12 accepted papers
Federated learning (FL) allows clients to collaboratively train models without exposing private data, but practical FL is simultaneously challenged by data heterogeneity and model heterogeneity. Prior heterogeneous FL (HtFL) approaches often fail to handle fine-grained feature shifts, leading to wea…
Vector images have attracted increasing attention in the field of information hiding in recent years due to their scalability and structural properties. However, existing steganographic methods for vector images often introduce noticeable modifications to the files themselves, resulting in potential
Generative image steganography (GIS) is an emerging technique that conceals secret messages in the generation of images. Compared to GAN-based or flow-based GIS schemes, diffusion model-based solutions can provide high-quality and more diverse images, thus receiving considerable attention recently.…
Turbo codes have been successfully adopted in 4G LTE, which can approach the channel capacity with Bahl-Cocke-Jelinek-Raviv (BCJR) decoding. With the evolution from 4G LTE to 5G NR, there is a demand to design a unified channel decoder that supports both LTE Turbo codes and NR low-density parity-che…
For the scenarios with high throughput requirements, the belief propagation (BP) decoding is one of the most promising decoding strategies for polar codes. By pruning the redundant variable nodes (VNs) and check nodes (CNs) in the original factor graph, the graph is condensed to a sparse bipartite g…
For massive multiple-input multiple-output (MIMO) detection, belief propagation (BP) based on graphical models has become a popular detection algorithm since it provides a good tradeoff between performance and complexity. To further lower the complexity of BP detection, an efficient BP detection bas…
Polar code is increasing its popularity recently for its capacity-achieving property for B-DMCs. However, when designing decoders for polar code, it has always been an inevitable concern for us to balance the decoding performance and the hardware consumption. In this paper, we propose an approximate…
The design of measurement matrices is an important part in compressed sensing (CS). Random matrices superior to incoherence are considered to be optimal measurement matrices to achieve successful recovery. However, they are deficient in memory cost. Structure matrices like circulant matrices are pre…
Recently, low-precision weight method has been considered as a promising scheme to efficiently implement inference of deep convolutional neural networks (DCNN). But it suffers from expensive retraining cost and accuracy degradation. In this paper, a low-bit and retraining-free quantization method, w…
For polar codes, both successive cancellation list (SCL) decoding and list sphere decoding (LSD) aim to balance performance and complexity. The same list structure but different decoding schedules of SCL and LSD can lead to a combination of both schemes. In this paper, an efficient joint list decode…
In recent years stochastic computing (SC) is re-gaining increasing attention for its unique advantages on low hardware cost and strong error resilience that are the key metrics for nanoscale CMOS era. However, the potential deployment of SC in practical applications is impeded by the long latency of…
In this paper, a low-complexity stochastic belief propagation (BP) detector for large-scale MIMO is first proposed. Its efficient hardware architecture, with parallel pipeline, is presented in detail. Thanks to the stochastic approach, all arithmetic operations of the detector are implemented with s…