rician included in the presence of noise errors. The results show that the SLNR scheme outperforms ZF and BD schemes overall scenario. This implied that when the value of SNR increased the performance of SLNR increased by 21.4% and 45.7% for ZF and BD respectively.

Handover initiation performance of a new multi-cell cellular configuration with a developed base-station multi-beam antenna

A new multi-cell cellular configuration networks is provided for analysis of handover initiation probability, which is based on multi-beam base-station antenna splitting in the elevation-radiating plane. The sum of the received signal power in the mobile station, including both desired and interference signal power, has been introduced into the handover initiation algorithm. Along with the idea, we present three models of handover initiation algorithm with the shadowing process of Gaussian distribution. The formulation of handover initiation probability of those algorithms is also analyzed. The validity of the presented models has been checked through the comparison with simulation results. The results present the performance characteristics of handover initiation vary with cluster number and base-station antenna elevation angle.

Beam interference suppression in multi-cell millimeter wave communications

Due to the increase in the number of users, beam switching is used for suppressing interference, which leads to higher computational complexity in multi-cell millimeter wave communications. In order to resolve this problem, a beam interference model is introduced, and a lower complexity beam interference suppression algorithm based on user grouping is proposed. The proposed algorithm operates beam switching and mnlti-cell cooperative transmission for a part of the users when there exists beam interference due to high user density. In particular, considering the distinct interference suffered by each user, the proposed dual-threshold user grouping method can effectively solve the frequent switching problem at the base station caused by multi-cell cooperative transmission in multi-cell environments. Simulation results show that the proposed algorithm can reduce the computational complexity of beam switching and approach ideal system capacity, compared with conventional interference suppression algorithms that do not involve grouping of users.

Resource provisioning for computation and communication in multi-cell wireless networks

The convergence of computation and communication at network edges plays a significant role in coping with computation-intensive and delay-critical tasks.During the stage of network planning,the resource provisioning problem for edge nodes has to be investigated to provide prior information for future system configurations.This work focuses on how to quantify the computation capabilities of access points at network edges when provisioning resources of computation and communication in multi-cell wireless networks.The problem is formulated as a discrete and non-convex minimization problem,where practical constraints including delay requirements,the inter-cell interference,and resource allocation strategies are considered.An iterative algorithm is also developed based on decomposition theory and fractional programming to solve this problem.The analysis shows that the necessary computation capability needed for certain delay guarantee depends on resource allocation strategies for delay-critical tasks.For delay-tolerant tasks,it can be approximately estimated by a derived lower bound which ignores the scheduling strategy.The efficiency of the proposed algorithm is demonstrated using numerical results.

Multi Multi-Cell Uplink Interference Management Cell Uplink Interference Management: A Distributed Power Control Method

This paper investigates a multi-cell uplink network,where the orthogonal frequency division multiplexing(OFDM)protocol is considered to mitigate the intra-cell interference.An optimization problem is formulated to maximize the user sup-porting ratio for the uplink multi-cell system by optimizing the transmit power.This paper adopts the user supporting ratio as the main performance metric.Our goal is to improve the user supporting ratio of each cell.Since the formulated optimization problem is non-convex,it cannot be solved by using traditional convex-based optimi-zation methods.Thus,a distributed method with low complexity and a small amount of multi-cell interaction is proposed.Numerical results show that a notable perfor-mance gain achieved by our proposed scheme compared with the traditional one is without inter-cell interaction.

Resource Allocation for Channel Estimation in Reconfigurable Intelligent Surface-Aided Multi-Cell Networks

Reconfigurable intelligent surface(RIS)is a promising solution to deal with the blockage-sensitivity of millimeter wave band and reduce the high energy consumption caused by network densification. However, deploying large scale RISs may not bring expected performance gain due to significant channel estimation overhead and non-negligible reflected interference.In this paper,we derive the analytical expressions of the coverage probability, area spectrum efficiency(ASE)and energy efficiency (EE)of a downlink RIS-aided multi-cell network.In order to optimize the network performance, we investigate the conditions for the optimal number of training symbols of each antenna-to-antenna and antenna-to-element path (referred to as the optimal unit training overhead) in channel estimation.Our study shows that:1)RIS deployment is not“the more, the better”, only when blockage objects are dense should one deploy more RISs;2) the coverage probability is maximized when the unit training overhead is designed as large as possible;3)however,the ASE-and-EE-optimal unit training overhead exists. It is a monotonically increasing function of the frame length and a monotonically decreasing function of the average signal-to-noise-ratio (in the high signal-to-noise-ratio region). Additionally,the optimal unit training overhead is smaller when communication nodes deploy particularly few or many antennas.

