Scenario Modeling-Aided AP Placement Optimization Method for Indoor Localization and Network Access

Owing to the ubiquity of wireless networks and the popularity of WiFi infrastructures,received signal strength(RSS)-based indoor localization systems have received much attention.The placement of access points(APs)significantly influences localization accuracy and network access.However,the indoor scenario and network access are not fully considered in previous AP placement optimization methods.This study proposes a practical scenario modelingaided AP placement optimization method for improving localization accuracy and network access.In order to reduce the gap between simulation-based and field measurement-based AP placement optimization methods,we introduce an indoor scenario modeling and Gaussian process-based RSS prediction method.After that,the localization and network access metrics are implemented in the multiple objective particle swarm optimization(MOPSO)solution,Pareto front criterion and virtual repulsion force are applied to determine the optimal AP placement.Finally,field experiments demonstrate the effectiveness of the proposed indoor scenario modeling method and RSS prediction model.A thorough comparison confirms the localization and network access improvement attributed to the proposed anchor placement method.

Modelling a Learning-Based Dynamic Tree Routing Model for Wireless Mesh Access Networks

Link asymmetry in wireless mesh access networks(WMAN)of Mobile ad-hoc Networks(MANETs)is due mesh routers’transmission range.It is depicted as significant research challenges that pose during the design of network protocol in wireless networks.Based on the extensive review,it is noted that the substantial link percentage is symmetric,i.e.,many links are unidirectional.It is identified that the synchronous acknowledgement reliability is higher than the asynchronous message.Therefore,the process of establishing bidirectional link quality through asynchronous beacons underrates the link reliability of asym-metric links.It paves the way to exploit an investigation on asymmetric links to enhance network functions through link estimation.Here,a novel Learning-based Dynamic Tree routing(LDTR)model is proposed to improve network performance and delay.For the evaluation of delay measures,asymmetric link,interference,probability of transmission failure is evaluated.The proportion of energy consumed is used for monitoring energy conditions based on the total energy capacity.This learning model is a productive way for resolving the routing issues over the network model during uncertainty.The asymmetric path is chosen to achieve exploitation and exploration iteratively.The learning-based Dynamic Tree routing model is utilized to resolve the multi-objective routing problem.Here,the simulation is done with MATLAB 2020a simulation environment and path with energy-efficiency and lesser E2E delay is evaluated and compared with existing approaches like the Dyna-Q-network model(DQN),asymmetric MAC model(AMAC),and cooperative asymmetric MAC model(CAMAC)model.The simulation outcomes demonstrate that the anticipated LDTR model attains superior network performance compared to others.The average energy consump-tion is 250 J,packet energy consumption is 6.5 J,PRR is 50 bits/sec,95%PDR,average delay percentage is 20%.

0.6μm CMOS Laser Diode Driver for Optical Access Networks

Using native CMOS technology,EDA tool,and adopting full-custom design methodology,a laser diode driver for the use of STM-1 and STM-4 optical access network,is realized by CSMC-HJ 0.6μm CMOS technology to modulate laser diodes at 155Mb/s (STM-1),622Mb/s (STM-4) with adjustable modulation current from 0 to 50mA for an equivalent 50Ω load.The maximum modulation voltage is over 2.5V pp corresponding to a 3V DC bias for output stage.The time range of rise and fall from 360ps to 471ps is measured from the output voltage pulse.The RMS jitter is no more than 30ps for four bit rates.The power consumption is less than 410mW under a power supply voltage of 5V.According to the experimental results,the laser diode driver achieves the same level as their counterparts worldwide.

