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Publications
Embracing Channel Estimation in Multi-Packet Reception of ZigBee
Zhe Wang
L. Kong
Xuemei Liu
Guihai Chen
As a low-power and low-cost wireless protocol, the promising ZigBee has been widely used in sensor networks and cyber-physical systems. Sinc… (voir plus)e ZigBee based networks usually adopt tree or cluster topology, the convergecast scenarios are common in which multiple transmitters send packets to one receiver, leading to the severe collision problem. The conventional ZigBee adopts carrier sense multiple access with collisions avoidance to avoid collisions, which introduces additional time/energy overhead. The state-of-the-art methods resolve collisions instead of avoidance, in which mZig decomposes a collision by the collision itself and reZig decodes a collision by comparing with reference waveforms. However, mZig falls into high decoding errors only exploiting the signal amplitudes while reZig incurs high computational complexity for waveform comparison. In this paper, we propose CmZig to embrace channel estimation in multiple-packet reception (MPR) of ZigBee, which effectively improves MPR via lightweight computing used for channel estimation and collision decomposition. First, CmZig enables accurate collision decomposition with low computational complexity, which uses the estimated channel parameters modeling both signal amplitudes and phases. Second, CmZig adopts reference waveform comparison only for collisions without chip-level time offsets, instead of the complex machine learning based method. We implement CmZig on USRP-N210 and establish a six-node testbed. Results show that CmZig achieves a bit error rate in the order of
For polar-coded MIMO systems, separate detection and decoding (SDD) is the traditional scheme. In SDD systems, sphere decoding (SD) is one o… (voir plus)f the competitive MIMO detection schemes. However, SD may not utilize the coding information sufficiently in SDD systems, causing an error-correction performance loss. The existed joint detection and decoding using breadth-first SD (BSD) improves the performance than SDD, whereas the limited search space still causes a performance loss. In this paper, we propose joint detection and decoding based on SD (SD JDD) for polar-coded MIMO systems to reach maximum likelihood (ML) bound. Subsequently, two approaches are further proposed to reduce the computational complexity. The first approach reduces the layers of the SD search tree by exploiting symbol synchro sets, which could accelerate the convergence of SD JDD. The second efficient approach performs multiple tree searches. A small initial radius of the sphere for the first search is assigned to reduce the search space. The ML optimality could be preserved by the following multiple tree searches with increasing radius. It is shown from the numerical results that the proposed JDD outperforms SDD by 3.1 dB at FER
2023-05-01
IEEE Transactions on Vehicular Technology (publié)
Large pre-trained models have proved to be remarkable zero- and (prompt-based) few-shot learners in unimodal vision and language tasks. We p… (voir plus)ropose MAPL, a simple and parameter-efficient method that reuses frozen pre-trained unimodal models and leverages their strong generalization capabilities in multimodal vision-language (VL) settings. MAPL learns a lightweight mapping between the representation spaces of unimodal models using aligned image-text data, and can generalize to unseen VL tasks from just a few in-context examples. The small number of trainable parameters makes MAPL effective at low-data and in-domain learning. Moreover, MAPL’s modularity enables easy extension to other pre-trained models. Extensive experiments on several visual question answering and image captioning benchmarks show that MAPL achieves superior or competitive performance compared to similar methods while training orders of magnitude fewer parameters. MAPL can be trained in just a few hours using modest computational resources and public datasets. We release our code and pre-trained model weights at https://github.com/oscmansan/mapl.
2023-05-01
Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics (publié)
Protective effectiveness of previous SARS-CoV-2 infection and hybrid immunity against the omicron variant and severe disease: a systematic review and meta-regression