Classification of Human Protein in Multiple Cells Microscopy Images Using CNN

The subcellular localization of human proteins is vital for understanding the structure of human cells.Proteins play a significant role within human cells,as many different groups of proteins are located in a specific location to perform a particular function.Understanding these functions will help in discoveringmany diseases and developing their treatments.The importance of imaging analysis techniques,specifically in proteomics research,is becoming more prevalent.Despite recent advances in deep learning techniques for analyzing microscopy images,classification models have faced critical challenges in achieving high performance.Most protein subcellular images have a significant class imbalance.We use oversampling and under sampling techniques in this research to overcome this issue.We have used a Convolutional Neural Network(CNN)model called GapNet-PL for the multi-label classification task on the Human Protein Atlas Classification(HPA)Dataset.Authors have found that the ParametricRectified LinearUnit(PreLU)activation function is better than the Scaled Exponential LinearUnit(SeLU)activation function in the GapNet-PL model in most classification metrics.The results showed that the GapNet-PL model with the PReLU activation function achieved an area under the ROC curve(AUC)equal to 0.896,an F1 score of 0.541,and a recall of 0.473.

Energy efficiency optimization for a RIS-assisted multi-cell communication system based on a practical RIS power consumption model

Reconfigurable intelligent surface(RIS)is widely accepted as a potential technology to assist in communication between base stations(BSs)and users in edge areas.We study the energy efficiency of a RIS-assisted multi-cell communication system with a realistic RIS power consumption model.With the goal of maximizing the energy efficiency of the system,we optimize the transmit beamforming vectors at the BS and the RIS phase shift matrix by a proposed alternative optimization algorithm.First,the transmit beamforming vector is optimized by solving the transformed weighted minimum mean square error(WMMSE)problem.Subsequently,to solve the inconvenience incurred by the discrete relationship between the RIS reflecting unit power consumption and its discrete phase shift,we use a continuous function to approximate their relationship.With this approximation,we can use the majorization minimization(MM)technique to optimize the continuous RIS phase shifts,and then quantize the obtained phase shifts to discrete ones.Simulation results demonstrate that the energy efficiency of the system is effectively optimized by the proposed algorithm.

北京市东南郊再生水灌区地下水多环芳烃污染风险评估

为研究污染物随再生水进入地下环境后其迁移衰减情况及对地下水的潜在危害性,以Multi-cell模型为基础,结合污染物质量守恒、在水土中吸附再分配、生物降解等机理,针对地下水污染风险评估构建了计算污染物随水在土壤剖面的垂向迁移衰减一维模型,并以北京通州大兴再生水灌区为研究区域,以再生水中持久性有机污染物多环芳烃萘和菲为研究对象,根据当地钻孔资料及灌溉水水质、地下水水质资料,应用该模型进行试算。结果表明,经过多年连续灌溉后,通州大兴大部分地区进入潜水含水层的萘、菲浓度较低,整体污染风险较低,仅在通州区潞城镇等个别地区萘、菲浓度较高,应引起重视;由于大兴区整体包气带较厚,其污染风险低于通州区。土壤粘土层是萘、菲积累的主要层位,其吸附容量远大于细砂等粗颗粒介质,在土壤表层低环多环芳烃迁移性更强。应用这一模型,能够较为宏观地掌握通州大兴再生水灌区不同区域地下水中多环芳烃萘和菲的污染风险差异。

多小区OFDMA网络中用于有效无线资源分配的ICI管理技术(英文)

Two Inter-cell Interference (ICI) management algorithms: Primary Interference Balancing (PIB) algorithm and Interfering Bits Loading Avoidance (IBLA) algorithm are proposed for canceling the ICI effects which the existing efficient radio resource allocation algorithms do not consider. The efficient radio resource allocation algorithm, i.e., Pre-assignment and Reassignment (PR) algorithm, obtains the lowest complexity and achieves good throughput performance in single cell OFDMA system. However, in multi-cell multi-sector OFDMA networks, PR algorithm is not applicable because it does not take ICI into consideration. The proposed PIB algorithm balances the number of loading bits for the desired User Equipment (UE) and the major interfering UE, as well as optimizes the SINR performance; meanwhile, IBLA avoids loading certain number of interfering bits which would make SINR unqualified. Simulations confirm the ICI management effectiveness and feasibility of both the proposals.

System utility based resource allocation for multi-cell OFDM system

Utility based resource allocation strategy in multi-cell orthogonal frequency-division multiplexing （OFDM） system plays a critical role in next generation mobile communication systems. Based on the analysis of risk aversion utility functions, this article proposed the system utility based utility, which is named the customer satisfaction （CS） utility. Compared with the proportional fairness （PF） utility, the CS utility reflects the user demands better, and enables the system to adjust its resource allocation according to both the traffic requirements and the resource situation.

