Robust Rate Adaptation and Proportional Fair Scheduling With Imperfect CSI

Fritzsche, Richard; Rost, Peter; Fettweis, Gerhard

doi:10.1109/twc.2015.2420564

Cited by 20 publications

(8 citation statements)

References 28 publications

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“…In this letter, we analyze the outage probability of max-based user scheduling with a delay constraint and imperfect CSIT. Our results differ from those in [5] in which perfect CSIT is assumed, and in [3] in which delay constraint was not imposed. We derive the lower bounds on outage probability for flat Rayleigh fading, which are shown to be tight for moderate required rate or when the number of users is close to or larger than that of time slots.…”

contrasting

confidence: 99%

“…We also compare the results of max-based scheduling with simulation results of random and PF scheduling [3]. Random scheduler performs much worse than max-based one.…”

Section: Numerical Resultsmentioning

confidence: 99%

“…An average throughput with max-based scheduling is then determined. In [3], the authors consider proportional fairness (PF) scheduling and rate adaptation when imperfect CSIT is assumed, and analyze the outage probability that a required data rate is not supported in a single time slot. In [4], outage probability is derived for various schedulers, assuming that the transmitter obtains delayed feedback of channel information.…”

mentioning

confidence: 99%

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Outage Bound for Max-Based Downlink Scheduling With Imperfect CSIT and Delay Constraint

2016

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Abstract-We consider downlink max-based scheduling in which the base station and each user are equipped with a single antenna. In each time slot, the base station obtains channel gains of all users and selects the user with the largest squared channel gain. Assuming that channel state information at the transmitter (CSIT), i.e., squared channel gain, can be inaccurate, we derive lower bounds for probability of outage, which occurs when a required data rate is not satisfied under a delay constraint. The bounds are tight for Rayleigh fading and show how required rate and CSIT error affect outage performance.Index Terms-Outage probability, delay constraint, channel state information at transmitter (CSIT), scheduling, downlink.

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contrasting

confidence: 99%

“…We also compare the results of max-based scheduling with simulation results of random and PF scheduling [3]. Random scheduler performs much worse than max-based one.…”

Section: Numerical Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

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Outage Bound for Max-Based Downlink Scheduling With Imperfect CSIT and Delay Constraint

2016

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“…Fairness concepts have been widely studied in the context of fair division problems, see e.g. [4] for a general overview, and in many other application scenarios (mostly in telecommunications systems [10,19] and, more recently, in cloud computing [12,25]). In particular, in [25] the authors point out that resource allocation in computing systems is one of the hottest topics of interest for both computer scientists and economists.…”

Section: Related Literaturementioning

confidence: 99%

Price of Fairness for allocating a bounded resource

Nicosia

Pacifici

Pferschy

2017

European Journal of Operational Research

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In this paper we study the problem of allocating a scarce resource among several players (or agents). A central decision maker wants to maximize the total utility of all agents. However, such a solution may be unfair for one or more agents in the sense that it can be achieved through a very unbalanced allocation of the resource. On the other hand fair/balanced allocations may be far from optimal from a central point of view. So, in this paper we are interested in assessing the quality of fair solutions, i.e. in measuring the system efficiency loss under a fair allocation compared to the one that maximizes the sum of agents utilities. This indicator is usually called the Price of Fairness and we study it under three different definitions of fairness, namely maximin, Kalai-Smorodinski and proportional fairness.Our results are of two different types. We first formalize a number of properties holding for any general multi-agent problem without any special assumption on the agents utilities. Then we introduce an allocation problem, where each agent can consume the resource in given discrete quantities (items). In this case the maximization of the total utility is given by a Subset Sum Problem. For the resulting Fair Subset Sum Problem, in the case of two agents, we provide upper and lower bounds on the Price of Fairness as functions of an upper bound on the items size.

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“…In [25], the resource allocation problem with the proportional fair constraint condition based on quantised feedback for multiuser orthogonal frequency division multiplexing access system has been proposed. Besides, the proportional fair scheduling has been studied under imperfect channel state information (CSI) in [26]. However, few works have thought about the IA and fairness together.…”

Section: Introductionmentioning

confidence: 99%

Interference Alignment and Fairness Algorithms for MIMO Cognitive Radio Systems

Zhao

Wen

Chen

2015

Mobile Information Systems

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Interference alignment (IA) is an effective technique to eliminate the interference among wireless nodes. In a multiinput multi-output (MIMO) cognitive radio system, multiple secondary users can coexist with the primary user without generating any interference by using the IA technology. However, few works have considered the fairness of secondary users. In this paper, not only is the interference eliminated by IA, but also the fairness of secondary users is considered by two kinds of algorithms. Without losing generality, one primary user andKsecondary users are considered in the network. Assuming perfect channel knowledge at the primary user, the interference from secondary users to the primary user is aligned into the unused spatial dimension which is obtained by water-filling among primary user. Also, the interference between secondary users can be eliminated by a modified maximum signal-to-interference-plus-noise algorithm using channel reciprocity. In addition, two kinds of fairness algorithms, max-min fairness and proportional fairness, among secondary users are proposed. Simulation results show the effectiveness of the proposed algorithms in terms of suppressed interference and fairness of secondary nodes. What is more, the performances of the two fairness algorithms are compared.

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Robust Rate Adaptation and Proportional Fair Scheduling With Imperfect CSI

Cited by 20 publications

References 28 publications

Outage Bound for Max-Based Downlink Scheduling With Imperfect CSIT and Delay Constraint

Outage Bound for Max-Based Downlink Scheduling With Imperfect CSIT and Delay Constraint

Price of Fairness for allocating a bounded resource

Interference Alignment and Fairness Algorithms for MIMO Cognitive Radio Systems

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