Joint Scheduling and Power Allocations for Traffic Offloading via Dual-Connectivity

TL;DR

This paper proposes joint scheduling and power control algorithms for traffic offloading in dual-connectivity mobile networks, significantly reducing user costs while managing interference.

Contribution

It introduces both centralized and distributed algorithms for joint traffic scheduling and power control in dual-connectivity systems, addressing non-convex optimization challenges.

Findings

Achieves near-optimal solutions with less computational complexity.

Reduces mobile user costs by over 75% compared to no offloading.

Significantly improves traffic offloading efficiency and interference management.

Abstract

With the rapid growth of mobile traffic demand, a promising approach to relieve cellular network congestion is to offload users' traffic to small-cell networks. In this paper, we investigate how the mobile users (MUs) can effectively offload traffic by taking advantage of the capability of dual-connectivity, which enables an MU to simultaneously communicate with a macro base station (BS) and a small-cell access point (AP) via two radio-interfaces. Offloading traffic to the AP usually reduces the MUs' mobile data cost, but often at the expense of suffering increased interferences from other MUs at the same AP. We thus formulate an optimization problem that jointly determines each MU's traffic schedule (between the BS and AP) and power control (between two radio-interfaces). The system objective is to minimize all MUs' total cost, while satisfying each MU's transmit-power constraints through proper interference control. In spite of the non-convexity of the problem, we design both a centralized algorithm and a distributed algorithm to solve the joint optimization problem. Numerical results show that the proposed algorithms can achieve the close-to-optimum results comparing with the ones achieved by the LINGO (a commercial optimization software), but with significantly less computational complexity. The results also show that the proposed adaptive offloading can significantly reduce the MUs' cost, i.e., save more than 75% of the cost without offloading traffic and 65% of the cost with a fixed offloading.