Facing the development of future 5G, the emerging technologies such as Internet of things, big data, cloud computing, and artificial intelligence is enhancing an explosive growth in data traffic. Radical changes in communication theory and implement technologies, the wireless communications and wireless networks have entered a new era. Among them, wireless big data (WBD) has tremendous value, and artificial intelligence (AI) gives unthinkable possibilities. However, in the big data development and artificial intelligence application groups, the lack of a sound theoretical foundation and mathematical methods is regarded as a real challenge that needs to be solved. From the basic problem of wireless communication, the interrelationship of demand, environment and ability, this paper intends to investigate the concept and data model of WBD, the wireless data mining, the wireless knowledge and wireless knowledge learning (WKL), and typical practices examples, to facilitate and open up more opportunities of WBD research and developments. Such research is beneficial for creating new theoretical foundation and emerging technologies of future wireless communications.

The ability to intelligently utilize resources to meet the need of growing diversity in services and user behavior marks the future of wireless communication systems. Intelligent wireless communications aims at enabling the system to perceive and assess the available resources, to autonomously learn to adapt to the perceived wireless environment, and to reconfigure its operating mode to maximize the utility of the available resources. The perception capability and reconfigurability are the essential features of cognitive radio while modern machine learning techniques project great potential in system adaptation. In this paper, we discuss the development of the cognitive radio technology and machine learning techniques and emphasize their roles in improving spectrum and energy utility of wireless communication systems. We describe the state-of-the-art of relevant techniques, covering spectrum sensing and access approaches and powerful machine learning algorithms that enable spectrum and energy-efficient communications in dynamic wireless environments. We also present practical applications of these techniques and identify further research challenges in cognitive radio and machine learning as applied to the existing and future wireless communication systems.

Efficient massive MIMO detection for practical deployment, which is with spatially correlated channel and high-order modulation, is a challenging topic for the fifth generation mobile communication (5G). In this paper, we propose a lattice reduction aided expectation propagation (LRA-EP) algorithm for massive MIMO detection. LRA-EP applies expectation propagation in lattice reduced MIMO system to approach the distribution of lattice reduced constellation point by iterative refinement on its parameters (mean and covariance). The parameter refinement is based on the lattice reduced, well-conditioned MIMO channel. Numerical result shows that LRA-EP outperforms classic EP based MIMO detection (EPD) with 5~7dB in terms of required signal-to-noise ratio (SNR) for 1% packet error rate in spatially correlated channel for 256-QAM. We also show that LRA-EP has lower computation complexity than EPD.

For the existing support vector machine, when recognizing more questions, the shortcomings of high computational complexity and low recognition rate under the low SNR are emerged. The characteristic parameter of the signal is extracted and optimized by using a clustering algorithm, support vector machine is trained by grading algorithm so as to enhance the rate of convergence, improve the performance of recognition under the low SNR and realize modulation recognition of the signal based on the modulation system of the constellation diagram in this paper. Simulation results show that the average recognition rate based on this algorithm is enhanced over 30% compared with methods that adopting clustering algorithm or support vector machine respectively under the low SNR. The average recognition rate can reach 90% when the SNR is 5 dB, and the method is easy to be achieved so that it has broad application prospect in the modulating recognition.

A more general narrowband regular-shaped geometry-based statistical model (RS-GBSM) combined with the line of sight (LoS) and single bounce (SB) rays for unmanned aerial vehicle (UAV) multiple-input multiple-output (MIMO) channel is proposed in this paper. The channel characteristics, including space-time correlation function (STCF), Doppler power spectral density (DPSD), level crossing rate (LCR) and average fade duration (AFD), are derived based on the single sphere reference model for a non-isotropic environment. The corresponding sum-of-sinusoids (SoS) simulation models including both the deterministic model and statistical model with finite scatterers are also proposed for practicable implementation. The simulation results illustrate that the simulation models well reproduce the channel characteristics of the single sphere reference model with sufficient simulation scatterers. And the statistical model has a better approximation of the reference model in comparison with the deterministic one when the simulation trials of the stochastic model are sufficient. The effects of the parameters such as flight height, moving direction and Rice factor on the characteristics are also studied.