Distributed interference suppression scheme for multi-cell uplink OFDMA system based on non-cooperative game

In this paper,a distributed interference suppression scheme is proposed for multi-cell uplink orthogonal frequency division multiple access（OFDMA） system.Firstly,we model resource allocation process as a non-cooperative game.Then we show the concept of Nash equilibrium（NE） and investigate its existence and uniqueness in detail.To enhance the performance of multi-cell OFDMA system,the further improvement process based on NE is given.Several adjustable parameters are set to make the system achieve different tradeoffs between the total capacity and complexity.Simulation results show that the proposed scheme can greatly improve the system performance comparing with conventional scheme.

Adaptive bit allocation scheme in multi-cell cooperative block diagonalization systems with delayed and limited feedback

In multi-cell cooperative multi-input multi-output （MIMO） systems, base station （BS） can exchange and utilize channel state information （CSI） of adjacent cell users to manage co-channel interference. Users quantize the CSIs of desired channel and interference channels using finite-rate feedback links, then BS can generate cooperative block diagonalization （BD） precoding matrices using the obtained quantized CSI at transmitter to supress co-channel interference. In this paper, a novel adaptive bit allocation scheme is proposed to minimize the rate loss due to imperfect CSI. We derive the closed-form expression of rate loss caused by both channel delay and limited feedback. Based on the derived rate loss expression, the proposed scheme can adaptively allocate more bits to quantize the better channels with smaller delays and fewer bits to worse channels with larger delays. Simulation results show that the proposed scheme yields higher performance than other allocation schemes.

Review of Crashworthiness Studies on Cellular Structures

The application of lightweight structures with excellent energy absorption performance is crucial for enhancing vehicle safety and energy efficiency.Cellular structures,inspired by the characteristics observed in natural organisms,have exhibited exceptional structural utilization in terms of energy absorption compared with traditional structures.In recent years,various innovative cellular structures have been proposed to meet different engineering needs,resulting in significant performance improvements.This paper provides a comprehensive overview of novel cellular structures for energy absorption applications.In particular,it outlines the application forms and design concepts of cellular structures under typical loading conditions in vehicle collisions,including axial loading,oblique loading,bending loading,and blast loading.Cellular structures have evolved to meet the demands of complex loading conditions and diverse research methods,focusing on achieving high-performance characteristics across multiple load cases.Moreover,this review discusses manufacturing techniques and strate-gies for enhancing the manufacturing performance of cellular structures.Finally,current key challenges and future research directions for cellular structures are discussed.The aim of this study is to provide valuable guidelines for researchers and engineers in the development of next-generation lightweight cellular structures.

Deep reinforcement learning for dynamic computation offloading and resource allocation in cache-assisted mobile edge computing systems

Mobile Edge Computing(MEC)is one of the most promising techniques for next-generation wireless communication systems.In this paper,we study the problem of dynamic caching,computation offloading,and resource allocation in cache-assisted multi-user MEC systems with stochastic task arrivals.There are multiple computationally intensive tasks in the system,and each Mobile User(MU)needs to execute a task either locally or remotely in one or more MEC servers by offloading the task data.Popular tasks can be cached in MEC servers to avoid duplicates in offloading.The cached contents can be either obtained through user offloading,fetched from a remote cloud,or fetched from another MEC server.The objective is to minimize the long-term average of a cost function,which is defined as a weighted sum of energy consumption,delay,and cache contents’fetching costs.The weighting coefficients associated with the different metrics in the objective function can be adjusted to balance the tradeoff among them.The optimum design is performed with respect to four decision parameters:whether to cache a given task,whether to offload a given uncached task,how much transmission power should be used during offloading,and how much MEC resources to be allocated for executing a task.We propose to solve the problems by developing a dynamic scheduling policy based on Deep Reinforcement Learning(DRL)with the Deep Deterministic Policy Gradient(DDPG)method.A new decentralized DDPG algorithm is developed to obtain the optimum designs for multi-cell MEC systems by leveraging on the cooperations among neighboring MEC servers.Simulation results demonstrate that the proposed algorithm outperforms other existing strategies,such as Deep Q-Network(DQN).

Improved resource allocation strategy in SU-CoMP network

Coordinated multi-point transmission and reception （CoMP） for single user, named as SU-CoMP, is considered as an efficient approach to mitigate inter-cell interference in orthogonal frequency division multiple access （OFDMA） systems. Two prevalent approaches in SU-CoMP are coordinated scheduling （CS） and joint processing （JP）. Although JP in SU-CoMP has been proved to achieve a great link performance improvement for the cell-edge user, efficient resource allocation （RA） on the system level is quite needed. However, so far limited work has been done considering JP, and most existing schemes achieved the improvement of cell-edge performance at cost of the cell-average performance degradation compared to the single cell RA. In this paper, a two-phase strategy is proposed for SU-CoMP networks. CS and JP are combined to improve both cell-edge and cell-average performance. Compared to the single cell RA, simulation results demonstrate that, the proposed strategy leads to both higher cell-average and cell-edge throughput.