A Stackelberg game for resource allocation in multiuser cooperative transmission networks

Abstract

In this paper, we consider the problem of stimulating cooperation and resource allocation in cooperative transmission networks. We formulate this problem as a sellers' market competition where a relay is willing to share its resource with multiple users. We use a Stackelberg game to jointly consider the benefits of the relay and the users. Firstly, the relay determines the price of relaying according to the user demand. Secondly, the users purchase the optimal amount of resources to maximize their utilities. Although the Nash equilibrium, i.e., the solution of the game, can be obtained in a centralized manner, we develop a distributed algorithm to search the Nash equilibrium, which is more applicable in practical systems. Also, the convergence conditions of the algorithm are analyzed. Simulation results show, by using the distributed algorithm, the relay and the users could determine what price should ask for and how much bandwidth should buy, respectively.

Fair and Efficient Resource Sharing for Selfish Cooperative Communication Networks Using Cooperative Game Theory

“…To tackle this problem, game theory [8] is a natural and powerful tool which studies how autonomous users interact and cooperate with each other. In this area, Zhang [9] and Cong [10] studied the problem of how a relay should allocate its bandwidth among multiple competing source nodes using the noncooperative game theory. Based on the Stackberg game theory, Wang [11] studied another case in which multiple relays compete with each other in terms of price to gain the highest profit from offering relaying power to a single source node.…”

A smart scheme for relay selection in cooperative wireless communication systems

“…Noticing that from (14), the theoretical maximum channel capacity for a 2 × 2 MIMO is C max = 4.11 bit/s/Hz, Table 2 presents the theoretical maximum capacity gain that the cooperative system can achieve, taking into account the values presented in Table 1. This gain is defined as…”

“…The previous cited works [10][11][12][13][14][15][16][17], which present relay selection algorithms, only consider frequency-flat fading channels. Nevertheless, broadband communication wireless systems have a transmission bandwidth larger than the coherence bandwidth of the channel, yielding a frequency-selective channel.…”

“…Compared to cooperative diversity schemes where all relays transmit their message to the destination concurrently, several recent results have demonstrated the performance and implementation advantages of one or a subset of relays assisting transmissions in a wireless network [10][11][12][13][14][15][16][17][18][19][20][21]. With relay selection, the complexity, synchronization, and overhead of the cooperative system are relaxed, leading to an increase in the spectral efficiency.…”

“…The authors of [11] presented a centralized method for grouping and partner selection to achieve full network-wide cooperative diversity, in which enough partners are assigned to each transmitter in order to improve the probability of decoding messages successfully. Other works followed a game theory approach to find a solution to the relay selection problem [12][13][14]; this problem is formulated as a market competition in which multiple relays compete with each other to offer resources (e.g., power, bandwidth) to a single user. In [15], a power allocation scheme along with a relay subset selection scheme considering generic noise and interference were proposed in order to reduce the signaling overhead required for CSI acquisition.…”