Low‐complexity user scheduling for MMSE relaying with large‐scale arrays

2015 ◽  
Vol 51 (2) ◽  
pp. 147-149
Author(s):  
Haijing Liu ◽  
Hui Gao ◽  
Tiejun Lv
Keyword(s):  
Large Scale ◽  
Low Complexity ◽  
User Scheduling

scholarly journals Low-Complexity User Scheduling with Switched Tilting for 3D Cellular Systems

2015 ◽  
Vol 2015 ◽  
pp. 1-9
Author(s):  
Haijing Liu ◽  
Hui Gao ◽  
Tiejun Lv
Keyword(s):  
Large Scale ◽  
Matrix Theory ◽  
Low Complexity ◽  
Base Station ◽  
Optimal Number ◽  
Cellular Systems ◽  
Rate Performance ◽  
User Scheduling ◽  
Sum Rate ◽  
Computational Resources

We propose a low-complexity user scheduling scheme to enhance the sum rate performance for a multicell downlink system, in which the base station (BS) is equipped with a large-scale active antenna array. First, we divide each cell intoNregions according to the vertical beamwidth of the BS antennas. Next, candidate user equipment (UE) items are assigned to corresponding groups to their locations. Each scheduling slot is also divided intoNequal-time subslots. Then, at each subslot, we focus on one UE group, select the optimal number,K*, of UEs for simultaneous data transmission in the manner of round-robin scheduling, and adjust the BS antenna tilting to the optimal angleθtilt*. In particular,K*andθtilt*for each UE group are both obtained by means of large-system asymptotic analysis. Benefiting from the random matrix theory tools, the asymptotic analytical results are independent of instantaneous channel state information of UE, which make it possible to solveK*andθtilt*offline, therefore saving the online computational resources significantly. Numerical results verify that the proposed scheme achieves good sum rate performance with extremely low computational complexity.

Low-Complexity Joint Antenna Tilting and User Scheduling for Large-Scale ZF Relaying

2015 ◽  
Vol 22 (3) ◽  
pp. 361-365 ◽  
Author(s):  
Haijing Liu ◽  
Hui Gao ◽  
Cong Zhang ◽  
Tiejun Lv
Keyword(s):  
Large Scale ◽  
Low Complexity ◽  
User Scheduling

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.

Performance Analysis of Low Complexity Multiuser Scheduling Algorithms in MIMO System

2013 ◽  
Vol 392 ◽  
pp. 867-871
Author(s):  
Ming Xia Lv ◽  
Yan Kun Lai ◽  
Dong Tang
Keyword(s):  
Mimo System ◽  
Scheduling Algorithm ◽  
Low Complexity ◽  
Base Station ◽  
User Scheduling ◽  
Sum Capacity ◽  
Multiuser Scheduling ◽  
The Common ◽  
The Cost ◽  
Selection Algorithms

The total throughput of the communication system can be maximized by allocating the common radio resource to the user or the user group having the best channel quality at a given time and the multiuser diversity gain can be obtained when multiple users share the same channel at one time. The object to select the users is to select the users with the maximum sum capacity. As for a scheduling algorithm, exhaustive algorithm can get the largest capability of the system by multi-user scheduling. However, this algorithm is quite complex hence the cost of operation to a base station has substantial increased. We compare the multiuser performance of two fast user selection algorithms with low complexity in MIMO-MRC systems with co-channel interferences. From the simulation results, these two algorithms not only decrease the computational complexity of the scheduling algorithm but also retain large capability of the MIMO system.

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
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