In this paper, we propose a distributed joint computation offloading and resource allocation optimization (JCORAO) scheme in heterogeneous networks (HetNets) with mobile edge computing (MEC). An optimization problem is formulated to provide the optimal computation offloading strategy policy, uplink subchannel allocation, uplink transmission power allocation and computation resource scheduling. The optimization problem is decomposed into two sub-problems due to the NP-hard property. In order to analyse the offloading strategy, a sub-algorithm named distributed potential game is built. The existence of Nash equilibrium (NE) is proved. To jointly allocate uplink subchannel, uplink transmission power and computation resource for the offloading MTs, a sub-algorithm named cloud and wireless resource allocation algorithm (CWRAA) is designed. The solutions for subchannel allocation consist of uniform zero frequency reuse (UZFR) method without interference and fractional frequency reuse method based on Hungarian and graph coloring (FFR-HGC) with interference. A distributed JCORAO scheme is proposed to solve the optimization problem by the mutual iteration of the two sub-algorithms. Simulation results show that the distributed JCORAO scheme can effectively decrease the energy consumption and task completion time with lower complexity.

In order to identify non-line-of-sight (NLOS) propagations for a high-resolution localization system in wireless networks, a strong NLOS classifier is proposed via a machine learning meta algorithm named AdaBoost. Signal features are extracted to characterize the distinctions between line-of-sight (LOS) and NLOS, based on which a series of weak learners are primarily given. By training and re-sampling data iteratively, weak learners with the minimal classification error are selected and the weighted sum is boosted into a strong classifier. Simulations demonstrate that the proposed AdaBoost-based NLOS classifier can improve NLOS identification performance, leading to reduced positioning errors.

The problem of joint radio and cloud resources allocation is studied for heterogeneous mobile cloud computing networks. The objective of the proposed joint resource allocation schemes is to maximize the total utility of users as well as satisfy the required quality of service (QoS) such as the end-to-end response latency experienced by each user. We formulate the problem of joint resource allocation as a combinatorial optimization problem. Three evolutionary approaches are considered to solve the problem: genetic algorithm (GA), ant colony optimization with genetic algorithm (ACO-GA), and quantum genetic algorithm (QGA). To decrease the time complexity, we propose a mapping process between the resource allocation matrix and the chromosome of GA, ACO-GA, and QGA, search the available radio and cloud resource pairs based on the resource availability matrixes for ACO-GA, and encode the difference value between the allocated resources and the minimum resource requirement for QGA. Extensive simulation results show that our proposed methods greatly outperform the existing algorithms in terms of running time, the accuracy of final results, the total utility, resource utilization and the end-to-end response latency guaranteeing.

This paper mainly focuses on developing a joint cloud and wireless resource allocation algorithm based on evolutionary game (JRA-EG) considering mobile terminals' energy consumption and time delay as well as monetary cost in mobile edge computing environment. The mobile terminals (MTs) in different service areas form different populations. In addition, the competition among populations of MTs in different service areas is to share the limited amount of bandwidth and computation resources in the accessible service providers (SPs). We analyse the stability property of the evolutionary game model and obtain the evolutionary equilibrium (EE). EE is acquired by replicator dynamics approach. Simulation results show that our JRA-EG algorithm can converge to the EE rapidly. Compared with the existing algorithms, the algorithms can save more energy and have less time delay when input data size becomes larger.

In this paper, a three-dimensional (3D) non-stationary geometry-based stochastic model (GBSM) is proposed for low-altitude unmanned aerial vehicles (UAVs) multiple-input-multiple-output (MIMO) air-to-ground (A2G) channel. In the proposed model, both UAV rotation and arbitrary trajectories are taken into account. UAV rotation is described by a 2D Gauss-Markov random mobility model (RMM), while UAV arbitrary trajectories are characterized by a 3D revised Gauss-Markov RMM. The time-varying parameters of multipath components (MPCs), such as distances, angles of arrival (AoAs), and angles of departure (AoDs), are calculated based on the proposed model. In addition, the expressions of the space-time correlation function (CF) and Doppler power spectral density (PSD) are also derived. Numerous numerical simulations are conducted and the simulation results reveal that the UAV rotational motion causes channel non-stationarity in space domain, and the mobility characteristics of UAV along with arbitrary trajectories cause channel time domain non-stationary. Finally, the accuracy of the model is verified by comparing with the available measurement data.