A Review of Transmission Rate over Wireless Fading Channels: Classifications, Applications, and Challenges

Yadav, Poonam; Kumar, Sandeep; Kumar, Rajesh

doi:10.1007/s11277-021-08968-1

Cited by 8 publications

(2 citation statements)

References 260 publications

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“…Brief investigation of the already existing combining methods is as follows: The simplest combining technique is the selection combining (SC) which evaluates the signal to noise ratio (SNR) of each received version and then selects the signal with the highest SNR value [17]. However, this selection ignores the non-selected versions' valuable information that can be used for more efficient detection.…”

Section: B the Combining Algorithmmentioning

confidence: 99%

Collaborative Network of Ground Stations with a Virtual Platform to Perform Diversity Combining

Al-Anbagi

Veřtát

2022

2022 International Conference on Applied Electronics (AE)

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A conventional ground station can establish a single downlink with only one satellite at a time through steerable high-gain antenna. In addition to the lack of tracking more than one satellite at once, such single radio communication is highly vulnerable to outages when experiencing severe degrading circumstances or even with steering engine failures. Accordingly, such problematic single radio link would leave the operator with no alternative options to overcome the consequences. This work exhibits a solution to the ground station through networking. Multiple ground stations, with omnidirectional antennas instead of the steerable directive ones, can be engaged in a collaborative network to receive multiple versions of the same transmitted data for processing and combining. The suggested receive diversity combining is performed at a virtual ground station which utilizes a combining algorithm to help detect the original data from the received versions with less errors and hence reflecting more efficient and reliable services. To exploit this aimed diversity gain, a simple combining algorithm is also developed in this article. The simulation results from the proposed scheme have indicated significant performance enhancement over the single site ground station. This cooperative scheme will not only improve the system performance but also offer to track more than one satellite at a time.

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Section: B the Combining Algorithmmentioning

confidence: 99%

Collaborative Network of Ground Stations with a Virtual Platform to Perform Diversity Combining

Al-Anbagi

Veřtát

2022

2022 International Conference on Applied Electronics (AE)

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“…The effects of various types of fading, multipath, and shadowing on spectrum sensing have been inves-tigated in [8][9][10]. The capacity of fading channels and the data transmission rate through these channels are extremely important for reliable communication between SU and PU [11]. Reliable, efficient, and secured data transmission over wireless channels requires physical layer security and a controllable wireless propagation environment [12][13].…”

Section: Introductionmentioning

confidence: 99%

Multi-Head Attention-Based Spectrum Sensing for Cognitive Radio

Devarakonda

Nandanavam²

2023

Int. j. electr. comput. eng. syst. (Online)

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Spectrum sensing is one of the key tasks of cognitive radio to monitor the activity of the primary user. The sensing accuracy of the secondary user is dependent on the signal-to-noise ratio of the primary user signal. A novel Multi-head Attention-based spectrum sensing for Cognitive Radio is proposed through this work to increase the detection probability of the primary user at a low signal- to-noise ratio condition. A radio machine learning dataset with a variety of digital modulation schemes and varying signal-to-noise ratios served as a training source for the proposed model. Further, the performance metrics were evaluated to assess the performance of the proposed model. The experimental results indicate that the proposed model is optimized in terms of the amount of training time required which also has an increase of 27.6% in the probability of detection of the primary user under a low signal-to-noise ratio when compared to other related works that use deep learning.

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