Learning Automata-Based Solutions to the Nonlinear Fractional Knapsack Problem With Applications to Optimal Resource Allocation

2007 ◽  
Vol 37 (1) ◽  
pp. 166-175 ◽  
Author(s):  
Ole-Christoffer Granmo ◽  
B. John Oommen ◽  
Svein Arild Myrer ◽  
Morten Goodwin Olsen
Keyword(s):  
Resource Allocation ◽  
Knapsack Problem ◽  
Learning Automata ◽  
Optimal Resource Allocation ◽  
Optimal Resource

scholarly journals End-to-End Data Rate Performance of Decode-and-Forward Relaying with Different Resource Allocation Schemes

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Inam Ullah ◽  
Alexis Dowhuszko ◽  
Zhong Zheng ◽  
David González González ◽  
Jyri Hämäläinen
Keyword(s):  
Resource Allocation ◽  
Closed Form ◽  
Data Rate ◽  
Allocation Scheme ◽  
Optimal Resource Allocation ◽  
Decode And Forward ◽  
Optimal Resource ◽  
The Mean ◽  
Resource Allocation Scheme ◽  
Resource Allocation Schemes

This paper studies the end-to-end (e2e) data rate of dual-hop Decode-and-Forward (DF) infrastructure relaying under different resource allocation schemes. In this context, we first provide a comparative analysis of the optimal resource allocation scheme with respect to several other approaches in order to provide insights into the system behavior and show the benefits of each alternative. Then, assuming the optimal resource allocation, a closed form expression for the distribution of the mean and outage data rates is derived. It turns out that the corresponding mean e2e data rate formula attains an expression in terms of an integral that does not admit a closed form solution. Therefore, a tight lower bound formula for the mean e2e data rate is presented. Results can be used to select the most convenient resource allocation scheme and perform link dimensioning in the network planning phase, showing the explicit relationships that exist between component link bandwidths, SNR values, and mean data rate.

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