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Unbalanced traffic distribution in cellular networks results in congestion and degrades spectrum efficiency. To tackle this problem, we propose an Unmanned Aerial Vehicle (UAV)-assisted wireless network in which the UAV acts as an aerial relay to divert some traffic from the overloaded cell to its adjacent underloaded cell. To fully exploit its potential, we jointly optimize the UAV position, user association, spectrum allocation, and power allocation to maximize the sum-log-rate of all users in two adjacent cells. To tackle the complicated joint optimization problem, we first design a genetic-based algorithm to optimize the UAV position. Then, we simplify the problem by theoretical analysis and devise a low-complexity algorithm according to the branch-and-bound method, so as to obtain the optimal user association and spectrum allocation schemes. We further propose an iterative power allocation algorithm based on the sequential convex approximation theory. The simulation results indicate that the proposed UAV-assisted wireless network is superior to the terrestrial network in both utility and throughput, and the proposed algorithms can substantially improve the network performance in comparison with the other schemes.

Energy harvesting has been considered as a promising technique to decrease the conventional grid energy expenditure. However, most renewable energy sources are unreliable and random. To overcome these drawbacks, besides the commonly adopted approaches such as purchasing power from the grid and deploying batteries, energy cooperation is an appealing solution. In this paper, we investigate the energy management problem by jointly optimizing the data admission rate, transmit power, energy sharing among base stations (BSs), battery charging and discharging rate, and the energy purchased from the grid in hybrid energy powered cellular networks. First, the long-term average total cost minimization problem under the constraints on limited battery size and users' data rate requirements is formulated as a stochastic optimization problem. Employing the Lyapunov optimization technique and alternating direction method of multipliers (ADMM), we propose an online distributed algorithm, referred to as distributed online energy management algorithm (DOEMA), where the current system states are needed, without requiring the system statistic or future information. Furthermore, the proposed algorithm can be implemented in a parallel and completely distributed fashion, which could provide more engineering guidelines for practical communication protocols compared with the centralized algorithm. The extensive simulation results are conducted to demonstrate the correctness of the theoretical analysis and validate the performance improvement against other algorithms in terms of system total cost reduction.

The aerial base stations (ABSs) can be quickly deployed to provide emergency communications and airborne network infrastructures. How to ensure wide coverage, reliable links, and high throughput for ground users under the conditions of limited onboard power supply, large propagation distance, and restricted frequency resource is a critical and challenging issue. In this work, we propose a hybrid-spectrum scheme for ABS-based airborne access networks, named dual-band aerial access (DBAA) where the ABS employs both the UHF and S-bands to provide connectivity for ground users. The DBAA can improve coverage range and reliability by taking advantage of the preferable radio propagation characteristics of the low-frequency band and meanwhile improve network throughput by exploiting the large spectrum bandwidth in the high-frequency band. Following the cross-layer approach, we first conducted a measurement campaign on the large-scale fading of the air-to-ground (A2G) channels at 785 and 2160 MHz simultaneously. We installed an ABS with two antennas on an airship that hovered at several altitudes from 50 to 950 m. We measured the signal power attenuation from the ABS to a ground terminal that moved in rural, suburban, and urban scenarios with the horizontal distance up to 70 km from the airship. Based on the measurement data, we establish the large-scale fading channel model for ABS at different operating frequencies. Then, we design the joint spectrum-and-power allocation algorithm to maximize the network throughput for the dual-band airborne access network. We evaluate the performance of the optimal resource allocation based on the proposed channel model. The simulation results show that the DBAA scheme with the optimal resource allocation can achieve substantial performance improvement in comparison with the single-band solution given the total spectrum bandwidth and onboard power supply.

This paper investigates the application of non-orthogonal multiple access (NOMA) and device-to-device (D2D) into the scenario of massive Machine Type Communications (mMTC). Specifically, we first propose a new NOMA-and-D2D integrated network, where NOMA is utilized to deal with the cross-tier and co-tier interference at the base station side. To fully exploit the advantages of the network, we formulate a joint channel allocation and power control problem with the objective to maximize the performance of the D2D communications under the constraints of the rate requirements of the cellular users. For solving the formulated problem efficiently, we first adopt the sequential convex approximation method to solve the channel allocation subproblem, and then transform the power control subproblem into a convex optimization problem. To further reduce the computational complexity, we employ the convolutional neural network (CNN) to devise a resource management framework, where the relation between the system states and the control policies is established by multiple neurons. Finally, simulation results indicate that the convex approximation based algorithm outperforms the other algorithms in terms of utility, sum-rate, and user satisfaction, and the CNN based algorithm achieves orders of magnitude speedup in computational time with only slight loss of performance.

