Energy Efficient Resource Allocation for UAV-Assisted Space-Air-Ground Internet of Remote Things Networks

Abstract

Internet of remote things (IoRT) networks are regarded as an effective approach for providing services to smart devices, which are often remote and dispersed over in a wide area. Due to the fact that the ground base station deployment is difficult and the power consumption of smart devices is limited in IoRT networks, the hierarchical Space-Air-Ground architecture is very essential for these scenarios. This paper aims to investigate energy efficient resource allocation problem in a two-hop uplink communication for Space-Air-Ground Internet of remote things (SAG-IoRT) networks assisted with unmanned aerial vehicle (UAV) relays. In particular, the optimization goal of this paper is to maximize the system energy efficiency by jointly optimizing sub-channel selection, uplink transmission power control and UAV relays deployment. The optimization problem is a mix-integer non-linear non-convex programming, which is hard to tackle. Therefore, an iterative algorithm that combines two sub-problems is proposed to solve it. First, given UAV relays deployment position, the optimal sub-channel selection and power control policy are obtained by the Lagrangian dual decomposition method. Next, based on the obtained sub-channel allocation and power control policy, UAV relays deployment is obtained by successive convex approximation (SCA). These two sub-problems are alternatively optimized to obtain the maximum system energy efficiency. Numerical results verify that the proposed algorithm has at least 21.9% gain in system energy efficiency compared to the other benchmark scheme.

Joint Stochastic Computational Resource and UAV Trajectory for Wireless-Powered Space-Air-Ground IoRT Networks

“…Next, the Karush-Kuhn-Tucker (KKT) conditions of P 5 should be satisfied to obtain the optimal power control, which has a lower computational complexity [26]. Associated with the Lagrangian function (32), the optimal power control P U,opt k (t) and P S,opt k (t) can be given by…”

Joint Sub-Carrier and Power Allocation for Efficient Communication of Cellular UAVs

“…The associated terms reflect the mean SNR from the interfering BS to the user v (γ tv in the case of a UE, and A tv and B tv in the case of a UAV). The formulas for the mean SNR are expressed in (4) and (10). Therefore, the terms related to the links tv depend on the power employed by the interfering BS for transmitting data to the served users.…”

“…Initially, the maximum transmission power is used (line 1). For each BS u ∈ B, if it is serving a user v through the sub-carrier c (lines 5 and 6), a power optimization is considered; The transmission power of the BS u is reduced until having the maximum outage probability smaller than a threshold P out,th , while maintaining the power larger than the minimum value,P uv (lines [8][9][10]). As explained in the previous paragraph, when the transmission powers from the interfering BSs are unchanged, the outage probability for the user v increases as the transmission power from the serving BS decreases.…”

“…Since changing the transmission power of a BS also influences the outage probability of users subject to interference, this power optimization is repeated. Lines [11][12][13][14] evaluate if the transmission power has changed after executing lines [8][9][10]. The variable 'Stable' is used to characterize the stable situation, where no BS needs to reduce its transmission power.…”

“…The complexity of Algorithm 1 in the worst case scenario is O(Ṡ * B * ( P max −Puv P stp + 1)), wherė S reflects the number of unstable solutions found before the effective one. Indeed, in the worst case scenario, lines [8][9][10] will be executed P max −Puv…”