Automated spectrum trading mechanisms: understanding the big picture

López-Martínez, Mario; Alcaraz, Juan J.; Vales‐Alonso, Javier; García-Haro, Joan

doi:10.1007/s11276-014-0812-0

Cited by 16 publications

(13 citation statements)

References 90 publications

(222 reference statements)

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“…According to Appendix , the second derivative of

false(U t i l i t y_{LEO‐sat}^{d} false(i, n, s false) false)

versus P LEO − sat ( i , s ) is less than zero; it can be concluded that the objective function is a concave function. Therefore, maximization of a concave function is a convex optimization problem . Hence, to obtain optimum power for i th LEO satellites, the Lagrange function based on subgame 1 and Karush‐Kuhn‐Tucker (KKT) condition is formed according to Equation .…”

Section: Power Control Of Each Leo Satellite In Downlink Based On Intmentioning

confidence: 99%

“…One of the solutions for bandwidth allocation is obtained by a demand from any mobile users based on pricing trade off which is known as spectrum trading . In this section, we provide a solution for bandwidth allocation among mobile users in each small cell based on spectrum trading mechanism.…”

Section: Bandwidth Sharing Algorithm From Leo Satellite Among Mobile mentioning

confidence: 99%

“…Based on Lashgari et al and Boyd and Vandenbergh, there are three SINR equations to distribute bandwidth among i th LEO satellite system, l th ground station and n th mobile user as follows: The SINR for the i th LEO satellite can be expressed as follows:

\begin{matrix} alignleft \\ align-1 & align-2 R_{L E O - s a t} (i, l, s) = (1 - λ) \times W \times \\ align-1 & align-2 \log_{2} (1 + \frac{{P^{'}}_{L E O - s a t} (i, s) \times {G^{d}}_{L E O - s a t} (i, s) \times {G^{}}_{g s} (l, s) \times | h_{LEO‐sat(i,s) - g s (l, s)} |^{2}}{{false\sum}_{j = 1, j \neq i}^{I} {P^{'}}_{L E O - s a t} (j, s) \times {G^{d}}_{L E O - s a t} (j, s) \times {G^{}}_{g s} (l, s) \times | h_{LEO‐sat(j,s) - g s (l, s)} |^{2} + P_{n 3}}), \\ align-1 & align-2 I3 = \sum_{j = 1, j \neq i}^{I} {P^{'}}_{L E O - s a t} (j, s) \end{matrix}

…”

Section: Bandwidth Sharing Algorithm From Leo Satellite Among Mobile mentioning

confidence: 99%

“…Subgame 6 is considered as a leader game to obtain maximum Lagrange multiplier ( λ ) constraint in Equation . According to the explanations provided in this section, the utility function( Utility LEO − sat ( i , l , s )) for subgame 6 can be obtained in Parsaeefard et al and Lopez‐Martinez et al as follows:

\begin{matrix} alignleft \\ align-1 & align-2 subgame 6 : (\underset{λ}{Max} {U t i l i t y}_{L E O - s a t} (i, l, s) s . t . 0 \leq λ \leq 1) . \\ align-1 & align-2 U t i l i t y_{L E O - s a t} (i, l, s) = {D^{'}}_{n, i} \times K^{'} \times L n (\frac{R_{L E O - s a t} (i, l, s)}{{R^{'}}_{\min}}) + \sum_{l = 1}^{L} (1 - λ) \times w_{l} \times P + λ \times w_{l} \times P . \end{matrix}

…”

Section: Bandwidth Sharing Algorithm From Leo Satellite Among Mobile mentioning

confidence: 99%

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Interference management for spectral coexistence in a heterogeneous satellite network

Hajipour

Shahzadi

Ghazi‐Maghrebi

2020

Satell Commun Network

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Summary With the advent of the fifth generation of mobile radio communication by 2020, there will be many challenges such as increasing service demand with low delay in providing billions of end users called the satellite mobile users. It is expected that terrestrial communication systems will be faced with a dense network having many small cells anywhere and anytime. Therefore, there are some remote regions in the world where terrestrial systems cannot provide any services to end users. Furthermore, because of lack of spectral resources, it is very important that the spectrum is shared between satellite systems and terrestrial equipment by a suitable solution to interference management. In this paper, a heterogeneous satellite network that includes low earth orbit (LEO) satellite constellation and terrestrial equipment is proposed to provide low delay services. In this type of structure, interference management based on transmission power control between LEO satellite systems and mobile users is very important for obtaining high throughput. Moreover, in order to mitigate interference, transmission power control is shown based on noncooperative Stackelberg game under many subgames through pricing‐based algorithm and convex optimization method. Finally, the simulation results show that the performance of this study's system model will be improved through the proposed algorithm.