Unmanned aerial vehicle (UAV) and reconfigurable intelligent surface (RIS) methods are promising techniques for improving the energy and spectrum efficiency of Fifth Generation/Beyond Fifth Generation (5G/B5G) networks. In order to take advantage of both techniques, we propose an RIS-assisted UAV secure communication scheme, where an UAV is equipped with RIS to facilitate secure transmission. To maximize the average secrecy rate, we jointly optimize the beamforming power, reflect phase shift, and UAV’s trajectory. For this non-convex problem, we decompose it into the power beamforming problem, the phase shift optimization problem, and the UAV’s trajectory design problem, and proposed an efficient iterative algorithm to solve the problem. The numerical results verify the superiority of the proposed scheme that can improve the average secure transmission rate by about 20% compared to that of Eavesdropping elimination methods.

Sparse code multiple access (SCMA) has been proposed as a candidate air interface (AI) technique for 5G wireless networks. However, the existing resource management schemes with predesigned SCMA codebooks cannot fully exploit user diversities in the frequency domain, thus degrading the performance of SCMA systems. To fully exploit the potential of SCMA, in this paper, we design a more flexible and configurable SCMA through adaptively adjusting the codebook design and assignment according to the user's features. Specifically, for the uplink networks, first we formulate a detection complexity minimization problem by jointly considering the codebook design (i.e., mapping matrix and constellation graph design) and codebook assignment, which is an integer linear program and NP-hard in general. To tackle this hard problem effectively, first we borrow the idea of dual coordinate search to devise a suboptimal but computational efficient algorithm to determine the mapping matrix and codebook assignment. Based on the obtained mapping matrix, we use the multi-dimensional modulation characteristic of SCMA to carefully design the constellations for each codebook to further reduce the detection complexity. For the downlink networks, we formulate a total power consumption minimization problem by jointly considering the codebook design and assignment and power allocation. Exploiting the special structure of the problem, we employ the Lagrangian dual decomposition technique to propose a fast iterative algorithm, which can solve the problem optimally with low complexity. Finally, we present extensive simulations to exhibit the performance improvement against other algorithms in terms of detection complexity and power consumption. The modified SCMA in this paper can be intelligently optimized based on service and user awareness, which can provide some guidelines for the design of software-defined AI in future wireless networks.

In this paper, we propose a mobile edge computing (MEC)-enabled unmanned aerial vehicle (UAV)-assisted vehicular ad hoc network (VANET) architecture, based on which a number of vehicles are served by UAVs equipped with computation resource. Each vehicle has to offload its computing tasks to the proper MEC server on the UAV due to the limited computation ability. To counter the problems above, we first model and analyze the transmission model and the security assurance model from the vehicle to the MEC server on UAV, and the task computation model of the local vehicle and the edge UAV. Then, the vehicle offloading problem is formulated as a multi-objective optimization problem by jointly considering the task offloading, the resource allocation, and the security assurance. For tackling this hard problem, we decouple the multi-objective optimization problem as two subproblems and propose an efficient iterative algorithm to jointly make the MEC selection decision based on the criteria of load balancing and optimize the offloading ratio and the computation resource according to the Lagrangian dual decomposition. Finally, the simulation results demonstrate that our proposed scheme achieves significant performance superiority compared with other schemes in terms of the successful task processing ratio and the task processing delay.

In this paper, we investigate the relay selection problem for the unmanned aerial vehicle (UAV)-assisted vehicular ad-hoc networks (VANETs). For the considered network, we first model and analyze the link quality of service (LQoS) from the source node (SN) to the neighbor node and the node forward capacity (NFC) from the neighbor node to the destination node (DN). Then, the relay selection problem is formulated as a multi-objective optimization problem by jointly considering the LQoS and the NFC. Afterward, we decompose the problem into two subproblems and propose a relay selection protocol with the storage-carry-forward (SCF) method. Moreover, we define a utility function with the node encounter frequency (NEF) and the message time-to-live (TTL) taken into account, based on which a redundant copy-deleting approach is devised. Furthermore, we analyze the security of the designed protocol. Finally, the simulation results demonstrate that the proposed relay selection protocol can improve the message delivery ratio, reduce the average end-to-end delay, and limit the overhead.

As one of the main applications of the Internet of things (IoT), the vehicular ad-hoc network (VANET) is the core of the intelligent transportation system (ITS). Air−ground integrated vehicular networks (AGIVNs) assisted by unmanned aerial vehicles (UAVs) have the advantages of wide coverage and flexible configuration, which outperform the ground-based VANET in terms of communication quality. However, the complex electromagnetic interference (EMI) severely degrades the communication performance of UAV sensors. Therefore, it is meaningful and challenging to design an efficient anti-interference scheme for UAV data links in AGIVNs. In this paper, we propose an anti-interference scheme, named as Mary-MCM, for UAV data links in AGIVNs based on multi-ary (M-ary) spread spectrum and multi-carrier modulation (MCM). Specifically, the Mary-MCM disperses the interference power by expanding the signal spectrum, such that the anti-interference ability of AGIVNs is enhanced. Besides, by using MCM and multiple-input multiple-output (MIMO) technologies, the Mary-MCM improves the spectrum utilization effectively while ensuring system performance. The simulation results verify that the Mary-MCM achieves excellent anti-interference performance under different EMI combinations.