Adaptive Channel Allocation Spectrum Etiquette for Cognitive Radio Networks

By Nie Nie, Cristina Comaniciu. First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005., 2005

722 citations recorded by the source corpus.

In this work, we propose a game theoretic framework to analyze the behavior of cognitive radios for distributed adaptive channel allocation. We define two different objective functions for the spectrum sharing games, which capture the utility of selfish users and cooperative users, respectively. Based on the utility definition for cooperative users, we show that the channel allocation problem can be formulated as a potential game, and thus converges to a deterministic channel allocation Nash equilibrium point. Alternatively, a no-regret learning implementation is proposed for both scenarios and it is shown to have similar performance with the potential game when cooperation is enforced, but with a higher variability across users. The no-regret learning formulation is particularly useful to accommodate selfish users. Non-cooperative learning games have the advantage of a very low overhead for information exchange in the network. We show that cooperation based spectrum sharing etiquette improves the overall network performance at the expense of an increased overhead required for information exchange.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/bc1289d5cf2579204021b92c5d5af22adc7973fc

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Citations in the corpus

Competitive Pricing for Spectrum Sharing in Cognitive Radio Networks: Dynamic Game, Inefficiency of Nash Equilibrium, and Collusion (2008, background): “In [9], a game-theoretic adaptive channel allocation scheme was proposed for cognitive radio networks.”
CRAHNs: Cognitive radio ad hoc networks (2009, neutral): “In the absence of standard spectrum etiquette, competition based approaches may allow sharing of the spectrum among the CRAHN users, and both cooperative and selfish approaches are discussed in [63].”
Cooperative Communications for Cognitive Radio Networks (2009, background): “In [5], a game theoretic framework to analyze the behavior of cognitive radios for distributed adaptive channel allocation was described.”
Spectrum Sharing for Multi-Hop Networking with Cognitive Radios (2008, methodology): “For example, in [4], [22], game theory was applied to study spectrum sharing, while in [13], [14], pricing mechanism was used.”
Game theory for cognitive radio networks: An overview (2010, neutral): “Channel allocation [9].”
Spectrum Assignment in Cognitive Radio Networks: A Comprehensive Survey (2013, methodology): “[33], [48], [49]) consider the SINR at each CR node in order to perform an efficient channel assignment.”
Resource allocation for spectrum underlay in cognitive radio networks (2008, background): “For the spectrum underlay paradigm, it is required that an interference limit corresponding to an interference temperature level be maintained at receiving points of the primary network.”
Price-Based Spectrum Management in Cognitive Radio Networks (2007, methodology): “However, the works in [5] and [33] provide only channel/power allocation algorithms and”
Machine Learning for Resource Management in Cellular and IoT Networks: Potentials, Current Solutions, and Open Challenges (2019, background): “such as collisions, increased outages, and decreased system throughput [36].”
Cooperative Spectrum Sensing in Cognitive Radio, Part II: Multiuser Networks (2007, background): “In [9] [ 8 ], a game-theoretic approach is adapted for decentralized channel assignment.”
Cooperative Spectrum Sensing in Cognitive Radio, Part I: Two User Networks (2007, background): “In [9] [8], a game-theoretic approach is adapted for decentralized channel assignment.”
Spectrum Trading in Cognitive Radio Networks: A Contract-Theoretic Modeling Approach (2011, background): “In [6], a game-theoretic adaptive channel allocation scheme was proposed for cognitive radio networks.”
Opportunistic Spectrum Access in Cognitive Radio Networks: Global Optimization Using Local Interaction Games (2012, background): “, best response dynamic [39], [40], no-regret learning [11], [20] and fictitious play [42], [43], a main drawback is that they may converge to some suboptimal NE points.”
NeXt generation/dynamic spectrum access/cognitive radio wireless networks: A survey (2006, background): “Especially, the comparison between cooperative and non-cooperative approaches has been presented in [49] through game theoretical analysis.”
Competitive spectrum sharing in cognitive radio networks: a dynamic game approach (2008, background): “In [17], a game-theoretic adaptive channel allocation scheme was proposed for cognitive radio networks.”