4.8 Article

Backhaul-Capacity-Aware Interference Mitigation Framework in 6G Cellular Internet of Things

Journal

IEEE INTERNET OF THINGS JOURNAL
Volume 8, Issue 12, Pages 10071-10084

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JIOT.2021.3050013

Keywords

Interference; Resource management; Internet of Things; 6G mobile communication; Computer architecture; Optimization; Load modeling; Convex optimization; Internet of Things (IoT); matching theory; millimeter wave (mmWave)

Funding

  1. National Natural Science Foundation of China [62022020, 61941102]
  2. National Key Research and Development Program of China [2020YFB1807603]
  3. 111 Project of China [B16006]
  4. NSF [EARS-1839818, CNS1717454, CNS1731424, CNS-1702850]

Ask authors/readers for more resources

This article proposes a joint traffic load-balancing and interference mitigation framework to maximize the network capacity for 6G cellular IoT services. A novel two-step resource allocation scheme is designed by optimizing the user equipment association and the transmit power allocation. Simulation results prove that the proposed algorithms can significantly improve the network sum rate and the successful transmission probability can be well guaranteed.
To meet the increasing demands for wide-band communications and network densification, a new paradigm of millimeter-wave (mmWave)-enabled integrated access and backhaul (IAB) is urgently needed in the sixth-generation (6G) cellular Internet-of-Things (IoT) network. However, the mmWave-enabled IAB technology brings new challenges on network capacity in terms of the differentiated backhaul capacities of small-cell base stations and the various interferences among access links in the 6G cellular IoT network. Therefore, this article proposes a joint traffic load-balancing and interference mitigation framework to maximize the network capacity for 6G cellular IoT services. A novel two-step resource allocation scheme is designed by optimizing the user equipment association and the transmit power allocation (PA), iteratively. Moreover, to minimize both the backhaul burden and the interference, a novel backhaul capacity and interference-aware matching utility function using the many-to-many matching model is designed to measure the interference penalty and the backhaul capacity. By transforming the nonconvex PA subproblem into a convex problem using the successive convex approximation method, both upper and lower bounds of the optimal transmit power are theoretically achieved. Simulation results prove that the proposed algorithms can significantly improve the network sum rate by 71.9% compared to the conventional algorithms, and the successful transmission probability can be well guaranteed.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.8
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available