Search arXiv⌕ Search

arXiv · 1401.6633

Payoff Allocation of Service Coalition in Wireless Mesh Network: A Cooperative Game Perspective

Abstract

In wireless mesh network (WMN), multiple service providers (SPs) can cooperate to share resources (e.g., relay nodes and spectrum), to serve their collective subscribed customers for better service. As a reward, SPs are able to achieve more individual benefits, i.e., increased revenue or decreased cost, through efficient utilization of shared network resources. However, this cooperation can be realized only if fair allocation of aggregated payoff, which is the sum of the payoff of all the cooperative SPs, can be achieved. We first formulate such cooperation as a coalitional game with transferable utility, specifically, a linear programming game, in which, each SP should obtain the fair share of the aggregated payoff. Then we study the problem of allocating aggregated payoff which leads to stable service coalition of SPs in WMN based on the concepts of dual payoff and Shapley value.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Xiao Lu, Ping Wang, Dusit Niyato. 2014-01-26. Payoff Allocation of Service Coalition in Wireless Mesh Network: A Cooperative Game Perspective. https://arxiv.org/abs/1401.6633

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Money Burning Mechanism Design: From Welfare to Surplus

We settle the worst-case approximability of consumer-surplus maximization in general multidimensional mechanism-design environments. We do so through two black-box reductions from welfare maximization to the agents' total utility. Our first reduction turns exact welfare maximization into a prior-free, universally truthful and ex-post individually rational mechanism that preserves at least a $1/H_n$ fraction of optimal welfare as expected consumer surplus. The guarantee holds for $n$ agents with arbitrary nonnegative valuations over a finite outcome space, where $H_n$ is the $n$-th harmonic number. The factor $H_n$ is worst-case optimal, including its constant, even for a single-item auction with a known i.i.d. prior and Bayesian incentive compatibility. Our second reduction allows existing truthful welfare approximation mechanisms to be reused for surplus maximization. For valuation classes closed under scaling, it converts any ex-post individually rational, truthful $α$-approximation for welfare with nonnegative payments into an $O(α\log(n))$-approximation for surplus. Our sharp guarantee resolves the welfare-approximation aspect of the open question of Hartline and Roughgarden [2008] on the power of money burning beyond $k$-unit auctions, and the question of Ezra et al. [2025] concerning optimal surplus guarantees for broader valuation classes. It also replaces the outcome-dependent $O(\log|\mathcal{O}|)$ guarantee of Fotakis et al. [2015] with the tight agent-dependent factor $H_n$. These results yield polynomial-time mechanisms with the exact $H_n$ guarantee for gross-substitutes. They also give prior-free, universally truthful approximations of $O(H_n\log^2\log m)$ for XOS valuations and $O(H_n\log^3\log m)$ for subadditive valuations using demand and value queries, where $m$ is the number of items.

cs.GT↗

Improved Revenue Guarantees for Selling Separately and Bundling

We study how much revenue a seller can lose by restricting attention to selling separately or grand bundling, in the setting of a single additive buyer with independent item values. Although revenue-optimal mechanisms can require lotteries and infinite menus, Babaioff, Immorlica, Lucier, and Weinberg showed that the better of these two simple formats always achieves a constant fraction of optimal revenue. We prove that $\mathrm{OPT} \le 3.52 \max\{\mathrm{SREV}, \mathrm{BREV}\}$, where $\mathrm{SREV}$ and $\mathrm{BREV}$ are the optimal revenues from selling separately and grand bundling, respectively. This improves the previous best-known approximation factor of $5.2$ due to Ma and Simchi-Levi and narrows the gap to the known lower bound of $2$.

cs.GT↗

Diverse Representation in Approval-Based Committee Voting

The study of approval-based committee (ABC) voting has so far focused predominantly on proportional representation. The canonical notion of diverse representation, based on the Chamberlin--Courant score, counts the number of voters with at least one representative in the committee, making it an individualistic notion. We develop a more comprehensive theory that instead requires the representation of many groups of voters, grounding it in the justified representation (JR) axiom, which we strengthen in two directions. First, we study the existing axioms of Strong JR (SJR) and Semi-Strong JR (SSJR), which consider the same cohesive groups as JR but demand stricter representation. We show that neither can be optimized efficiently on general domains (unless P=NP), but both can be on the Candidate Interval domain. Second, to capture the unique and defining opinions of a group, we introduce Distinctive Representation (DR) and its local optimization variant, Local DR. We address satisfiability and computation time, and show (Local) DR to be distinct from known proportionality and diversity axioms. Experiments on real-world and synthetic data show Local DR performs well on multiple empirical measures of diversity.

cs.GT↗