Hierarchical Content Caching in Fog Radio Access Networks:Ergodic Rate and Transmit Latency

In order to alleviate capacity constraints on the fronthaul and decrease the transmit latency, a hierarchical content caching paradigm is applied in the fog radio access networks(F-RANs). In particular, a specific cluster of remote radio heads is formed through a common centralized cloud at the baseband unit pool, while the local content is directly delivered at fog access points with edge cache and distributed radio signal processing capability. Focusing on a downlink F-RAN, the explicit expressions of ergodic rate for the hierarchical paradigm is derived. Meanwhile, both the waiting delay and latency ratio for users requiring a single content are exploited. According to the evaluation results of ergodic rate on waiting delay, the transmit latency can be effectively reduced through improving the capacity of both fronthaul and radio access links. Moreover, to fully explore the potential of hierarchical content caching, the transmit latency for users requiring multiple content objects is optimized as well in three content transmission cases with different radio access links. The simulation results verify the accuracy of the analysis, further show the latency decreases significantly due to the hierarchical paradigm.

The Interplay between Artificial Intelligence and Fog Radio Access Networks

The interplay between artificial intelligence(AI) and fog radio access networks(F-RANs) is investigated in this work from two perspectives: how F-RANs enable hierarchical AI to be deployed in wireless networks and how AI makes F-RANs smarter to better serve mobile devices. Due to the heterogeneity of processing capability, the cloud, fog, and device layers in F-RANs provide hierarchical intelligence via centralized, distributed, and federated learning. In addition, cross-layer learning is also introduced to further reduce the demand for the memory size of the mobile devices. On the other hand, AI provides F-RANs with technologies and methods to deal with massive data and make smarter decisions. Specifically, machine learning tools such as deep neural networks are introduced for data processing, while reinforcement learning(RL) algorithms are adopted for network optimization and decisions. Then, two examples of AI-based applications in F-RANs, i.e., health monitoring and intelligent transportation systems, are presented, followed by a case study of an RL-based caching application in the presence of spatio-temporal unknown content popularity to showcase the potential of applying AI to F-RANs.

Challenges for beyond 5G:ultra-densification of radio access network

Ultra-densification of radio access network(RAN)is a key to efficiently support the exponentially growing mobile data traffic in 5 G era.Furthermore,extremely high frequency band like mm Wave band was utilized to solve the bandwidth shortage problem.However,untra-dense reusing the same radio resource produced severe interference.And the mm Wave link was very harsh due to frequent blockage by obstacles.Therefore a new RAN architecture needed to be introduced to realize ultra-reliable communications in such a severe radio propagation environment.An architecture of distributed MIMO based RAN was presented.Then,enhanced interference coordination(e IC)was described.Finally,the effectiveness of distributed MIMO based RAN with e IC by computer simulation was showed.

Energy-efficient Scheme for Multiple Access Network Selection Using Principal Component Analysis

This paper brings forward a novel dynamic multiple access network selection scheme(NDMAS),which could achieve less energy loss and improve the poor adaptive capability caused by the variable network parameters.Firstly,a multiple access network selection mathematical model based on information theory is presented.From the perspective of information theory,access selection is essentially a process to reduce the information entropy in the system.It can be found that the lower the information entropy is,the better the system performance fulfills.Therefore,this model is designed to reduce the information entropy by removing redundant parameters,and to avoid the computational cost as well.Secondly,for model implementation,the Principal Component Analysis(PCA) is employed to process the observation data to find out the related factors which affect the users most.As a result,the information entropy is decreased.Theoretical analysis proves that system loss and computational complexity have been decreased by using the proposed approach,while the network QoS and accuracy are guaranteed.Finally,simulation results show that our scheme achieves much better system performance in terms of packet delay,throughput and call blocking probability than other currently existing ones.

A SDN-Based Energy Saving Strategy in Wireless Access Networks

This paper investigates on the base stations(BSs) sleeping control and energy saving in wireless network. The objective is to find the sleeping control and energy saving configuration between total power consumption and average video's quality. On the Software Defined Network(SDN) access network architecture, a type of sleeping control and active BSs' optimal transmitting time strategy is considered, the BS sleeps when there is no active users, and wakes up after a period of vacation time. In this paper, we study the active users grouping strategy, In order to spare more BSs into sleeping mode. Then this paper proposes an active BS transmitting time optimal strategy according to the users' Qo S. In the proposed strategy, the active BSs' transmitting time is minimized in order to save energy. This paper employs the mixed integer-programming model to present this optimization problem. Then we utilized a novel algorithm to save the energy in access networks and also meet the Qo S requirements. Both the analytical and simulation results show that the algorithm can effectively save energy in the access network BSs.

