Autonomous vehicles are expected to emerge as a main trends in vehicle development over the next decade. To support autonomous vehicles, ultra-reliable low-latency communications (URLLC) is required between autonomous vehicles and infrastructure networks, e.g., a fifth-generation (5G) cellular networks. Hence, reliability and latency must be jointly investigated in 5G autonomous vehicular networks. In this paper, utilizing the Euclidean norm theory, we first propose a reliability and latency joint function to evaluate the joint impact of reliability and latency in 5G autonomous vehicular networks. The interactions between reliability and latency are illustrated via Monte Carlo simulations of 5G autonomous vehicular networks. To improve both the reliability and latency performance and implement URLLC, a new network slicing solution that extends from resource slicing to service and function slicing is presented for 5G autonomous vehicular networks. The simulation results indicate that the proposed network slicing solution can improve both the reliability and latency performance and ensure URLLC in 5G autonomous vehicular networks.

Ultra-Reliable Low-Latency Communications in Autonomous Vehicular Networks

Very recent studies showed that the area spectral efficiency (ASE) of downlink cellular networks will continuously decrease and finally crash to zero as the base station (BS) density increases toward infinity if the absolute height difference between BS antenna and user equipment antenna is larger than zero. Such a phenomenon is referred to as the ASE crash. We revisit this issue by considering optimizing the BS antenna downtilt in cellular networks. It is common to adjust antenna pattern to tune the direction of the vertical beamforming and thus increasing received signal power and/or reducing inter-cell interference power to improve network performance. This paper focuses on investigating the relationship between the BS antenna downtilt and the downlink network performance in terms of the coverage probability and the ASE. Our results reveal an interesting find that there exists an optimal antenna downtilt to achieve the maximum coverage probability for each BS density. Numerically solvable expressions are derived for such optimal antenna downtilt, which is a function of the BS density. Our numerical results show that after applying the optimal antenna downtilt, the network performance can be significantly improved, and hence the ASE crash can be delayed by nearly one order of magnitude in terms of the BS density. Our results also give guidance on setting the optimum downtilt angle to maximize network performance given a fixed BS density.

Optimal Base Station Antenna Downtilt in Downlink Cellular Networks

In this paper, new enhanced cognitive radio networks (E-CRNs) based on spectrum sharing (SS) and spectrum aggregation (SA) are proposed for fifth generation (5G) wireless networks. The E-CRNs jointly exploit the licensed spectrum shared with the primary user (PU) networks and the unlicensed spectrum aggregated from the industrial, scientific, and medical bands. The PU networks include TV systems in TV white space and different incumbent systems in the long term evolution time division duplexing bands. The harmful interference from the E-CRNs to the PU networks are delicately controlled. Furthermore, the coexistence between the E-CRNs and other unlicensed systems, such as WiFi, is studied. The E-CRNs framework including dynamic spectrum management (DSM) is designed for the key parameters of licensed SS and unlicensed SA. The essential tradeoff between sharing efficiency and aggregation efficiency for the E-CRNs is discussed. Based on this tradeoff, a spectrum lean-management scheme is proposed to fulfill the DSM. Moreover, a water-filling algorithm is designed to dynamically access the available spectrum. Numerical results demonstrate that the proposed E-CRNs can significantly improve the system performance in terms of data rate, outage probability, and spectrum efficiency. In particular, the E-CRNs framework provides a spectrum usage prototype for 5G wireless communication networks.

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Enhanced 5G Cognitive Radio Networks Based on Spectrum Sharing and Spectrum Aggregation

