Source localization based on field signatures:Laboratory ultrasonic validation

Location awareness in wireless networks is essential for emergency services,navigation,gaming,and many other applications.This article presents a method for source localization based on measuring the amplitude-phase distribution of the field at the base station.The existing scatterers in the target area create unique scattered field interference at each source location.The unique field interference at each source location results in a unique field signature at the base station which is used for source localization.In the proposed method,the target area is divided into a grid with a step of less than half the wavelength.Each grid node is characterized by its field signature at the base station.Field signatures corresponding to all nodes are normalized and stored in the base station as fingerprints for source localization.The normalization of the field signatures avoids the need for time synchronization between the base station and the source.When a source transmits signals,the generated field signature at the base station is normalized and then correlated with the stored fingerprints.The maximum correlation value is given by the node to which the source is the closest.Numerical simulations and results of experiments on ultrasonic waves in the air show that the ultrasonic source is correctly localized using broadband field signatures with one base station and without time synchronization.The proposed method is potentially applicable for indoor localization and navigation of mobile robots.

Multibeam Antenna Based on Butler Matrix for 3G/LTE/5G/B5G Base Station Applications

With the rapid development of mobile communication technology and the explosion of data traffic,high capacity communication with high data transmission rate is urgently needed in densely populated areas.Since multibeam antennas are able to increase the communication capacity and support a high data transmission rate,they have attracted a lot of research interest and have been actively investigated for base station applications.In addition,since multi-beam antennas based on Butler matrix(MABBMs)have the advantages of high gain,easy design and low profile,they are suitable for base station applications.The purposes of this paper is to provide an overview of the existing MABBMs.The specifications,principles of operation,design method and implementation of MABBMs are presented.The challenge of MABBMs for 3G/LTE/5G/B5G base station applications is discussed in the end.

Rule Based Collector Station Selection Scheme for Lossless Data Transmission in Underground Sensor Networks

There are fundamentally two different communication media in wireless underground sensor networks. The first of these is a solid medium where the sensor nodes are buried underground and wirelessly transmit data from underground to aboveground. The second is an underground medium such as tunnel, cave etc. and the data is transmitted from underground to the aboveground through partially solid medium. The quality of communication is greatly influenced by the humidity of the soil in both environments. The placement of wireless underground sensor nodes at hard-to-reach locations makes energy efficient work compulsory. In this paper, rule based collector station selection scheme is proposed for lossless data transmission in underground sensor networks. In order for sensor nodes to transmit energy-efficient lossless data, rulebased selection operations are carried out with the help of fuzzy logic. The proposed wireless underground sensor network is simulated using Riverbed software, and fuzzy logic-based selection scheme is implemented utilizing Matlab software. In order to evaluate the performance of the sensor network;the parameters of delay, throughput and energy consumption are investigated. Examining performance evaluation results, it is seen that average delay and maximum throughput are accomplished in the proposed underground sensor network. Under these conditions, it has been shown that the most appropriate collector station selection decision is made with the aim of minimizing energy consumption.

Location correction technique based on mobile communication base station for earthquake population heat map

China has mobile phone penetration rate of over 96.2%.Mobile phone has become the largest Internet terminal for Chinese Internet users.Population geographic distribution in earthquake zones can be got based on mobile phone positioning and map matching.For reducing earthquake black-box stage,we propose a real-time collection,correction and schedule algorithm of population position data by four stream processing environments(Redis,Hbase,Kafka,and Spark Streaming)in this paper.For labeling precisely population geographic distribution on the network map,matching of population geographic coordinates and map coordinates are optimized by sample comparison based on location data of mobile communication base stations and prefecture level cities.The test result shows the proposed system is high efficient and can rapidly respond to any emerging parallel tasks during the earthquake.A high-precision heat map of affected population can be produced and published on-line within 2 min after the devastating earthquake happened.

SMDP-based sleep policy for base stations in heterogeneous cellular networks

A dense heterogeneous cellular network can effectively increase the system capacity and enhance the network coverage.It is a key technology for the new generation of the mobile communication system.The dense deployment of small base stations not only improves the quality of network service,but also brings about a significant increase in network energy consumption.This paper mainly studies the energy efficiency optimization of the Macro-Femto heterogeneous cellular network.Considering the dynamic random changes of the access users in the network,the sleep process of the Femto Base Stations(FBSs)is modeled as a Semi-Markov Decision Process(SMDP)model in order to save the network energy consumption.And further,this paper gives the dynamic sleep algorithm of the FBS based on the value iteration.The simulation results show that the proposed SMDP-based adaptive sleep strategy of the FBS can effectively reduce the network energy consumption.

