Backhaul-Aware Uplink Communications in Full-Duplex DBS-Aided HetNets
Drone-mounted base stations (DBSs) are promising solutions to provide ubiquitous connections to users and support many applications in the fifth generation of mobile networks while full duplex communications has the potential to improve the spectrum efficiency. In this paper, we have investigated the backhaul-aware uplink communications in a full-duplex DBS-aided HetNet (BUD) problem with the objective to maximize the total throughput of the network, and this problem is decomposed into two sub-problems: the DBS Placement problem (including the vertical position and horizontal position) and the joint UE association, power and bandwidth assignment (Joint-UPB) problem. Since the BUD problem is NP- hard, we propose approximation algorithms to solve the sub-problems and another, named the AA-BUD algorithm, to solve the BUD problem with guaranteed performance. The performance of the AA- BUD algorithm has been demonstrated via extensive simulations, and results show that the AA-BUD algorithm is superior to two benchmark algorithms.
Authors:
;
Award ID(s):
Publication Date:
NSF-PAR ID:
10185647
Journal Name:
2019 IEEE Global Communications Conference (GLOBECOM)
Page Range or eLocation-ID:
1 to 6
3. In this paper we consider the problem of minimizing composite objective functions consisting of a convex differentiable loss function plus a non-smooth regularization term, such as $L_1$ norm or nuclear norm, under Rényi differential privacy (RDP). To solve the problem, we propose two stochastic alternating direction method of multipliers (ADMM) algorithms: ssADMM based on gradient perturbation and mpADMM based on output perturbation. Both algorithms decompose the original problem into sub-problems that have closed-form solutions. The first algorithm, ssADMM, applies the recent privacy amplification result for RDP to reduce the amount of noise to add. The second algorithm, mpADMM, numerically computes the sensitivity of ADMM variable updates and releases the updated parameter vector at the end of each epoch. We compare the performance of our algorithms with several baseline algorithms on both real and simulated datasets. Experimental results show that, in high privacy regimes (small ε), ssADMM and mpADMM outperform baseline algorithms in terms of classification and feature selection performance, respectively.