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Signal Processing for Communications and Networking

OPTIMUM DECISION FUSION IN COGNITIVE WIRELESS SENSOR NETWORKS WITH UNKNOWN USERS LOCATION


We consider a cooperative cognitive wireless network sce- nario where a primary wireless network is co-located with a cognitive (or secondary) network. In the considered scenario, the nodes of the secondary network make local binary de- cisions about the presence of a signal emitted by a primary node. Then, they transmit their decisions to a fusion center (FC). The final decision about the channel state is up to the FC by means of a proper fusion rule.

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Authors:
Andrea Abrardo
Submitted On:
23 February 2016 - 1:44pm
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ICASSP15_Abrardo_Barni.pdf

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[1] Andrea Abrardo, "OPTIMUM DECISION FUSION IN COGNITIVE WIRELESS SENSOR NETWORKS WITH UNKNOWN USERS LOCATION", IEEE SigPort, 2014. [Online]. Available: http://sigport.org/122. Accessed: Dec. 17, 2017.
@article{122-14,
url = {http://sigport.org/122},
author = {Andrea Abrardo },
publisher = {IEEE SigPort},
title = {OPTIMUM DECISION FUSION IN COGNITIVE WIRELESS SENSOR NETWORKS WITH UNKNOWN USERS LOCATION},
year = {2014} }
TY - EJOUR
T1 - OPTIMUM DECISION FUSION IN COGNITIVE WIRELESS SENSOR NETWORKS WITH UNKNOWN USERS LOCATION
AU - Andrea Abrardo
PY - 2014
PB - IEEE SigPort
UR - http://sigport.org/122
ER -
Andrea Abrardo. (2014). OPTIMUM DECISION FUSION IN COGNITIVE WIRELESS SENSOR NETWORKS WITH UNKNOWN USERS LOCATION. IEEE SigPort. http://sigport.org/122
Andrea Abrardo, 2014. OPTIMUM DECISION FUSION IN COGNITIVE WIRELESS SENSOR NETWORKS WITH UNKNOWN USERS LOCATION. Available at: http://sigport.org/122.
Andrea Abrardo. (2014). "OPTIMUM DECISION FUSION IN COGNITIVE WIRELESS SENSOR NETWORKS WITH UNKNOWN USERS LOCATION." Web.
1. Andrea Abrardo. OPTIMUM DECISION FUSION IN COGNITIVE WIRELESS SENSOR NETWORKS WITH UNKNOWN USERS LOCATION [Internet]. IEEE SigPort; 2014. Available from : http://sigport.org/122

JOINT CHANNEL AND CARRIER FREQUENCY ESTIMATION FOR M-ARY CPM OVER FREQUENCY-SELECTIVE CHANNEL USING PAM DECOMPOSITION

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7 March 2017 - 2:01pm
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[1] , "JOINT CHANNEL AND CARRIER FREQUENCY ESTIMATION FOR M-ARY CPM OVER FREQUENCY-SELECTIVE CHANNEL USING PAM DECOMPOSITION", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1692. Accessed: Dec. 17, 2017.
@article{1692-17,
url = {http://sigport.org/1692},
author = { },
publisher = {IEEE SigPort},
title = {JOINT CHANNEL AND CARRIER FREQUENCY ESTIMATION FOR M-ARY CPM OVER FREQUENCY-SELECTIVE CHANNEL USING PAM DECOMPOSITION},
year = {2017} }
TY - EJOUR
T1 - JOINT CHANNEL AND CARRIER FREQUENCY ESTIMATION FOR M-ARY CPM OVER FREQUENCY-SELECTIVE CHANNEL USING PAM DECOMPOSITION
AU -
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1692
ER -
. (2017). JOINT CHANNEL AND CARRIER FREQUENCY ESTIMATION FOR M-ARY CPM OVER FREQUENCY-SELECTIVE CHANNEL USING PAM DECOMPOSITION. IEEE SigPort. http://sigport.org/1692
, 2017. JOINT CHANNEL AND CARRIER FREQUENCY ESTIMATION FOR M-ARY CPM OVER FREQUENCY-SELECTIVE CHANNEL USING PAM DECOMPOSITION. Available at: http://sigport.org/1692.
. (2017). "JOINT CHANNEL AND CARRIER FREQUENCY ESTIMATION FOR M-ARY CPM OVER FREQUENCY-SELECTIVE CHANNEL USING PAM DECOMPOSITION." Web.
1. . JOINT CHANNEL AND CARRIER FREQUENCY ESTIMATION FOR M-ARY CPM OVER FREQUENCY-SELECTIVE CHANNEL USING PAM DECOMPOSITION [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1692

