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In this study, an innovative architecture is proposed to enhance the performance of semantic communication networks by leveraging deep learning and joint source‐channel coding. A fundamental challenge in this field is the strong dependence of conventional networks on a fixed signal‐to‐noise ratio (SNR) during training, which leads to performance degradation under varying channel conditions. To address this limitation, we introduce a novel attention‐based approach that enables dynamic adaptation to different SNR levels, ensuring more stable and optimized communication performance. The proposed model learns more generalized features that exhibit greater resilience to channel variations. To evaluate its effectiveness, extensive simulations were conducted, comparing the performance of the proposed architecture with DeepSC, a state‐of‐the‐art benchmark model in the field. While the baseline model, trained at a single SNR, experiences performance drops under mismatched conditions, the proposed model, trained across a range of SNRs, achieves improvement of 16.2%, 30.8%, 42.8%, and 53.8% for 1, 2, 3, and 4‐gram precisions, respectively, in bilingual evaluation understudy score and an 11.4% increase in sentence similarity across challenging low‐SNR conditions. Furthermore, the model maintains robust performance with 48% less training data, highlighting its efficiency and data efficiency under practical constraints. These gains confirm the model's superior adaptability and high‐quality data reconstruction under diverse conditions. The results of this study underscore the significant benefits of attention‐based architectures in semantic communication, particularly in environments with unpredictable channel variations, and highlight their potential for reliable deployment in real‐world applications. In this study, an innovative architecture is proposed to enhance the performance of semantic communication networks by leveraging deep learning and Joint Source‐Channel Coding. A fundamental challenge in this field is the strong dependence of conventional networks on a fixed Signal‐to‐Noise Ratio (SNR) during training, which leads to performance degradation under varying channel conditions. To address this limitation, we introduce a novel attention‐based approach that enables dynamic adaptation to different SNR levels, ensuring more stable and optimized communication performance.

Semantic secure communication is an emerging field that combines the principles of source-channel coding with the need for secure data transmission. It is of great significance in modern communications to protect the confidentiality and privacy of sensitive information and prevent information leaks and malicious attacks. This paper presents a novel approach to semantic secure communication through the utilization of joint source-channel coding, which is based on the design of an automated joint source-channel coding algorithm and an encryption and decryption algorithm based on semantic security. The traditional and state-of-the-art joint source-channel coding algorithms are selected as two baselines for different comparison purposes. Experimental results demonstrate that our proposed algorithm outperforms the first baseline algorithm, the traditional source-channel coding, by 61.21% in efficiency under identical channel conditions (). In security, our proposed method can resist 2 more types of attacks compared to the two baselines, exhibiting nearly no increases in time consumption and error rate compared to the state-of-the-art joint source-channel coding algorithm while the secure semantic communication is supported.

A new joint cryptography-channel coding technique is introduced which employs punctured quasi-cyclic-low density parity check (QC-LDPC) codes obtained from extended difference families. The absence of permutation and scrambling matrices reduces the key size compared with similar code-based cryptosystems, while having an acceptable level of security. The main advantage of this system is that, provided the system parameters are chosen appropriately, even if the code employed is revealed the system remains secure. Performance results are presented which show that the punctured code outperforms a random low density parity check (LDPC) code of the same length and rate.

Let ( S 1 , i , S 2 , i ) ∼ i . i . d p ( s 1 , s 2 ) , i = 1 , 2 , ⋯ be a memoryless, correlated partial side information sequence. In this work, we study channel coding and source coding problems where the partial side information ( S 1 , S 2 ) is available at the encoder and the decoder, respectively, and, additionally, either the encoder’s or the decoder’s side information is increased by a limited-rate description of the other’s partial side information. We derive six special cases of channel coding and source coding problems and we characterize the capacity and the rate-distortion functions for the different cases. We present a duality between the channel capacity and the rate-distortion cases we study. In order to find numerical solutions for our channel capacity and rate-distortion problems, we use the Blahut-Arimoto algorithm and convex optimization tools. Finally, we provide several examples corresponding to the channel capacity and the rate-distortion cases we presented.

The authors consider applying the systematic low-density parity-check codes with the parity based approach to the lossless (or near lossless) distributed joint source channel coding (DJSCC) with the decoder side information for the non-uniform sources over the asymmetric memoryless transmission channel. By using an equivalent channel coding model, which consists of two parallel subchannels: a correlation and a transmission sub-channel, respectively, they derive the codeword averaged density evolution (DE) for the DJSCC with the decoder side information for the asymmetrically correlated non-uniform sources over the asymmetric memoryless transmission channel. A new code ensemble definition of the irregular codes is introduced to distinguish between the source and the parity variable nodes, respectively. Extensive simulations demonstrate the effectiveness of the codeword averaged DE.

