A high‐throughput multimode low‐density parity‐check decoder for 5G New Radio

Pourjabar, Sina; Choi, Gwan

doi:10.1002/cta.3208

Cited by 10 publications

(6 citation statements)

References 23 publications

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“…In typical hardware implementation, regardless of the parallel architecture, a shift network is always utilized to route the intermediate messages between VNUs and CNUs [45], and it is the key factor that determines the reconfigurability, parallelism, area, and routing delay of a decoder [46]. Typical MET LDPC codes, e.g., 5G-NR LDPC codes, owns relatively high routing complexity due to their highly irregular base graph structure.…”

Section: Adaptive Asymmetric Quantization Strategy For Met Ldpc Codesmentioning

confidence: 99%

Adaptive Quantized and Normalized MSA Based on Modified MET-DE and Its Application for 5G-NR LDPC Codes

An,

Zhang,

Peng

et al. 2023

IEEE Access

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5G new radio (5G-NR) enhanced Mobile Broadband (eMBB) scenario demands a high data throughput of up to 20Gb/s, leading to an urgent need for low complexity and high throughput decoding algorithms for 5G-NR low-density parity-check (LDPC) codes, the coding scheme of 5G eMBB data channel. Quantization of input and intermediate signals enables the data throughput enhancement and the computation and storage overhead reduction of LDPC decoder, but confronts the problem of performance degradation. In addition, conventional analysis tools show limitation on the accurate asymptotic performance prediction of quantized and normalized LDPC decoding algorithms. In this paper, we first introduce modified multi-edge type density evolution (MET-DE) to the asymptotic performance prediction of quantized and adaptive normalized minsum algorithm (ANMSA). Then we adopt adaptive asymmetric quantization strategy for MET LDPC codes, combine it with conventional adaptive normalization technique, and further propose adaptive quantized and normalized min-sum algorithm (AQNMSA), which significantly alleviates the performance degradation of fixed quantized and normalized min-sum algorithm (NMSA) with negligible increment of computational complexity. Concurrently, we provide a novel look-up table design algorithm for AQNMSA based on the analysis tool of modified MET-DE. Finally, we apply AQNMSA to 5G-NR LDPC codes with a very low quantization bit-width of 4 bits for intermediate signals, and observe a comparable or superior decoding performance compared with its float-point non-adaptive counterparts under multiple code rates.3rd Generation Partnership Project (3GPP) adopted 5G new radio (5G-NR) low-density parity-check (LDPC)

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Section: Adaptive Asymmetric Quantization Strategy For Met Ldpc Codesmentioning

confidence: 99%

Adaptive Quantized and Normalized MSA Based on Modified MET-DE and Its Application for 5G-NR LDPC Codes

An,

Zhang,

Peng

et al. 2023

IEEE Access

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“…The algorithm developed by David Huffman is used to decrease coding redundancy without losing data quality. Using data repetition is the main idea in the Huffman coding algorithm [27,28]. In this method, the symbols of the alphabet are assigned to the variable code words considering their frequency.…”

Section: Huffman Coding Algorithmmentioning

confidence: 99%

Improve Performances of Wireless Sensor Networks for Data Transfer Based on Fuzzy Clustering and Huffman Compression

et al. 2022

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In today’s world, the main challenge is to save use energy optimally. The IoT devices generate a large amount of data for wide applications. Considering the application perspective in the IoT market, in one instance of the IoT technology, that is a wireless sensor network, factors like energy, storage capacity, computation power, and limitations of communication bandwidth resources are the reason for using data fusion. Data fusion and aggregation in wireless sensor networks (WSNs) such that minimum energy is consumed are an essential issue. In most clustering models, data aggregation is carried out by the cluster-head (CH). In the proposed algorithm, data aggregation in the cluster-head is carried out using the lossless cascode Huffman compression algorithm. Due to the correlation among data of nodes, the data sensed by each node is compared with the data of the cluster-head node; after removing redundancy, the coded data is transmitted to the main node. The CH node is selected by an algorithm based on fuzzy logic according to the residual energy of the node and the distance of the node from the sink node. Various fuzzy type-I systems of Mamdani and Takagi-Sugeno and type-II systems are used. In this paper, the CH selection algorithms are evaluated using three scenarios in terms of the number of live nodes, received packets and CHs, proper distribution rate, and other parameters of the LEACH protocol, network lifetime, and network energy. In the following, to demonstrate the performance of this new algorithm, simulations are performed in MATLAB based on the proposed method. The results show that the proposed compression algorithm in environments with high data correlation improves the compression rate by 8% compared to the conventional Huffman compression, while in environments with low data correlation, these two algorithms perform almost the same. This compression helps reduce the energy consumption of the network.

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“…𝐷 𝑗 : The communication between the physical machine j and other physical machines Moreover, the approach presented in [24] is used to model resource usage. The model is predicated on the idea that the devices' power consumption and processor productivity truly follow a linear relationship.…”

Section: A Formulation Of the Solutionmentioning

confidence: 99%

“…Almost all of these studies have not covered the idea of lowering energy usage and scheduling. The references [20,21,24,25] have used edge computing methodologies. They also employ cloud computing in many aspects of their work.…”

Section: Comparative Analysismentioning

confidence: 99%

Energy Consumption Reduction Strategy and a Load Balancing Mechanism for Cloud Computing in IoT Environment

Zhang¹,

Li²

2022

IJACSA

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Modern networks are built to be linked, agile, programmable, and load-efficient in order to overcome the drawbacks of an unbalanced network, such as network congestion, elevated transmission costs, low reliability, and other problems. The many technological devices in our environment have a considerable potential to make the connected world concept a reality. The Internet of Things (IoT) is a research community initiative to bring this idea to life. Cloud computing is crucial to making it happen. The load balancing and scheduling significantly increase the possibility of using resources and provide the grounds for reliability. Even if the intended node is under low or high loading, the load balancing techniques can increase its efficiency. This paper presents a scheduling technique for optimal resource allocation with enhanced particle swarm optimization and virtual machine live migration technique. The proposed technique prevents excessive or low server overloads through optimal allocation and scheduling tasks to physical servers. The proposed strategy was implemented in the cloudsim simulator environment and compared and showed that the proposed method is more effective and is well suited to decreasing execution time and energy consumption. This solution provides grounds to reduce energy consumption in the cloud environment while decreasing execution time. The simulation results showed that the amount of energy consumption compared to particle crowding has decreased by 10% and compared to PSO (Particle Swarm Optimization) scheduling by more than 8%. Also, the execution time has been reduced by 18% compared to particle swarm scheduling and by 8% compared to PSO.

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A high‐throughput multimode low‐density parity‐check decoder for 5G New Radio

Cited by 10 publications

References 23 publications

Adaptive Quantized and Normalized MSA Based on Modified MET-DE and Its Application for 5G-NR LDPC Codes

Adaptive Quantized and Normalized MSA Based on Modified MET-DE and Its Application for 5G-NR LDPC Codes

Improve Performances of Wireless Sensor Networks for Data Transfer Based on Fuzzy Clustering and Huffman Compression

Energy Consumption Reduction Strategy and a Load Balancing Mechanism for Cloud Computing in IoT Environment

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