Comparison of Channel State Information Estimation Using SLM and Clipping-based PAPR Reduction Methods

AbstractChannel estimation is a crucial issue in orthogonal frequency-division multiplexing (OFDM) as well as in all multicarrier systems. However, OFDM suffers from a major setback, the peak-to-average power ratio (PAPR). PAPR can be solved using a number of available techniques in literature, such as coding, active constellation extension, amplitude clipping, and selected mapping. The coding approach presents a disadvantage, represented by redundant data that significantly reduce the bit rate. The active constellation extension is an effective method; however, it requires higher transmission power. The clipping method is the simplest, but it produces high bit error rate (BER) degradation. Selected mapping (SLM) is the best among the available methods; however, it sends several bits as side information. In this study, we compare the clipping and SLM methods and show how the channel state information (CSI) estimation is affected in both techniques. Simulation results show that the SLM method is more effective than the clipping technique. The BER significantly increases when the clipping method is used because of the inaccurate estimation of CSI when the high peaks are clipped, such as in the case of the inserted pilots

Comparison of Channel State Information Estimation Using SLM and Clipping-based PAPR Reduction Methods

AbstractChannel estimation is a crucial issue in orthogonal frequency-division multiplexing (OFDM) as well as in all multicarrier systems. However, OFDM suffers from a major setback, the peak-to-average power ratio (PAPR). PAPR can be solved using a number of available techniques in literature, such as coding, active constellation extension, amplitude clipping, and selected mapping. The coding approach presents a disadvantage, represented by redundant data that significantly reduce the bit rate. The active constellation extension is an effective method; however, it requires higher transmission power. The clipping method is the simplest, but it produces high bit error rate (BER) degradation. Selected mapping (SLM) is the best among the available methods; however, it sends several bits as side information. In this study, we compare the clipping and SLM methods and show how the channel state information (CSI) estimation is affected in both techniques. Simulation results show that the SLM method is more effective than the clipping technique. The BER significantly increases when the clipping method is used because of the inaccurate estimation of CSI when the high peaks are clipped, such as in the case of the inserted pilots

Performance comparison between 802.11 and 802.11p for high speed vehicle in VANET

Vehicular ad-hoc networks (VANETs) technology has been emerged as a critical research area. Being ad-hoc in nature, VANET is a type of networks that is created from the concept of establishing a network of cars for a specific need or situation. Communication via routing packets over the high-speed vehicles is a challenging task. Vehicles mobility, speed can vary depending on the road specification. However, on highway, the speed can be increase up to 120 – 200 Km/H. Moving at high speed can affect the efficiency of data delivery. In particular V2I traffic where moving car trying to deliver data to fixed space units which are designed to collect and process data from vehicles. Different protocols have been proposed to be implemented for VANET infrastructure, including 802.11 and 802.11p. In this paper, the performance of the most widely deployed MAC protocols for handling wireless communication which is 802.11 and the 802.11p have been compared, which is a customized version for high speed modes. Performance is investigated in term of data delivery evaluation metrics including network throughput, delay and packet delivery ration. Results show that 802.11p has efficiently enhanced the network performance where network throughput is increased, delay is decreased, and packet delivery ratio is increased as well

A Comparision of Node Detection Algorithms Over Wireless Sensor Network

MANET is standing for Network as Mobile Ad-hoc which is a self-directed mobile handlers group which communicates over relatively bandwidth constrained wireless channels. Many services with different classes of Quality of Services (QoS) could be provided through the MANET such as data, voice, and video streaming. Thus, efficient packets routing is an essential issue especially over this kind of burst channel. To settle this issue, many scheduling techniques are proposed to reduce the packets dropping and channel collision when a huge demand of data is transferred from a sender to a receiver. In this paper, four MANET scheduling algorithms are selected and investigated in mobile ad hoc network which are Strict Preference (SP), Round Robin (RR), Weighted Round Robin (WRR), and Weighted Fair (WF). The network simulator EXata 2.0.1 is used to build the scenario which is consist of 50 nodes and performed the simulation. The results showed the performance metrics difference of the network such as the throughput and the end-end delay as well as queuing metrics like peak queue size, average queue length, in queue average time, and droppe of whole packets. Regrading throughput, the SP algorithm has a greater throughput than WF, RR, and WRR by 4.5%, 2.4%, and 1.42%, but WRR has outperformed others regarding the end-end delay. Moreover, WRR represents the best scheduling algorithm regarding both peak queue size since its greater than RP, WF, and WRR by 10.13%, 9.6%, and 5.32%, in order, and average output queue length in contrast, WRR worsts more time in queuing but it is the best in preventing the packets from dropping

Visualizing neuroscience through AI: A systematic review

The field of neuroscience explains how the neural networks in the brain work together to perform a variety of tasks, including pattern recognition, relative memory, object recognition, and more. The mental activity that makes different jobs possible is difficult to understand. Understanding the various patterns present in natural neural networks requires a combination of artificial intelligence and neuroscience, which requires less computation. As a result, it is possible to understand a large number of brain reactions in relation to the activity that each person is engaged in. Human brain neurons need to be trained by experience in order to perform activities like moving the hands, arms, and legs while also considering how to respond to each activity. In the past 10 years, artificial intelligence (AI), with its potential to uncover patterns in vast, complex data sets, has made amazing strides, in part by emulating how the brain does particular computations. This chapter reviews the replication of neuroscience via AI in a real-time scenario

Path reader and intelligent lane navigator by autonomous vehicle

Internet of Things (IoT) is a physical network of physical devices, such as widgets, structures, and other objects, which can store program, sensors, actuators, and screen configurations to allow the objects to assemble, control, display, and exchange data. The aim of this research was to develop an autonomous system with automated navigation. Using this approach, we are able to make use of deep neural networks for automatic navigation as well as the identification of pot holes and road conditions. Additionally, it displays potholes in traffic and the correct lane on the screen. The system stresses how important it is to select the path from one node to the next