LoRaWAN is a prominent technology for Low Power Wide Area Networks (LPWAN). However, the increasing network size has introduced a significant challenge: packet collisions resulting from concurrent transmissions in LoR...
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LoRaWAN is a prominent technology for Low Power Wide Area Networks (LPWAN). However, the increasing network size has introduced a significant challenge: packet collisions resulting from concurrent transmissions in LoRaWAN. Previous studies either overlooked the issue by examining limited features or tackled it with intricate receivers employing up to eight antennas. To achieve a more favorable balance between implementation cost and system performance, we introduceHi(2)LoRa-a solution utilizing highly dimensional and accurate features for LoRa concurrent decoding, implemented with only two receiving antennas. The feature dimensions are expanded through an exploration of various hardware imperfections and inherent channel state information specific to each transceiver pair. To enhance feature accuracy, low pass filters and BiLSTM networks are applied to capture and learn their temporal patterns. Additionally, an efficient collision suppression strategy is introduced to mitigate feature corruption from concurrently transmitted packets. Extensive real-world testbed evaluations demonstrate that the achievable concurrency in Hi(2)LoRa approaches that of state-of-the-art approaches with significantly higher complexity (e.g., utilizing eight antennas) or exceeds prior work by a factor of 2.7 with comparable complexity (e.g., using two antennas).
Due to its simplicity and scalability, the Irregular Repetition Slotted ALOHA (IRSA) system that uses the successive interference cancellation (SIC) technique is a promising solution for uncoordinated multiple access ...
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ISBN:
(数字)9781665480536
ISBN:
(纸本)9781665480536
Due to its simplicity and scalability, the Irregular Repetition Slotted ALOHA (IRSA) system that uses the successive interference cancellation (SIC) technique is a promising solution for uncoordinated multiple access of a massive number of Internet-of-Things (IoT) devices. However, the peeling (iterative) decoder for IRSA is sequential in nature, and it might lead to cascading errors due to imperfect SIC. In this paper, we propose a parallel decoding algorithm for IRSA in an Additive White Gaussian Noise (AWGN) channel. Inspired by a recent advance in collision resolution for random access, our approach is to find a SIC-decoupling matrix so that the receiver can perform interference cancellation based on the received signals only. We propose a message-passing algorithm to find the optimal SIC-decoupling matrix when the induced user-slot bipartite graph of an IRSA system is acyclic. This includes the Contention Resolution Diversity Slotted ALOHA (CRDSA) system that sends exactly two copies for each packet. Using a random graph analysis, we derive the throughput for parallel decoding of CRDSA in a threshold-based decoding model. We also conduct various numerical experiments to illustrate the tradeoffs between sequential decoding with a limited number of iterations and parallel decoding with a predefined signal-to-noise ratio (SNR) threshold. Our numerical results show that one can significantly reduce the decoding time and achieve comparable throughput by parallel decoding when the SNR is substantially larger than the decoding threshold.
The transducer architecture is becoming increasingly popular in the field of speech recognition, because it is naturally streaming as well as high in accuracy. One of the drawbacks of transducer is that it is difficul...
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As a mainstream approach, grammar-based models have achieved high performance in text-to-SQL parsing task, but suffer from low decoding efficiency since the number of actions for building SQL trees are much larger tha...
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ISBN:
(纸本)9783031171895;9783031171888
As a mainstream approach, grammar-based models have achieved high performance in text-to-SQL parsing task, but suffer from low decoding efficiency since the number of actions for building SQL trees are much larger than the number of tokens in SQL queries. Meanwhile, intuitively it is beneficial from the parsing performance perspective to incorporate alignment information between SQL clauses and question segments. This paper proposes clause-level parallel decoding and alignment loss to enhance two high-performance grammar-based parsers, i.e., RATSQL and LGESQL. Experiments on the Spider dataset show our approach improves the decoding speed of RATSQL and LGESQL by 18.9% and 35.5% respectively, and also achieves consistent improvement in parsing accuracy, especially on complex questions.
Handwritten mathematical expression recognition (HMER) is a challenging task in the field of computer vision due to the complex two-dimensional spatial structure and diverse handwriting styles of mathematical expressi...
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Handwritten mathematical expression recognition (HMER) is a challenging task in the field of computer vision due to the complex two-dimensional spatial structure and diverse handwriting styles of mathematical expressions (MEs). Recent mainstream approach treats MEs as objects with tree structures, modeled by sequence decoders or tree decoders. These decoders recognize the symbols and relationships between symbols in MEs in depth-first order, resulting in long decoding steps that can harm their performance, particularly for MEs with complex structures. In this paper, we propose a novel tree-based model with branch parallel decoding for HMER, which parses the structures of ME trees by explicitly predicting the relationships between symbols. In addition, a query constructing module is proposed to assist the decoder in decoding the branches of ME trees in parallel, thus reducing the number of decoding time steps and alleviating the problem of long sequence attention decoding. As a result, our model outperforms existing models on three widely-used benchmarks and demonstrates significant improvements in HMER performance.
This paper studies distributed storage for protecting the confidentiality of partial data in the presence of storage node failures. It is required that not only the original data can be reconstructed from the remainin...
