Abstract

Impact of Frequency Reuse and Flexible Cell Association on the Performance of Dense Heterogeneous Cellular Networks Using Dual-Slope Path Loss Model

Highlights

  • The past decade has witnessed an exponential increase in mobile data traffic, owing primarily to the drastic rise in the demand of smart devices supporting numerous high data rate applications

  • Using DS-path loss model (PLM) further avoids the overestimation of benefits/drawbacks associated with small BSs (SBSs) biasing and frequency reuse mechanisms

  • The DS-PLM incorporation has resulted in a significant change in the performance of dense heterogeneous cellular networks (HCNs), as both tier association probability (T-AP) and coverage probability (CP) have shown some striking results in contrast to single slope (SS)-PLM

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Summary

INTRODUCTION

The past decade has witnessed an exponential increase in mobile data traffic, owing primarily to the drastic rise in the demand of smart devices supporting numerous high data rate applications. The authors in [31] proposed an improved model of HCN which permits flexible cell association to UEs while providing additional insights on important performance metrics like T-AP and average load per tier but their work is limited to a unity reuse factor. 1.) The nearest BS UE-AS which is extensively employed in analyzing the single-tier cellular networks becomes invalid in case of multi-tier scenario as there is a high likelihood that UEs might be residing within near proximity of SBSs but still preferring association with distant BSs, 2.) The irregularities associated with cell coverage areas (footprints) due to disparity in transmit power of BS tiers and antenna heights strongly affect the composition of interference in densified HCNs. the load balancing and/or interference avoiding strategies can be more effectively utilized by employing DS-PLM.

SYSTEM MODEL
TIER ASSOCIATION PROBABILITY
DISTRIBUTION OF THE STATISTICAL DISTANCES BETWEEN T-UE AND ITS SERVING BS
COVERAGE PROBABILITY
COVERAGE PROBABILITY OF T-UE ASSOCIATED WITH MACRO TIER
COMPUTATIONAL COMPLEXITY COMPARISON
NUMERICAL AND SIMULATION RESULTS
23: Small Coverage
26: Macro Coverage
Findings
CONCLUSION
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