Novel Wi-Fi Throughput Estimation Method Considering CSMA/CA Behavior

Imagaki, Yuichi; Kashiki, Kanshiro; Yamazaki, Kosuke; Yamaguchi, A.

doi:10.1109/vetecs.2012.6240239

Cited by 6 publications

(4 citation statements)

References 4 publications

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“…As decreases, the probability of two different devices choosing the same spread sequence increases. This situation is called access collision and it can happen in a grant-free multiple access, such as in Carrier Sense Multiple Access (CSMA) employed in Wi-Fi networks [25].…”

Section: Principles Of the Musa Transmittermentioning

confidence: 99%

MUSA Grant-Free Access Framework and Blind Detection Receiver

Aquino¹,

Barbosa²,

Chafii³

et al. 2021

JCIS

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Recently, a non-orthogonal multiple access scheme called multi-user shared access (MUSA) was proposed to provide massive connection capability of low-complexity devices in the 5G networks. MUSA achieves higher spectral efficiency allowing independent devices to transmit data on the same physical layer time-frequency resources. Furthermore, MUSA introduces a grant-free transmission and a blind multi-user detection at the receiver, reducing the complexity on the transmit side. This approach is interesting for Internet of Things applications over mobile communication networks, where the devices have limited power and processing capacity. The references available in the literature about this multiple access scheme do not bring sufficient details about the MUSA multi-user detector. This limitation makes it difficult to evaluate the MUSA performance and to propose improvements for this new technique. The main goal of this paper is to provide a framework describing the entire communication chain using MUSA as multiple access. This paper also brings a proposal for a blind multi-user detection, where the information about the MUSA parameters and the channel state information are unknown at the receiver side. The performance of the MUSA multi-user detector is improved by a deep learning based processing that enhances the quality of the channel estimation provided by a initial minimum mean square error estimator. The proposed deep neural network architecture employed to improve the channel estimation allows more users to share the same time-frequency resources for a given target block error rate, increasing the overall spectrum efficiency of the system.

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Section: Principles Of the Musa Transmittermentioning

confidence: 99%

MUSA Grant-Free Access Framework and Blind Detection Receiver

Aquino¹,

Barbosa²,

Chafii³

et al. 2021

JCIS

View full text Add to dashboard Cite

show abstract

“…WiFi capacity can be estimated using theoretical formulas as in [24]- [26] or using practical methods as in [28]- [30]. Constraints (5c) represent the QoS of SUEs r (i) ∀i that has to be met by the licensed and/or unlicensed band transmissions.…”

Section: Joint Optimization Formulationmentioning

confidence: 99%

Resource Allocation and Inter-Cell Interference Management for Dual-Access Small Cells

Elsherif

Chen

Itô

et al. 2015

IEEE J. Select. Areas Commun.

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In this paper, we present a method for resource allocation for small cells that integrate licensed and unlicensed RF operations motivated by the widespread WiFi hotspots and the common inclusion of WiFi interface in most cellular terminals. Small cells have proven popular for cell coverage enhancement and traffic offloading from macrocells. We formulate an optimization problem that jointly allocates resources over both licensed and unlicensed bands with the goal of maximizing sum small cell user equipment (SUE) rate while achieving fairness among these user equipments and controlling inter-cell interference to neighboring macrocell users. The proposed solution further considers the Quality of Service (QoS) requirement of SUE traffics to be distributed over both licensed and unlicensed bands. We show the formulation of the proposed optimization problem as an efficient and low complexity linear programming. We further show that our problem formulation can be modified to maximize the revenue of mobile network operators. Our proposed solution achieves better performance than several existing solutions.

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“…represents the available WiFi capacity, which can be calculated by using theoretical formulas as in [11]- [13] or by using more practical methods for estimating WiFi capacity as in [14]- [16]. Constraint (1d) is for the maximum transmission power for HeNB n, P i,c,max , on the unlicensed band rate for connection c for HUE i.…”

Section: A Load Estimationmentioning

confidence: 99%

Design of dual-access-technology femtocells in enterprise environments

Elsherif

Chen

Itô

et al. 2013

2013 IEEE 24th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC)

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This work studies dual-access-technology femtocells equipped with both cellular and non-cellular air interfaces with the goal of increasing per-user throughput as well as achieving user fairness. We target enterprise environments characterized by high data rates and/or dense user terminals such as corporate premises, shopping malls, or stadiums. To meet this high data demand, multiple femtocells are deployed for which the major challenge is interference management. We propose an architecture for interference management and resource allocation over both cellular and non-cellular bands. Numerical evaluation shows that our proposed solution outperforms other existing solutions.

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Novel Wi-Fi Throughput Estimation Method Considering CSMA/CA Behavior

Cited by 6 publications

References 4 publications

MUSA Grant-Free Access Framework and Blind Detection Receiver

MUSA Grant-Free Access Framework and Blind Detection Receiver

Resource Allocation and Inter-Cell Interference Management for Dual-Access Small Cells

Design of dual-access-technology femtocells in enterprise environments

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