Optimal Positions of Relay Stations for Cluster-Based Two-Hop Cellular Network

Multihop cellular networks is an exciting and a fledgling area of wireless communication which offers huge potential in terms of coverage enhancement, data-rates, power reduction, and various other quality of service improvements. However, resource allocation in MCN is an NP-hard problem. Hence, significant research needs to be done in this field in order to efficiently design the radio network. In this paper, optimal position of relay stations in a hierarchical cluster-based two-hop cellular network is investigated. Vector algebra has been used to derive general equation for carrier-to-interference ratio (C/I) of a mobile station. It has been observed that when the transmit power of base station (BS) and the gateway (GTW)/relay station (RS) are same, the RSs should be located close to mid-point of BS and the edge of the cell. However, significantly, when the transmit power of the BS is greater than that of the GTW, then the RSs should be placed closer to the edge of the cell, in order to maximize the minimum C/I at any point in the cell. This in turn results in higher modulation technique at the physical layer, and hence, a higher data-rate to all the users in the system.

A Trajectory Prediction Based Intelligent Handover Control Method in UAV Cellular Networks

The airborne base station(ABS) can provide wireless coverage to the ground in unmanned aerial vehicle(UAV) cellular networks.When mobile users move among adjacent ABSs,the measurement information reported by a single mobile user is used to trigger the handover mechanism.This handover mechanism lacks the consideration of movement state of mobile users and the location relationship between mobile users,which may lead to handover misjudgments and even communication interrupts.In this paper,we propose an intelligent handover control method in UAV cellular networks.Firstly,we introduce a deep learning model to predict the user trajectories.This prediction model learns the movement behavior of mobile users from the measurement information and analyzes the positional relations between mobile users such as avoiding collision and accommodating fellow pedestrians.Secondly,we propose a handover decision method,which can calculate the users' corresponding receiving power based on the predicted location and the characteristic of air-to-ground channel,to make handover decisions accurately.Finally,we use realistic data sets with thousands of non-linear trajectories to verify the basic functions and performance of our proposed intelligent handover controlmethod.The simulation results show that the handover success rate of the proposed method is 8% higher than existing methods.

Probe Selection for Over-the-Air Test in 5G Base Stations with Massive Multiple-Input Multiple-Output

In 5G systems, massive multiple-input multiple-output (MIMO) has been adopted in base stations (BSs) to improve spectral efficiency and coverage. The traditional conductive performance test techniques are challenging due to the unaffordable cost and high complexity when testing a large number of antennas. To solve this problem, the over-the-air (OTA) test has been presented, in which probe selection is the key to reduce the number of channel emulators and probes. In this paper, a novel artificial bee colony (ABC) algorithm is introduced to enhance the efficiency and accuracy of probe selection procedure. A sectoring- based multi-probe anechoic chamber (MPAC) is built to evaluate the throughput performance of massive MIMO equipped in 5G BS. In addition, link level simulation is carried out to evaluate the proposal’s performance gain under the commercial network assumptions, where the average throughput of three velocity is given with different SNR region. The results suggest that OTA chamber and multi-probe wall are available not only for 5G BSs, but also for user equipments (UEs) with end-to-end communication.

A long-term-based handover decision algorithm for dense macro-femto coexistence networks

For the dense macro-femto coexistence networks scenario, a long-term-based handover（LTBH） algorithm is proposed. The handover decision algorithm is jointly determined by the angle of handover（AHO） and the time-tostay（TTS） to reduce the unnecessary handover numbers.First, the proposed AHO parameter is used to decrease the computation complexity in multiple candidate base stations（CBSs） scenario. Then, two types of TTS parameters are given for the fixed base stations and mobile base stations to make handover decisions among multiple CBSs. The simulation results show that the proposed LTBH algorithm can not only maintain the required transmission rate of users, but also effectively reduce the unnecessary numbers of handover in the dense macro-femto networks with the coexisting mobile BSs.

