A review on reconfigurable orthogonal frequency division multiplexing (OFDM) system for wireless communication

Drakshayini, M N; Singh, Arun Vikas

doi:10.1109/icatcct.2016.7911969

Cited by 4 publications

(7 citation statements)

References 8 publications

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“…Lastly, OFDM symbols are converted into a serial sequence of OFDM symbols that modulates high-frequency carriers and is transmitted via a channel. The processes in the receiver are reversed to the described processes in the transmitter [13][14][15]. The function of this type of modulation is based on the concept of orthogonality.…”

Section: Orthogonal Frequency Division Multiplexing-ofdmmentioning

confidence: 99%

“…Consequently, the orthogonality provides high spectral efficiency. Interference between individual subcarriers occurs only in the case of loss of orthogonality [13,14]. Figure 3 shows the principle of generation and demodulation of a signal with OFDM.…”

Section: Orthogonal Frequency Division Multiplexing-ofdmmentioning

confidence: 99%

“…Consequently, the orthogonality provides high spectral efficiency. Interference between individual subcarriers occurs only in the case of loss of orthogonality [13,14].…”

Section: Orthogonal Frequency Division Multiplexing-ofdmmentioning

confidence: 99%

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A Survey on the Use of Deep Learning Techniques for UAV Jamming and Deception

Simon

Götthans

2022

Electronics

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Unmanned aerial vehicles (UAVs) can be used for a variety of illegal activities (e.g., industrial espionage, smuggling, terrorism). Given their growing popularity and availability, and advances in communications technology, more sophisticated ways to disable these vehicles must be sought. Various forms of jamming are used to disable drones, but more advanced techniques such as deception and UAV takeover are considerably difficult to implement, and there is a large research gap in this area. Currently, machine and deep learning techniques are popular and are also used in various drone-related applications. However, no detailed research has been conducted so far on the use of these techniques for jamming and deception of UAVs. This paper focuses on exploring the current techniques in the area of jamming and deception. A survey on the use of machine or deep learning specifically in UAV-related applications is also conducted. The paper provides insight into the issues described and encourages more detailed research in this area.

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Section: Orthogonal Frequency Division Multiplexing-ofdmmentioning

confidence: 99%

Section: Orthogonal Frequency Division Multiplexing-ofdmmentioning

confidence: 99%

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A Survey on the Use of Deep Learning Techniques for UAV Jamming and Deception

Simon

Götthans

2022

Electronics

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“…The Orthogonal Frequency-Division Multiplexing (OFDM) and Multiple Input Multiple Output (MIMO) [ 21 , 22 ] techniques have been widely employed in the IEEE 802.11 standard; thus, the modulated WiFi signals are separated into a number of subchannels in parallel frequencies with different subcarriers and are transmitted concurrently among multipath antennas. A subcarrier is a sideband of a radio frequency carrier wave which is modulated to send additional information.…”

Section: Introductionmentioning

confidence: 99%

WiFi Indoor Localization with CSI Fingerprinting-Based Random Forest

Wang

Xiu

Zhang

et al. 2018

Sensors

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WiFi fingerprinting indoor positioning systems have extensive applied prospects. However, a vast amount of data in a particular environment has to be gathered to establish a fingerprinting database. Deficiencies of these systems are the lack of universality of multipath effects and a burden of heavy workload on fingerprint storage. Thus, this paper presents a novel Random Forest fingerprinting localization (RFFP) method using channel state information (CSI), which utilizes the Random Forest model trained in the offline stage as fingerprints in order to economize memory space and possess a good anti-multipath characteristic. Furthermore, a series of specific experiments are conducted in a microwave anechoic chamber and an office to detail the localization performance of RFFP with different wireless channel circumstances, system parameters, algorithms, and input datasets. In addition, compared with other algorithms including K-Nearest-Neighbor (KNN), Weighted K-Nearest-Neighbor (WKNN), REPTree, CART, and J48, the RFFP method provides far greater classification accuracy as well as lower mean location error. The proposed method offers outstanding comprehensive performance including accuracy, robustness, low workload, and better anti-multipath-fading.

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“…Sedangkan DVB merupakan standar sistem televisi digital yang sudah diakui secara internasional dan dikelola oleh konsorsium industri. OFDM diklaim jauh lebih tangguh daripada generasi sebelumnya, termasuk dalam efisiensi penggunaan bandwidth disebabkan penggunaan multi-carrier yang saling ortogonal [3]. Dengan multi-carrier yang saling ortogonal tersebut, juga membuat sistem lebih tahan terhadap interferensi antar frekuensi.…”

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Estimasi Kanal Sistem OFDM pada Kanal Fading Rayleigh dengan Metode Piece-wise Linear

Hadiansyah¹

2021

COMPLETE

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Transmisi data kecepatan tinggi menjadi sorotan masyarakat saat ini karena semakin banyak masyarakat menggunakan teknologi nirkabel pita lebar untuk memenuhi kebutuhan sehari-hari. Long Term Evolution (LTE) masih menjadi teknologi eksisting yang memanfaatkan sistem Orthogonal Frequency Division Multiplexing (OFDM) sebagai solusi penghematan bandwidth. Untuk mempertahankan kualitas data dari distorsi, maka dibutuhkan teknik estimasi kanal. Dengan mengetahui hasil estimasi kanal, maka akan didapatkan solusi untuk meminimalisasi efek distorsi. Respon impuls diasumsikan berupa kanal fading Rayleigh. Estimasi kanal yang dipakai adalah metode estimasi Piece-wise Linear dengan 2 slope. Serangkaian penelitian yang bisa dilakukan setelah estimasi kanal adalah estimasi efek doppler, minimalisasi interferensi antar-simbol, dan efisiensi serta optimalisasi sistem. Dari hasil simulasi menggunakan program Matlab dengan 100 kali iterasi, dapat diambil kesimpulan bahwa semakin besar Signal to Noise Ratio (SNR), semakin kecil error yang terjadi. Nilai rata-rata error estimasi dari SNR=9-40 dB adalah 2.4%-38.2%.

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A review on reconfigurable orthogonal frequency division multiplexing (OFDM) system for wireless communication

Cited by 4 publications

References 8 publications

A Survey on the Use of Deep Learning Techniques for UAV Jamming and Deception

A Survey on the Use of Deep Learning Techniques for UAV Jamming and Deception

WiFi Indoor Localization with CSI Fingerprinting-Based Random Forest

Estimasi Kanal Sistem OFDM pada Kanal Fading Rayleigh dengan Metode Piece-wise Linear

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