Evolving digital technologies in remote health monitoring require an energy-efficient method for secure and reliable transmission of patient’s/user’s confidential information from the sensor nodes to the cloud/medical server. Thus, a united scheme of the physiological signal steganography and its communication by benefitting from the unequal significance between different parts of the physiological data are emphasized. We believe higher steganography coding strength and more robust source-channel coding would protect extremely vital parts of the physiological data. Therefore, data integrity and transmission efficiency of packet information achieved in a resilient way. We formulate our idea of joint steganography-source-channel coding ( J S 2 C 2 ) as an optimization problem to simultaneously securing and minimizing the transmission energy consumption. A low-complexity deep learning-based ECG classification algorithm along with its secure and energy-efficient neural J S 2 C 2 transmission for real-time monitoring has been realized. The optimal parameters for our united framework have been calculated by J S 2 C 2 optimization method. Our steganography algorithm unequal steganography embedding (USE) achieves very low wavelet-based weighted percent root-mean-squared difference lower than 0.5 % . Furthermore, the high correlation between cover and stego and low end-to-end mean-square error (MSE) indicates resilient imperceptibility and maintains the diagnosability of the physiological signal. Moreover, low MSE between embedded and extracted data validates that embedded confidential data has been extracted with negligible distortion. In addition, for the given distortion, the USE-based framework’s energy consumption is much smaller (by 55% in typical application scenario) as compared with the equal steganography embedding-based approach’s energy consumption.

The aim of this work is to develop a dynamic coding strategy for a Multi user MIMO NOMA 5G downlink communication by means of concatenation of coding methods. The ultimate motive behind 5G technology is to deliver data at a ultra-high speed of multi - Gbps rate with extremely low latency, being highly reliable, offering huge network capacity, readily available channels and a much stable user experience accommodating multiple simultaneous users. This in turn demands a highly flexible and an effective channel coding method as it helps out the communication to be almost error free by reducing the bit errors of the transmitted data by saving it from the channel noise and the available interference in the channel. This paper provides an efficient approach based on the concatenation of Polar codes that is suited for a multi user mimo NOMA system that meets the criteria of the 5G standard. To compare the performance of mimo NOMA systems with that of concatenated PDCCH (Physical Downlink Control CHannel) polar codes  (Symbol Energy to Noise Ratio) versus BLER (BLock Error Rate) simulations have been performed. The results show that the suggested approach performs better in terms of Sum Rate Capacity versus SNR in multiuser mimo NOMA system.

It is envisioned that healthcare systems of the future will be revolutionized with the development and integration of body-centric networks into future generations of communication systems, giving rise to the so-called “Internet of Bio-nano things”. Molecular communications (MC) emerge as the most promising way of transmitting information for in-body communications. One of the biggest challenges is how to minimize the effects of environmental noise and reduce the inter-symbol interference (ISI) which in an MC via diffusion scenario can be very high. To address this problem, channel coding is one of the most promising techniques. In this paper, we study the effects of different channel codes integrated into MC systems. We provide a study of Tomlinson, Cercas, Hughes (TCH) codes as a new attractive approach for the MC environment due to the codeword properties which enable simplified detection. Simulation results show that TCH codes are more effective for these scenarios when compared to other existing alternatives, without introducing too much complexity or processing power into the system. Furthermore, an experimental proof-of-concept macroscale test bed is described, which uses pH as the information carrier, and which demonstrates that the proposed TCH codes can improve the reliability in this type of communication channel.

It is envisioned that healthcare systems of the future will be revolutionized with the development and integration of body-centric networks into future generations of communication systems, giving rise to the so-called “Internet of Bio-nano things”. Molecular communications (MC) emerge as the most promising way of transmitting information for in-body communications. One of the biggest challenges is how to minimize the effects of environmental noise and reduce the inter-symbol interference (ISI) which in an MC via diffusion scenario can be very high. To address this problem, channel coding is one of the most promising techniques. In this paper, we study the effects of different channel codes integrated into MC systems. We provide a study of Tomlinson, Cercas, Hughes (TCH) codes as a new attractive approach for the MC environment due to the codeword properties which enable simplified detection. Simulation results show that TCH codes are more effective for these scenarios when compared to other existing alternatives, without introducing too much complexity or processing power into the system. Furthermore, an experimental proof-of-concept macroscale test bed is described, which uses pH as the information carrier, and which demonstrates that the proposed TCH codes can improve the reliability in this type of communication channel.