scholarly journals Low-complexity and Low-overhead Receiver for OTFS via Large-scale Antenna Array

2021 ◽  
pp. 1-1
Author(s):  
Yaru Shan ◽  
Fanggang Wang
Keyword(s):  
Antenna Array ◽  
Large Scale ◽  
Low Complexity

scholarly journals Constant Envelope MIMO-OFDM Precoding for Low Complexity Large-Scale Antenna Array Systems

2020 ◽  
Vol 19 (12) ◽  
pp. 7973-7985
Author(s):  
Stavros Domouchtsidis ◽  
Christos G. Tsinos ◽  
Symeon Chatzinotas ◽  
Bjorn Ottersten
Keyword(s):  
Antenna Array ◽  
Large Scale ◽  
Low Complexity ◽  
Constant Envelope ◽  
Mimo Ofdm

Symbol-Level Precoding for Low Complexity Transmitter Architectures in Large-Scale Antenna Array Systems

2019 ◽  
Vol 18 (2) ◽  
pp. 852-863 ◽  
Author(s):  
Stavros Domouchtsidis ◽  
Christos G. Tsinos ◽  
Symeon Chatzinotas ◽  
Bjorn Ottersten
Keyword(s):  
Antenna Array ◽  
Large Scale ◽  
Low Complexity

scholarly journals Low-Complexity Failed Element Diagnosis for Radar-Communication mmWave Antenna Array with Low SNR

Electronics ◽  
2019 ◽  
Vol 8 (8) ◽  
pp. 904
Author(s):  
Qingyu Li ◽  
Keren Dai ◽  
Xiaofeng Wang ◽  
Yu Zhang ◽  
He Zhang ◽  
...  
Keyword(s):  
Antenna Array ◽  
Large Scale ◽  
High Reliability ◽  
Doa Estimation ◽  
Low Complexity ◽  
Working Environment ◽  
Joint Decision ◽  
Communication Signals ◽  
Low Snr ◽  
Communication Signal

The millimeter-wave (mmWave) antenna array plays an important role in the excellent performance of wireless sensors networks (WSN) or unmanned aerial vehicle (UAV) clusters. However, the array elements are easily damaged in its harsh working environment but hard to be repaired or exchanged timely, resulting in a serious decline in the beamforming performance. Thus, accurate self-diagnosis of the failed elements is of great importance. In previous studies, there are still significant difficulties for large-scale arrays under extremely low SNR. In this paper, a diagnosis algorithm with low complexity and high reliability for the failed elements is proposed, which is based on a joint decision of communication signal and sensing echoes. Compared with the previous studies, the complexity of the algorithm is reduced by the construction of low-dimensional feature vectors for classification, the decoupling of the degree of arrival (DOA) estimation and the failed pattern diagnosis, with the help of the sub-array division. Simulation results show that, under an ultra-low SNR of −12.5 dB for communication signals and −16 dB for sensing echoes, an accurate self-diagnosis with a block error rate lower than 8% can be realized. The study in this paper will effectively promote the long-term and reliable operation of the mmWave antenna array in WSN, UAV clusters and other similar fields.

scholarly journals Low-complexity sparse-aware multiuser detection for large-scale MIMO systems

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Rong Ran ◽  
Hayoung Oh
Keyword(s):  
Large Scale ◽  
Mimo Systems ◽  
Minimum Mean Square Error ◽  
Multiple Input Multiple Output ◽  
Low Complexity ◽  
Finite Alphabet ◽  
Multiuser Detectors ◽  
Input Multiple Output ◽  
Error Detector ◽  
Significant Performance

AbstractSparse-aware (SA) detectors have attracted a lot attention due to its significant performance and low-complexity, in particular for large-scale multiple-input multiple-output (MIMO) systems. Similar to the conventional multiuser detectors, the nonlinear or compressive sensing based SA detectors provide the better performance but are not appropriate for the overdetermined multiuser MIMO systems in sense of power and time consumption. The linear SA detector provides a more elegant tradeoff between performance and complexity compared to the nonlinear ones. However, the major limitation of the linear SA detector is that, as the zero-forcing or minimum mean square error detector, it was derived by relaxing the finite-alphabet constraints, and therefore its performance is still sub-optimal. In this paper, we propose a novel SA detector, named single-dimensional search-based SA (SDSB-SA) detector, for overdetermined uplink MIMO systems. The proposed SDSB-SA detector adheres to the finite-alphabet constraints so that it outperforms the conventional linear SA detector, in particular, in high SNR regime. Meanwhile, the proposed detector follows a single-dimensional search manner, so it has a very low computational complexity which is feasible for light-ware Internet of Thing devices for ultra-reliable low-latency communication. Numerical results show that the the proposed SDSB-SA detector provides a relatively better tradeoff between the performance and complexity compared with several existing detectors.