Joint Resource Allocation and Admission Control in Sliced Fog Radio Access Networks

Network slicing based fog radio access network(F-RAN) has emerged as a promising architecture to support various novel applications in 5 G-and-beyond wireless networks. However, the co-existence of multiple network slices in F-RANs may lead to significant performance degradation due to the resource competitions among different network slices. In this paper, the downlink F-RANs with a hotspot slice and an Internet of Things(Io T) slice are considered, in which the user equipments(UEs) of different slices share the same spectrum. A novel joint resource allocation and admission control scheme is developed to maximize the number of UEs in the hotspot slice that can be supported with desired quality-of-service, while satisfying the interference constraint of the UEs in the Io T slice. Specifically, the admission control and beamforming vector optimization are performed in the hotspot slice to maximize the number of admitted UEs, while the joint sub-channel and power allocation is performed in the Io T slice to maximize the capability of the UEs in the Io T slice tolerating the interference from the hotspot slice. Numerical results show that our proposed scheme can effectively boost the number of UEs in the hotspot slice compared to the existing baselines.

An Offloading Scheme Leveraging on Neighboring Node Resources for Edge Computing over Fiber-Wireless (FiWi) Access Networks

The computation resources at a single node in Edge Computing(EC)are commonly limited,which cannot execute large scale computation tasks.To face the challenge,an Offloading scheme leveraging on NEighboring node Resources(ONER)for EC over Fiber-Wireless(FiWi)access networks is proposed in this paper.In the ONER scheme,the FiWi network connects edge computing nodes with fiber and converges wireless and fiber connections seamlessly,so that it can support the offloading transmission with low delay and wide bandwidth.Based on the ONER scheme supported by FiWi networks,computation tasks can be offloaded to edge computing nodes in a wider range of area without increasing wireless hops(e.g.,just one wireless hop),which achieves low delay.Additionally,an efficient Computation Resource Scheduling(CRS)algorithm based on the ONER scheme is also proposed to make offloading decision.The results show that more offloading requests can be satisfied and the average completion time of computation tasks decreases significantly with the ONER scheme and the CRS algorithm.Therefore,the ONER scheme and the CRS algorithm can schedule computation resources at neighboring edge computing nodes for offloading to meet the challenge of large scale computation tasks.

A DYNAMIC SPECTRUM ACCESS NETWORK BASED ON COGNITIVE RADIO

Dynamic spectrum access technologies based on Cognitive Radio(CR) is under intensive research carried out by the wireless communication society and is expected to solve the problem of spectrum scarcity.However,most enabling technologies related to dynamic spectrum access are con-sidered individually.In this paper,we consider these key technologies jointly and introduce a new implementation scheme for a Dynamic Spectrum Access Network Based on Cognitive Radio(DSAN-BCR).We start with a flexible hardware platform for DSAN-BCR,as well as a flexible protocol structure that dominates the operation of DSAN-BCR.We then focus on the state of the art of key technologies such as spectrum sensing,spectrum resources management,dynamic spectrum access,and routing that are below the network layer in DSAN-BCR,as well as the development of technologies related to higher layers.Last but not the least,we analyze the challenges confronted by these men-tioned technologies in DSAN-BCR,and give the perspectives on the future development of these technologies.The DSAN-BCR introduced is expected to provide a system level guidance to alleviate the problem of spectrum scarcity.

Accessing Multi-Source Geological Data through Network in MORPAS Software System

MORPAS is a special GIS (geographic information system) software system, based on the MAPGIS platform whose aim is to prospect and evaluate mineral resources quantificationally by synthesizing geological, geophysical, geochemical and remote sensing data. It overlays geological database management, geological background and geological abnormality analysis, image processing of remote sensing and comprehensive abnormality analysis, etc.. It puts forward an integrative solution for the application of GIS in basic-level units and the construction of information engineering in the geological field. As the popularization of computer networks and the request of data sharing, it is necessary to extend its functions in data management so that all its data files can be accessed in the network server. This paper utilizes some MAPGIS functions for the second development and ADO (access data object) technique to access multi-source geological data in SQL Server databases. Then remote visiting and congruous management will be realized in the MORPAS system.

