Fog-cloud computing is one of cores techniques in wireless networks, where fog and cloud nodes provide high-speed and large-scale computing services to mobile users through a collaborative method. For the conventional fog-cloud computing schemes, the computing abilities of nodes and the computation task offloading methods are the main factors affecting the latency and energy consumption. However, when computing abilities of nodes reach saturation, the latency and energy consumption caused by task data transmission are approximate to those caused by computation. In order to reduce latency and energy consumption of data transmission and computation, this paper proposes an opportunistic access fog-cloud computing network (OFCN), where each mobile user selects fog node through opportunistic access method. Then, we formulate an optimization problem, considering both resource allocation and computation offloading under the constraints of users’ quality of service (QoS) requirements. Due to the difficulty of solving, we divide the original problem into four suboptimal problems and develop an iterative algorithm to approach the global optimal solution. Numerical results show that the proposed OFCN can achieve lower latency and energy consumption than the conventional fog-cloud computing networks.

Millimeter-wave cell-free and user-centric massive multiple input multiple output (MIMO) systems are promising, due to the reliable ultra-high data rate services. Thus, it is a significant issue to design low-complexity hybrid precoders. In this paper, a simplified sum-mean-square-error (SMSE) is used to design hybrid precoders, based on the array gain and spatial macro-diversity. Then, a heuristic hybrid pre-coding scheme is proposed, with two analog design algorithms that are with or without the knowledge of statistical information. The influence of the imperfect state information of beams is theoretically analyzed, from the instantaneous and ergodic aspects, verifying the robustness of the proposed scheme. Simulation results show that our proposed low-complexity hybrid precoder can be easily realized with acceptable bit error rate (BER) and sum-rate performances, especially when the total transmit power is small.

This paper investigates the performances of multiuser downlink opportunistic beamforming (OBF) systems in both Rayleigh and Rician fading channels with the limited feedback information. The statistical characteristics of the equivalent channels show how OBF affects the channel gain. The optimal OBF weight is derived to achieve the maximum capacity. Moreover, the relationship between the capacity and feedback information is analyzed in details. In order to meet different quality of service requirements, we propose two transmission mechanisms that are, respectively, based on maximum-capacity and fairness criterions. Since the closed-form expression of the bit error ratio (BER) is hard to obtain, the upper and lower bounds of BER are derived to approximate the theoretical values. Finally, numerical results are presented to validate our analysis and provide further insights.

Due to high flexible deployment and enhanced transmission capabilities, unmanned aerial vehicle (UAV) has attracted significant attention in recent years. UAV can serve as base station to provide communication coverage for emerging internet of things (IoT) in hot-spot areas. Stable topology plays an important role in improving connection and efficiency of UAV-IoT networks. However, when UAV-IoT nodes fail, network topology is destoryed and reconstruction becomes a formidable challenge, particularly in extremely harsh scenarios. Traditional topology reconstruction schemes predominantly rely on node movement to restore network connectivity, which neglect the performances of UAV-IoT networks. To remain stability and promote performances simultaneously, this paper proposes a distributed resource scheduling topology reconstruction (DRSTR) scheme, where both connectivity and throuphput of UAV nodes are jointly considered. Then, an optimization problem combining topology reconstruction and resource allocation is presented under the constraints of UAV-IoT nodes’ quality of service (QoS) requirements. Since the difficulty in solving the problem, the original problem is divided into three sub-optimal issues and an iterative approach is introduced to approximate the global optimal solution. Numerical results show that the proposed shceme achieves higher performances in UAV-IoT networks, compared to the traditional topology reconstruction shcemes.

Opportunistic beamforming (OBF) is an attractive research topic in wireless communications, which can provide multi-user diversity with a low complexity. The conventional OBF technique requires a large number of waiting users, and cannot simultaneously support multiple users. In order to solve the mentioned problem, this paper proposes a downlink non-orthogonal multi-user and multi-weight opportunistic beamforming (MW-OBF-NOMA) system. An objective function is formulated to maximize the sum spectrum efficiency (SE) of the proposed system, however, it is a non-convex issue and intractable to derive an optimal solution. To solve this non-convex issue, we divide it into two sub-optimal issues and solve them one-by-one. Based on the solutions of the two sub-optimal issues, a joint iterative resource allocation algorithm is presented. Moreover, we discuss the influences of SNR (signal-to-power ratio) estimation, and deduce a closed-form expression of bit error ratio (BER). Numerical results show that our proposed system can simultaneously serve multiple users without extra resources, and achieve an improved sum SE with a low complexity.

Opportunistic beamforming (OBF) is a promising multiple-input–multiple-output (MIMO) downlink precoding technique, where multiuser diversity gain is achieved to deal with the performance loss caused by low-complexity channel estimation. Due to high-performance and low-complexity channel estimation, OBF is appropriate for Internet of Things (IoT) networks. However, the conventional OBF cannot achieve satisfactory performance in post-disaster communication, and the low signal-to-noise ratio (SNR) makes it hard to establish communication between the base station (BS) and users. Therefore, we propose an unmanned aerial vehicle (UAV)-assisted OBF IoT network (UON) to satisfy the Quality-of-Service (QoS) requirements of users. Then, an optimization problem is formulated to maximize the weighted energy and spectrum efficiencies, while the optimization problem is nonconvex and hard to solve. To obtain the solution, we divide the optimization problem into three suboptimal issues, and then a joint iterative algorithm is applied. According to numerical results, the proposed scheme achieves higher system performances compared with the conventional OBF scheme in both Rayleigh and Rician channels. Moreover, we discuss the objective functions with different weights of spectrum efficiency (SE) and energy efficiency (EE) to adapt the QoS requirements of different scenarios. It is indicated that the proposed scheme can achieve high performance with low computational complexity, regardless of the weighted energy and SE.