scholarly journals Vlsi Interconnection Modelling Using a Finite Element Approach

1995 ◽  
Vol 18 (3) ◽  
pp. 179-202
Author(s):  
Umesh Kumar
Keyword(s):  
Integrated Circuit ◽  
Large Scale ◽  
Low Complexity ◽  
Stratified Medium ◽  
Base Function ◽  
Large Scale Integration ◽  
Element Approach ◽  
Scale Integration ◽  
High Degree ◽  
Important Shift

In the last decade, an important shift has taken place in the design of hardware with the advent of smaller and denser integrated circuit packages. Analysis techniques are required to ensure the proper electrical functioning of this hardware. An efficient method is presented to model the parasitic capacitance of VLSI (very large scale integration) interconnections. It is valid for conductors in a stratified medium, which is considered to be a good approximation for theSi−SiO2system of which present day ICs are made. The model approximates the charge density on the conductors as a continuous function on a web of edges. Each base function in the approximation has the form of a “spider” of edges. Here the method used [1] has very low complexity, as compared to other models used previously [2], and achieves a high degree of precision within the range of validity of the stratified medium.

scholarly journals Engineering Bi-Connected Component Overlay for Maximum-Flow Parallel Acceleration in Large Sparse Graph

2018 ◽  
Vol 36 (5) ◽  
pp. 955-962
Author(s):  
Yang Liu ◽  
Wei Wei ◽  
Heyang Xu
Keyword(s):  
Large Scale ◽  
Flow Problem ◽  
Maximum Flow ◽  
Low Complexity ◽  
Connected Components ◽  
Sparse Graphs ◽  
Fully Integrated ◽  
Flow Acceleration ◽  
Maximum Flow Problem ◽  
Graph Contraction

Network maximum flow problem is important and basic in graph theory, and one of its research directions is maximum-flow acceleration in large-scale graph. Existing acceleration strategy includes graph contraction and parallel computation, where there is still room for improvement:(1) The existing two acceleration strategies are not fully integrated, leading to their limited acceleration effect; (2) There is no sufficient support for computing multiple maximum-flow in one graph, leading to a lot of redundant computation. (3)The existing preprocessing methods need to consider node degrees and capacity constraints, resulting in high computational complexity. To address above problems, we identify the bi-connected components in a given graph and build an overlay, which can help split the maximum-flow problem into several subproblems and then solve them in parallel. The algorithm only uses the connectivity in the graph and has low complexity. The analyses and experiments on benchmark graphs indicate that the method can significantly shorten the calculation time in large sparse graphs.

scholarly journals Does Tail Label Help for Large-Scale Multi-Label Learning

2018 ◽  
Author(s):  
Tong Wei ◽  
Yu-Feng Li
Keyword(s):  
Large Scale ◽  
Performance Metrics ◽  
Learning Algorithm ◽  
Predictive Performance ◽  
Low Complexity ◽  
Tail Distribution ◽  
Prediction Time ◽  
Unseen Data ◽  
Model Size ◽  
Fast Prediction

Large-scale multi-label learning annotates relevant labels for unseen data from a huge number of candidate labels. It is well known that in large-scale multi-label learning, labels exhibit a long tail distribution in which a significant fraction of labels are tail labels. Nonetheless, how tail labels make impact on the performance metrics in large-scale multi-label learning was not explicitly quantified. In this paper, we disclose that whatever labels are randomly missing or misclassified, tail labels impact much less than common labels in terms of commonly used performance metrics (Top-$k$ precision and nDCG@$k$). With the observation above, we develop a low-complexity large-scale multi-label learning algorithm with the goal of facilitating fast prediction and compact models by trimming tail labels adaptively. Experiments clearly verify that both the prediction time and the model size are significantly reduced without sacrificing much predictive performance for state-of-the-art approaches.

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