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“…According to Appendix , the second derivative of

false(U t i l i t y_{LEO‐sat}^{d} false(i, n, s false) false)

Section: Power Control Of Each Leo Satellite In Downlink Based On Intmentioning

confidence: 99%

Section: Bandwidth Sharing Algorithm From Leo Satellite Among Mobile mentioning

confidence: 99%

\begin{matrix} alignleft \\ align-1 & align-2 R_{L E O - s a t} (i, l, s) = (1 - λ) \times W \times \\ align-1 & align-2 \log_{2} (1 + \frac{{P^{'}}_{L E O - s a t} (i, s) \times {G^{d}}_{L E O - s a t} (i, s) \times {G^{}}_{g s} (l, s) \times | h_{LEO‐sat(i,s) - g s (l, s)} |^{2}}{{false\sum}_{j = 1, j \neq i}^{I} {P^{'}}_{L E O - s a t} (j, s) \times {G^{d}}_{L E O - s a t} (j, s) \times {G^{}}_{g s} (l, s) \times | h_{LEO‐sat(j,s) - g s (l, s)} |^{2} + P_{n 3}}), \\ align-1 & align-2 I3 = \sum_{j = 1, j \neq i}^{I} {P^{'}}_{L E O - s a t} (j, s) \end{matrix}

…”

Section: Bandwidth Sharing Algorithm From Leo Satellite Among Mobile mentioning

confidence: 99%

\begin{matrix} alignleft \\ align-1 & align-2 subgame 6 : (\underset{λ}{Max} {U t i l i t y}_{L E O - s a t} (i, l, s) s . t . 0 \leq λ \leq 1) . \\ align-1 & align-2 U t i l i t y_{L E O - s a t} (i, l, s) = {D^{'}}_{n, i} \times K^{'} \times L n (\frac{R_{L E O - s a t} (i, l, s)}{{R^{'}}_{\min}}) + \sum_{l = 1}^{L} (1 - λ) \times w_{l} \times P + λ \times w_{l} \times P . \end{matrix}

…”

Section: Bandwidth Sharing Algorithm From Leo Satellite Among Mobile mentioning

confidence: 99%

See 2 more Smart Citations

Interference management for spectral coexistence in a heterogeneous satellite network

Hajipour

Shahzadi

Ghazi‐Maghrebi

2020

Satell Commun Network

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“…However, when economic transactions take place on top of the spectrum allocation scenarios, then spectrum trading dominates, as described in Section 1. Presently, different approaches have been adopted to cope with the spectrum trading problem, like game theory, auction theory, and market equilibrium …”

Section: Related Workmentioning

confidence: 99%

Matching theory as enabler of efficient spectrum management in 5G networks

Tsirakis

López-Aguilera

Agapiou

et al. 2019

Trans Emerging Tel Tech

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This paper analyzes the spectrum trading problem in virtualized fifth generation networks to enhance the network performance with respect to the spectrum utilization. The problem is modeled as a many‐to‐many matching game with utility‐based preferences and determines the matching between mobile network operators and mobile virtual network operators. The two proposed versions of utility functions for each set aim at maximizing the satisfaction of both sets with conflicting interests and improving the overall spectrum efficiency. In the simulation evaluation, the proposed scheme is compared with three different schemes in terms of the system utility, individual, and pair matching satisfaction. We also investigate the scalability aspects, the strategy plan impact on the matching performance of our proposed scheme, and at the same time, we attempt to make appropriate assumptions closer to reality. Our proposed scheme shows much better performance than the other schemes achieving a quite high level of satisfaction for the matching result on both sets.

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Game theoretic approach for multi-tier 5G heterogeneous network optimization based on joint power control and spectrum trading

Zanjani

Shahzadi

2018

Wireless Netw

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Automated spectrum trading mechanisms: understanding the big picture

Cited by 16 publications

References 90 publications

Interference management for spectral coexistence in a heterogeneous satellite network

Interference management for spectral coexistence in a heterogeneous satellite network

Matching theory as enabler of efficient spectrum management in 5G networks

Game theoretic approach for multi-tier 5G heterogeneous network optimization based on joint power control and spectrum trading

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