Optimal configuration for photovoltaic storage system capacity in 5G base station microgrids

Base station operators deploy a large number of distributed photovoltaics to solve the problems of high energy consumption and high electricity costs of 5G base stations.In this study,the idle space of the base station’s energy storage is used to stabilize the photovoltaic output,and a photovoltaic storage system microgrid of a 5G base station is constructed.Aiming at the capacity planning problem of photovoltaic storage systems,a two-layer optimal configuration method is proposed.The inner layer optimization considers the energy sharing among the base station microgrids,combines the communication characteristics of the 5G base station and the backup power demand of the energy storage battery,and determines an economic scheduling strategy for each photovoltaic storage system with the goal of minimizing the daily operation cost of the base station microgrid.The outer model aims to minimize the annual average comprehensive revenue of the 5G base station microgrid,while considering peak clipping and valley filling,to optimize the photovoltaic storage system capacity.The CPLEX solver and a genetic algorithm were used to solve the two-layer models.Considering the construction of the 5G base station in a certain area as an example,the results showed that the proposed model can not only reduce the cost of the 5G base station operators,but also reduce the peak load of the power grid and promote the local digestion of photovoltaic power.

Optimal configuration of 5G base station energy storage considering sleep mechanism

The high-energy consumption and high construction density of 5G base stations have greatly increased the demand for backup energy storage batteries.To maximize overall benefits for the investors and operators of base station energy storage,we proposed a bi-level optimization model for the operation of the energy storage,and the planning of 5G base stations considering the sleep mechanism.A multi-base station cooperative system composed of 5G acer stations was considered as the research object,and the outer goal was to maximize the net profit over the complete life cycle of the energy storage.Furthermore,the power and capacity of the energy storage configuration were optimized.The inner goal included the sleep mechanism of the base station,and the optimization of the energy storage charging and discharging strategy,for minimizing the daily electricity expenditure of the 5G base station system.Additionally,genetic algorithm and mixed integer programming were used to solve the bi-level optimization model,analyze the numerical example test comparison of the three types of batteries and the net income of the configuration,and finally verify the validity of the model.Furthermore,the sleep mechanism,the charging and discharging strategy for energy consumption,and the economic benefits for the operators were investigated to provide reference for the 5G base station energy storage configuration.

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.

Node deployment strategy optimization for wireless sensor network with mobile base station

The optimization of network performance in a movement-assisted data gathering scheme was studied by analyzing the energy consumption of wireless sensor network with node uniform distribution. A theoretically analytical method for avoiding energy hole was proposed. It is proved that if the densities of sensor nodes working at the same time are alternate between dormancy and work with non-uniform node distribution. The efficiency of network can increase by several times and the residual energy of network is nearly zero when the network lifetime ends.

Design and Implementation of Dynamic High-Speed Switches in Super Base Station Architectures

Novel centralized base station architectures integrating computation and communication functionalities have become important for the development of future mobile communication networks.Therefore,the development of dynamic high-speed interconnections between baseband units(BBUs)and remote radio heads(RRHs)is vital in centralized base station design.Herein,dynamic high-speed switches(HSSs)connecting BBUs and RRHs were designed for a centralized base station architecture.We analyzed the characteristics of actual traffic and introduced a switch traffic model suitable for the super base station architecture.Then,we proposed a data-priority-aware(DPA)scheduling algorithm based on the traffic model.Lastly,we developed the dynamic HSS model based on the OPNET platform and the prototype based on FPGA.Our results show that the DPA achieves close to 100%throughput with lower latency and provides better run-time complexity than iOCF and HE-iSLIP,thereby demonstrating that the proposed switch system can be adopted in centralized base station architectures.

Malicious Base Station and Detecting Malicious Base Station Signal

This paper introduces the background,illustrates the hardware structure and software features of malicious base station,explains its work principle,presents a method of detecting malicious base station,analyses the experiment and evaluates the experimental results to verify the reliability of this method.Finally proposes the future work.

