This document discusses cell reference signals (RS) in LTE networks. It describes how cell RS are used for cell search and acquisition, downlink channel estimation, and channel quality measurements. It then provides technical details on:
- The frequency and time spacing of downlink RS
- The pseudo-random sequence generation process used to calculate cell RS values
- How the calculated cell RS values are mapped to resource elements for transmission
Redistribution is necessary when routing protocols connect and must pass routes between the two.
Route Redistribution involves placing the routes learned from one routing domain, such as RIP, into
another routing domain, such as EIGRP.
While running a single routing protocol throughout your entire IP internetwork is desirable, multiprotocol routing is common for a number of reasons, such as company mergers, multiple departments
managed by multiple network administrators, and multi-vendor environments. Running different
routing protocols is often part of a network design.
Redistribution is necessary when routing protocols connect and must pass routes between the two.
Route Redistribution involves placing the routes learned from one routing domain, such as RIP, into
another routing domain, such as EIGRP.
While running a single routing protocol throughout your entire IP internetwork is desirable, multiprotocol routing is common for a number of reasons, such as company mergers, multiple departments
managed by multiple network administrators, and multi-vendor environments. Running different
routing protocols is often part of a network design.
Design of all digital phase locked loop (d pll) with fast acquisition timeeSAT Journals
Abstract
A Digital PLL is designed with improved acquisition time and power efficiency. The implemented D-PLL can operate
from 6.54MHz to 105MHz with a power dissipation of is 7.763μW (at 210MHz) with 1.2V supply voltage. The D-PLL is
synthesized using cadence RTL compiler in 45nm CMOS process technology.
Keywords: Digital PLL, Digital Phase/Frequency detector, NCO, Divide by N counter.
Addressing mode and instruction set using 8051logesh waran
1. Immediate addressing mode:
In this type, the operand is specified in the instruction along with the opcode. In simple way, it means data is provided in instruction itself.
Ex: MOV A,#05H -> Where MOV stands for move, # represents immediate data. 05h is the data. It means the immediate date 05h provided in instruction is moved into A register.
2.Register addressing mode:
Here the operand in contained in the specific register of microcontroller. The user must provide the name of register from where the operand/data need to be fetched. The permitted registers are A, R7-R0 of each register bank. Ex: MOV A,R0-> content of R0 register is copied into Accumulator.
3. Direct addressing mode:
In this mode the direct address of memory location is provided in instruction to fetch the operand. Only internal RAM and SFR's address can be used in this type of instruction.
Ex: MOV A, 30H => Content of RAM address 30H is copied into Accumulator.
4. Register Indirect addressing mode:
Here the address of memory location is indirectly provided by a register. The '@' sign indicates that the register holds the address of memory location i.e. fetch the content of memory location whose address is provided in register.
Ex: MOV A,@R0 => Copy the content of memory location whose address is given in R0 register.
5. Indexed Addressing mode:
This addressing mode is basically used for accessing data from look up table. Here the address of memory is indexed i.e. added to form the actual address of memory.
Ex: MOVC A,@A+DPTR => here 'C' means Code. Here the content of A register is added with content of DPTR and the resultant is the address of memory location from where the data is copied to A register.
Es'hail2 1er satellite radioamateur géostationnaire QATAR OSCAR-100Passion Radio Amateur
Présentation complète (en anglais) du premier satellite radioamateur géostationnaire QATAR OSCAR-100 / Es'hail2.
Ce satellite a été développé par QARS (Qatar Amateur Radio Society) et es’hailSat (The Qatar satellite Company), sous la direction technique d’AMSAT-DL.
Design of all digital phase locked loop (d pll) with fast acquisition timeeSAT Journals
Abstract
A Digital PLL is designed with improved acquisition time and power efficiency. The implemented D-PLL can operate
from 6.54MHz to 105MHz with a power dissipation of is 7.763μW (at 210MHz) with 1.2V supply voltage. The D-PLL is
synthesized using cadence RTL compiler in 45nm CMOS process technology.
Keywords: Digital PLL, Digital Phase/Frequency detector, NCO, Divide by N counter.
Addressing mode and instruction set using 8051logesh waran
1. Immediate addressing mode:
In this type, the operand is specified in the instruction along with the opcode. In simple way, it means data is provided in instruction itself.
Ex: MOV A,#05H -> Where MOV stands for move, # represents immediate data. 05h is the data. It means the immediate date 05h provided in instruction is moved into A register.
2.Register addressing mode:
Here the operand in contained in the specific register of microcontroller. The user must provide the name of register from where the operand/data need to be fetched. The permitted registers are A, R7-R0 of each register bank. Ex: MOV A,R0-> content of R0 register is copied into Accumulator.
3. Direct addressing mode:
In this mode the direct address of memory location is provided in instruction to fetch the operand. Only internal RAM and SFR's address can be used in this type of instruction.
Ex: MOV A, 30H => Content of RAM address 30H is copied into Accumulator.
4. Register Indirect addressing mode:
Here the address of memory location is indirectly provided by a register. The '@' sign indicates that the register holds the address of memory location i.e. fetch the content of memory location whose address is provided in register.
Ex: MOV A,@R0 => Copy the content of memory location whose address is given in R0 register.
5. Indexed Addressing mode:
This addressing mode is basically used for accessing data from look up table. Here the address of memory is indexed i.e. added to form the actual address of memory.
Ex: MOVC A,@A+DPTR => here 'C' means Code. Here the content of A register is added with content of DPTR and the resultant is the address of memory location from where the data is copied to A register.