Joint Design of Coalition Formation and Semi-Blind Channel Estimation in Fog Radio Access Networks

Coordinated signal processing can obtain a huge transmission gain for Fog Radio Access Networks(F-RANs).However,integrating into large scale,it will lead to high computation complexity in channel estimation and spectral efficiency loss in transmission performance.Thus,a joint cluster formation and channel estimation scheme is proposed in this paper.Considering research remote radio heads(RRHs)centred serving scheme,a coalition game is formulated in order to maximize the spectral efficiency of cooperative RRHs under the conditions of balancing the data rate and the cost of channel estimation.As the cost influences to the necessary consumption of training length and estimation error.Particularly,an iterative semi-blind channel estimation and symbol detection approach is designed by expectation maximization algorithm,where the channel estimation process is initialized by subspace method with lower pilot length.Finally,the simulation results show that a stable cluster formation is established by our proposed coalition game method and it outperforms compared with full coordinated schemes.

Channel Estimation for Full-Duplex Relay Transmission in Cloud Radio Access Networks

Relay in full-duplex(FD) mode can achieve higher spectrum efficiency than that in half-duplex mode,while it is crucial to suppress relay self-interference to ensure transmission quality which requires instantaneous channel state information(CSI). In this paper,the channel estimation issue in FD amplify-andforward relay networks is considered,where the training-based estimation technique is adopted. Firstly,the least square(LS) estimation is implemented to obtain composite channel coefficients of source-relay-destination(SRD) channel and relay loop-interference(LI) channel in order to assist destination in performing data detection. Secondly,both LS and maximum likelihood estimation methods are utilized to perform individual channel estimation aiming at supporting successive interference cancelation at destination. Finally,simulation results demonstrate the effectiveness of both composite and individual channel estimation,and the presented ML method can achieve lower MSEs than LS solution.

Deep Learning Based Channel Estimation in Fog Radio Access Networks

As a promising paradigm of the fifth generation networks,fog radio access network(F-RAN)has attracted lots of attention nowadays.To fully utilize the promising gain of F-RANs,the acquisition of accurate channel state information is significant.However,conventional channel estimation approaches are not suitable in F-RANs due to the large training and feedback overhead.In this paper,we consider the channel estimation in F-RANs with fog access point(F-AP)equipped with massive antennas.Thanks to the computing ability of F-AP and the sparsity of channel matrices in angular domain,Gated Recurrent Unit(GRU),a data-driven based channel estimation is proposed at F-AP to reduce the training and feedback overhead.The GRU-based method can capture the hidden sparsity structure automatically through the network training.Moreover,to further improve the channel estimation,a bidirectional GRU based method is proposed,whose target channel structure is decided by previous and subsequent structures.We compare the performance of our proposed channel estimation with traditional methods(Orthogonal Matching Pursuit(OMP)and Simultaneous OMP(SOMP)).Simulation results show that the proposed approaches have better performance compared with the traditional OMP and SOMP methods.

Theory Analysis of the Handover Challenge in Express Train Access Networks （ETAN）

To handle the handover challenge in Express Train Access Networks(ETAN).mobility fading effects in high speed railway environments should be addressed first.Based on the investigation of fading effects in this paper,we obtain two theoretical bounds:HOTiming upper bound and HO-Margin lower bound,which are helpful guidelines to study the handover challenge today and in the future.Then,we apply them to analyze performance of conventional handover technologies and our proposal in ETAN.This follow-up theory analyses and simulation experiment results demonstrate that the proposed handover solution can minimize handover time up to 4ms(which is the fastest one so far),and reduce HO-Margin to 0.16 dB at a train speed of 350km/h.

