Channel Modeling and Characteristics for 6G Wireless Communications

[EN] Channel models are vital for theoretical analysis, performance evaluation, and system deployment of the communication systems between the transmitter and receivers. For sixth-generation (6G) wireless networks, channel modeling and characteristics analysis should combine different technologies and disciplines, such as high-mobil-ity, multiple mobilities, the uncertainty of motion trajectory, and the non-stationary nature of time/frequency/space domains. In this article, we begin with an overview of the salient characteristics in the modeling of 6G wireless channels. Then, we discuss the advancement of channel modeling and characteristics analysis for next-generation communication systems. Finally, we outline the research challenges of channel models and characteristics in 6G wireless communications.This research was supported by the National Key R&D Program of China under grant 2018YFB1801101; the National Nature Science Foundation of China (No. 61771248 and 61971167); the Jiangsu Province Research Scheme of Nature Science for Higher Education Institution (No. 14KJA510001); and the Open Research Fund of the National Mobile Communications Research Laboratory, Southeast University (No. 2020D14).Jiang, H.; Mukherjee, M.; Zhou, J.; Lloret, J. (2021). Channel Modeling and Characteristics for 6G Wireless Communications. IEEE Network. 35(1):296-303. https://doi.org/10.1109/MNET.011.200034829630335

Robust Resource Allocation to Secure Physical Layer Using UAV-Assisted Mobile Relay Communications in 5G Technology

The unmanned aerial vehicles (UAVs) are also known as drones. Recently, UAVs have attracted the next generation researchers due to their flexible, dynamic, and cost-effective deployment, etc. Moreover, the UAVs have a wide range of application domains, such as rescue operation in the remote area, military surveillance, emergency application, etc. Given the UAVs are appropriately deployed, the UAVs provide continuous and reliable connectivity, on-demand, and cost-effective features to the desired destination in the wireless communication system. Thus, the UAVs can be a great choice to deploy as a mobile relay in co-existence with the base stations (BSs) on the ground to serve the 5G wireless users. In this thesis, the UAV-assisted mobile relay (UAV-MR) in the next generation wireless networks has been studied, which also considers the UAV-MR physical layer security. The proposed system also considers one ground user, one BS on the ground, and active presence of multiple eavesdroppers, situated nearby the ground user. The locations of these nodes (i.e., the ground user, the BS, and the eavesdroppers) are considered fixed on the ground. Moreover, the locations of the eavesdroppers are not precisely known to the UAV-MR. Thus, this thesis aims to maximize the achievable secrecy rate, while the BS sends the secure information to the ground user via the UAV-MR. However, the UAV-MR has some challenges to deploy in wireless networks, such as 3D deployment, robust resource allocation, secure UAV-MR to ground communication, the channel modeling, the UAV-MR flight duration, and the UAV-MR robust trajectory design, etc. Thus, this project investigates the UAV-MR assisted wireless networks, which addresses those technical challenges to guarantee efficient UAV-MR communication. Moreover, the mathematical frameworks are formulated to support the proposed model. An efficient algorithm is proposed to maximize the UAV-MR achievable secrecy rate. Finally, the simulation results show the improved performance for the UAV-MR assisted next-generation networks

Signal processing for distributed nodes in smart networks

With increasing environmental concern for energy conservation and mitigating climate change, next generation smart networks are bound to provide improved performance in terms of security, reliability, and energy efficiency. For instance, future smart networks will work in highly complex and dynamic environments and will have distributed nodes that need to interact with each other and may also interact with an energy provider in order to improve their performance. In this context, advanced signal processing tools such as game theory and distributed transmit beamforming can yield tremendous performance gains in terms of energy efficiency for demand management and signal trans-mission in smart networks. The central theme of this dissertation is the modeling of energy usage behavior of self-seeking distributed nodes in smart networks. The thesis mainly looks into two key areas of smart networks: 1) smart grid networks and 2) wireless sensor networks, and contains: an analytical framework of the economics of electric vehicle charging in smart grids in an energy constrained environment; a study of a consumer-centric energy management scheme for encouraging the consumers in a smart grid to voluntarily take part in energy management; an outage management scheme for efficiently curtailing energy from the consumers in smart grids in the event of a power outage; a comprehensive study of power control of sensors in a wireless sensor network using game theory and distributed transmit beamforming; and finally, an energy aware distributed transmit beamfoming technique for long distance signal transmission in a wireless sensor network. This thesis addresses the challenges of modeling the energy usage behavior of distributed nodes through studying the propriety of energy users in smart networks, 1) by capturing the interactions between the energy users and energy provider in smart grids using non-cooperative Stackelberg and generalized Nash games, and showing that the socially optimal energy management for users can be achieved at the solution of the games, and 2) by studying the power control of sensors in wireless sensor networks, using a non-cooperative Nash game and distributed transmit beamforming that demonstrates significant transmit energy savings for the sensors. To foster energy efficient transmission, the thesis also studies a distributed transmit beamforming technique that does not require any channel state information for long distance signal transmission in sensor networks. The contributions of this dissertation are enhanced by proposing suitable system models and appropriate signal processing techniques. These models and techniques can capture the different cost-benefit tradeoffs that exist in these networks. All the proposed schemes in this dissertation are shown to have significant performance improvement when compared with existing solutions. The work in this thesis demonstrates that modeling power usage behavior of distributed nodes in smart networks is both possible and beneficial for increasing the energy efficiency of these networks

HetHetNets: Heterogeneous Traffic Distribution in Heterogeneous Wireless Cellular Networks

A recent approach in modeling and analysis of the supply and demand in heterogeneous wireless cellular networks has been the use of two independent Poisson point processes (PPPs) for the locations of base stations (BSs) and user equipments (UEs). This popular approach has two major shortcomings. First, although the PPP model may be a fitting one for the BS locations, it is less adequate for the UE locations mainly due to the fact that the model is not adjustable (tunable) to represent the severity of the heterogeneity (non-uniformity) in the UE locations. Besides, the independence assumption between the two PPPs does not capture the often-observed correlation between the UE and BS locations. This paper presents a novel heterogeneous spatial traffic modeling which allows statistical adjustment. Simple and non-parameterized, yet sufficiently accurate, measures for capturing the traffic characteristics in space are introduced. Only two statistical parameters related to the UE distribution, namely, the coefficient of variation (the normalized second-moment), of an appropriately defined inter-UE distance measure, and correlation coefficient (the normalized cross-moment) between UE and BS locations, are adjusted to control the degree of heterogeneity and the bias towards the BS locations, respectively. This model is used in heterogeneous wireless cellular networks (HetNets) to demonstrate the impact of heterogeneous and BS-correlated traffic on the network performance. This network is called HetHetNet since it has two types of heterogeneity: heterogeneity in the infrastructure (supply), and heterogeneity in the spatial traffic distribution (demand).Comment: JSA