Bandwidth Distribution Algorithm of Applications in LTE-Femtocell Network: Advanced Study | Chapter 4 | Emerging Trends in Engineering Research and Technology Vol. 5

 

The wireless technology demand is growing exponentially for higher data rate more than 1Gbit/s. The capacity of wireless networks must grow continuously in order to satisfy the demand of traffic from users and applications. Long-Term Evolution (LTE) and LTE-Advanced (LTE-A) have been standardized by 3GPP to target data rate of 100Mbits/s in the downlink and 50Mbits/s in the uplink. In LTE, the concept of self-optimization includes optimization of coverage, capacity, handover and interference. During Handover, in the dense deployment of femtocells, the issues of cell selection and reselection need to optimize. The mobile broadband networks support multiple applications like voice, data, video and email, single IP-based infrastructure. These applications have unique traffic challenging and Quality of Service requirements. In the LTE network the bandwidth is distributed among all the users and active applications using rate requirement of user equipment (UE). To increase user satisfaction in terms of rate of applications, the bandwidth allocation among applications needs to be optimized by dynamic scheduling. The objectives which we focused are Handover optimization and bandwidth optimization in the LTE integrated with femtocell networks. Aiming to our objective we proposed Dynamic Cell Selection (DCS) mechanism for handover optimization and Efficient Bandwidth Distribution (EBD) mechanism for allocating dynamic bandwidth to applications. We can perform the simulation of DCS and EBD mechanism in the NS-3 or similar simulation software to evaluate the performance of these algorithms. The real time experimentation of these mechanisms using Software Defined Radio is also possible. For real time set-up, possible supported technical tools are Xilinx(R) Zynq-Based Radio Support Package, Analog Devices AD9361 and LTE System Toolbox of MATLAB.

 

Author(s) Details

Anita B. Jadhav

1Ramrao Adik Institute of Technology (RAIT), Nerul, Mumbai, India.

 

Y. Srinivasa Rao

3Sardar Patel Institute of Technology (SPIT), Andheri, Mumbai, India.

 

Please see the book here :- https://doi.org/10.9734/bpi/etert/v5

Comparative Analysis of Different CPU Scheduling Algorithms with Respect to Average Waiting Time and Average Turnaround Time | Chapter 11 | Mathematics and Computer Science: Research Updates Vol. 5

CPU scheduling is one of the fundamental concepts of an Operating System. Most CPU scheduling algorithms concentrate on maximising CPU utilisation and throughput and minimising turnaround time, waiting time, response time and number of context switches for a set of requests. Some of the popular CPU scheduling algorithms are First-Come-First-Served (FCFS), Shortest Job First (SJF), Priority Based (PB) and Round Robin (RR). It has been found that CPU scheduling algorithms serve as the basis of multiprogramming. This paper presents the relationship between existing CPU scheduling algorithms with respect to their average waiting time and average turnaround time through different cases. There are five cases where each of which has four parts, and each part represents different existing CPU scheduling algorithms, i.e. FCFS, SJF, RR, and PB. Each part assumes that there are four different processes with different or the same priority and arrival time depending upon the particular case, as designed in the Section 4 of this paper. The goal of this paper is to find out the relationship with respect to average waiting time and average turnaround time, when different cases are implemented on the existing CPU scheduling algorithms and find the fastest algorithm amongst the existing CPU scheduling algorithms taken in this article.

Case Formulation-

·         In case 1, the CPU bursts taken are 4, 3, 5 and 2 for all the respective 4 processes.

·         In case 2, the CPU burst taken is an addition of 1 from the CPU burst of case 1 of each process.

·         In case 3, the CPU burst taken is the subtraction of 2 from the CPU burst of case 2 of each process.

·         In case 4, the CPU burst taken is an addition of 3 from the CPU burst of case 3 of each process.

·         In case 5, the CPU burst taken is the addition of 1 from the CPU burst of case 4 of each process.

 

Author (s) Details

 

Biswarup Samanta
Sarala Birla University, Ranchi, Jharkhand, India.

 

Arinjay Biswas
Adobe Systems, Noida, India.