A Dynamic Distributed Spectrum Allocation Mechanism Based on Game Model in Fog Radio Access Networks

With the explosive growth of highspeed wireless data demand and the number of mobile devices, fog radio access networks(F-RAN) with multi-layer network structure becomes a hot topic in recent research. Meanwhile, due to the rapid growth of mobile communication traffic, high cost and the scarcity of wireless resources, it is especially important to develop an efficient radio resource management mechanism. In this paper, we focus on the shortcomings of resource waste, and we consider the actual situation of base station dynamic coverage and user requirements. We propose a spectrum pricing and allocation scheme based on Stackelberg game model under F-RAN framework, realizing the allocation of resource on demand. This scheme studies the double game between the users and the operators, as well as between the traditional operators and the virtual operators, maximizing the profits of the operators. At the same time, spectrum reuse technology is adopted to improve the utilization of network resource. By analyzing the simulation results, it is verified that our proposed scheme can not only avoid resource waste, but also effectively improve the operator's revenue efficiency and overall network resource utilization.

Semi-Blind Pilot-Aided Channel Estimation in Uplink Cloud Radio Access Networks

In this paper, a quasi-Newton method fbr semi-blind estimation is derived for channel estimation in uplink cloud radio access networks （C-RANs）. Different from traditional pilot-aided estimation, semiblind estimation utilizes the unknown data symbols in addition to the known pilot symbols to estimate the channel. An initial channel state information （CSI） obtained by least-squared （LS） estimation is needed in semi-blind estimation. BFGS （Brayben, Fletcher, Goldfarb and Shanno） algorithm, which employs data as well as pilot symbols, estimates the CSI though solving the problem provided by maximum-likelihood （ML） principle. In addition, mean-square-error （MSE） used to evaluate the estimation performance can be further minimized with an optimal pilot design. Simulation results show that the semi-blind estimation achieves a significant improvement in terms of MSE performance over the conventional LS estimation by utilizing data symbols instead of increasing the number of pilot symbols, which demonstrates the estimation accuracy and spectral efficiency are both improved by semiblind estimation for C-RANs.

Spatial Accessibility of Road Network in Wuhan Metropolitan Area Based on Spatial Syntax

Based on space syntax theory, the spatial accessibility of the road network in Wuhan Metropolitan Area has been quantitatively analyzed by building a series of accessibility variables. Topologic connectivity in the accessible rings appears to be broken;traffic axis network is in spatial structure of hub-and-spoke and fishbone-like. Meanwhile, the differences in classified road network have led to inefficiency of its network servo and its ever-worsening capability to respond to traffic jams. Besides, two band-like integrated cores of which one is east to west along the Yangtze River and the other is north to south along Beijing to Guangzhou Railway, have become the first level traffic axis in the whole network, which is responsible for the connectivity of the entire metropolitan area network. This consequently has strengthened the dominant position of Wuhan which is located on the bands’ crossing. In short, the spatial accessibility of that classified space morphology, the urban system, the transport infrastructure as well as the social and economic development of Wuhan Metropolitan Area are highly interrelated to each other, especially to the high level highway network featured by freeways, the development level of which is well in line with that of road network accessibility.

Regional accessibility of land traffic network in the Yangtze River Delta

In a given district, the accessibility of any point should be the synthetically evaluation of the internal and external accessibilities. Using MapX component and Delphi, the author presents an information system to calculate and analyze regional accessibility according to the shortest travel time, generating thus a mark diffusing figure. Based on land traffic network, this paper assesses the present and the future regional accessibilities of sixteen major cities in the Yangtze River Delta. The result shows that the regional accessibility of the Yangtze River Delta presents a fan with Shanghai as its core. The top two most accessible cities are Shanghai and Jiaxing, and the bottom two ones are Taizhou （Zhejiang province） and Nantong With the construction of Sutong Bridge, Hangzhouwan Bridge and Zhoushan Bridge, the regional internal accessibility of all cities will be improved. Especially for Shaoxing, Ningbo and Taizhou （Jiangsu province）, the regional internal accessibility will be decreased by one hour, and other cities will be shortened by about 25 minutes averagely. As the construction of Yangkou Harbor in Nantong, the regional external accessibility of the harbor cities in Jiangsu province will be speeded up by about one hour.