LEARNING DICTIONARY FOR EFFICIENT SIGNAL COMPRESSION

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Authors:
Afshin Abdi, Ali Payani, Faramarz Fekri
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4 March 2017 - 9:50pm
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[1] Afshin Abdi, Ali Payani, Faramarz Fekri, "LEARNING DICTIONARY FOR EFFICIENT SIGNAL COMPRESSION", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1630. Accessed: Dec. 17, 2017.
@article{1630-17,
url = {http://sigport.org/1630},
author = {Afshin Abdi; Ali Payani; Faramarz Fekri },
publisher = {IEEE SigPort},
title = {LEARNING DICTIONARY FOR EFFICIENT SIGNAL COMPRESSION},
year = {2017} }
TY - EJOUR
T1 - LEARNING DICTIONARY FOR EFFICIENT SIGNAL COMPRESSION
AU - Afshin Abdi; Ali Payani; Faramarz Fekri
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1630
ER -
Afshin Abdi, Ali Payani, Faramarz Fekri. (2017). LEARNING DICTIONARY FOR EFFICIENT SIGNAL COMPRESSION. IEEE SigPort. http://sigport.org/1630
Afshin Abdi, Ali Payani, Faramarz Fekri, 2017. LEARNING DICTIONARY FOR EFFICIENT SIGNAL COMPRESSION. Available at: http://sigport.org/1630.
Afshin Abdi, Ali Payani, Faramarz Fekri. (2017). "LEARNING DICTIONARY FOR EFFICIENT SIGNAL COMPRESSION." Web.
1. Afshin Abdi, Ali Payani, Faramarz Fekri. LEARNING DICTIONARY FOR EFFICIENT SIGNAL COMPRESSION [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1630

Combining Belief Propagation and Successive Cancellation List Decoding of Polar Codes on a GPU Platform


The decoding performance of polar codes strongly depends on the decoding algorithm used, while also the decoder throughput and its latency mainly depend on the decoding algorithm. In this work, we implement the powerful successive cancellation list (SCL) decoder on a GPU and identify the bottlenecks of this algorithm with respect to parallel computing and its difficulties. The inherent serial decoding property of the SCL algorithm naturally limits the achievable speed-up gains on GPUs when compared to CPU implementations.

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Authors:
Sebastian Cammerer, Benedikt Leible, Matthias Stahl, Jakob Hoydis, Stephan ten Brink
Submitted On:
28 February 2017 - 7:34am
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Poster Presentation ICASSP'17

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[1] Sebastian Cammerer, Benedikt Leible, Matthias Stahl, Jakob Hoydis, Stephan ten Brink, "Combining Belief Propagation and Successive Cancellation List Decoding of Polar Codes on a GPU Platform", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1506. Accessed: Dec. 17, 2017.
@article{1506-17,
url = {http://sigport.org/1506},
author = {Sebastian Cammerer; Benedikt Leible; Matthias Stahl; Jakob Hoydis; Stephan ten Brink },
publisher = {IEEE SigPort},
title = {Combining Belief Propagation and Successive Cancellation List Decoding of Polar Codes on a GPU Platform},
year = {2017} }
TY - EJOUR
T1 - Combining Belief Propagation and Successive Cancellation List Decoding of Polar Codes on a GPU Platform
AU - Sebastian Cammerer; Benedikt Leible; Matthias Stahl; Jakob Hoydis; Stephan ten Brink
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1506
ER -
Sebastian Cammerer, Benedikt Leible, Matthias Stahl, Jakob Hoydis, Stephan ten Brink. (2017). Combining Belief Propagation and Successive Cancellation List Decoding of Polar Codes on a GPU Platform. IEEE SigPort. http://sigport.org/1506
Sebastian Cammerer, Benedikt Leible, Matthias Stahl, Jakob Hoydis, Stephan ten Brink, 2017. Combining Belief Propagation and Successive Cancellation List Decoding of Polar Codes on a GPU Platform. Available at: http://sigport.org/1506.
Sebastian Cammerer, Benedikt Leible, Matthias Stahl, Jakob Hoydis, Stephan ten Brink. (2017). "Combining Belief Propagation and Successive Cancellation List Decoding of Polar Codes on a GPU Platform." Web.
1. Sebastian Cammerer, Benedikt Leible, Matthias Stahl, Jakob Hoydis, Stephan ten Brink. Combining Belief Propagation and Successive Cancellation List Decoding of Polar Codes on a GPU Platform [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1506