Es'hail2 1er satellite radioamateur géostationnaire QATAR OSCAR-100Passion Radio Amateur
Présentation complète (en anglais) du premier satellite radioamateur géostationnaire QATAR OSCAR-100 / Es'hail2.
Ce satellite a été développé par QARS (Qatar Amateur Radio Society) et es’hailSat (The Qatar satellite Company), sous la direction technique d’AMSAT-DL.
In this paper, we discussed about LTE system throughput calculation for both TDD and FDD system.
3GPP LTE technology support both TDD and FDD multiplexing. The paper describes all the factors which affect the throughput like Bandwidth, Modulation, UE category and mulplexing. It also describes how we get throughput 300Mbps in DL and 75Mbps in UL and what are assumptions taken to calculate the same.
Paper describes the steps and formulae to calculate the throughput for FDD system for TDD Config 1 and Config 2.
The throughput calculations shown in this paper is theoretical and limited by the assumptions taken to calculate for calculations
Analysis of Space Time Codes Using Modulation TechniquesIOSR Journals
Abstract: In this Paper, Analysis of channel codes for improving the data rate and reliability of communication over fading channels using multiple transmit antennas has been considered. The codes, namely ’Space Time Codes’ render full diversity and amend coding gain. Performance criteria for designing such codes, under this assumption that the fading is slow and nonselective frequency, is also analysed. Under this research, Study of Frame Error Rate(FER) and outage capacity is compared for different no. Of transmit and receive antennas as well as for different modulation techniques. According to theoretical results FER decreases with increasing SNR and No. Of receiving antennas. Numerical and practical result shows that FER decreases with increasing SNR and no. Of receiving antennas. Keywords: Space time Block Codes ,Space time trellis Codes,Frame Error Rate(FER),Outage capacity,Pairwise Error Probability
Analysis and Simulation of Pseudo Ranging Noise codes for Geo-Stationary Sate...IDES Editor
The Geo-Stationary Navigation Satellite System
will provides basically two types of services 1) Standard
Positioning Service (SPS) and 2) Restricted Service (RS).
Both of these services are provided at two frequencies of L
and S-Band. The code sequences used in SPS and RS are
Pseudo Ranging Noise (PRN) codes. In SPS downlink, it is
planned to use Gold Codes for navigation data transmission.
The RS navigation down link has signals with pilot component
and data component. The pilot component uses primary code
and secondary code to get final code known as tiered code.
The primary code is truncated Gold code. The secondary
code is PRN sequence code. The data component of RS service
uses truncated PRN sequence code. This paper presents the
performance analysis and simulation results of auto
correlation function (ACF) and Cross correlation function
(CCF) properties for Gold code, Kasami codes and it’s
truncation effect. Apart from ACF and CCF, Doppler
frequency shift on L & S-band carrier frequencies and
Doppler frequency shift on L & S band Codes are carried
out. The simulations of ACF & CCF on codes and Doppler
effects were analyzed using Matlab and System View design
tool and results are compared with Welch bound. The
simulated test results are well within the theoretical limits.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
2. Cell Reference Signal are used for…
• Cell search and initial acquisition
• Downlink channel estimation at the UE
• Downlink channel quality measurements
4. Downlink RS (Reference Signals)
• Transmitted by each antenna (currently using
single antenna)
– Freq. domain spacing is 6 subcarrier
– Time domain spacing is 4 OFDM symbols
8. Cell RS Equation
• Shall be transmitted in all downlink subframes
• Shall be transmitted in one or several antennas
for number_of_slot=1:20
for ofdm_symbol=1:7
calculate rofdm_symbol,number_of_slot(m)
m = 0,1,…,2*N_RB - 1
9. Pseudo-Random Sequence Generator
• Initialized with
• Then, initialize the the Gold-Sequence m_seq1
and m_seq2. Each has 31-bit length.
for length=1:31
if length=1
m_seq1[length] = 1
else
m_seq1[length] = 0
10. …more
• m_seq2 denoted by (need cinit)
for length=1:31
m_seq2[length] = c_init mod 2
c_init = c_init / 2
11. So far…
for length=1:31
{
if length=1
m_seq1[length] = 1
else
m_seq1[length] = 0
}
for number_of_slot=1:20
for ofdm_symbol=1:7
{
calc_c_init()
for length=1:31
{
m_seq2[length] = c_init mod 2
c_init = c_init / 2
}
calc_pseudo_rand()
calc_cell_RS()
}
calc_pseudo_rand()
{
for n=1:M_pn+Nc
{
m_seq1[n+31]=m_seq1[n+3]^m_seq1[n]
m_seq2[n+31]=m_seq2[n+3]^m_seq2[n+2]^m_seq2[n+1]^m_seq2[n]
}
for n=1:M_pn
{
pseudo_rand[n]=m_seq1[n+Nc]^m_seq2[n+Nc]
}
}
12. The cell RS value
…init m_seq1
for number_of_slot=1:20
for ofdm_symbol=1:7
{
…init c_init
…init m_seq2
…calc_pseudo_rand()
for m_index=1:N_RB*2
{
out_signal[number_of_slot][ofdm_symbol][m_index].re =
(1-2*pseudo_rand_seq[2*m_index])/SQRT_2;
out_signal[number_of_slot][ofdm_symbol][m_index].im =
((1-2*pseudo_rand_seq[2*m_index+1])/SQRT_2);
}
}