In this paper, we investigate the interference coordination for downlink full-dimension multiple-input multiple-output (FD-MIMO) systems with device-to-device (D2D) communications underlaying. With three-dimensional (3D) beamforming transmission applied for cellular users (CUEs), an approximation of the interference to signal ratio for CUEs is derived, and a coordination strategy is proposed to mitigate the interference from D2D pairs to CUEs. Based on the lower bound of the interference to signal ratio for D2D pairs, we propose coordination strategies for D2D pairs to mitigate the interference caused by base station (BS) and the interference between D2D pairs. The proposed strategies require only some statistical channel state information (CSI) of each user and the reduced-dimensional effective CSI of a few CUEs and D2D pairs. Simulation results show that the proposed coordination strategy performs well in terms of achieving good tradeoff between the achievable rate of CUEs and D2D pairs.

In this paper, a multi-hop relay channel model based on unmanned aerial vehicles (UAVs) is established by taking into account of the propagation loss, shadowing, and multi-path fading. Based on the proposed channel model, the cascaded propagation loss of relay link and the cascaded probability density function (PDF) of channel fading are derived. Moreover, the theoretical performance of the UAV-based relay system, i.e., the outage probability, bit error rate (BER), and channel capacity, is also analysed and derived. Simulation results show agreement with theoretical results for the hill, mountain, and sea scenarios, indicating the accuracy of both the simulations and derivations.

Modern cars are mostly computerized and equipped with passive keyless entry and start (PKES) system. PKES is based on Radio Frequency Identification (RFID) technology for authentication of the authorized drivers. RFID technology has replaced the conventional ways of identification and authorization in order to facilitate users while introducing new security challenges. In this article, we focused on verifying the presence of authorized key in the physical proximity of car by employing multiple antennas. Application of multiple antennas to the currently developed cryptographic algorithms opens a new approach for researchers to improve security of RFID based systems. We propose an advanced security system for PKES using multiple antennas wherein an authorized key passes through multiple vicinities to allow driver to access and start the car. Furthermore, we modified a light-weight cryptographic protocol named as HB (Hopper and Blum) protocol to integrate it with the proposed design based on multiple antennas. Simulation results show improvement in security functionality while keeping in view the efficiency constraints.

This article explores the use of network-connected unmanned aerial vehicle (UAV) communications as a compelling solution to achieve high-rate information transmission and support ultra-reliable UAV remote command and control. We first discuss the use cases of UAVs and the resulting communication requirements, accompanied with a flexible architecture for network-connected UAV communications. Then, the signal transmission and interference characteristics are theoretically analyzed, and subsequently we highlight the design and optimization considerations, including antenna design, nonorthogonal multiple access communications, as well as network selection and association optimization. Finally, case studies are provided to show the feasibility of network-connected UAV communications.

This paper proposes a novel unmanned aerial vehicle (UAV)-based illegal radio station (IRS) localization scheme, where the transmit power of the IRS, the channel model and the noise model are unknown to the UAV. A direction-aware Q-learning algorithm is developed to process received signal strength (RSS) values collected by a directional antenna, as well as directions corresponding to the RSS values. This algorithm determines the direction the UAV flies towards and thereby finds the IRS. The proposed scheme is compared to two baseline schemes. One baseline locates the IRS by a UAV equipped with an omnidirectional antenna, where conventional Q-learning is exploited to process the measured RSS and determine the UAV’s trajectory. The other baseline locates the IRS by a directional-antenna UAV, where the UAV flies towards the direction with respect to the maximum RSS value. Numerical results show that, especially for a low receive SNR, the proposed scheme can outperform the two baselines in terms of the localization efficiency, providing a smoother trajectory for the UAV.

This paper proposes a mode selection scheme to improve the spectral efficiency for coordinated multi-point (CoMP) transmission with phase synchronization errors (PSE). Upper bounds of average achievable rate for different CoMP transmission modes, such as coordinated beamforming (CB) and joint processing (JP), are derived by random matrix theory and asymptotic mathematical approximation. According to these upper bounds, the proposed scheme switches CoMP transmission mode between CB and JP adaptively to enhance the average achievable rate. Simulation results show that these upper bounds agree well with the average achievable rates for both JP and CB, and the proposed scheme outperforms traditional single mode CoMP transmission when PSE exist.

The wireless communication systems based on Unmanned Aerial Vehicles (UAVs) have found a wide range of applications recently. In this paper, we propose a new three-dimensional (3D) non-stationary multiple-input multiple-output (MIMO) channel model for the communication links between the UAV and mobile terminal (MT). The new model originates the traditional geometry-based stochastic models (GBSMs) but considers the non-stationary propagation environment due to the rapid movements of the UAV, MT, and clusters. Meanwhile, the upgrade time evolving algorithms of time-variant channel parameters, i.e., the path number based on birth-death processes of clusters, path delays, path powers, and angles of arrival and departure, are developed and optimized. In addition, the statistical properties of proposed GBSM including autocorrelation function (ACF), cross-correlation function (CCF), and Doppler power spectrum density (DPSD) are investigated and analyzed. Simulation results demonstrate that our proposed model provides a good agreement on the statistical properties with the corresponding derived theoretical ones, which indicates its usefulness for the performance evaluation and validation of the UAV based communication systems.

