Game-based multi-hop broadcast including power control and MRC in wireless networks

Mousavi, Mahdi; Al-Shatri, Hussein; Le, Hong Quy; Kuehne, Alexander J. C.; Wichtlhuber, Matthias; Hausheer, David; Klein, Anja

doi:10.1109/pimrc.2015.7343552

Cited by 3 publications

(8 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The importance of multi-hop broadcast for future communication networks implies the need for a more comprehensive algorithm capable of addressing both the energy efficiency and incentive issue which, to the best of our knowledge, has not been addressed in the literature. In [1], [16], we proposed game-theoretic algorithms for minimum-power multihop broadcast. Although [16] considers MRC, the decision making at the nodes is based on a heuristic iterative approach which may not necessarily be the best decision for the node.…”

Section: State Of the Art And Main Contributionsmentioning

confidence: 99%

“…when it changes its PNs from W i to W i . According to (16), the change in the cost of node i can be written as ∆ i…”

Section: Remarkmentioning

confidence: 99%

“…Since the cost function in G, defined in (16), is linearly separable with respect to the cost paid by a CN to each of its chosen PNs, we can write the right side of (24) as ∆ i…”

Section: Remarkmentioning

confidence: 99%

“…In [1], [16], we proposed game-theoretic algorithms for minimum-power multihop broadcast. Although [16] considers MRC, the decision making at the nodes is based on a heuristic iterative approach which may not necessarily be the best decision for the node. In [1], we showed that the decision-making problem at the nodes can be solved optimally by a linear program in contrast to the heuristic approach of [16].…”

Section: State Of the Art And Main Contributionsmentioning

confidence: 99%

“…• Cost function: Assigns a real-valued cost to every node i ∈ P as C i (a) : (W, P req ) → R + . The cost function is defined in (16).…”

Section: A Game-theoretic Modelmentioning

confidence: 99%

See 4 more Smart Citations

Energy and Social Cost Minimization for Data Dissemination in Wireless Networks: Centralized and Decentralized Approaches

Mousavi

Klein

2020

IEEE Trans. Veh. Technol.

Self Cite

View full text Add to dashboard Cite

We study multi-hop data-dissemination in a wireless network from one source to multiple nodes where some of the nodes of the network act as re-transmitting nodes and help the source in data dissemination. In this network, we study two scenarios; i) the transmitting nodes do not need an incentive and ii) they do need an incentive and are paid by their corresponding receiving nodes by virtual tokens. We investigate two problems; P1) network power minimization for the first scenario and P2) social cost minimization for the second scenario, defined as the total cost paid by the nodes of the network for receiving data. In this paper, we propose centralized and decentralized approaches for each of the problems. The proposed decentralized approach is based on a non-cooperative cost-sharing game (CSG). In our proposed game, every receiving node chooses its respective transmitting nodes and consequently, a cost is assigned to it according to the power imposed on its chosen transmitting nodes. For the sake of energy efficiency, in our model, we employ maximal-ratio combining (MRC) at the receivers so that a receiver can be served by multiple transmitters. Moreover, besides the radio-link power, we capture the power required by the passive modules of wireless devices involved in reception/transmission. We discuss how the network is formed in a decentralized way, find the actions of the nodes in the game and show that the proposed game reaches a Nash equilibrium. To find the centralized global optimum, which is a benchmark to our decentralized approach, we use a mixed-integer-liner-program (MILP). The proposed MILP can be used for both of the problems P1 and P2 with a minor change. Simulation results show that our proposed decentralized approach outperforms the conventional algorithms in terms of energy efficiency and social cost while it can address the need for an incentive for collaboration. 1

show abstract

Section: State Of the Art And Main Contributionsmentioning

confidence: 99%

“…when it changes its PNs from W i to W i . According to (16), the change in the cost of node i can be written as ∆ i…”

Section: Remarkmentioning

confidence: 99%

“…Since the cost function in G, defined in (16), is linearly separable with respect to the cost paid by a CN to each of its chosen PNs, we can write the right side of (24) as ∆ i…”

Section: Remarkmentioning

confidence: 99%

Section: State Of the Art And Main Contributionsmentioning

confidence: 99%

“…• Cost function: Assigns a real-valued cost to every node i ∈ P as C i (a) : (W, P req ) → R + . The cost function is defined in (16).…”

Section: A Game-theoretic Modelmentioning

confidence: 99%

See 3 more Smart Citations

Energy and Social Cost Minimization for Data Dissemination in Wireless Networks: Centralized and Decentralized Approaches

Mousavi

Klein

2020

IEEE Trans. Veh. Technol.

Self Cite

View full text Add to dashboard Cite

show abstract

Multi-hop data dissemination with selfish nodes: Optimal decision and fair cost allocation based on the Shapley value

Mousavi

Müller

Al-Shatri

et al. 2016

2016 IEEE International Conference on Communications (ICC)

View full text Add to dashboard Cite

Buyers Collusion in Incentivized Forwarding Networks: A Multi-Agent Reinforcement Learning Study

Ibrahim,

Ekin,

Imran

2024

Trans. Mach. Learn. Comm. Netw.

View full text Add to dashboard Cite

We present the issue of monetarily incentivized forwarding in a multi-hop mesh network architecture from an economic perspective. It is anticipated that credit-incentivized forwarding and relaying will be a simple method of exchanging transmission power and spectrum for connectivity. However, gateways and forwarding nodes, like any other free market, may create an oligopolistic market for the users they serve. In this study, a coalition scheme between buyers aims to address price control by gateways or nodes closer to gateways. In a Stackelberg competition game, buyer agents (users) and sellers (gateways) make decisions using reinforcement learning (RL), with decentralized Deep Q-Networks to buy and sell forwarding resources. We allow communication links between the buyers with a limited messaging space, without defining a collusion mechanism. The idea is to demonstrate that through messaging, and RL tacit collusion can emerge between agents in a decentralized setup. The multi-agent reinforcement learning (MARL) system is presented and analyzed from a machine-learning perspective. Moreover, MARL dynamics are discussed via mean field analysis to better understand divergence causes and make implementation recommendations for such systems. Finally, the simulation results show the results of coordination among the users.

show abstract

Game-based multi-hop broadcast including power control and MRC in wireless networks

Cited by 3 publications

References 13 publications

Energy and Social Cost Minimization for Data Dissemination in Wireless Networks: Centralized and Decentralized Approaches

Energy and Social Cost Minimization for Data Dissemination in Wireless Networks: Centralized and Decentralized Approaches

Multi-hop data dissemination with selfish nodes: Optimal decision and fair cost allocation based on the Shapley value

Buyers Collusion in Incentivized Forwarding Networks: A Multi-Agent Reinforcement Learning Study

Contact Info

Product

Resources

About