KEYWORDS: Holography, Data transmission, Holograms, Image transmission, Signal to noise ratio, Data communications, Image compression, Digital holography, Mobile communications, Modulation
The article discusses the problem of reducing the transmission speed of images and any other digital data in mobile networks when errors occur. The decrease in speed is explained by a different approach to the issue of data transmission quality in communication networks and in IP networks - if in communication networks a very small, but non-zero probability of error is acceptable, then in IP networks the TCP protocol ensures guaranteed delivery, and the data packet is delivered either without errors, or a connection failure is detected. The solution to the problem is to increase the noise immunity of the communication channel and reduce the probability of error at the decoder output to 0.000001. This requires a method of noise-resistant coding with a much greater correction capacity compared to the known codes used in communication networks. This method is the holographic coding method. When using it, the average speed of information transmission increases, the load on channels with retransmitted packets decreases, and freeing up channels will make it possible to do without additional frequency resources and an increase in the number of base stations. To ensure that the redundancy of the holographic code does not reduce the transmission speed in channels with good communication quality, it must be turned on only in those channels where a drop in speed of more than 10 times is recorded.
The article discusses the possibilities of using holography for parallel transmission of information in communication channels. Existing channels use serial transmission. At the same time, there is an effect that can be considered as a parallel transmission of information – holography. To transmit a hologram over a communication channel, it is necessary to transpose the space-time matrix. In this case, the information is deployed in space, and two local points are used in time – the moment of the formation of the object and the moment of the formation of the hologram. One option is optical holography, where an array of lasers pointed at the receiver matrix forms an interference pattern on it. The second option is the use of radio holography, the third is the transfer of the communication channel from the space-time domain to the time-frequency domain. To do this, one can use the transfer of the information block into a linear array of the spectrum in the frequency domain (time-frequency transposition). In this case, the hologram is aligned in the frequency space – the shape of the hologram corresponding to the transmitted information block contains the signal spectrum. The approaches considered are a problem statement for developing methods for parallel holographic transmission and creating communication channels that practically do not have an upper bandwidth limit. The theoretical limit is the transmission rate of a hologram – the amount of information contained in a three-dimensional image of a complex object, within the duration of one period of an electromagnetic wave.
KEYWORDS: Modulation, Orthogonal frequency division multiplexing, Radio over Fiber, Signal processing, Energy efficiency, Phase shift keying, Quadrature amplitude modulation, Optical fibers, Interference (communication), Distortion
Future 6G networks will be able to support a wide range of services with different technical requirements and in different frequency bands. To achieve this goal, the use of radio over fiber (RoF) technology is an important foundation for both the transport architecture and the hybrid radio-optical centralized architecture or 6G fronthaul cloud radio access network (CRAN). At the same time, the 6G generated signal processing technologies and modulation schemes must meet the stringent requirements of a mobile data network. In this paper we estimate energy efficiency of DFT-s-OFDM in RoF systems in terms of peak-to-average power ratio (PAPR).
The paper discusses a method of error-correcting coding based on the transfer of holographic coding of an arbitrary digital signal into the spectral range. The process of coding a code message consists in generating N radio pulses at orthogonal frequencies (a set of frequencies is determined by a holographic encoder for each message) by switching the outputs of N harmonic signal generators operating in a constant mode. The group signal is formed by summing the manipulated harmonics at the input of the transmitter amplifier or directly in the antenna. Signal decoding in the receiver is performed by calculating the spectrum of the received signal and applying an inverse holographic transform to it, which restores the value of the transmitted data block. Spectral holographic coding provides a 7-8 dB gain in noise immunity compared to coding the signal itself. Another advantage of the spectral code is the lower complexity of encoding and decoding when the redundancy is changed over a wide range, as well as high secrecy due to the use of a noise-like signal.
In this paper we present an effective and simple method of image compression based on spectral analysis rather than redundancy reduction. A significant portion of traffic transmitted over communication channels is static and dynamic images, and the volume of this traffic is growing at a faster rate than the capacity of communication channels is increasing. One of the ways to solve this problem is to compress the transmitted images. Data compression can be done in two ways – with and without loss of information. A distinctive feature of images as a form of information presentation is the presence of large internal redundancy, which allows the use of lossy compression methods. To exclude the loss of meaningful information, it is necessary to take into account the specifics of specific signals and divide them into groups according to the predominant concentration of information in the frequency or in spatial domain. To do this, one can use the analysis of the spatial spectrum of images and remove some part of the spectrum with an acceptable loss of information.
KEYWORDS: Holograms, Holography, Digital holography, Data storage, Error analysis, Binary data, Remote sensing, Data modeling, Reliability, Digital imaging
The holographic coding method improves the reliability of storing digital information in memory systems that are subject to external influences that introduce a large number of errors. The method is based on writing to memory instead of the initial data of a digital hologram created in the virtual space by a wave from an input source. The property of the hologram divisibility is used, allowing to recover the recorded data block by its fragment. The achieved level of noise immunity is determined by the size of the hologram. For an 8-bit data block, a 256-bit hologram recording provides recovery of information when 75% of the recorded hologram is lost. The developed decoder corrects the packet of dependent (grouped) errors that distort all bits of the hologram. The number of random independent errors that the decoder corrects can be up to 40% of the recorded information.
KEYWORDS: Holograms, Holography, Signal to noise ratio, Digital holography, Interference (communication), Signal attenuation, Binary data, Image processing, Computer programming, Wavefronts
The article describes the method of error-correcting coding based on the holographic representation of the digital signal. The coding process of a code message is a mathematical modeling of a hologram generated in virtual space by a wave from an input source. It is shown that the holographic representation of the signal has significantly greater noise immunity and allows you to restore the original digital combination when the majority of the code message is lost and when the encoded signal is distorted by noise that exceeds the signal level several times. The achieved level of noise immunity is determined by the level of redundancy. For example, with a redundancy factor of q=32, error-free decoding of the signal occurs when a 70% of the signal is lost, while with a redundancy factor of q=512, the loss of the signal can reach 90%.
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