On the Location-Dependent SIR Gain in Cellular Networks

Feng, Ke; Haenggi, Martin

doi:10.1109/lwc.2019.2892422

Cited by 18 publications

(10 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…MODELS The locations of the users play a crucial part in their performance. Conditioning the regions (e.g., cell center and boundary) of the user of interest allows a location-dependent analysis [99], [100] for a location-specific user (LSU) in cellular networks. The objective of this section is to demonstrate the effect of the location of the target user on the SIR distribution.…”

Section: Location-dependent Analysis Of Cellularmentioning

confidence: 99%

Stochastic Geometry Analysis of Spatial-Temporal Performance in Wireless Networks: A Tutorial

Lu¹,

Salehi²,

Haenggi³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

The performance of wireless networks is fundamentally limited by the aggregate interference, which depends on the spatial distributions of the interferers, channel conditions, and user traffic patterns (or queueing dynamics). These factors usually exhibit spatial and temporal correlations and thus make the performance of large-scale networks environment-dependent (i.e., dependent on network topology, locations of the blockages, etc.). The correlation can be exploited in protocol designs (e.g., spectrum-, load-, location-, energy-aware resource allocations) to provide efficient wireless services. For this, accurate system-level performance characterization and evaluation with spatio-temporal correlation are required. In this context, stochastic geometry models and random graph techniques have been used to develop analytical frameworks to capture the spatio-temporal interference correlation in large-scale wireless networks. The objective of this article is to provide a tutorial on the stochastic geometry analysis of large-scale wireless networks that captures the spatio-temporal interference correlation (and hence the signal-to-interference ratio (SIR) correlation). We first discuss the importance of spatiotemporal performance analysis, different parameters affecting the spatio-temporal correlation in the SIR, and the different performance metrics for spatio-temporal analysis. Then we describe the methodologies to characterize spatio-temporal SIR correlations for different network configurations (independent, attractive, repulsive configurations), shadowing scenarios, user locations, queueing behavior, relaying, retransmission, and mobility. We conclude by outlining future research directions in the context of spatiotemporal analysis of emerging wireless communications scenarios.

show abstract

Section: Location-dependent Analysis Of Cellularmentioning

confidence: 99%

Stochastic Geometry Analysis of Spatial-Temporal Performance in Wireless Networks: A Tutorial

Lu¹,

Salehi²,

Haenggi³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Please refer to [18] for further details. Once both f k (I(t + 1), I D (t)) and f k (I D (t)) have been computed, we use (11) to obtain the conditional PDF…”

Section: Interference Power Estimationmentioning

confidence: 99%

“…Rayleigh fading channels are considered in [11], and shadowing is considered as noise source. BSs locations are assumed to follow a Poisson point process and user coordination is obtained by assigning each user to the nearest BS.…”

Section: Introductionmentioning

confidence: 99%

“…BSs locations are assumed to follow a Poisson point process and user coordination is obtained by assigning each user to the nearest BS. Defining as success probability the probability of the signal to interference ratio being above a certain threshold, [11] provides the distribution of the success probability conditioned on UE being a cell center or cell boundary type.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Interference Distribution Prediction for Link Adaptation in Ultra-Reliable Low-Latency Communications

Brighente

Mohammadi

Baracca

2020

2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring)

View full text Add to dashboard Cite

The strict latency and reliability requirements of ultra-reliable low-latency communications (URLLC) use cases are among the main drivers in fifth generation (5G) network design. Link adaptation (LA) is considered to be one of the bottlenecks to realize URLLC. In this paper, we focus on predicting the signal to interference plus noise ratio at the user to enhance the LA. Motivated by the fact that most of the URLLC use cases with most extreme latency and reliability requirements are characterized by semi-deterministic traffic, we propose to exploit the time correlation of the interference to compute useful statistics needed to predict the interference power in the next transmission. This prediction is exploited in the LA context to maximize the spectral efficiency while guaranteeing reliability at an arbitrary level. Numerical results are compared with state of the art interference prediction techniques for LA. We show that exploiting time correlation of the interference is an important enabler of URLLC.

show abstract

“…Due to the ergodicity of the PPP, the area fraction of two regions are equivalent to the probabilities that the typical user at the origin falls into the two regions, respectively, given by Fig. 2 shows the area fraction of the two regions with R. Remark 1: The user classification in [25] is based on the relative distance between the user and the serving BS and the interfering BSs, where the area fraction can be from 0 to 1. The user classification in this paper is based on the exclusion regions.…”

Section: User Classificationmentioning

confidence: 99%

Performance Analysis of D2D and Cellular Coexisting Networks With Interference Management

2020

View full text Add to dashboard Cite

Integrating device-to-device (D2D) communication into cellular networks is considered as a promising technology to support high data rate and improve spectrum utilization. However, the severe interference issue in D2D and cellular coexisting networks might erode the benefits brought by D2D communications. To address this critical issue, we first propose two exclusion-based D2D activation schemes with hybrid spectrum allocation (named EDAH) and spectrum sharing (named EDAS), where D2D users (DUs) outside the exclusion regions of base stations are active. Based on the activation scheme, cellular users can be classified into central users (CUs) within the exclusion regions and the remaining edge users (EUs). Secondly, we establish a stochastic geometry-based framework to characterize the performance of D2D and cellular coexisting networks, where the distribution of the active D2D pairs follow a bipolar Poisson hole process. Specifically, we provide tight bounds and accurate approximations for the success probabilities of CUs and DUs. Due to the different spectrum allocation, we provide an exact analytical expression for the success probability of EUs in EDAH and a tight bound in EDAS. With these results, we further analyze the area spectral efficiency (ASE) for network-level performance evaluations. The results show that both schemes are effective in suppressing interference. In particular, the EDAH scheme can significantly improve the transmission reliabilities of users anywhere, while the EDAS scheme generally gains higher ASE at the expense of the transmission reliability of EUs. INDEX TERMS Stochastic geometry, D2D and cellular coexisting network, Poisson hole process, success probability, area spectrum efficiency.

show abstract

On the Location-Dependent SIR Gain in Cellular Networks

Cited by 18 publications

References 7 publications

Stochastic Geometry Analysis of Spatial-Temporal Performance in Wireless Networks: A Tutorial

Stochastic Geometry Analysis of Spatial-Temporal Performance in Wireless Networks: A Tutorial

Interference Distribution Prediction for Link Adaptation in Ultra-Reliable Low-Latency Communications

Performance Analysis of D2D and Cellular Coexisting Networks With Interference Management

Contact Info

Product

Resources

About