The progress of the technology in the recent decades has caused the video transmission by communication Channels to meet high demands. Therefore, many methods have been proposed to improve the quality of the video under Channel errors. The aim of this paper is to increase the Peak Signal to the Noise Ratio (PSNR) for video reconstructed by the receiver. This is achieved by increasing Channel encoder rate but in constant transmission rate. In the proposed method the video frames’ parts containing more energy are coded by the local Channel Coding inside the source encoder. Then, the resulted data is embedded in the low power frames’ parts. The proposed method is able to increase the Channel Coding rates without increasing the amount of the information for any frame. This method provides more robustness for video frames against Channel errors. The proposed method is tested for different source Coding rates and several Signals to Noise Ratio (SNRs) for Channel and the obtained results are compared with a new method.

In this article, polar codes that have recently been presented by Arikan, to integrate Channel Coding and encryption is used. Polar coded bits are divided into two categories. The first batch are the bits that virtual high-capacity Channels of the passage that briefly bits of high-capacity are called and the information on it. The second group are bits that are virtual Channels with capacity for short passes that "constant bits" are called. In the first proposal of fixed bits as we use encryption key, and on all bits of the plan (data bits and fixed bits) are key. In fact, the plan proposed 8-bit Arikan, we use the 8 key. Then, the proposed method is that it can be applied to the number of key bits can be reduced. The encryption system is effective and desirable that, in addition to the high complexity and lack of correlation between bits, the least it used to be key.

Performance and capabilities of security forces during operations depend strictly on their ability in gaining information from the desired resources. Therefore, these forces always try to improve their productivity and efficiency using telecommunications and electronic surveillance systems. In surveillance systems, Coding detection is done after identification of modulation. Accurate and timely recognition of Coding in these systems is so critical that the performance of these systems is tied to it. This paper presents an algorithm that is based on entropy and information theory and is able to detect block and convolution codes. Simulation results show that the proposed algorithm is able to detect Coding type in the Additive White Gaussian Noise Channels (AWGN) with a minimum signal to noise ratio of 3dB, in binary symmetric Channel (BSC) with an error probability of less than 0.1 and in binary erasure Channel (BEC) with an error probability of less than 0.2.

In this paper, we propose a new framework for joint encryption enCoding scheme based on polar codes, namely efficient and secure joint secret key encryption Channel Coding scheme. The issue of using new Coding structure, i.e. polar codes in Rao-Nam (RN) like schemes is addressed. Cryptanalysis methods show that the proposed scheme has an acceptable level of security with a relatively smaller key size in comparison with the previous works. The results indicate that the scheme provides an efficient error performance and benefits from a higher code rate which can approach the Channel capacity for large enough polar codes. The most important property of the proposed scheme is that if we increase the block length of the code, we can have a higher code rate and higher level of security without significant changes in the key size of the scheme. The resulting characteristics of the proposed scheme make it suitable for high-speed communications, such as deep space communication systems.

Progress of technology in recent decade causes that video transmission via communication Channels has met high demands. Therefore, several methods have been proposed to improve the quality of video under Channel errors. The aim of this paper is to increase PSNR for synthesized video by increasing Channel encoder rate but in constant transmission rate. This is achieved by using intelligent neural network and Huffman used in the MPEG standard to compress transmitted data significantly. Then, depending to the amount of compression by the proposed method, the compressed data is coded again using secondary Channel encoder. The proposed method is able to increase Channel Coding rate without increasing the amount of information for each frame. This method provides more robustness for video frames against Channel errors. The proposed method is tested for different source Coding rates and several SNRs for Channel and the obtained results are compared with state-of-the-art methods.

Orthogonal frequency division multiplexing (OFDM) is an effective technique for high bit rate applications in multipath Channels. Many researches have been carried out to develop and enhance OFDM efficiency. Because of the special structure of OFDM, the Channel Coding is very important. In this paper, Channel Coding in OFDM, the efficiency of various codes such as convolutional code, turbo code and their concatenated components with Reed-Solomon code are studied. The techniques of improving the effectiveness of Channel Coding with soft deCoding and the use of Channel state information are presented with simulation results. It can be seen that in a high signal to noise ratio, the Coding gain of concatenated codes is more than the pure codes. The Coding scheme can be chosen according to the complexity, suitable delay, and desired oding gain for the system.

Both secure and error control Coding are very extensive subjects, each with a variety of sub- disciplines. A secure Channel Coding (joint encryption-Channel Coding) scheme provides both data secrecy and data reliability in one process to combat problems in an insecure and unreliable Channel. In this paper, a joint encryption-Channel Coding scheme is developed, based on concatenated turbo codes for more efficient and secure transmission of LEO satellite data. Reliability and security are achieved by adapting the pseudo-random puncturing strategy with a change of distance between satellites and ground stations in the communication séance, an issue further burdened by reducing energy consumption or increasing bit rate of data transmission. Simulation results show the relevance and superior performance of the proposed scheme compared with the traditional data transmission system.

In this paper, we consider cognitive radio network in which two cognitive radio sources communicate with two cognitive destinations via a relay node. The decode and forward (DF) relay node employs physical layer network Coding (PLNC) to improve the data rate. Based on the availability of the spectrum bands at the source, relay and destination, the network employs three different diversity schemes namely source to relay diversity, relay to destination diversity and combination of earlier two diversity schemes with overall source to destination diversity schemes. The optimal allocation of Channel and power with per band and sum power constraints of a node in the network is formulated as convex optimization problem to improve the end to end throughput of the cognitive radio network. Simulation results show that the resultant joint Channel and power allocation are superior to the equal power allocation in terms of both end to end throughput and outage probability.

As source and Channel Coding are performed independently from each other without any feedback, source Coding tries to remove redundancy of the information whereas Channel Coding tries to increase reliability of transmitted data. As known, Channel capacity restricts volume of the transmitted data and so depending to conditions, it is required to have a tradeoff between source and Channel Coding. The aim of this paper is to improve the quality of the synthesized video by increasing the robustness against Channel errors in fixed transmission rate. In other words, the robustness of the transmitted video frames increases without any increment in bit rate. This results in improvement in the quality of the synthesized video. In the proposed method, the transmitted information is considerably compressed using neural network with Huffman Coding in H.264. Then a secondary Channel Coding whose rate depends on the amount of the compression is applied on the compressed information. This causes that the proposed method is able to increase Channel Coding rate and therefore provides higher protection for the transmitted information and more robustness against Channel errors. The obtained results by the proposed method are compared to the other methods for different source Coding rates and SNRs.