Based on a set of designed test scenarios, we then carry out an extensive performance evaluation of our approach. In order to analyze the communication distance of UWOC, the performance of optical transmitter and receiver, underwater channel characterization and modulation schemes were discussed. The communication link model was established based the on Beer-Lambert Law. This paper not only provides comprehensive understanding of UWOC, but also aims to provide a theoretical foundation for designing the underwater wireless optical communication system and optimizing the system parameters.
In addition, the current channel state is predicted by observing multiple previous states. Extensive simulations are conducted and results verify that the prediction accuracy is significantly improved using the proposed H2BMM. Particularly, we first deploy a gateway to the center of gravity of all nodes in each MANET so that the network topology is optimized. Vis extensive simulations, we validate the effectiveness of our proposed scheme.
Our experimental results demonstrate that the CDO scheme can greatly improve network performance. Finally, we discuss some other issues. Gomez, Martin B. Pairing this regulatory approach with WNV permits us to present a sharing proposal where emphasis is made on increasing resource availability and providing flexible methods for negotiating for resource access. We include an economics framework that aims at presenting an additional perspective on the attainable outcomes of our sharing proposal.
We find that by pairing regulatory flexibility with an enabling technology, within an appropriate economics context, we can increase resource access opportunities and enhance current sharing arrangements. Kokkinen Published: 12th Dec Abstract: This paper discusses the regulatory and standardization status of the Licensed Shared Access LSA , compares it with the US Citizens Broadband Radio Service concept, and reviews results from the ongoing feasibility study in the European Telecommunications Standards Institute on temporary spectrum a… This paper discusses the regulatory and standardization status of the Licensed Shared Access LSA , compares it with the US Citizens Broadband Radio Service concept, and reviews results from the ongoing feasibility study in the European Telecommunications Standards Institute on temporary spectrum access for local high-quality wireless networks.
Based on comparative analysis, a new LSA evolution concept and functional architecture is proposed, and the early results of the world first LSAevo validation are presented. Introduced system architecture can be applied to 3. In the end-to-end field trial, local high-quality wireless network use case for an industrial automation Micro-operator is validated. Then the controller estimates the right amount of power level of these vehicles to connect a new assigned RSU. We propose a novel index based algorithm named QoS-UCB that balances exploration in terms of occupancy and quality, e.
To the best of our knowledge, this is the first learning algorithm which adapts to non-stationary Markov MAB framework and also quantifies channel quality information. Where there is overlapping coverage of multiple Radio Access Technologies, with no cooperation between them, a vehicle can use the different techn… Reliable wireless communications between vehicles V2V and between vehicles and infrastructure V2I will play a key role in future transport networks. Where there is overlapping coverage of multiple Radio Access Technologies, with no cooperation between them, a vehicle can use the different technologies simultaneously.
MISS aims to find all the available networks that can meet multiple criteria based on user preference and required performance. Simulation results show that safety critical traffic can be prioritized where the resources are insufficient for all the services. Video delivery quality is also improved by prioritizing the most important frames. In the second proposed prioritization method, shorter packets in the primary and secondary networks are given a higher priority compared to longer packets and are transmitted over channels with lower service time.
The average waiting time of the packets and the primary and secondary throughput are derived. Simulation results show that the proposed schemes improve the average waiting time of packets and average throughput compared to other existing MAC policies in cognitive networks. Rawat, M. Song, C. Xin Published: 10th Jan Abstract: Software defined wireless networking is regarded as an emerging technology to enhance spectrum efficiency and improve the overall network performance.
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In the following section, we will investigate this issue by using learning automata. In practice, the primary user usually has no prior information of the secondary users and even has no knowledge of how many secondary users are in a cognitive radio network; therefore, it may incur a large amount of overheads for the primary user to obtain the perfect information about all secondary users. Likewise, the secondary users in practice are unable to have the perfect information, and they even have no knowledge of the auction mechanisms the primary user may apply.
Thus, it is impractical to employ a centralized decision making process to attain the equilibrium of the game. Based on the concept of learning automata, we propose a distributed stochastic learning algorithm to adaptively update the bidding strategies of the secondary users and the auction mechanism of the primary user in a non-cooperative manner.
Stochastic learning technique has been used for price dynamics [ 22 ], discrete power control [ 23 ], multi-mode precoding strategy selection [ 24 ], and so on. Our proposed distributed algorithm involves only limited amount of feedback and is computationally simple and efficient. To construct the learning algorithm, we consider the game in its mixed-strategy form.
Then, the expected utility g i for secondary user i is given as follows:. From the learning automata point of view, the secondary users and the primary user are the automata; the bidding strategies of the secondary users and the mechanism design strategies of the primary user are the actions of the automata.
Furthermore, the normalized utilities of the secondary users and the normalized revenue of the primary user are the reactions of the automata. Then, we can propose a distributed learning automata DLA algorithm based on learning automata. In our proposed DLA algorithm, the primary user learns probability distribution q over the set of auction mechanisms i. We assume that the primary user acts after the secondary users at each updating round, and thus, it can observe all the actions of the secondary users. That is, first, the secondary users learn and update their bidding strategies, and afterwards, the primary user learns and updates its auction mechanism.