The relentless development of the Internet of Things (IoT) communication technologies and the gradual maturity of Artificial Intelligence (AI) have led to a powerful cognitive computing ability. Users can now access efficient and convenient smart services in smart-city, green-IoT and heterogeneous networks. AI has been applied in various areas, including the intelligent household, advanced health-care, automatic driving and emotional interactions. This paper focuses on current wireless-communication technologies, including cellular-communication technologies (4G, 5G), low-power wide-area (LPWA) technologies with an unlicensed spectrum (LoRa, SigFox), and other LPWA technologies supported by 3GPP working with an authorized spectrum (EC-GSM, LTE-M, NB-IoT). We put forward a cognitive low-power wide-area-network (Cognitive-LPWAN) architecture to safeguard stable and efficient communications in a heterogeneous IoT. To ensure that the user can employ the AI efficiently and conveniently, we realize a variety of LPWA technologies to safeguard the network layer. In addition, to balance the demand for heterogeneous IoT devices with the communication delay and energy consumption, we put forward the AI-enabled LPWA hybrid method, starting from the perspective of traffic control. The AI algorithm provides the smart control of wireless-communication technology, intelligent applications and services for the choice of different wireless-communication technologies. As an example, we consider the AIWAC emotion interaction system, build the Cognitive-LPWAN and test the proposed AI-enabled LPWA hybrid method. The experimental results show that our scheme can meet the demands of communication-delay applications. Cognitive-LPWAN selects appropriate communication technologies to achieve a better interaction experience.

Cognitive-LPWAN: Towards Intelligent Wireless Services in Hybrid Low Power Wide Area Networks

From very recent studies, the area spectral efficiency (ASE) performance of downlink (DL) cellular networks will continuously decrease and finally to zero with the network densification in a fully loaded ultra-dense network (UDN) when the absolute height difference between a base station (BS) antenna and a user equipment (UE) antenna is larger than zero, which is referred as the ASE Crash. We revisit this issue by considering the impact of the BS antenna downtilt on the downlink network capacity. In general, there exists a height difference between a BS and a UE in practical networks. It is common to utilize antenna downtilt to adjust the direction of the vertical antenna pattern, and thus increase received signal power or reduce inter-cell interference power to improve network performance. This paper focuses on investigating the relationship between the base station antenna downtilt and the downlink network capacity in terms of the coverage probability and the ASE. The analytical results of the coverage probability and the ASE are derived, and we find that there exists an optimal antenna downtilt to achieve the maximal coverage probability for each base station density. Moreover, we derive numerically solvable expressions for the optimal antenna downtilt, which is a function of the base station density. Our theoretical and numerical results show that after applying the optimal antenna downtilt, the network performance can be improved significantly. Specifically, with the optimal antenna downtilt, the ASE crash can be delayed by nearly one order of magnitude in terms of the base station density.

With the seamless coverage of wireless cellular networks in modern society, it is interesting to consider the shape of wireless cellular coverage. Is the shape a regular hexagon, an irregular polygon, or another complex geometrical shape? Based on fractal theory, the statistical characteristic of the wireless cellular coverage boundary is determined by the measured wireless cellular data collected from Shanghai, China. The measured results indicate that the wireless cellular coverage boundary presents an extremely irregular geometrical shape, which is also called a statistical fractal shape. Moreover, the statistical fractal characteristics of the wireless cellular coverage boundary have been validated by values of the Hurst parameter estimated in angular scales. The statistical fractal characteristics of the wireless cellular coverage boundary can be used to evaluate and design the handoff scheme of mobile user terminals in wireless cellular networks.

Wireless fractal cellular networks

Wireless Fractal Cellular Networks

To meet massive wireless traffic demand in the future fifth generation (5G) cellular networks, small-cell networks are emerging as an attractive solution for 5G network deployments. The cellular coverage characteristic is a key issue for the deployment of the small-cell networks. Considering the anisotropic path loss in wireless channels of real cellular scenarios, in this paper the fractal coverage characteristic is first used to evaluate the performance of the small-cell networks. Moreover, the coverage probability, average achievable rate, and the area spectral efficiency are derived for fractal small-cell networks. Compared with the average achievable rate and area spectral efficiency with isotropic path loss models, the average achievable rate and area spectral efficiency with anisotropic path loss models have been underestimated in the fractal small-cell networks. Considering the impact of the anisotropic path loss on wireless channels, most of performances of wireless cellular networks need to be re-evaluated. This paper provides a tractable method to investigate the performance of the small-cell networks with fractal coverage characteristics.

Yehong Qiu

Papers

Wireless fractal cellular networks

Wireless Fractal Cellular Networks

Small-Cell Networks With Fractal Coverage Characteristics