SAMPLING AND DISTORTION TRADEOFFS FOR INDIRECT SOURCE RETRIEVAL


We study the problem of remote reconstruction of a continuous signal from its multiple corrupted versions. We are interested in the optimal number of samples and their locations for each corrupted signal to minimize the total reconstruction distortion of the remote signal. The correlation among the corrupted signals can be utilized to reduce the sampling rate.

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7 December 2016 - 3:17am
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[1] , "SAMPLING AND DISTORTION TRADEOFFS FOR INDIRECT SOURCE RETRIEVAL", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1399. Accessed: Dec. 17, 2017.
@article{1399-16,
url = {http://sigport.org/1399},
author = { },
publisher = {IEEE SigPort},
title = {SAMPLING AND DISTORTION TRADEOFFS FOR INDIRECT SOURCE RETRIEVAL},
year = {2016} }
TY - EJOUR
T1 - SAMPLING AND DISTORTION TRADEOFFS FOR INDIRECT SOURCE RETRIEVAL
AU -
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1399
ER -
. (2016). SAMPLING AND DISTORTION TRADEOFFS FOR INDIRECT SOURCE RETRIEVAL. IEEE SigPort. http://sigport.org/1399
, 2016. SAMPLING AND DISTORTION TRADEOFFS FOR INDIRECT SOURCE RETRIEVAL. Available at: http://sigport.org/1399.
. (2016). "SAMPLING AND DISTORTION TRADEOFFS FOR INDIRECT SOURCE RETRIEVAL." Web.
1. . SAMPLING AND DISTORTION TRADEOFFS FOR INDIRECT SOURCE RETRIEVAL [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1399

Generalized Approximate Message Passing for One-Bit Compressed Sensing with AWGN


Compressed sensing recovery techniques allow for reconstruction of an unknown sparse vector from an underdetermined system of linear equations. Recently, a lot of attention was drawn to the problem of recovering the sparse vector from quantized CS measurements. Especially interesting is the case, when extreme quantization is enforced that captures only the sign of the measurements. The problem becomes even more difficult if the measurements are corrupted by noise. In this paper we consider \ac{AWGN}.

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Authors:
Osman Musa,Gabor Hannak,Norbert Goertz
Submitted On:
6 December 2016 - 12:16pm
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[1] Osman Musa,Gabor Hannak,Norbert Goertz, "Generalized Approximate Message Passing for One-Bit Compressed Sensing with AWGN", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1368. Accessed: Dec. 17, 2017.
@article{1368-16,
url = {http://sigport.org/1368},
author = {Osman Musa;Gabor Hannak;Norbert Goertz },
publisher = {IEEE SigPort},
title = {Generalized Approximate Message Passing for One-Bit Compressed Sensing with AWGN},
year = {2016} }
TY - EJOUR
T1 - Generalized Approximate Message Passing for One-Bit Compressed Sensing with AWGN
AU - Osman Musa;Gabor Hannak;Norbert Goertz
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1368
ER -
Osman Musa,Gabor Hannak,Norbert Goertz. (2016). Generalized Approximate Message Passing for One-Bit Compressed Sensing with AWGN. IEEE SigPort. http://sigport.org/1368
Osman Musa,Gabor Hannak,Norbert Goertz, 2016. Generalized Approximate Message Passing for One-Bit Compressed Sensing with AWGN. Available at: http://sigport.org/1368.
Osman Musa,Gabor Hannak,Norbert Goertz. (2016). "Generalized Approximate Message Passing for One-Bit Compressed Sensing with AWGN." Web.
1. Osman Musa,Gabor Hannak,Norbert Goertz. Generalized Approximate Message Passing for One-Bit Compressed Sensing with AWGN [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1368

