Register Allocation Compilation Technique for ASIP in 5G Micro Base Stations

The currently available compilation techniques are for general computing and are not optimized for physical layer computing in 5G micro base stations.In such cases,the foreseeable data sizes and small code size are application specific opportunities for baseband algorithm optimizations.Therefore,the special attention can be paid,for example,the specific register allocation algorithm has not been studied so far.The compilation for kernel sub-routines of baseband in 5G micro base stations is our focusing point.For applications of known and fixed data size,we proposed a compilation scheme of parallel data accessing,while operands can be mainly allocated and stored in registers.Based on a small register group(48×32b),the target of our compilation scheme is the optimization of baseband algorithms based on 4×4 or smaller matrices,maximizing the utilization of register files,and eliminating the extra register data exchanging.Meanwhile,when data is allocated into register files,we used VLIW(Very Long Instruction Word)machine to hide the time of data accessing and minimize the cost of data accessing,thus the total execution time is minimum.Experiments indicate that for algorithms with small data size,the cost of data accessing and extra addressing can be minimized.

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.

General Galois Processor for Transmitters in 5G/6G Base Stations

This paper proposes a flexible eight-mode high parallel Galois SIMD ASIP(Application Specific Instruction Set Processor).It supports parallel executions of Gold,Scrambling,CRC,CC,Turbo,RM,PSS,SSS encoding LFSR(linear feedback shift registers)algorithms with high performance and flexibility.It can perform also general bit processing and m-sequence.Our design is based on proposed table conversion and a datapath for unified eight-mode encoding.Based on 28 nm digital CMOS technology,the total area is 0.177 mm2 and the clock frequency can be up to 1 GHz.The throughputs of Gold,Scrambling,CRC32,CRC24,CRC16,CRC8,CC,Turbo are 64 Gb/s,64 Gb/s,128 Gb/s,168 Gb/s,256 Gb/s,512 Gb/s,3×80 Gb/s,and 72 Gb/s,respectively.

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.

Practical Non-Uniform Channelization for Multistandard Base Stations

A multistandard software-defined radio base station must perform non-uniform channelization of multiplexed frequency bands. Non-uniform channelization accounts for a significant portion of the digital signal processing workload in the base station receiver and can be difficult to realize in a physical implementation. In non-uniform channelization methods based on generalized DFT filter banks, large prototype filter orders are a significant issue for implementation. In this paper, a multistage filter design is applied to two different non-uniform generalized DFT-based channelizers in order to reduce their filter orders. To evaluate the approach, a TETRA and TEDS base station is used. Experimental results show that the new multistage design reduces both the number of coefficients and operations and leads to a more feasible design and practical physical implementation.

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.

A Broadband45°Dual-Polarized Multiple-Input Multiple-Output Antenna for 5G Base Stations with Extra Decoupling Elements

A dual-polarized multiple-input multiple-output(MIMO)antenna is proposed for 5G base stations.Each antenna element consists of two orthogonally placed fan-shaped dipole elements and45°dual-polarized feed-ing structures are used to achieve broadband operation.The resonant frequency of the array element is from 3.3 to 4.2 GHz,which covers the mainstream spectrum allocations of 5G mobile networks.H-shaped coupling elements are used to improve the isolation between the neighboring antenna elements and the mutual coupling is reduced by over 4 dB.The envelope correlation of a 2×2 array is provided for verification.

Base Station Energy Management in 5G Networks Using Wide Range Control Optimization

The traffic activity offifth generation(5G)networks demand for new energy management techniques that is dynamic deep and longer duration of sleep as compared to the fourth generation(4G)network technologies that demand always for varied control and data signalling based on control base station(CBS)and data base station(DBS).Hence,this paper discusses the energy management in wireless cellular networks using wide range of control for twice the reduction in energy conservation in non-standalone deployment of 5G network.As the new radio(NR)based 5G network is configured to transmit signal blocks for every 20 ms,the proposed algorithm implements withstanding capacity of on or off based energy switching,which in-turn operates in wide range control by carrying out reduced computational complexity.The proposed Wide range of control for base station in green cellular network using sleep mode for switch(WGCNS)algorithm toon and off the base station will work in heavy load with neighbouring base station.For reducing the overhead duration in air,heuristic versions of the algorithm are proposed at the base station.The algorithm operates based on the specification with suggested protocol-level to give best amount of energy savings.The proposed algorithm reduces 40%to 83%of residual energy based on the traffic pattern of the urban scenario.

Muti-Fusion Swarm Intelligence Optimization Algorithm in Base Station Coverage Optimization Problems

As millimeter waves will be widely used in the Internet of Things(IoT)and Telematics to provide high bandwidth communication and mass connectivity,the coverage optimization of base stations can effectively improve the quality of communication services.How to optimize the convergence speed of the base station coverage solution is crucial for IoT service providers.This paper proposes the Muti-Fusion Sparrow Search Algorithm(MFSSA)optimize the situation to address the problem of discrete coverage maximization and rapid convergence.Firstly,the initial swarm diversity is enriched using a sine chaotic map,and dynamic adaptive weighting is added to the discoverer location update strategy to improve the global search capability.Diverse swarms have a more remarkable ability to forage for food and avoid predation and are less likely to fall into a“precocious”state.Such a swarm is very suitable for solving NP-hard problems.Secondly,an elite opposition-based learning strategy is added to expand the search range of the algorithm,and a t-distribution-based one-fifth rule is introduced to reduce the probability of falling into a local optimum.This fusion mutation strategy can significantly optimize the adaptability and searchability of the algorithm.Finally,the experimental results show that the MFSSA algorithm can effectively improve the coverage of the deployment scheme in the base station coverage optimization problem,and the convergence speed is better than other algorithms.MFSSA is improved by more than 10%compared to the original algorithm.

