To accommodate the rapidly increasing demand for connected infrastructure, automation for industrial sites and building smart cities, the development of Internet of Things (IoT)-based solutions is considered one of the major trends in modern day industrial revolution. In particular, providing high precision indoor positioning services for such applications is a key challenge. Wi-Fi fingerprint-based indoor positioning systems have been adapted as promising candidates for such applications. The performance of such indoor positioning systems degrade drastically due to several impairments like noisy datasets, high variation in Wi-Fi signals over time, fading of Wi-Fi signals due to multipath propagation caused by hurdles, people walking in the area under consideration and the addition/removal of Wi-Fi access points (APs). In this paper, we propose data pre- and post-processing algorithms with deep learning classifiers for Wi-Fi fingerprint-based indoor positioning, in order to provide immunity against limitations in the database and the indoor environment. In addition, we investigate the performance of the proposed system through simulation as well as extensive experiments. The results demonstrate that the pre-processing algorithm can efficiently fill in the missing Wi-Fi received signal strength fingerprints in the database, resulting in a success rate of 88.96% in simulation and 86.61% in a real-time experiment. The post-processing algorithm can improve the results from 9.05–10.94% for the conducted experiments, providing the highest success rate of 95.94% with a precision of 4 m for Wi-Fi fingerprint-based indoor positioning.

Ultra‐dense cellular networks (UDNs) represent the trend for 5G networks in dense urban environments. With the aim of exploring the optimal extent of network densification under different performance requirements and the trade‐off between the network capacity and deployment cost in UDNs, a multiple‐objective optimization model is proposed. This novel optimization design consists of a multiattribute user type in which users are grouped based on their propagation conditions and an infinitesimal dividing modeling method termed the ring method for network capacity dimensioning. The optimal cell size is estimated to maximize the total network capacity and minimize the deployment cost under different levels of user capacity demand. Additionally, the corresponding total network capacity and the required number of base stations are presented. Furthermore, two conventional frequency bands, 800 MHz and 1.8 GHz, and two new bands, 3.5 GHz and mmWave 28 GHz, are considered to investigate their feasibility and the potential of higher frequency bands in the 5G network.

Frequency bands above 1.5 GHz have traditionally been used for 3G and 4G mobile communication services and new frequency bands will be allocated for 5G services in the near future. In this paper, the spectrum value in rural environments is analyzed using an engineering-economic modeling. In order to evaluate the spectrum values, three metrics are selected: maximum cell radius, annual infrastructure cost per square kilometer, and spectrum value per MHz-POP. Frequency bands ranging from 1.5 GHz to 3 GHz and 3 GHz to 11 GHz, are evaluated.

For vehicle-to-vehicle (V2V) communication, such issues as continuity and reliability still have to be solved. Specifically, it is necessary to consider a more scalable physical layer due to the high-speed mobility of vehicles and the complex channel environment. Adaptive transmission has been adapted in channel-dependent scheduling. However, it has been neglected with regards to the physical topology changes in the vehicle network. In this paper, we propose a physical topology-triggered adaptive transmission scheme which adjusts the data rate between vehicles according to the number of connectable vehicles nearby. Also, we investigate the performance of the proposed method using computer simulations and compare it with the conventional methods. The numerical results show that the proposed method can provide more continuous and reliable data transmission for V2V communications.

Frequency bands higher than 3 GHz will be allocated for 5G telecommunication services. Therefore, investigating the spectrum values at these frequency bands is important for developing an appropriate deployment strategy. In this paper, spectrum values above 1.5 GHz are investigated in suburban and urban environments. In the suburban environment, the relative spectrum values at different positions in a cell are analyzed for frequencies ranging from 500 MHz to 1.5 GHz, 1.5 GHz to 3 GHz, and 3 GHz to 11 GHz. In the urban environment, the maximum achievable capacity of users is calculated for frequencies ranging from 500 MHz to 40 GHz. Numerical results show that spectrum values at lower frequency bands below 5 GHz present higher values at the cell-edge in the suburban environment and in non-line-of-sight (NLoS) situations in the urban environment. However, higher frequency bands have nearly the same impact as low frequency bands at the cell center in suburban environments above 5 GHz but show advantages in outdoor urban environments with line-of-sight (LoS) above 20 GHz. It should be noted that 3.5 GHz shows the highest value in NLoS situations and the indoor domain environment in UMi scenarios, while 28 GHz gives the optimum value at the cell-center and indoor areas with LoS in UMa scenarios. When considering the spectrum value for different frequency bands, the primary consideration should be whether the environment is NLoS or LoS, and the secondary consideration should be whether the environment is indoor or outdoor.