Inter-operator spectrum sharing from a game theoretic perspective

This is different from our system model, as described in the next section, where BSs are not supposed to have sensing capabilities and such a hierarchy is not present. In [9], a more complex CR environment is considered, where a CR network operates in the coverage area of three Primary Network Operators PNO and is subject to some rate and interference constraints. PNOs take advantage of the relaying capabilities of the secondary operator, which accepts to cooperate as long as it In an alternative co-primary spectrum sharing model, multiple operators are willing to jointly use a part or the whole of their licensed spectrum [2], [3].

Operators may have individual licenses to access exclusive frequency bands, or a group authorization to use a common pool of spectral resources. Joint use of the licensed spectrum of operators can be realized by sharing the frequency resources either orthogonally or non-orthogonally.

In the case of non-orthogonal spectrum sharing, operators simultaneously use common block of spectral resources, creating inter-operator interference. The operators might implement inter-operator interference mitigation schemes, see e.

Most of the works on co-primary spectrum sharing rely on operator specific information which in some cases needs to be exchanged.

In [8], [9], operator specific information such as full inter-operator Channel State Information CSI is assumed to be available for all operators, in order to apply inter-operator interference The detailed derivation of the expressions may be found in [9].

Operators bargaining—first phase As stated at the beginning of this section, the price p and the amount of spectrum b are subject to a bargaining process between the MO and the FO, which is conducted before the subscription prices are advertised by the operators and the subscription decision is made by the users. We model the bargaining as a non-cooperative game where the incumbent operator the MO has full bargaining power and therefore offers a take-it-or-leav Documents: Advanced Search Include Citations.

Authors: Advanced Search Include Citations. Results 1 - 4 of 4. Abstract-Static spectrum allocation leads to resource wastage and inter-operator spectrum sharing is a possible way to improve spectrum efficiency. In this work, we assume that two cellular network operators agree upon sharing part of their spectrum, which can then be dynamically accessed by either Abstract - Cited by 3 1 self - Add to MetaCart Abstract-Static spectrum allocation leads to resource wastage and inter-operator spectrum sharing is a possible way to improve spectrum efficiency.

In this work, we assume that two cellular network operators agree upon sharing part of their spectrum, which can then be dynamically accessed by either of them in a mutually exclusive way. Our goal is to numerically assess the gain, in terms of cell capacity, due to such orthogonal spectrum sharing. Hence, we propose a centralized algorithm that performs coordinated scheduling, in order to numerically evaluate an upper bound on the achievable sum capacity.

The algorithm is centralized and exploits complete information on both networks to perform the optimum allocation. The simulation results illustrate the impact of the multiuser diversity and the asymmetry in the traffic load among the networks on the overall achievable gain.

Metrics details. We address the problem of spectrum sharing where competitive operators coexist in the same frequency band. First, we model this problem as a strategic non-cooperative game where operators simultaneously share the spectrum according to the Nash Equilibrium NE. Given a set of channel realizations, several Nash equilibria exist which renders the outcome of the game unpredictable.

Then, in a cognitive context with the presence of primary and secondary operators, the inter-operator spectrum sharing problem is reformulated as a Stackelberg game using hierarchy where the primary operator is the leader. The Stackelberg Equilibrium SE is reached where the best response of the secondary operator is taken into account upon maximizing the primary operator's utility function.

Moreover, an extension to the multiple operators spectrum sharing problem is given. It is shown that the Stackelberg approach yields better payoffs for operators compared to the classical water-filling approach. Finally, we assess the goodness of the proposed distributed approach by comparing its performance to the centralized approach. You can also search for this author in PubMed Google Scholar.

Correspondence to Mehdi Bennis. Reprints and Permissions. Bennis, M. Signal Process. Download citation.