An Enhanced Group Key-Based Security Protocol to Protect 5G SON Against FBS

Network operators are attempting many innovations and changes in 5G using self-organizing networks(SON).The SON operates on the measurement reports(MR),which are obtained from user equipment(UE)and secured against malware and userspace programs.However,the synchronization signal block that the UE relies on to measure the wireless environment configured by a base station is not authenticated.As a result,the UE will likely gauge the wrong wireless environment configured by a false base station(FBS)and transmit the corresponding MR to the serving base station,which poisons the data used for 5G SONs.Therefore,the serving base stations must verify the authenticity of the MR.The 3GPP has advocated numerous solutions for this issue,including the use of public key certificates,identity-based keys,and group keys.Although the solution leveraging group keys have better efficiency and practicality than the other two,they are vulnerable to security threats caused by key leaks via insiders or malicious UE.In this paper,we analyze these security issues and propose an improved group key protocol that uses a new network function,called a broadcast message authentication network function(BMANF),which validates broadcasted messages on behalf of the UE.The protocol operates in two phases:initial and verification.During the initial phase,the 5G core network distributes a shared secret key to the BMANF and UE,allowing the latter to request an authentication ticket from the former.During the verification phase,the UE requests the BMANF to validate the broadcasted messages received from base stations using the ticket and its corresponding shared key.For evaluation,we formally verified the proposed protocol,which was then compared with alternative methods in terms of computing cost.As a result,the proposed protocol fulfills the security requirements and shows a lower overhead than the alternatives.

Bio-Inspired Intelligent Routing in WSN: Integrating Mayfly Optimization and Enhanced Ant Colony Optimization for Energy-Efficient Cluster Formation and Maintenance

Wireless Sensor Networks(WSNs)are a collection of sensor nodes distributed in space and connected through wireless communication.The sensor nodes gather and store data about the real world around them.However,the nodes that are dependent on batteries will ultimately suffer an energy loss with time,which affects the lifetime of the network.This research proposes to achieve its primary goal by reducing energy consumption and increasing the network’s lifetime and stability.The present technique employs the hybrid Mayfly Optimization Algorithm-Enhanced Ant Colony Optimization(MFOA-EACO),where the Mayfly Optimization Algorithm(MFOA)is used to select the best cluster head(CH)from a set of nodes,and the Enhanced Ant Colony Optimization(EACO)technique is used to determine an optimal route between the cluster head and base station.The performance evaluation of our suggested hybrid approach is based on many parameters,including the number of active and dead nodes,node degree,distance,and energy usage.Our objective is to integrate MFOA-EACO to enhance energy efficiency and extend the network life of the WSN in the future.The proposed method outcomes proved to be better than traditional approaches such as Hybrid Squirrel-Flying Fox Optimization Algorithm(HSFLBOA),Hybrid Social Reindeer Optimization and Differential Evolution-Firefly Algorithm(HSRODE-FFA),Social Spider Distance Sensitive-Iterative Antlion Butterfly Cockroach Algorithm(SADSS-IABCA),and Energy Efficient Clustering Hierarchy Strategy-Improved Social Spider Algorithm Differential Evolution(EECHS-ISSADE).

Effects of Exposure to GSM Mobile Phone Base Station Signals on Salivary Cortisol,Alpha-Amylase,and Immunoglobulin A

Objective The present study aimed to test whether exposure to radiofrequency electromagnetic fields （RF-EMF） emitted by mobile phone base stations may have effects on salivary alpha-amylase, immunoglobulin A （IgA）, and cortisol levels. Methods Fifty seven participants were randomly allocated to one of three different experimental scenarios （22 participants to scenario 1, 26 to scenario 2, and 9 to scenario 3）. Each participant went through five 50-minute exposure sessions. The main RF-EMF source was a GSM-900-MHz antenna located at the outer wall of the building. In scenarios 1 and 2, the first, third, and fifth sessions were ＂low＂ （median power flux density 5.2 μW/m^2） exposure. The second session was ＂high＂ （2126.8 μW/m^2）, and the fourth session was ＂medium＂ （153.6 μW/m^2） in scenario 1, and vice versa in scenario 2. Scenario 3 had four ＂low＂ exposure conditions, followed by a ＂high＂ exposure condition. Biomedical parameters were collected by saliva samples three times a session. Exposure levels were created by shielding curtains. Results In scenario 3 from session 4 to session 5 （from ＂low＂ to ＂high＂ exposure）, an increase of cortisol was detected, while in scenarios 1 and 2, a higher concentration of alpha-amylase related to the baseline was identified as compared to that in scenario 3. IgA concentration was not significantly related to the exposure. Conclusions RF-EMF in considerably lower field densities than ICNIRP-gnidelines may influence certain psychobiological stress markers.

Energy Estimation and Optimization Platform for 4G and the Future Base Station System Early-Stage Design

There are already several power models to estimate the power consumption of base stations at system level. However, there is so far no model that can predict power consumption of the future base station designs based on algorithms and hardware selections with insufficient physical information. We present such an energy model for typical base stations. This model can help designers in estimating, evaluating and optimizing energy/power consumption of candidate designs in early design stages. The proposed model is verified by an LTE extreme scenario. The estimated results show that digital front-end, channel equalization and channel decoding are three major power greedy modules(consuming 39.4%, 16.3%, 13.4%) in a digital baseband subsystem. The power estimation error of the proposed power amplifier(PA) power model is 3.5%(macro cell). The major contribution of this paper is that the proposed models can rapidly estimate energy/power consumption of 4G and the future base stations(such as 5G) in early design stages with well acceptable precision, even without sufficient implementation information.

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.

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.

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.

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.

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.

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.