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This paper studies distributed storage for protecting the confidentiality of partial data in the presence of storage node failures. It is required that not only the original data can be reconstructed from the remaining surviving nodes, but also the data lost by a failed node can be repaired from as few nodes as possible. The minimum number of surviving nodes required to repair a failed node is called the repair degree. Inspired by the zigzag-decodable secret sharing scheme, we propose a new shift-add secret sharing scheme based on the XOR and bitwise-shift operations, in which confidential data is protected by using random keys generated from non-confidential data. The reliability and repairability of the proposed scheme are measured by the message loss probability and the maximum repair degree among all nodes, respectively, and then compared with three benchmark schemes. In contrast to conventional zigzag-decodable codes, the special structure of our proposed scheme allows the design of fast parallel algorithms for modern devices with multi-core processors, which have a linear speedup in decoding time compared with various versions of serial zigzag decoding. Experiments are implemented on a multi-core computer, and the empirical results on decoding time are consistent with our theoretical observations.
The commodity passive RFID system employs slotted ALOHA protocol to interrogate the tags within the reader's communication range. So, at each time slot, there is only one RFID tag communicating with the reader. Th...
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ISBN:
(纸本)9781665440905
The commodity passive RFID system employs slotted ALOHA protocol to interrogate the tags within the reader's communication range. So, at each time slot, there is only one RFID tag communicating with the reader. This degrades the network throughput, especially in large-scale RFID deployments such as warehouses. Recently, parallel decoding techniques are proposed, which can only read less than ten tags at each time slot. So, it is not applicable for warehouse applications, where there are thousands of RFID tags. In this paper, we propose to achieve parallel decoding with compressive sensing technique for multi-reader large-scale RFID system. Since it is difficult to decode the collisions from multiple tags at one reader, we distributively deploy multiple readers. However, we have to consider the inter-reader interference. Even though there are thousands of tags deployed in the large warehouse, they may not backscatter the signals at each time slot simultaneously due to the heterogeneity. Therefore, this sparsity property of backscattering signals can allow us to leverage compressive sensing to decode multiple tags simultaneously with multiple readers. Our simulation results reveal that compressive sensing can efficiently achieve parallel decoding in multi-reader large-scale RFID system.
We proposed a new automatic speech recognition (ASR) service architecture that is extendable to medium-scale ASR service and more flexible than the previous architecture. Improvement aims to substitute the distributed...
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ISBN:
(纸本)9781665438315
We proposed a new automatic speech recognition (ASR) service architecture that is extendable to medium-scale ASR service and more flexible than the previous architecture. Improvement aims to substitute the distributed processing approach with an asynchronous parallel thread for decoding multiple voice streams. We replace our TCP-based communication protocol with a remote procedure call developed by Google (gRPC) that makes our ASR service become a developer-friendly, less overhead connection. Besides, the API gateway is employed to reinforce the ASR services by multiple servers so that we can increase our new ASR service to a larger scale. The experimental result shows that our new architecture performs faster than the previous architecture in terms of real-time factor.
LoRa has emerged as a promising Low-Power Wide Area Network (LP-WAN) technology to connect a huge number of Internet-of-Things (IoT) devices. The dense deployment and an increasing number of IoT devices lead to intens...
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LoRa has emerged as a promising Low-Power Wide Area Network (LP-WAN) technology to connect a huge number of Internet-of-Things (IoT) devices. The dense deployment and an increasing number of IoT devices lead to intense collisions due to uncoordinated transmissions. However, the current MAC/PHY design of LoRaWAN fails to recover collisions, resulting in degraded performance as the system scales. This article presents FTrack, a novel communication paradigm that enables demodulation of collided LoRa transmissions. FTrack resolves LoRa collisions at the physical layer and thereby supports parallel decoding for LoRa transmissions. We propose a novel technique to separate collided transmissions by jointly considering both the time domain and the frequency domain features. The proposed technique is motivated from two key observations: (1) the symbol edges of the same frame exhibit periodic patterns, while the symbol edges of different frames are usually misaligned in time;(2) the frequency of LoRa signal increases continuously in between the edges of symbol, yet exhibits sudden changes at the symbol edges. We detect the continuity of signal frequency to remove interference and further exploit the time-domain information of symbol edges to recover symbols of all collided frames. We substantially optimize computation-intensive tasks and meet the real-time requirements of parallel LoRa decoding. We implement FTrack on a low-cost software defined radio. Our testbed evaluations show that FTrack demodulates collided LoRa frames with low symbol error rates in diverse SNR conditions. It increases the throughput of LoRaWAN in real usage scenarios by up to 3 times.
LoRa has emerged as a promising Low-Power Wide Area Network (LP-WAN) technology to connect a huge number of Internet-ofThings (IoT) devices. The dense deployment and an increasing number of loT devices lead to intense...
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ISBN:
(纸本)9781450369503
LoRa has emerged as a promising Low-Power Wide Area Network (LP-WAN) technology to connect a huge number of Internet-ofThings (IoT) devices. The dense deployment and an increasing number of loT devices lead to intense collisions due to uncoordinated transmissions. However, the current MAC/PHY design of LoRaWAN fails to recover collisions, resulting in degraded performance as the system scales. This paper presents FTrack, a novel communication paradigm that enables demodulation of collided LoRa transmissions. FTrack resolves LoRa collisions at the physical layer and thereby supports parallel decoding for LoRa transmissions. We propose a novel technique to separate collided transmissions by jointly considering both the time domain and the frequency domain features. The proposed technique is motivated from two key observations: (1) the symbol edges of the same frame exhibit periodic patterns, while the symbol edges of different frames are usually misaligned in time;(2) the frequency of LoRa signal increases continuously in between the edges of symbol, yet exhibits sudden changes at the symbol edges. We detect the continuity of signal frequency to remove interference and further exploit the time-domain information of symbol edges to recover symbols of all collided frames. We implement Mack on a low-cost software defined radio. Our testbed evaluations show that FTrack demodulates collided LoRa frames with low symbol error rates in diverse SNR conditions. It increases the throughput of LoRaWAN in real usage scenarios by up to 3 times.
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