Fog computing is an emerging technology that aims at reducing the load on cloud data centers by migrating some computation and storage toward end-users. It leverages the intermediate servers for local processing and storage while making it possible to offload part of the computation and storage to the cloud. Inspired by the benefits of fog computing, we present a novel paradigm that considers the context of social phenomena. Online and off-line human interactions and the mobile social network’s relentless growth allowed real-world data and created users’ traces. We categorize social phenomena into two main groups to integrate with fog computing from social interactions’ continuous development. In this regard, the first contribution addresses the social relationship between the end-users and fog nodes based on personal benefits. The social relationship considers trust, reciprocity, incentives, and selfishness mechanisms. The second contribution describes the group-based social behavior, i.e., centrality, community, and colocation in fog computing networks (FCNs). We also discuss the impact of social phenomena on FCNs in network performance, resource allocations, security, and privacy. We present open challenges and highlight future directions on social perception to encourage follow-up work.

Millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) offers a promising technique to fulfil the high data demand and connectivity of the Internet-of-Things (IoT) and 5G communications because it owns valuable and unknown spectrum resources. Using massive antennas with recently introduced drone-enabled aerial computing platforms, named unmanned aerial vehicles (UAVs), can cast high energy consumption if fully-digital precoding is employed at the UAVs. Using hybrid precoding at a UAV can reduce hardware complexity and energy consumption but is challenging with a need for joint optimization of three precoding matrices at the UAV (sixth-order polynomial objective function). In this paper, we propose to decompose the original UAV hybrid precoding challenge into three subproblems and develop a coordinated descent optimization (CDO) algorithm to solve the three problems recursively. In addition, the convergence and complexity of this new technique are analyzed. Numerical studies indicate the improved effectiveness of the proposed solution over existing solutions.

Hybrid precoding is an effective method for millimeter-wave (mmWave) massive multiple input multiple output (MIMO) system to reduce the number of radio frequency (RF) chains without an obvious sum-rate loss. However, the current literature on hybrid precoding considers either the high resolution (HR) phase shifters (PSs) with high power consumption or the impractical narrowband mmWave channel model. To this end, in this paper, we investigate an energy-efficient hybrid precoding scheme using one-bit PSs for the practical frequency-selective wideband mmWave massive MIMO systems. Specifically, we provide energy consumption analysis to reveal the fact that the energy consumed by the one-bit PSs is much less as compared to the HR-PSs and the array gain loss incurred by using one-bit PSs is also very small. In addition, motivated by the cross- entropy optimization (CEO) algorithm evolved for machine learning, we propose the CEO-based hybrid precoding scheme to maximize the achievable sum- rate of the considered system. The performance evaluation demonstrates that our proposed scheme can obtain the near-optimal sum-rate and considerably higher energy efficiency than some existing works.

Heterogeneous networks (HetNets), which involve densely deployed femtocells underlaid traditional macrocell network, is a promising solution to the extremely high data rate requirements of the future 5G communications. In this paper, we analyze the closed-form optimal deployment of femtocells in HetNets to maximize the network throughput under the outage constraints from both macrocells and femtocells. Specifically, we model the random distribution of macro cell users (MUEs) and femtocell base stations (FBSs) as Poisson Point Processes (PPPs). Then, the closed form expressions for outage probabilities in both uplink and downlink transmissions are derived. Further, we study the network throughput maximization problem under the outage probability constraints. Finally, With the help of convex optimization, the interval of FBS density, which contains the maximum network throughput is obtained in closed form. Simulation results validate the impact of the system parameters on the different optimal FBS density as well as the influence of interference to the maximum network throughput.