Power Control for D2D Underlaid Cellular Networks: Modeling, Algorithms, and Analysis

By Namyoon Lee, Xingqin Lin, Jeffrey G. Andrews, Robert W. Heath. IEEE Journal on Selected Areas in Communications, 2013

449 citations recorded by the source corpus.

This paper proposes a random network model for a device-to-device (D2D) underlaid cellular system using stochastic geometry and develops centralized and distributed power control algorithms. The goal of centralized power control is twofold: ensure that the cellular users have sufficient coverage probability by limiting the interference created by underlaid D2D users, while scheduling as many D2D links as possible. For the distributed power control method, the optimal on-off power control strategy is proposed, which maximizes the sum rate of the D2D links. Expressions are derived for the coverage probabilities of cellular, D2D links, and the sum rate of the D2D links in terms of the density of D2D links and the path-loss exponent. The analysis reveals the impact of key system parameters on the network performance. For example, the bottleneck of D2D underlaid cellular networks is the cross-tier interference between D2D links and the cellular user, not the D2D intratier interference when the density of D2D links is sparse. Simulation results verify the exactness of the derived coverage probabilities and the sum rate of D2D links.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/78b1403ad859e8d2eebfd6779e3276329ad762c6

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Citations in the corpus

A Survey of Device-to-Device Communications: Research Issues and Challenges (2018, background): “With the addition of D2D users in cellular networks, the issue of interference becomes more complex [100]–[103], [105]–[107], [110].”
Deep Learning Based Transmit Power Control in Underlaid Device-to-Device Communication (2019, background): “Transmit power control becomes more important in underlaid D2D communication, where DUEs share radio resources with cellular user equipment (CUE) [3], which results in the extensive study of transmit power control for underlaid D2D communication [4]–[6].”
A Tutorial on UAVs for Wireless Networks: Applications, Challenges, and Open Problems (2018, background): “Moreover, we assume that a D2D receiver connects to its corresponding D2D transmitter located at a fixed distance away from it [147].”
Unmanned Aerial Vehicle With Underlaid Device-to-Device Communications: Performance and Tradeoffs (2015, methodology): “In particular, a comprehensive analysis to evaluate this coexistence in terms of different performance metrics, such as coverage and rate, is lacking in the current state-of-the-art [9], [14], [19]–[21].”
Channel Hardening and Favorable Propagation in Cell-Free Massive MIMO With Stochastic Geometry (2017, background): “, users located at the network boundary are still likely to have nearby dominant APs that makes their received signal distribution similar to network-center users [29].”
Transmit Power Control Using Deep Neural Network for Underlay Device-to-Device Communication (2018, background): “In [2], centralized and distributed transmit power control strategies to maximize the sum rate of DUE were considered which guarantee the coverage probability of”
5G D2D Networks: Techniques, Challenges, and Future Prospects (2018, methodology): “[41] Channel selection and power control No D2D network performance in heterogeneous environment [36] CoMP technology Yes System power consumption versus throughput analysis [27] ILA-based on SIR Yes D2D multicast system analysis [29] Centralized and distributed power control scheme No Coverage prob”
Deep Power Control: Transmit Power Control Scheme Based on Convolutional Neural Network (2018, unknown): “, device-to-device (D2D) communication [1].”