A utility proportional fairness approach for resource allocation in 4G-LTE

The uplink data arriving at the Machine-to-Machine (M2M) Application Server (AS) via M2M Aggregators (MAs) is fairly heterogeneous along several dimensions such as maximum tolerable packet delay, payload size and arrival rate, thus necessitating the design of Quality-of-Service (QoS) aware packet scheduler. In this paper, we classify the M2M uplink data into multiple QoS classes and use sigmoidal function to map the delay requirements of each class onto utility functions. We propose a proportionally fair delay-optimal multiclass packet scheduler at AS that maximizes a system utility metric. We note that the average class delay under any work-conserving scheduling policy can be realized by appropriately time-sharing between all possible preemptive priority policies. Therefore the optimal scheduler is determined using an iterative process to determine the optimal time-sharing between all priority scheduling policies, such that it results in maximum system utility. The proposed scheduler can be implemented online with reduced complexity due to the iterative optimization process. We then extend this work to determine jointly optimal MA-AS channel allocation and packet scheduling scheme at the MAs and AS. We first formulate a joint optimization problem that is solved centrally at the AS and then propose a low complexity distributed optimization problem solved independently at MAs and AS. We show that the distributed optimization solution converges quickly to the centralized optimization result with minimal information exchange overhead between MAs and AS. Using Monte-Carlo simulations, we verify the optimality of the proposed scheduler and show that it outperforms other state-of-the-art packet schedulers such as weighted round robin, max-weight scheduler etc. Another desirable feature of proposed scheduler is low delay jitter for delay-sensitive traffic.

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“…We first use a generic sigmoidal function [26], [27] to map the latency 4 requirements of i th class, l i , onto a utility function as,…”

Section: Problem Formulationmentioning

confidence: 99%

“…priority orders during the n th iteration, i.e.,γ n m = {γ n j,m : j ∈ R}. 2) Optimization at AS: For a given set A r of r classes, if the AS serves class i ∈ A r with highest priority for α i fraction of time, then using (10) and (26), the average latency l i of class i at n th iteration is,…”

Section: B Distributed Optimizationmentioning

confidence: 99%

A Delay Efficient MAC and Packet Scheduler for Heterogeneous M2M Uplink

Kumar

Abdelhadi

Clancy

2016

2016 IEEE Globecom Workshops (GC Wkshps)

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“…In , the authors proposed a distributed resource allocation algorithm under a PF‐based policy. They expressed the problem as a convex optimization problem that allows to build an optimal algorithm for this problem.…”

Section: Long‐term Evolutionmentioning

confidence: 99%

An efficient and generic downlink resource allocation procedure for pre-5G networks

Ragaleux

Baey

Fladenmuller

2016

Wirel. Commun. Mob. Comput.

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This paper addresses the downlink resource allocation problem in pre-5G (LTE-B) networks. At each time slot, the problem is to share the radio resources between users in order to maximize a given objective function. We expressed this problem considering the LTE standard constraints, which are rarely considered in the literature. Mainly, for any given user, the base station is constrained to transmit with a single modulation and coding scheme. We show that this problem is nondeterministic polynomial-time hard, and therefore, we propose a generic approximation algorithm that covers a large variety of objectives. This algorithm is composed of three routines that enable an effective resource sharing procedure. The first routine computes a solution for a relaxation of our main problem, while the second routine selects the most suitable modulation and coding scheme for each user. Finally, the last routine effectively distributes the unallocated resources. For the max rate policy, simulation results show that our algorithm outperforms other existing algorithms in terms of capacity and remains close to the optimal. Under the proportional fairness policy, our solution also provides a very good fairness while maintaining a near-optimal capacity.

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“…, and k i is the utility increase with enlarging the r i . Based on [9], r i = b i is the inflection point of the sigmoidal utility function (1) such that only for r i > b i the user is satisfied. Parameters The i th UE rate allocated by its eNB l th sector, where l ∈ {1, ..., L}, includes constituents from radars spectrum, r l i,radar , and communications spectrum, r l i,comm , so that r l i,ag = r l i,radar + r l i,comm .…”

Section: Related Workmentioning

confidence: 99%

A Utility Proportional Fairness Resource Allocation in Spectrally Radar-Coexistent Cellular Networks

Ghorbanzadeh

Abdelhadi

Clancy

2014

2014 IEEE Military Communications Conference

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Spectrum sharing is an elegant solution to addressing the scarcity of the bandwidth for wireless communications systems. This research studies the feasibility of sharing the spectrum between sectorized cellular systems and stationary radars interfering with certain sectors of the communications infrastructure. It also explores allocating optimal resources to mobile devices in order to provide with the quality of service for all running applications whilst growing the communications network spectrally coexistent with the radar systems. The rate allocation problem is formulated as two convex optimizations, where the radar-interfering sector assignments are extracted from the portion of the spectrum non-overlapping with the radar operating frequency. Such a double-stage resource allocation procedure inherits the fairness into the rate allocation scheme by first assigning the spectrally radar-overlapping resources.

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A utility proportional fairness approach for resource allocation in 4G-LTE

Cited by 82 publications

References 8 publications

A Delay Efficient MAC and Packet Scheduler for Heterogeneous M2M Uplink

A Delay Efficient MAC and Packet Scheduler for Heterogeneous M2M Uplink

An efficient and generic downlink resource allocation procedure for pre-5G networks

A Utility Proportional Fairness Resource Allocation in Spectrally Radar-Coexistent Cellular Networks

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