Rate and UE Selection Algorithms for Interference-Aware Receivers

Abdrashitov, Vitaly; Nam, Wooseok; Bai, Dingqun

doi:10.1109/vtcspring.2014.7023067

Cited by 13 publications

(10 citation statements)

References 6 publications

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“…The MAC rate region is obtained when the receiver does a joint decoding of both the desired and dominant interference signals. This rate region (denoted as R MAC (ij) for a UE u ij ) is governed by the following rate conditions [4]:…”

Section: System Modelmentioning

confidence: 99%

“…For a given cell i, the UE (say, U ij * ) that gives the largest PF metric when calculated based on its maximum feasible data rate obtained from its Reuse 1 rate region (R Reuse1 (ij * )) is selected. For these UEs selected in all the cooperating cells, the optimal rate allocation algorithm (denoted as Algorithm 2 in [4]) which is based on the max-sum message passing framework is performed.…”

Section: A Reference Lms Schemementioning

confidence: 99%

“…2) Cooperative Rate-set Selection: Perform cooperative rate selection for the selected set of UEs in the previous step using Algorithm 2 given in [4].…”

Section: A Reference Lms Schemementioning

confidence: 99%

“…Among all the NAICS receivers, the Interference Aware Successive Decoding (IASD) is one of the most advanced receivers that tries to decode and cancel the dominant interfering signal, thereby improving the receive signal-to-interference and noise-ratio (SINR) of the desired signal [4]. Their interference cancellation (IC) efficiency, which is a percenage estimate of the amount of the dominant interference that can be canceled, depends mainly on the receive signal quality and the transmission data rate of their dominant interference.…”

Section: Introductionmentioning

confidence: 99%

“…This paper provides enhancements to the best performing scheme given in [4], namely the Loosest constraint UE selection/max-sum rate selection (denoted as'LMS' in this paper). Their approach performs UE and rate selection separately by first choosing the UE (locally in each cell) that has the loosest rate constraint.…”

Section: Introductionmentioning

confidence: 99%

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Coordinated Scheduling for Advanced UE Receivers Using Belief Propagation

Natarajan

2015

2015 IEEE 81st Vehicular Technology Conference (VTC Spring)

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The newly introduced Network Assisted Interference Cancellation and Suppression (NAICS) feature in LTE Release-12 facilitates autonomous cancellation/suppression of a user equipment's (UE's) dominant interference. For one of the most-advanced UE receivers that benefit from NAICS, namely the interference-aware successive decoding (IASD) receiver, the achievable transmission rate is dependent on the dominant interferer's transmission rate. Hence, cooperative scheduling among the neighboring basestations (BSs) can further enhance their performance. This paper proposes distributed coordination schemes for joint UE, rate and BS transmit power selection (power on/off) based on a belief-propagation framework.

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Section: System Modelmentioning

confidence: 99%

Section: A Reference Lms Schemementioning

confidence: 99%

“…2) Cooperative Rate-set Selection: Perform cooperative rate selection for the selected set of UEs in the previous step using Algorithm 2 given in [4].…”

Section: A Reference Lms Schemementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Coordinated Scheduling for Advanced UE Receivers Using Belief Propagation

Natarajan

2015

2015 IEEE 81st Vehicular Technology Conference (VTC Spring)

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SAHCI: Scheduling Approach for Heterogeneous Content-Centric IoT Applications

et al. 2019

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The Internet of Things (IoT) is one of the dominating paradigms of the new era with its abilities to provide ubiquitous intelligence and pervasive interconnections to diverse physical objects. With the advancements, such as new generation 5G communication and cloud/edge computing-based paradigms, the degree of domination is expected to further increase. Therefore, improvements for quality of service have become a critical issue. Traditional packets scheduling algorithms cannot meet the requirements of the large-scale IoT systems. In this paper, a novel scheduling approach is proposed for different data classes (types) to be exchanged between heterogeneous nodes of a generic IoT infrastructure. The possible ways of organizing these classes depending on different metrics, such as time latency, reliability, and data loss, are investigated. The proposed approach has the advantage of being able to tune the priorities and network characteristics to reach a specifically desired performance state. Since each type of packets is considered separately, it is possible to prioritize them, by tuning the related parameters, which changes the priorities between packets. The numerical results presented show that the new approach performs better than the existing typical scheduling approaches. The developed approach can be used in the various IoT applications with the support of 5G communication and edge computing, such as agriculture, wearables, connected cars, smart retail, and smart cities.

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