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GlobalSIP 2019

The 7th IEEE Global Conference on Signal and Information Processing (GlobalSIP)  focuses on signal and information processing with an emphasis on up-and-coming signal processing themes. The conference features world-class plenary speeches, distinguished symposium talks, tutorials, exhibits, oral and poster sessions, and panels. GlobalSIP is comprised of co-located General Symposium and symposia selected based on responses to the call-for-symposia proposals.

Joint Subchannel and Power Allocation for Cognitive NOMA Systems with Imperfect CSI


In this paper, a robust power allocation and subchannel assignment algorithm was proposed to maximize the total EE
of SUs for cognitive NOMA systems under taking channel uncertainties and diverse QoS requirements of users into account. The RAA problem was formulated into a non-convex mixed-integer fractional programming problem with the outage probabilities of users and thus difficult to solve. Based on Gaussian CSI error models, we transformed the robust rate constraint and the robust interference power constraint into the convex constraints. By slacking the integer subchannel allocation factor into a continuous variable, the original problem was converted into a convex problem by using the subtractive-form auxiliary variable. Based on the Lagrangian dual approach and the subgradient updating methods, the closed-form solutions were obtained. The effectiveness of the proposed algorithm was verified by comparing it with the existing algorithms.

Paper Details

Authors:
Yang Yang, Guoquan Li, Zhengqiang Wang
Submitted On:
29 October 2019 - 3:04pm
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GlobalSIP_Xu_2019 ppt.pdf

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[1] Yang Yang, Guoquan Li, Zhengqiang Wang, "Joint Subchannel and Power Allocation for Cognitive NOMA Systems with Imperfect CSI", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4900. Accessed: Dec. 12, 2019.
@article{4900-19,
url = {http://sigport.org/4900},
author = {Yang Yang; Guoquan Li; Zhengqiang Wang },
publisher = {IEEE SigPort},
title = {Joint Subchannel and Power Allocation for Cognitive NOMA Systems with Imperfect CSI},
year = {2019} }
TY - EJOUR
T1 - Joint Subchannel and Power Allocation for Cognitive NOMA Systems with Imperfect CSI
AU - Yang Yang; Guoquan Li; Zhengqiang Wang
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4900
ER -
Yang Yang, Guoquan Li, Zhengqiang Wang. (2019). Joint Subchannel and Power Allocation for Cognitive NOMA Systems with Imperfect CSI. IEEE SigPort. http://sigport.org/4900
Yang Yang, Guoquan Li, Zhengqiang Wang, 2019. Joint Subchannel and Power Allocation for Cognitive NOMA Systems with Imperfect CSI. Available at: http://sigport.org/4900.
Yang Yang, Guoquan Li, Zhengqiang Wang. (2019). "Joint Subchannel and Power Allocation for Cognitive NOMA Systems with Imperfect CSI." Web.
1. Yang Yang, Guoquan Li, Zhengqiang Wang. Joint Subchannel and Power Allocation for Cognitive NOMA Systems with Imperfect CSI [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4900

Stochastic Tucker-Decomposed Recurrent Neural Networks for Forecasting


The growing edge computing paradigm, notably the vision of the internet-of-things (IoT), calls for a new epitome of lightweight algorithms. Currently, the most successful models that learn from temporal data, which is prevalent in IoT applications, stem from the field of deep learning. However, these models evince extended training times and heavy resource requirements, prohibiting training in constrained environments. To address these concerns, we employ deep stochastic neural networks from the reservoir computing paradigm.

Paper Details

Authors:
Zachariah Carmichael, Dhireesha Kudithipudi
Submitted On:
29 October 2019 - 2:12pm
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[1] Zachariah Carmichael, Dhireesha Kudithipudi, "Stochastic Tucker-Decomposed Recurrent Neural Networks for Forecasting", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4899. Accessed: Dec. 12, 2019.
@article{4899-19,
url = {http://sigport.org/4899},
author = {Zachariah Carmichael; Dhireesha Kudithipudi },
publisher = {IEEE SigPort},
title = {Stochastic Tucker-Decomposed Recurrent Neural Networks for Forecasting},
year = {2019} }
TY - EJOUR
T1 - Stochastic Tucker-Decomposed Recurrent Neural Networks for Forecasting
AU - Zachariah Carmichael; Dhireesha Kudithipudi
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4899
ER -
Zachariah Carmichael, Dhireesha Kudithipudi. (2019). Stochastic Tucker-Decomposed Recurrent Neural Networks for Forecasting. IEEE SigPort. http://sigport.org/4899
Zachariah Carmichael, Dhireesha Kudithipudi, 2019. Stochastic Tucker-Decomposed Recurrent Neural Networks for Forecasting. Available at: http://sigport.org/4899.
Zachariah Carmichael, Dhireesha Kudithipudi. (2019). "Stochastic Tucker-Decomposed Recurrent Neural Networks for Forecasting." Web.
1. Zachariah Carmichael, Dhireesha Kudithipudi. Stochastic Tucker-Decomposed Recurrent Neural Networks for Forecasting [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4899

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