Interference-aware time-based fairness for multihop wireless networks

Blough, Douglas M.; Resta, Giovanni; Santi, Paolo

doi:10.1109/infocom.2016.7524516

Cited by 1 publication

(1 citation statement)

References 17 publications

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“…The practical significance of stochastic asymmetric Blotto games is paramount for evolving the decision-making logic of next-generation communication systems by shifting from centrally-imposed to distributed-competitive notions of fairness, where the term "users" (players) can expand from mobile users to complete campus-wide networks, service providers, smart verticals, or even virtual network functions (VNFs), network nodes, etc., and wireless services (contests) can draw on enhanced Mobile BroadBand (eMBB) profiles with transformational Ultra Reliable Low Latency Communications (URLLC) and massive Machine Type Communications (mMTC) applications (3GPP Releases 15 & 16). For example, stochastic asymmetric Blotto game logic can jointly integrate eMBB, URLLC and mMTC services into single multi-network profile when applied on, e.g., (i) cognitive radios (CRs) by enabling unlicensed mobile users to simultaneously compete for spare licensed carriers in both good and bad channel conditions, which ensures that no resources are wasted [6]- [7], (ii) mobile ad-hoc networks (MANETs) by deliberating both best-effort and poorly-treated nodes, which maximises the number of active routes and, thus, device connectivity [8], (iii) software-defined networking (SDN) by hierarchising the coexisting network nodes according to their instantaneous capacity, which helps to self-configure the traffic load balancing rules in runtime [9]- [10], (iv) multi-hop networks by distributing hops that are close as well as away from the gateways, which maximises the number of data flows and, thus, the network throughput overall [11], (v) Fog computing by anticipating the benefit of each Edge-Cloud device considering both free and congested computation/storage resources [12], (vi) network sharing by enabling multiple mobile operators with their networks deployed in the same area to collaborate and maximise instantaneous service-critical requests, while competing to improve their profits [13]- [14], and so on.…”

Section: Introductionmentioning

confidence: 99%

Stochastic Asymmetric Blotto Game Approach for Wireless Resource Allocation Strategies

Chien

Zarakovitis

et al. 2019

IEEE Trans. Wireless Commun.

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The development of modellings and analytical tools to structurise and study the allocation of resources through noble user competitions become essential, especially considering the increased degree of heterogeneity in application and service demands that will be cornerstone in future communication systems. Stochastic asymmetric Blotto games appear promising to modelling such problems, and devising their Nash equilibrium (NE) strategies by anticipating the potential outcomes of user competitions. In this regard, this paper approaches the generic energy efficiency problem with a new stochastic asymmetric Blotto game paradigm to enable the derivation of joint optimal bandwidth and transmit power allocations by setting multiple users to compete in multiple auction-like contests for their individual resource demands. The proposed modelling innovates by abstracting the notion of fairness from centrally-imposed to distributed-competitive, where each user's pay-off probability is expressed as quantitative bidding metric, so as, all users' actions can be interdependent, i.e., each user attains its utility given the allocations of other users, which eliminates the chance of lowvalued carriers not being claimed by any user, and, in principle, enables the full utilisation of wireless resources. We also contribute by resolving the allocation problem with low complexity using new mathematical techniques based on Charnes-Cooper transformation, which eliminate the additional coefficients and multipliers that typically appear during optimisation analysis, and derive the joint optimal strategy as a set of linear singlevariable functions for each user. We prove that our strategy converges towards a unique, monotonous and scalable NE, and examine its optimality, positivity and feasibility properties in detail. Simulation comparisons with relevant studies confirm the superiority of our approach in terms of higher energy efficiency performance, fairness index and quality-of-service provision.

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