Standards-compliant LTE and LTE-A uplink power control

Zhang, Honghai; Prasad, Narayan; Rangarajan, Sampath; Mekhail, Sherif; Said, Said; Arnott, Rob

doi:10.1109/icc.2012.6364333

Cited by 16 publications

(25 citation statements)

References 3 publications

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“…Another approach is interference aware fractional power control (IAFPC), which limits the maximum amount of interference that a UE can generate. If the interference generated is greater than a maximum threshold, the UE adjusts its transmit power so as to reduce the interference to a given maximum value [7]. These few examples confirm the relevance that mitigating interference in cellular networks plays, as well as the research efforts that have been put towards this end.…”

Section: Introductionmentioning

confidence: 68%

“…The existing works that employ the TIN optimality condition for interference management are limited to D2D communications [12]- [14], whereas the works that employ interference awareness methods in cellular networks are based on heuristic criteria [7], [15]- [21]. In the present paper, we propose a scheduling algorithm based on the TIN optimality condition and develop a tractable analytical framework for its analysis and optimization by using tools from stochastic geometry.…”

Section: A Related Workmentioning

confidence: 99%

“…where max (·, ·) denotes the maximum operator, and its first and second arguments originate from the shortest distance cell association criterion and from the TIN-based optimality conditions in (3), respectively. From (7), the condition X 21 ≥ R I holds implicitly true.…”

Section: A Approximations For Tractable Analytical Modelingmentioning

confidence: 99%

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Treating Interference as Noise in Cellular Networks: A Stochastic Geometry Approach

Bacha

Renzo

Clerckx

2020

IEEE Trans. Wireless Commun.

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The interference management technique that treats interference as noise (TIN) is optimal when the interference is sufficiently low. Scheduling algorithms based on the TIN optimality condition have recently been proposed, e.g., for application to device-to-device communications. TIN, however, has never been applied to cellular networks. In this work, we propose a scheduling algorithm for application to cellular networks that is based on the TIN optimality condition. In the proposed scheduling algorithm, each base station (BS) first randomly selects a user equipment (UE) in its coverage region, and then checks the TIN optimality conditions. If the latter conditions are not fulfilled, the BS is turned off.In order to assess the performance of TIN applied to cellular networks, we introduce an analytical framework with the aid of stochastic geometry theory. We develop, in particular, tractable expressions of the signal-to-interference-and-noise ratio (SINR) coverage probability and average rate of cellular networks. In addition, we carry out asymptotic analysis to find the optimal system parameters that maximize the SINR coverage probability. By using the optimized system parameters, it is shown that TIN applied to cellular networks yields significant gains in terms of SINR coverage probability and average rate. Specifically, the numerical results show that average rate gains of the order of 21% over conventional scheduling algorithms are obtained. Index TermsTreating interference as noise (TIN), cellular networks, user scheduling, Poisson point processes, stochastic geometry, system level analysis.

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Section: Introductionmentioning

confidence: 68%

Section: A Related Workmentioning

confidence: 99%

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Treating Interference as Noise in Cellular Networks: A Stochastic Geometry Approach

Bacha

Renzo

Clerckx

2020

IEEE Trans. Wireless Commun.

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“…If an interference-aware scheme is used, the transmit power of the typical MT can be formulated as follows [16], [17]:…”

Section: Power Control Modelingmentioning

confidence: 99%

“…In the uplink, in addition, power control plays a vital role for striking a flexible trade-off between the coverage/rate and the power consumption of the Mobile Terminals (MTs) [14]. The 3rd Generation Partnership Project (3GPP), for example, adopts a fractional power control mechanism that depends on the path-loss and on the maximum transmit power of the MTs [15], as well as it accounts for uplink other-cell interference [16], [17].…”

Section: Introductionmentioning

confidence: 99%

Stochastic geometry analysis of uplink cellular networks with multi-antenna base stations and interference-aware fractional power control

Guan¹,

Renzo²

2015

2015 International Conference on Communications, Management and Telecommunications (ComManTel)

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In this paper, uplink cellular networks with multiantenna Base Stations (BSs) and interference-aware power control are studied. The locations of the multi-antenna BSs are modeled as points of a Poisson Point Process (PPP), and Maximum Ratio Combining (MRC) and Optimum Combining (OC) receivers are compared. Extensive system-level simulations are carried out and the impact of interference-unaware and interference-aware fractional power control is analyzed. Several numerical results and performance trends are illustrated in terms of coverage probability, rate and power consumption. It is shown that competing performance trends emerge for the achievable performance of cell-edge and cell-center Mobile Terminals (MTs).

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