 

Please see the book here:- https://doi.org/10.9734/bpi/mcsru/v5/4919

Performance Analysis of NOMA and OMA in Terms of Throughput and User Capacity | Chapter 9 | Exploration of Knowledge and Information in Sciences

Throughput, ergodic capacity, successive interference cancellation (SIC), NOMA, OMAAn increasing number of wireless devices in the present day putting a challenge of high spectral efficiency. The sixth generation (6G) is implemented to address this problem. But conventional orthogonal multiple access (OMA) approach is insufficient for the increased demand for spectrum. Non-orthogonal multiple access, or NOMA has drawn more and more interest in recent years from both academia and business and future 6G networks may find the NOMA technique to be advantageous as it offers spectrum efficiency, user fairness, better connectivity, and increased data rate, as compared to previous orthogonal multiple access approaches. Consequently, NOMA may be an appropriate multiple-access method due to its capacity to offer free random access and a further degree of flexibility for ultra-reliable low-latency communications (URLLC). This paper reviews the fundamentals of NOMA and related recent research on how beneficial it is in cooperative networks for improving performance analysis. The benefits of non-orthogonal multiple access (NOMA) have been well acknowledged in order to maximize the spectral efficiency. NOMA attracts many researchers as it solves the frequency deficiency problem by transmitting data at the same frequency and time. In NOMA data is transmitted in the power domain using Superposition coding at the transmitter side and successive interference cancellation at the receiver side. In this paper, we analyse the performance of NOMA in terms of received data rate (throughput) and ergodic capacity with the conventional OMA scheme.

 

Author (s) Details

 

Nidhi Chaudhary
ECE Department, NIT, Kurukshetra, 136119, India.

 

N. P. Singh
ECE Department, NIT, Kurukshetra, 136119, India.

 

Gaurav Verma
ECE Department, NIT, Kurukshetra, 136119, India.

 

Please see the book here:- https://doi.org/10.9734/bpi/mono/978-93-48859-02-0/CH9

Device to Device Mobile Communication with Novel Key Exchange Protocol | Chapter 12 | Current Approaches in Science and Technology Research Vol. 3

 The model decomposition and iteration techniques in Stochastic Petri Nets (SPNs) are used to derive its approximate steady state solution, which is used to obtain the approximate performance metrics of D2D communications in terms of average queue length, mean throughput, average packet delay, and packet dropping probability of each link. Simulations are carried out to validate the analytical results under various traffic loads and interference conditions. The protocol we're using here is the Novel Key Exchange Protocol. The goal of this technique is to present advancements on the current 3GPP LTEadvanced system in terms of Device-to-Device communication (D2D).

Author(s) Details

S. M. Asha Banu
Department of Electronics and Communication Engineering, Sri Krishna College of Engineering and Technology, Coimbatore, India.

Professor D. Mohana Geetha
Sri Krishna College of Engineering and Technology, Coimbatore, India.

Dr. V. Nandalal
Department of Electronics and Communication Engineering, Sri Krishna College of Engineering and Technology, Coimbatore, India.

View Book :- https://stm.bookpi.org/CASTR-V3/article/view/1417

Bandwidth Distribution Algorithm of Applications in LTE-Femtocell Network: Advanced Study | Chapter 4 | Emerging Trends in Engineering Research and Technology Vol. 5

The wireless technology demand is growing exponentially for higher data rate more than 1Gbit/s. The capacity of wireless networks must grow continuously in order to satisfy the demand of traffic from users and applications. Long-Term Evolution (LTE) and LTE-Advanced (LTE-A) have been standardized by 3GPP to target data rate of 100Mbits/s in the downlink and 50Mbits/s in the uplink. In LTE, the concept of self-optimization includes optimization of coverage, capacity, handover and interference. During Handover, in the dense deployment of femtocells, the issues of cell selection and reselection need to optimize. The mobile broadband networks support multiple applications like voice, data, video and email, single IP-based infrastructure. These applications have unique traffic challenging and Quality of Service requirements. In the LTE network the bandwidth is distributed among all the users and active applications using rate requirement of user equipment (UE). To increase user satisfaction in terms of rate of applications, the bandwidth allocation among applications needs to be optimized by dynamic scheduling. The objectives which we focused are Handover optimization and bandwidth optimization in the LTE integrated with femtocell networks. Aiming to our objective we proposed Dynamic Cell Selection (DCS) mechanism for handover optimization and Efficient Bandwidth Distribution (EBD) mechanism for allocating dynamic bandwidth to applications. We can perform the simulation of DCS and EBD mechanism in the NS-3 or similar simulation software to evaluate the performance of these algorithms. The real time experimentation of these mechanisms using Software Defined Radio is also possible. For real time set-up, possible supported technical tools are Xilinx(R) Zynq-Based Radio Support Package, Analog Devices AD9361 and LTE System Toolbox of MATLAB. 

Author(s) Details

Anita B. Jadhav
Ramrao Adik Institute of Technology (RAIT), Nerul and Usha Mittal Institute of Technology (UMIT), SNDT Women's University (SNDTWU), Mumbai, India

Dr. Y. Srinivasa Rao
Sardar Patel Institute of Technology (SPIT), Andheri, Mumbai, India

View Book :- http://bp.bookpi.org/index.php/bpi/catalog/book/187