Pricing for enabling forwarding in self-configuring ad hoc networks

Ileri, O.; Mau, Siun-Chuon; Mandayam, Narayan B.

doi:10.1109/wcnc.2004.1311330

Cited by 20 publications

(23 citation statements)

References 25 publications

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“…andR i,t−1 denotes the estimated accumulated regret of not playing action i. 14 Proof: The proof is a direct corollary of Theorem 6.6 of [33].…”

Section: Proof Of Propositionmentioning

confidence: 97%

“…In wireless resource allocation using auction games, bids must be submitted to some central controller that performs necessary computations and makes decisions [12], [13]. Finally, in supply-demand market models, prices and demands are exchanged among buyers and sellers [14], [15].…”

Section: B Distributed Resource Allocation In Infrastructureless Wirmentioning

confidence: 99%

“…13 Throughout this section and in order to simplify the notation, the player index (k) is omitted unless ambiguity arises. 14…”

Section: Proof Of Propositionmentioning

confidence: 99%

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Joint Channel Selection and Power Control in Infrastructureless Wireless Networks: A Multiplayer Multiarmed Bandit Framework

Maghsudi

Stańczak

2015

IEEE Trans. Veh. Technol.

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This paper deals with the problem of efficient resource allocation in dynamic infrastructureless wireless networks. Assuming a reactive interference-limited scenario, each transmitter is allowed to select one frequency channel (from a common pool) together with a power level at each transmission trial; hence, for all transmitters, not only the fading gain, but also the number of interfering transmissions and their transmit powers are varying over time. Due to the absence of a central controller and time-varying network characteristics, it is highly inefficient for transmitters to acquire global channel and network knowledge. Therefore a reasonable assumption is that transmitters have no knowledge of fading gains, interference, and network topology. Each transmitting node selfishly aims at maximizing its average reward (or minimizing its average cost), which is a function of the action of that specific transmitter as well as those of all other transmitters. This scenario is modeled as a multi-player multi-armed adversarial bandit game, in which multiple players receive an a priori unknown reward with an arbitrarily timevarying distribution by sequentially pulling an arm, selected from a known and finite set of arms. Since players do not know the arm with the highest average reward in advance, they attempt to minimize their so-called regret, determined by the set of players' actions, while attempting to achieve equilibrium in some sense. To this end, we design in this paper two joint power level and channel selection strategies.We prove that the gap between the average reward achieved by our approaches and that based on the best fixed strategy converges to zero asymptotically. Moreover, the empirical joint frequencies of the game converge to the set of correlated equilibria. We also characterize this set for two special cases Parts of the material in this paper were presented at

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“…andR i,t−1 denotes the estimated accumulated regret of not playing action i. 14 Proof: The proof is a direct corollary of Theorem 6.6 of [33].…”

Section: Proof Of Propositionmentioning

confidence: 97%

Section: B Distributed Resource Allocation In Infrastructureless Wirmentioning

confidence: 99%

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Joint Channel Selection and Power Control in Infrastructureless Wireless Networks: A Multiplayer Multiarmed Bandit Framework

Maghsudi

Stańczak

2015

IEEE Trans. Veh. Technol.

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“…The additional fifth category, effect of price, considers different pricing for channel models and could serve as a supplement for approaches from previous categories [11] [30].…”

Section: Related Workmentioning

confidence: 99%

A Novel Charging and Accounting Scheme in Mobile Ad-Hoc Networks

Kofman¹,

Huda²

2016

IJNSA

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“…We assume that the relay power is fixed to be 20 dB. The user 23 power is fixed as 10 dB but the number of users vary from 5 to 15. All channels are generated following the distribution CN (0, 1).…”

Section: A Network With Rayleigh Flat-fading Channelsmentioning

confidence: 99%

Power Allocation and Pricing in Multiuser Relay Networks Using Stackelberg and Bargaining Games

Cao

Zhao

Jing

2012

IEEE Trans. Veh. Technol.

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This paper considers a multi-user single-relay wireless network, where the relay gets paid for helping the users forward signals, and the users pay to receive the relay service. We study the relay power allocation and pricing problems, and model the interaction between the users and the relay as a two-level Stackelberg game. In this game, the relay, modeled as the service provider and the leader of the game, sets the relay price to maximize its revenue; while the users are modeled as customers and the followers who buy power from the relay for higher transmission rates. We use a bargaining game to model the negotiation among users to achieve a fair allocation of the relay power. Based on the proposed fair relay power allocation rule, the optimal relay power price that maximizes the relay revenue is derived analytically. Simulation shows that the proposed power allocation scheme achieves higher network sum-rate and relay revenue than the even power allocation. Furthermore, compared with the sum-rate-optimal solution, simulation shows that the proposed scheme achieves better fairness with comparable network sum-rate for a wide range of network scenarios. The proposed pricing and power allocation solutions are also shown to be consistent with the laws of supply and demand. Index terms:Multi-user relay network, power allocation, pricing, Stackelberg game, Kailai-Smorodinsky bargaining solution (KSBS). I. INTRODUCTIONCooperative communication has been shown to be a promising concept for future wireless networks (e.g., [1], [2]). The basic idea is to have multiple nodes in the network help each other's transmission to achieve diversity. Numerous cooperative relaying strategies aiming at 2 optimizing the global network performance have been proposed. Two widely used ones are amplify-and-forward (AF) and decode-and-forward (DF). For AF, the relays simply forward amplified versions of the signals they receive without decoding. For DF, the relays decode their received signals and transmit the re-encoded signals [3]. While pioneering efforts in cooperative networks focus on single-user networks (e.g., [4]-[9]), research on multi-user networks, in which multiple transmissions of different users can be supported, are important to meet the demands of future communication systems. One such model is the multi-user single-relay network (e.g., [10]- [16]). Capacity bounds of the network are investigated in [10], [11], and interference cancellation schemes are proposed in [12], [13]. In [14] and [15], network decoding is applied to combat interference among users in multi-user single-relay networks. In [16], the resource allocation problem, including both the subcarrier allocation and the relay power allocation, in a multi-user single-relay network is studied to maximize the sum-rate.In the aforementioned papers, nodes in a network are assumed to be altruistic and willing to cooperate to optimize the overall network performance. In many practical applications, however, nodes are selfish and aim to optimize their own benefits or qua...

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Pricing for enabling forwarding in self-configuring ad hoc networks

Cited by 20 publications

References 25 publications

Joint Channel Selection and Power Control in Infrastructureless Wireless Networks: A Multiplayer Multiarmed Bandit Framework

Joint Channel Selection and Power Control in Infrastructureless Wireless Networks: A Multiplayer Multiarmed Bandit Framework

A Novel Charging and Accounting Scheme in Mobile Ad-Hoc Networks

Power Allocation and Pricing in Multiuser Relay Networks Using Stackelberg and Bargaining Games

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