A Path-Based Approach for Data Aggregation in Grid-Based Wireless Sensor Networks

Sensor nodes in a wireless sensor network （WSN） are typically powered by batteries, thus the energy is constrained. It is our design goal to efficiently utilize the energy of each sensor node to extend its lifetime, so as to prolong the lifetime of the whole WSN. In this paper, we propose a path-based data aggregation scheme （PBDAS） for grid-based wireless sensor networks. In order to extend the lifetime of a WSN, we construct a grid infrastructure by partitioning the whole sensor field into a grid of cells. Each cell has a head responsible for aggregating its own data with the data sensed by the others in the same cell and then transmitting out. In order to efficiently and rapidly transmit the data to the base station （BS）, we link each cell head to form a chain. Each cell head on the chain takes turn becoming the chain leader responsible for transmitting data to the BS. Aggregated data moves from head to head along the chain, and finally the chain leader transmits to the BS. In PBDAS, only the cell heads need to transmit data toward the BS. Therefore, the data transmissions to the BS substantially decrease. Besides, the cell heads and chain leader are designated in turn according to the energy level so that the energy depletion of nodes is evenly distributed. Simulation results show that the proposed PBDAS extends the lifetime of sensor nodes, so as to make the lifetime of the whole network longer.

TDOA based assisted GNSS positioning algorithm

In order to improve the location performance of User Equipment (UE) , enhance the availability of satellites navigation systems, an assisted Global Navigation Satellite System (GNSS) positioning algorithm based on Time Difference of Arrival (TDOA) technology is proposed for only three visible satellites in this paper. Using the structure of assisted satellite position system, UE was guaranteed to acquire navigation information of the three visible satellites rapidly, and three satellite observation equation were acquired, then the arrival time difference between UE and two adjacent base stations was used as an auxiliary, and a full-rank equation was constructed to achieve positioning by least squares method. The results showed that the algorithm had good convergence ability and positioning accuracy, and the confidence of this method's positioning error, according to 3GPP request of assisted satellite navigation accuracy error within 50 m, reaches 94.56%, the average positioning accuracy was 16.8 m. The validity of this algorithm was demonstrated.

Energy-Efficient Joint Content Caching and Small Base Station Activation Mechanism Design in Heterogeneous Cellular Networks

Heterogeneous cellular networks(HCNs), by introducing caching capability, has been considered as a promising technique in 5 G era, which can bring contents closer to users to reduce the transmission delay, save scarce bandwidth resource. Although many works have been done for caching in HCNs, from an energy perspective, there still exists much space to develop a more energy-efficient system when considering the fact that the majority of base stations are under-utilized in the most of the time. Therefore, in this paper, by taking the activation mechanism for the base stations into account, we study a joint caching and activation mechanism design to further improve the energy efficiency, then we formulate the optimization problem as an Integer Linear Programming problem(ILP) to maximize the system energy saving. Due to the enormous computation complexity for finding the optimal solution, we introduced a Quantum-inspired Evolutionary Algorithm(QEA) to iteratively provide the global best solution. Numerical results show that our proposed algorithm presents an excellent performance, which is far better than the strategy of only considering caching without deactivation mechanism in the actual, normal situation. We also provide performance comparison amongour QEA, random sleeping algorithm and greedy algorithm, numerical results illustrate our introduced QEA performs best in accuracy and global optimality.

RF Technologies and Challenges for Future MBR Systems in Cellular Base Stations

This paper describes the advances and features of future cellular base stations. Software defined radio （SDR） evolves to cognitive radio （CR）, which is smart and has wideband, and multiband radio （MBR） with reconfigurable wideband can be regarded as the basis of CR and an advanced level of SDR. Based on the SDR platform, several radio frequency （RF） solutions for implementing MBR systems are proposed, and some challenges to MBR implementation are discussed.

Multilevel Power Modeling of Base Station and Its ICs

A new power estimation method is proposed for base station(BS) in this paper.Based on this method,a software platform for power estimation is developed.The proposed method models power consumption on different abstraction levels by splitting a typical base station into several basic components at different levels in the view of embedded system design.In particular,our focus is on baseband IC(Integrate Circuit) due to it's the dominant power consumer in small cells.Baseband power model is based on arithmetic computing costs of selected algorithms.All computing and storage costs are calibrated using algorithm complexity,hardware architecture,activity ratio,silicon technology,and overheads on all hierarchies.Micro architecture and IC technology are considered.The model enables power comparison of different types of base stations configured with different baseband algorithms,micro architectures,and ICs.The model also supports cellular operators in power estimation of different deployment strategies and transmission schemes.The model is verified by comparing power consumption with a real LTE base station.By exposing more configuration freedoms,the platform can be used for power estimation of current and future base stations.