Iterative Water-Filling for Gaussian Vector Multiple-Access Channels

Yu, Wei; Rhee, Wonjong; Boyd, Stephen; Cioffi, J.M.

doi:10.1109/tit.2003.821988

Cited by 915 publications

(789 citation statements)

References 12 publications

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“…Problem in (7) shows that the feasibility depends solely on the feasibility of satisfying the SINR targets of the RTUs under the power constraint. The feasibility of (7) can be evaluated by a power minimisation problem as discussed later.…”

Section: Problem Formulationmentioning

confidence: 99%

Game‐theoretic beamforming techniques for multiuser multi‐cell networks under mixed quality of service constraints

Basutli

Lambotharan

2017

IET signal process.

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The authors propose a game‐theoretic approach for the downlink beamformer design for a multiuser multi‐cell wireless network under a mixed quality of services (QoS) criterion. The network has real time users (RTUs) that must attain a specific set of signal‐to‐interference‐plus‐noise ratios (SINRs), and non‐RTUs whose SINRs should be balanced and maximised. They propose a mixed QoS strategic non‐cooperative game wherein base stations determine their downlink beamformers in a fully distributed manner. In the case of infeasibility, they have proposed a fallback mechanism which converts the problem to a pure max–min optimisation. They further propose the mixed QoS bargain game to improve the Nash equilibrium operating point through Egalitarian and Kalai–Smorodinsky bargaining solutions. They have shown that the results of bargaining games are comparable to that of the optimal solutions.

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Section: Problem Formulationmentioning

confidence: 99%

Game‐theoretic beamforming techniques for multiuser multi‐cell networks under mixed quality of service constraints

Basutli

Lambotharan

2017

IET signal process.

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“…Each subchannel may represent a single OFDM tone, or more likely a group of disjoint tones bundled together. 1 Each subchannel between each user and the receiver is modeled as a time-slotted, blockfading channel with frequency-flat fading and bandwidth W c . This is reasonable when all the tones in a subchannel lie within a single coherence band; when this is not the case, then this can be viewed as an approximation in which the channel gain represents the "average" gain for the subchannel.…”

Section: Model Descriptionmentioning

confidence: 99%

“…Such resource allocation problems have been widely studied, e.g. see [1]- [4]. Most of this prior work focuses on the case in which resource allocation decisions are made by a centralized controller with knowledge of every user's channel state.…”

Section: Introductionmentioning

confidence: 99%

Distributed Resource Allocation and Scheduling in OFDMA Wireless Networks

Qin

Berry

2006

2006 Fortieth Asilomar Conference on Signals, Systems and Computers

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Abstract-In this paper we develop distributed resource allocation and scheduling algorithms for the uplink of an orthogonal frequency division multiple access (OFDMA) wireless network. We consider a time-slotted model, where in each time-slot the users are assigned to subchannels consisting of groups of OFDM tones. Each user can also allocate its transmission power among the subchannels it is assigned. We consider distributed algorithms for accomplishing this, where each user's actions depend only on knowledge of their own channel gains. Assuming a collision model for each subchannel, we characterize an optimal policy which maximizes the system throughput and also give a simpler sub-optimal policy. We study the scaling behavior of these policies in several asymptotic regimes for a broad class of fading distributions.

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“…The capacity regions of multi-link MIMO have been extensively studied theoretically for both the downlink [4,14,12] and uplink [11,9,15]. In [3] the optimum signaling in terms of system capacity is derived.…”

Section: Measurement Setup 1 Introductionmentioning

confidence: 99%