Modeling and Performance Analysis of Clustered Device-to-Device Networks (1508.02668v2)

Abstract: Device-to-device (D2D) communication enables direct communication between proximate devices thereby improving the overall spectrum utilization and offloading traffic from cellular networks. This paper develops a new spatial model for D2D networks in which the device locations are modeled as a Poisson cluster process. Using this model, we study the performance of a typical D2D receiver in terms of coverage probability under two realistic content availability setups: (i) content of interest for a typical device is available at a device chosen uniformly at random from the same cluster, which we term uniform content availability, and (ii) content of interest is available at the $k^{th}$ closest device from the typical device inside the same cluster, which we term $k$-closest content availability. Using these coverage probability results, we also characterize the area spectral efficiency (ASE) of the whole network for the two setups. A key intermediate step in this analysis is the derivation of the distributions of distances from a typical device to both the intra- and inter-cluster devices. Our analysis reveals that an optimum number of D2D transmitters must be simultaneously activated per cluster in order to maximize ASE. This can be interpreted as the classical tradeoff between more aggressive frequency reuse and higher interference power. The optimum number of simultaneously transmitting devices and the resulting ASE increase as the content is made available closer to the receivers. Our analysis also quantifies the best and worst case performance of clustered D2D networks both in terms of coverage and ASE.

• The paper introduces a realistic spatial model for clustered D2D networks using a Poisson cluster process, capturing real-world device groupings.
• It derives expressions for network coverage probability and area spectral efficiency (ASE), analyzing performance under different content availability scenarios within clusters.
• The analysis provides system design insights, including the optimal number of simultaneous D2D transmissions and the performance impact of content availability distance.

Modeling and Performance Analysis of Clustered D2D Networks: A Comprehensive Framework

This paper presents a detailed paper on the spatial modeling and performance analysis of device-to-device (D2D) communication networks using a Poisson cluster process. Unlike prior models which typically treat device locations as randomly distributed across a plane, this work introduces a clustered approach, reflecting the practical situation where devices often form natural clusters in real-world environments.

The heart of this paper lies in modeling device locations as Poisson cluster processes, specifically a variant of a Thomas cluster process. This structure accounts for the spatial correlation of device locations, acknowledging that proximate devices may dynamically communicate directly. The primary focus is on two content availability scenarios within these clusters: "uniform content availability," where a device's content interest can be served by any random device within the cluster, and "k-closest content availability," where only the k-th nearest device provides the interested content.

Key Contributions

1. Realistic Spatial Model: A novel spatial framework where devices form clusters, modeling them as a Thomas cluster process with normally distributed members around randomly placed cluster centers. This setup provides a realistic approximation of typical proximity and spatial relations in D2D networks.
2. Coverage and ASE Analysis: The paper derives expressions for coverage probability (the likelihood a device experiences adequate signal quality) and area spectral efficiency (ASE) — a measure of the network's data transmission efficiency. These are calculated for both uniform and k-closest scenarios, offering insights into how content distribution strategies impact network performance.
3. Distance Distribution Characterization: Intermediate results critical to the main analysis, such as the distribution of distances from a typical device to intra- and inter-cluster devices, are robustly derived. The modeling of these distances as Rician random variables enables accurate interference modeling and signal quality assessments.
4. System Design Insights: The analysis reveals important design guidelines, recommending the optimal number of simultaneous D2D transmissions to maximize ASE. An intriguing finding is the tradeoff between aggressive frequency reuse and increased interference, showing that system performance peaks when content is available closer to receivers.

Numerical Results

The paper provides comprehensive numerical simulations, validating the derived expressions and approximations for coverage and ASE. These simulations confirm that considering cluster dynamics in D2D networks can significantly enhance understanding of performance metrics. Notably, the optimal number of simultaneous transmitting devices per cluster aligns closely with practical D2D networking principles, emphasizing the value of this clustered modeling approach.

Implications and Future Work

The implications of this work are twofold. Practically, it allows network designers to better configure D2D networks by accounting for the spatial clustering of devices. Theoretically, it opens new avenues in stochastic geometry applications for wireless network analysis, specifically through cluster point processes.

Future research could extend these models to incorporate adaptive content placement strategies or address interference dynamics in mixed cellular-D2D environments. Exploring different types of spatial processes and more sophisticated interference management strategies may also provide deeper insights into optimizing future wireless networks.

This paper stands as a fundamental contribution to the field of D2D communications, providing a realistic, tractable model for analyzing and designing such networks in clustered environments. Its blend of rigorous mathematical analysis and practical insights makes it a valuable resource for researchers and practitioners aiming to develop next-generation wireless networks.