Capacity analysis for distributed spectrum brokers in heterogeneous networks

2014 ◽  
Author(s):  
Roberto Carlos Hincapie ◽  
Leonardo Betancur Agudelo ◽  
Cristina Gomez ◽  
Alexander Galvis Quintero ◽  
Roberto Bustamante Miller
Keyword(s):  
Heterogeneous Networks ◽  
Capacity Analysis

Proposal of Capacity Analysis in Wireless Sensor Networks with Multi-Hop Transmissions and Hidden Nodes

2015 ◽  
Vol E98.B (9) ◽  
pp. 1749-1757
Author(s):  
Yun WEN ◽  
Kazuyuki OZAKI ◽  
Hiroshi FUJITA ◽  
Teruhisa NINOMIYA ◽  
Makoto YOSHIDA
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Wireless Sensor ◽  
Capacity Analysis ◽  
Hidden Nodes

Capacity Analysis for Correlated Multi-Hop MIMO Channels under Colored Noise

2015 ◽  
Vol 1 ◽  
pp. 41
Author(s):  
Nguyen N. Tran ◽  
Ha X. Nguyen
Keyword(s):  
Computational Complexity ◽  
Mutual Information ◽  
Closed Form Solution ◽  
Optimal Solution ◽  
Form Solution ◽  
Maximization Problem ◽  
Capacity Analysis ◽  
Mimo Channels ◽  
Average Mutual Information ◽  
Channel Output

A capacity analysis for generally correlated wireless multi-hop multi-input multi-output (MIMO) channels is presented in this paper. The channel at each hop is spatially correlated, the source symbols are mutually correlated, and the additive Gaussian noises are colored. First, by invoking Karush-Kuhn-Tucker condition for the optimality of convex programming, we derive the optimal source symbol covariance for the maximum mutual information between the channel input and the channel output when having the full knowledge of channel at the transmitter. Secondly, we formulate the average mutual information maximization problem when having only the channel statistics at the transmitter. Since this problem is almost impossible to be solved analytically, the numerical interior-point-method is employed to obtain the optimal solution. Furthermore, to reduce the computational complexity, an asymptotic closed-form solution is derived by maximizing an upper bound of the objective function. Simulation results show that the average mutual information obtained by the asymptotic design is very closed to that obtained by the optimal design, while saving a huge computational complexity.

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