In-Network Linear Regression with Arbitrarily Split Data Matrices


We address for the first time the question of how networked agents can collaboratively fit a Morozov-regularized linear model when each agent knows a summand of the regression data. This question generalizes previously studied data-splitting scenarios, which require that the data be partitioned among the agents. To answer the question, we introduce a class of network-structured problems, which contains the regularization problem, and by using the Douglas-Rachford splitting algorithm, we develop a distributed algorithm to solve these problems.

Poster.pdf

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Authors:
François D. Côté, Ioannis N. Psaromiligkos, Warren J. Gross
Submitted On:
5 December 2016 - 6:33pm
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[1] François D. Côté, Ioannis N. Psaromiligkos, Warren J. Gross, "In-Network Linear Regression with Arbitrarily Split Data Matrices", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1353. Accessed: Dec. 17, 2017.
@article{1353-16,
url = {http://sigport.org/1353},
author = {François D. Côté; Ioannis N. Psaromiligkos; Warren J. Gross },
publisher = {IEEE SigPort},
title = {In-Network Linear Regression with Arbitrarily Split Data Matrices},
year = {2016} }
TY - EJOUR
T1 - In-Network Linear Regression with Arbitrarily Split Data Matrices
AU - François D. Côté; Ioannis N. Psaromiligkos; Warren J. Gross
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1353
ER -
François D. Côté, Ioannis N. Psaromiligkos, Warren J. Gross. (2016). In-Network Linear Regression with Arbitrarily Split Data Matrices. IEEE SigPort. http://sigport.org/1353
François D. Côté, Ioannis N. Psaromiligkos, Warren J. Gross, 2016. In-Network Linear Regression with Arbitrarily Split Data Matrices. Available at: http://sigport.org/1353.
François D. Côté, Ioannis N. Psaromiligkos, Warren J. Gross. (2016). "In-Network Linear Regression with Arbitrarily Split Data Matrices." Web.
1. François D. Côté, Ioannis N. Psaromiligkos, Warren J. Gross. In-Network Linear Regression with Arbitrarily Split Data Matrices [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1353

DIGITAL PREDISTORTION FOR MITIGATING TRANSMITTER-INDUCED RECEIVER DESENSITIZATION IN CARRIER AGGREGATION FDD TRANSCEIVERS

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Authors:
Mahmoud Abdelaziz, Lauri Anttila, Mikko Valkama
Submitted On:
7 December 2016 - 10:49am
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[1] Mahmoud Abdelaziz, Lauri Anttila, Mikko Valkama, "DIGITAL PREDISTORTION FOR MITIGATING TRANSMITTER-INDUCED RECEIVER DESENSITIZATION IN CARRIER AGGREGATION FDD TRANSCEIVERS", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1347. Accessed: Dec. 17, 2017.
@article{1347-16,
url = {http://sigport.org/1347},
author = {Mahmoud Abdelaziz; Lauri Anttila; Mikko Valkama },
publisher = {IEEE SigPort},
title = {DIGITAL PREDISTORTION FOR MITIGATING TRANSMITTER-INDUCED RECEIVER DESENSITIZATION IN CARRIER AGGREGATION FDD TRANSCEIVERS},
year = {2016} }
TY - EJOUR
T1 - DIGITAL PREDISTORTION FOR MITIGATING TRANSMITTER-INDUCED RECEIVER DESENSITIZATION IN CARRIER AGGREGATION FDD TRANSCEIVERS
AU - Mahmoud Abdelaziz; Lauri Anttila; Mikko Valkama
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1347
ER -
Mahmoud Abdelaziz, Lauri Anttila, Mikko Valkama. (2016). DIGITAL PREDISTORTION FOR MITIGATING TRANSMITTER-INDUCED RECEIVER DESENSITIZATION IN CARRIER AGGREGATION FDD TRANSCEIVERS. IEEE SigPort. http://sigport.org/1347
Mahmoud Abdelaziz, Lauri Anttila, Mikko Valkama, 2016. DIGITAL PREDISTORTION FOR MITIGATING TRANSMITTER-INDUCED RECEIVER DESENSITIZATION IN CARRIER AGGREGATION FDD TRANSCEIVERS. Available at: http://sigport.org/1347.
Mahmoud Abdelaziz, Lauri Anttila, Mikko Valkama. (2016). "DIGITAL PREDISTORTION FOR MITIGATING TRANSMITTER-INDUCED RECEIVER DESENSITIZATION IN CARRIER AGGREGATION FDD TRANSCEIVERS." Web.
1. Mahmoud Abdelaziz, Lauri Anttila, Mikko Valkama. DIGITAL PREDISTORTION FOR MITIGATING TRANSMITTER-INDUCED RECEIVER DESENSITIZATION IN CARRIER AGGREGATION FDD TRANSCEIVERS [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1347