Recent years have witnessed a huge demand for ubiquitous communications services from continuously moving users. In order to provide seamless network services to high-mobility users, one of the promising solution proposed by 3GPP is the deployment of moving-relays. In this article, we introduce the concept of Moving-Personal-Cell (mPC), which is a type of moving-relays. mPC is a user-centric network, which aims to provide reliable network services to moving users. A mPC receives data-traffic from eNB and its neighboring mPCs via wireless backhaul and sidehual links respectively and forwards the received data to its serving users. In addition to this, mPC can also increase the network capacity by caching and distributing the popular contents to its serving users. Besides these pros, the mPC also has some limitations, as its performance is highly affected by cross-tier and co-tier interferences. In this article, we analyze the effect of these interferences on mPCs performance. Our results show that the performance of mPC network is equally affected by the capacity of wireless backhaul, sidehaul, and access links. Moreover, since mPCs accommodate data traffic from wireless backhaul, sidehaul links, and content cache, their performance is also affected by the ratio of data-traffic delivered via these links.

In this paper, the problem of communication security in an underlay wiretap cognitive radio network is addressed and statistically investigated. We rely on a simple dual-hop communication model of decode and forward relay assisted network. Regarding the interference from primary users, interference power and maximum transmit power constraints; this network is subjected to multiple eavesdropping attacks which employ a specific interception strategy. To confound this eavesdropping, proposed selection schemes are exploited that aim at maximizing the minimum of the dual secrecy rates in order to strengthen the physical layer security. Moreover, exact and asymptotic closed form expressions are derived for specific performance metrics over independent and identically distributed Rayleigh fading channels. At high signal to interference noise ratio (SINR), tangential system bounds are also derived and discussed. Monte Carlo simulation results emphasize our assumption. It is found out that at the full diversity of the system, any additional node that enters the cooperative eavesdropping system becomes significantly of no effect.

Virtual data center is a new form of cloud computing concept applied to data center. As one of the most important challenges, virtual data center embedding problem has attracted much attention from researchers. In data centers, energy issue is very important for the reality that data center energy consumption has increased by dozens of times in the last decade. In this paper, we are concerned about the cost-aware multi-domain virtual data center embedding problem. In order to solve this problem, this paper first addresses the energy consumption model. The model includes the energy consumption model of the virtual machine node and the virtual switch node, to quantify the energy consumption in the virtual data center embedding process. Based on the energy consumption model above, this paper presents a heuristic algorithm for cost-aware multi-domain virtual data center embedding. The algorithm consists of two steps: inter-domain embedding and intra-domain embedding. Inter-domain virtual data center embedding refers to dividing virtual data center requests into several slices to select the appropriate single data center. Intra-domain virtual data center refers to embedding virtual data center requests in each data center. We first propose an inter-domain virtual data center embedding algorithm based on label propagation to select the appropriate single data center. We then propose a cost-aware virtual data center embedding algorithm to perform the intra-domain data center embedding. Extensive simulation results show that our proposed algorithm in this paper can effectively reduce the energy consumption while ensuring the success ratio of embedding.

Pedestrian attributes recognition is a very important problem in video surveillance and video forensics. Traditional methods assume the pedestrian attributes are independent and design handcraft features for each one. In this paper, we propose a joint hierarchical multi-task learning algorithm to learn the relationships among attributes for better recognizing the pedestrian attributes in still images using convolutional neural networks (CNN). We divide the attributes into local and global ones according to spatial and semantic relations, and then consider learning semantic attributes through a hierarchical multi-task CNN model where each CNN in the first layer will predict each group of such local attributes and CNN in the second layer will predict the global attributes. Our multi-task learning framework allows each CNN model to simultaneously share visual knowledge among different groups of attribute categories. Extensive experiments are conducted on two popular and challenging benchmarks in surveillance scenarios, namely, the PETA and RAP pedestrian attributes datasets. On both benchmarks, our framework achieves superior results over the state-of-the-art methods by 88.2% on PETA and 83.25% on RAP, respectively.