Leveraging unmanned aerial vehicles (UAVs) as relays, the space-air-ground integrated Internet of Things (SAG-IoT) networks can provide wireless access services to the connected devices with high data rate, high flexibility, and wide coverage. However, besides bringing lots of benefits, the SAG-IoT systems also face many challenges due to the high mobility, unreliable satellite links, and limited resources. Especially, when multiple UAVs act relays for cross-tier communication to transfer the collected data from ground network tier to the satellite tier, how to select an appropriate number of UAVs as gateways to improve system spectral efficiency (SE) is an important issue. To the best of our knowledge, it is an entirely new problem since most existing works utilize all UAVs as relay nodes and neglect the spectrum allocation. Toward this end, we present this paper to discuss the joint optimization problem of gateway selection, bandwidth allocation, and UAV deployment with the objective of maximizing the system SE. A Dinkelbach method based iterative algorithm is proposed as our solution by alternately adopting simulated annealing and successively convex programming technologies. Experimental results show that our designed joint optimization schemes are able to significantly improve the system SE and can make optimal decisions of gateway selection, bandwidth allocation, as well as UAV deployment.

As an emerging network architecture and technology, mobile edge computing (MEC) can alleviate the tension between the computation-intensive applications and the resource-constrained mobile devices. However, most available studies on computation offloading in MEC assume that the edge severs host various applications and can cope with all kinds of computation tasks, ignoring limited computing resources and storage capacities of the MEC architecture. To make full use of the available resources deployed on the edge servers, in this paper, we study the cross-server computation offloading problem to realize the collaboration among multiple edge servers for multi-task mobile edge computing, and propose a greedy approximation algorithm as our solution to minimize the overall consumed energy. Numerical results validate that our proposed method can not only give near-optimal solutions with much higher computational efficiency, but also scale well with the growing number of mobile devices and tasks.

Orthogonal frequency-division multiplexing (OFDM) is an attractive multicarrier technique for the simplicity of equalization and high data throughput. However, the transmitted OFDM signal has a very high peak-to-average power ratio (PAPR), which severely degrades the performance of practical OFDM systems and reduces the efficiency of high-power amplifiers (HPA). The selected mapping (SLM) scheme is an effective PAPR reduction method of OFDM signals. However, this approach usually requires side information (SI) transmission, which increases the difficulty of the hardware implementation with high complexity and reduces the data transmission rate. In this paper, based on designing phase rotation vectors in the time domain, a novel blind SLM method with low complexity is proposed to reduce the PAPR of OFDM signals. At the transmitter, the proposed method properly designs the phase rotation vectors in the time domain, which can be considered as an equivalent wireless channel without SI transmission. At the receiver, the effect of phase rotation vectors can be removed by the conventional channel estimation method, and the data demodulation processing can be easily performed by the frequency domain equalization. Simulation results show that the proposed scheme can achieve low complexity in PAPR reduction and has great robustness in bit error rate (BER) performance compared to the other low-complexity SLM PAPR schemes.

This article unveils the importance of statistical quality of service (QoS) for resource allocation in a two-hop network. Particularly, an access point (AP) serves multiple IoT devices for information transfer with the assistance of the energy harvesting (EH) relaying. To explore the maximum constant data arrival metric, we aim to maximize the effective capacity (EC) under specified QoS requirements. Also, the statistical QoS inspired resource allocation policies are investigated for half/full duplex (HD/FD) modes, respectively, to jointly optimize power allocation and power splitting (PS) ratio. To solve the formulated problem, we first derive the closed-form solution of the optimal power allocation at the AP and the PS ratio. To gain more insights, we further derive the boundary conditions of optimal power allocation and PS ratio. Finally, numerical results are demonstrated to validate the theoretical derivations, which highlights the proposed scheme in terms of EC performance in comparison to the benchmark scheme.

Direct conversion receiver (DCR) architecture has drawn considerable attention in orthogonal frequency division multiplexing (OFDM) systems. However, OFDM DCRs are sensitive to non-ideal frontend effects and suffer from direct current offset (DCO) and carrier frequency offset (CFO). In this paper, a novel method to estimate DCO and CFO is presented in OFDM DCRs by exploring the sparse symbols embedded in the preamble. In the proposed method, CFO and DCO estimations can be easily decoupled. Next, a robust low-complexity DCO estimation method is proposed by using the sparse properties of preamble in the frequency domain. Moreover, a novel CFO estimation scheme independent of the DCO is presented based on the repeated preamble structure in which the DCO component focuses on the DC subcarrier when the received preamble signal was not CFO compensated in the frequency domain. Simulation results show that the proposed CFO and DCO estimators can achieve better performance than any available methods.