Semantic communication is considered the future of mobile communication, which aims to transmit data beyond Shannon’s theorem of communications by transmitting the semantic meaning of the data rather than the bit-by-bit reconstruction of the data at the receiver’s end. The semantic communication paradigm aims to bridge the gap of limited bandwidth problems in modern high-volume multimedia application content transmission. Integrating AI technologies with the 6G communications networks paved the way to develop semantic communication-based end-to-end communication systems. In this study, we have implemented a semantic communication-based end-to-end image transmission system, and we discuss potential design considerations in developing semantic communication systems in conjunction with physical channel characteristics. A Pre-trained GAN network is used at the receiver as the transmission task to reconstruct the realistic image based on the Semantic segmented image at the receiver input. The semantic segmentation task at the transmitter (encoder) and the GAN network at the receiver (decoder) is trained on a common knowledge base, the COCO-Stuff dataset. The research shows that the resource gain in the form of bandwidth saving is immense when transmitting the semantic segmentation map through the physical channel instead of the ground truth image in contrast to conventional communication systems. Furthermore, the research studies the effect of physical channel distortions and quantization noise on semantic communication-based multimedia content transmission.

Polar codes have been receiving increased attention for application in beyond 5G networks. They offer low-complexity decoding algorithm and can achieve symmetric channel capacity. However, the majority of research works have focused on the codes constructed by the binary kernel (2 × 2 polarization matrix) which bounds the code length to an integer power of 2. Multi-kernel polar codes have been proposed as a method that allows the construction of polar codes with sizes different from powers of 2 by mixing multiple kernels of different dimensions. A hardware implementation based on the successive cancellation (SC) algorithm found in the literature shows that it suffers from a long decoding latency. In this paper, we design and implement a multi-kernel decoder based on the fast-simplified SC (fast-SSC) algorithm to decrease the decoding latency. It can decode any code constructed by binary and ternary (3 × 3) kernels featuring flexible code length, code rate, and kernel sequence. FPGA implementation results reveal that a polar code of length N = 1536, rate R = 1/2 with Processing Element ( P_e ) value of P_e = 240, gains 84.6% lower latency compared to the original algorithm. Also, the architecture supports polar codes constructed by purely-binary and purely-ternary kernels. A polar code of length N = 1024, rate R = 1/2, and P_e = 120 achieves an information throughput of 432 Mbps.

Polar codes have received growing attention in the past decade and have been selected as the coding scheme for the control channel in the fifth generation (5G) wireless communication systems. However, the conventional polar codes have only been constructed by binary ( $2\times 2$ ) kernel, which poses block length limitation to powers of 2. To attain more flexible block lengths, multi-kernel polar codes are proposed. In this paper, a combinational architecture for multi-kernel polar codes with high throughput is proposed based on successive cancellation decoding algorithm. The proposed scheme can decode pure-binary, pure-ternary ( $3\times 3$ ), and binary-ternary mixed polar codes. The decoder’s architecture is rate-flexible, meaning that a new code rate can be assigned to the decoder at every clock cycle. The proposed architecture is validated by FPGA implementation, and the results reveal that a code of size $N=81$ achieves the coded throughput of 1664.5 Mbps. A Python-based polar compiler is also proposed to automatically generate the HDL modules for target decoders. A designer can input the target block length and kernel ordering of a polar code and get the required VHDL files automatically.

Semantic communication (SC) is a communication paradigm that has gained significant attention, as it offers a potential solution to move beyond Shannon’s formulation in bandwidth-limited communication channels by delivering the semantic meaning of the message rather than its exact form. In this paper, we propose an autoencoder-based SC system for transmitting images between two machines over error-prone channels to support emerging applications such as VIoT, XR, M2M, and M2H communications. The proposed autoencoder architecture, with a semantically modeled encoder and decoder, transmits image data as a reduced-dimension vector (latent vector) through an error-prone channel. The decoder then reconstructs the image to determine its M2M implications. The autoencoder is trained for different noise levels under various channel conditions, and both image quality and classification accuracy are used to evaluate the system’s efficacy. A CNN image classifier measures accuracy, as no image quality metric is available for SC yet. The simulation results show that all proposed autoencoders maintain high image quality and classification accuracy at high SNRs, while the autoencoder trained with zero noise underperforms other trained autoencoders at moderate SNRs. The results further indicate that all other proposed autoencoders trained under different noise levels are highly robust against channel impairments. We compare the proposed system against a comparable JPEG transmission system, and results reveal that the proposed system outperforms the JPEG system in compression efficiency by up to $50\%$ and in received image quality with an image coding gain of up to 17 dB.