A New Perspective on Randomized Gossip Algorithms


In this short note we propose a new approach for the design and analysis of randomized gossip algorithms which can be used to solve the average consensus problem. We show how the Randomized Block Kaczmarz (RBK) method—a method for solving linear systems—works as gossip algorithm when applied to a special system encoding the underlying network. The famous pairwise gossip algorithm arises as a special case.

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Authors:
Nicolas Loizou, Peter Richtarik
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5 December 2016 - 8:10am
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[1] Nicolas Loizou, Peter Richtarik , "A New Perspective on Randomized Gossip Algorithms", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1345. Accessed: Dec. 17, 2017.
@article{1345-16,
url = {http://sigport.org/1345},
author = {Nicolas Loizou; Peter Richtarik },
publisher = {IEEE SigPort},
title = {A New Perspective on Randomized Gossip Algorithms},
year = {2016} }
TY - EJOUR
T1 - A New Perspective on Randomized Gossip Algorithms
AU - Nicolas Loizou; Peter Richtarik
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1345
ER -
Nicolas Loizou, Peter Richtarik . (2016). A New Perspective on Randomized Gossip Algorithms. IEEE SigPort. http://sigport.org/1345
Nicolas Loizou, Peter Richtarik , 2016. A New Perspective on Randomized Gossip Algorithms. Available at: http://sigport.org/1345.
Nicolas Loizou, Peter Richtarik . (2016). "A New Perspective on Randomized Gossip Algorithms." Web.
1. Nicolas Loizou, Peter Richtarik . A New Perspective on Randomized Gossip Algorithms [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1345

A New Perspective on Randomized Gossip Algorithms


In this short note we propose a new approach for the design and analysis of randomized gossip algorithms which can be used to solve the average consensus problem. We show how the Randomized Block Kaczmarz (RBK) method—a method for solving linear systems—works as gossip algorithm when applied to a special system encoding the underlying network. The famous pairwise gossip algorithm arises as a special case.

Paper Details

Authors:
Nicolas Loizou, Peter Richtarik
Submitted On:
5 December 2016 - 8:10am
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PosterGlobalSip.pdf

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[1] Nicolas Loizou, Peter Richtarik , "A New Perspective on Randomized Gossip Algorithms", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1344. Accessed: Dec. 17, 2017.
@article{1344-16,
url = {http://sigport.org/1344},
author = {Nicolas Loizou; Peter Richtarik },
publisher = {IEEE SigPort},
title = {A New Perspective on Randomized Gossip Algorithms},
year = {2016} }
TY - EJOUR
T1 - A New Perspective on Randomized Gossip Algorithms
AU - Nicolas Loizou; Peter Richtarik
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1344
ER -
Nicolas Loizou, Peter Richtarik . (2016). A New Perspective on Randomized Gossip Algorithms. IEEE SigPort. http://sigport.org/1344
Nicolas Loizou, Peter Richtarik , 2016. A New Perspective on Randomized Gossip Algorithms. Available at: http://sigport.org/1344.
Nicolas Loizou, Peter Richtarik . (2016). "A New Perspective on Randomized Gossip Algorithms." Web.
1. Nicolas Loizou, Peter Richtarik . A New Perspective on Randomized Gossip Algorithms [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1344

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