Our proposed DLA algorithm for the mixed-strategy game is elaborated in Algorithm 1, and the corresponding flowchart is illustrated in Figure 3. Distributed learning automata algorithm for evolving the game in a mixed-strategy form. Since secondary users usually have no prior knowledge of their utilities, we employ a parameter self-adjusting scheme which can be stated as follows.
Its convergence can be set out by the following theorem. Then, according to theorem 3. From Theorem 1 , it is known that the convergence of the proposed algorithm is guaranteed.
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In addition, it is known from [ 13 ] that when a pure-strategy Nash equilibrium exists, it will be a locally asymptotically stable point of Algorithm 1. In this section, we evaluate the performance of our proposed algorithm via computer simulations. Furthermore, it is assumed that the channel gains of the secondary users include path loss and fading effects.
Mathematically, the channel gain of secondary user i is. Furthermore, it is assumed that there are five secondary users in the cognitive radio network unless otherwise stated. They are randomly distributed over interval [20 m,15 0m], i. First, we plot the evolution of probabilities q for the primary user for a specific channel realization in Figure 4. For illustration, we only plot the evolution of the choice probabilities p of bidding strategies for some selected secondary users, i.
From these two graphs, we can see that the proposed Algorithm 1 has good convergence, and the probabilities of the auction mechanisms converge faster. This is because the number of auction mechanisms is much smaller than the total number of the bidding strategies. And, we find that the proposed Algorithm 1 converges to a mixed-strategy Nash equilibrium in this simulation. Furthermore, since we assume that the duration of the auction is fixed, we should control the runtime of Algorithm 1 so that it will not be longer than the auction duration.
In practice, the adaptive stepsize scheme in [ 24 ] can be used to control the convergence speed of Algorithm 1 and limit the runtime of Algorithm 1. Evolution of the probabilities of auction mechanisms q for the primary user.
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Evolution of the probabilities of bidding strategies for the selected secondary users. In these figures, RAM-RB denotes the result where the primary user randomly chooses an auction mechanism and the secondary users randomly use their bidding strategies. It can be seen from Figures 6 , 7 , and 8 that our proposed Algorithm 1 always achieves the highest performance.
Furthermore, we plot the average revenues of the primary users and the average sum rates of the secondary users with different auction mechanisms in Figures 9 and 10 , respectively, for a particular channel realization. These two figures show that the proposed Algorithm 1 achieves higher average revenue than the other schemes, and the average sum rate of Algorithm 1 is the largest. Furthermore, we can see that the first-price mechanism fails to result in the highest revenue. Comparison of the average revenues of the primary user with different auction mechanisms by using Algorithm 1.
Comparison of the average sum rates of the secondary users with different auction mechanisms by using Algorithm 1. These results mean that the first-price auction or the second-price auction may not be optimal when the buyers have different risk attitudes. The results also indicate that our proposed algorithm can induce the secondary users to choose higher bids and achieve higher average sum utility.
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Note that in Figures 9 and 10 , the term 1st-price denotes the result where the primary user uses the traditional first-price auction mechanism and the secondary users use our proposed algorithm to choose their bidding strategies; the term 2nd-price denotes the result where the primary user uses the traditional second-price auction mechanism and the secondary users use our proposed algorithm to choose their bidding strategies.
Notice that Algorithm 1 converges in all cases. However, in Figures 11 and 12 , Algorithm 1 converges to a pure-strategy Nash equilibrium, while in Figures 13 and 14 , it converges to a mixed-strategy Nash equilibrium. The results in Figures 11 , 12 , 13 and 14 have verified that the mixed-strategy Nash equilibrium always exists in our defined game model, yet a pure-strategy Nash equilibrium may not. At last, we plot the average revenues of the secondary users with different types i.
The parameters are set as below. They are randomly distributed over interval [20m,m], i.
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Comparison of average revenue of the primary user with different risk attitudes by using Algorithm 1. In independent channel realizations.
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It is found that if the secondary users are all risk-averse, then the revenue of the primary users will be the lowest. In this paper, we have established a game theoretic framework for auction-driven dynamical spectrum access in cognitive radio networks with heterogeneous secondary users. We have studied the Nash equilibrium of the defined game and have proved that a unique pure-strategy Nash equilibrium exists in some particular situations. Due to the dynamic environment in cognitive radio networks, neither the primary user nor the secondary users have full information of the system. Thus, centralized algorithm is impractical.
To attain an equilibrium solution with only incomplete information in a distributed setting, we have proposed a distributed learning automata algorithm. We have proved that our proposed algorithm can converge to a Nash equilibrium of our defined game with proper stepsize parameters.
The simulation results have shown that our proposed algorithm can enable the auctioneer to obtain much higher revenue compared to the use of a fixed auction mechanism. At the same time, the proposed algorithm can significantly improve both the average sum capacities and the average sum utilities of secondary users. Mitola IJ: Cognitive radio for flexible mobile multimedia communications. Losifidis G, Koutsopoulos L: Challenges in auction theory driven spectrum management. IEEE Commun. Magz , 49 8 NBER, Cambridge, Accessed March Byde A: Applying evolutionary game theory to auction mechanism design.
Myerson R: Optimal auction design. Res , 6: Maskin E, Riley J: Optimal auctions with risk averse buyers. Econometrica , 52 6 Cambridge: Harvard University Press; Levin J: An optimal auction for complements. Behav , Mag , 25 6