Multiuser Scheduling using Equal Power in Allocated Subcarriers for OFDM Uplink

Giovanidis, Anastasios; Haustein, Thomas; Hadisusanto, Y.; Sezgin, Aydin; Kim, Dong‐Hee

doi:10.1109/acssc.2006.355076

Cited by 6 publications

(5 citation statements)

References 9 publications

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“…Representative data types are 16-bit/32-bit/64-bit integer. -Heuristics MUW: A heuristic power-allocation by multi-user waterfilling (a communication problem when scheduling transmissions from a base station to several receivers with frequency varying channels) (Giovanidis et al 2006). Representative data types are 32-bit integer and 32-bit/64-bit floating-point numbers.…”

Section: Methodsmentioning

confidence: 99%

Ssa-Ac

Metwalli

Hara-Azumi

2019

ACM Trans. Des. Autom. Electron. Syst.

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Recently, the quest to reduce energy consumption in digital systems has been the subject of a number of ongoing studies. One of the most researched focuses is approximate computing (AC). AC is a new computing paradigm in both hardware and software designs that aim to achieve energy-efficient digital systems. Although a variety of AC techniques have been studied so far, the main question, "How (in which section) can a program or a circuit be approximated?," has not been answered yet. This work addresses the above issue by developing a software framework Static Significance Analysis for Approximate Computing (SSA-AC) to analyze the target application program and guide the designers to identify parts of the program to which approximation can or cannot be applied. SSA-AC statically analyzes the significance of variables in the precise version of the program and thus needs no trial-and-error evaluation or specific test data. Experimental results show that SSA-AC can successfully extract the significance ranking of inputs/variables to be approximated in much shorter time than existing statistical works that are inevitably data dependent.

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Section: Methodsmentioning

confidence: 99%

Ssa-Ac

Metwalli

Hara-Azumi

2019

ACM Trans. Des. Autom. Electron. Syst.

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“…Without (15), the problem reduces to a classical sum rate maximization problem under FDMA constraint, where the optimal solution can be obtained using water filling technique [13]. However, suboptimal algorithms are more favorable in practice since they offer comparable solutions at much less cost.…”

Section: Greedy Algorithmmentioning

confidence: 98%

“…However, suboptimal algorithms are more favorable in practice since they offer comparable solutions at much less cost. Constant power water filling [14], [15], among them, has been shown to have very close performance to the optimum. In the algorithm, when an optimal number of channels for one user is determined, the user's total power is equally distributed into these channels.…”

Section: Greedy Algorithmmentioning

confidence: 99%

“…Since it explicitly gives the number of channels for allocation, there is no need to iteratively calculate the number as the original algorithm does. Under constraint (15), the power allocated to each channel of user k is simply P k L k , and the remaining problem is to assign channels under such power distributions to maximize the sum rate.…”

Section: Greedy Algorithmmentioning

confidence: 99%

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Resource Allocation with Load Balancing for Cognitive Radio Networks

Wang

Ren

2010

2010 IEEE Global Telecommunications Conference GLOBECOM 2010

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This paper considers channel and power allocation for cognitive radio (CR) networks. We assume that the total available spectrum is divided into several bands, each consisting of a group of channels. A centralized base station, enabled by spectrum sensing, is assumed to have the knowledge of all vacant channels, which will be assigned to various CRs according to their requests. The objective of resource allocation is to maximize the sum data rate of all CRs. Since the activities of primary users may cause heavy traffic in some bands while leaving other bands idle, load balancing is first performed to equalize the traffic. A multilevel subset sum algorithm as well as a simpler greedy algorithm is proposed to achieve excellent load balancing performance. After that, an algorithm incorporated with constant-power water filling is proposed to maximize the sum data rate. Simulation results are presented to illustrate the effectiveness of the proposed algorithms.

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“…Using equal power allocation [2], [7] a set of subchannels is chosen to achieve highest possible rate and minimize used frequency resources. Usually this can be achieved by moving to higher modulation schemes through increase of the power allocated to specific resources to provide higher spectral efficiency.…”

Section: Introductionmentioning

confidence: 99%

Dynamic User Grouping and Shared Frequency Resource Assignment Strategies for OFDMA

Giovanidis¹,

Sezgin²,

Monich³

et al. 2007

2007 IEEE 65th Vehicular Technology Conference - VTC2007-Spring

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In this paper strategies for dynamic user grouping and shared frequency resource assignments for OFDMA systems are proposed. The users are grouped based on their modulation type and a metric for this purpose is introduced which considers tradeoffs between maximum rate and minimum allocated resources per user. The available resources are distributed through the modulation groups based on fairness criteria. Equal power allocation on the loaded subcarriers of each user is further incorporated. Simulations show that under equal power assumptions the algorithm proposed in the following can bring a better exploitation of the available bandwidth and can provide good QoS to an increased number of users simultaneously.

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Multiuser Scheduling using Equal Power in Allocated Subcarriers for OFDM Uplink

Cited by 6 publications

References 9 publications

Ssa-Ac

Ssa-Ac

Resource Allocation with Load Balancing for Cognitive Radio Networks

Dynamic User Grouping and Shared Frequency Resource Assignment Strategies for OFDMA

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