12 accepted papers
The bisimulation metric (BSM) is a powerful tool for computing state similarities within a Markov decision process (MDP), revealing that states closer in BSM have more similar optimal value functions. While BSM has been successfully utilized in reinforcement learning (RL) for tasks like state repres…
In modern wireless networks, user mobility modeling plays a pivotal role in learning-based network optimization, particularly in tasks such as user association and resource allocation. Traditional random mobility models, e.g., random waypoint and Gauss Markov model, often fail to accurately capture
Graph neural networks have achieved state-of-the-art performance on collaborative filtering (NeuGCFs). The success of NeuGCFs is mainly attributed to the stacking of message aggregation layers. Recent studies have shown that the effectiveness of existing NeuGCFs largely relies on the selection of op…
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…
In this paper, we investigate the beamforming design for an integrated sensing and communication (ISAC) system involved full-duplex (FD) communications. Specifically, an FD ISAC base station (BS) performs target detection and communicates with multiple downlink users and uplink users reusing the sam…
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 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…