Wireless Powered Dense Cellular Networks: How Many Small Cells Do We Need?

This paper focuses on wireless powered 5G dense cellular networks, where base station (BS) delivers energy to user equipment (UE) via the microwave radiation in sub-6 GHz or millimeter wave (mmWave) frequency, and UE uses the harvested energy for uplink information transmission. By addressing the impacts of employing different number of antennas and bandwidths at lower and higher frequencies, we evaluate the amount of harvested energy and throughput in such networks. Based on the derived results, we obtain the required small cell density to achieve an expected level of harvested energy or throughput. Also, we obtain that when the ratio of the number of sub-6 GHz BSs to that of the mmWave BSs is lower than a given threshold, UE harvests more energy from a mmWave BS than a sub-6 GHz BS. We find how many mmWave small cells are needed to perform better than the sub-6 GHz small cells from the perspectives of harvested energy and throughput. Our results reveal that the amount of harvested energy from the mmWave tier can be comparable to the sub-6 GHz counterpart in the dense scenarios. For the same tier scale, mmWave tier can achieve higher throughput. Furthermore, the throughput gap between different mmWave frequencies increases with the mmWave BS density.Comment: pages 1-14, accepted by IEEE Journal on Selected Areas in Communication

Power Beacon-Assisted Millimeter Wave Ad Hoc Networks

Deployment of low cost power beacons (PBs) is a promising solution for dedicated wireless power transfer (WPT) in future wireless networks. In this paper, we present a tractable model for PB-assisted millimeter wave (mmWave) wireless ad hoc networks, where each transmitter (TX) harvests energy from all PBs and then uses the harvested energy to transmit information to its desired receiver. Our model accounts for realistic aspects of WPT and mmWave transmissions, such as power circuit activation threshold, allowed maximum harvested power, maximum transmit power, beamforming and blockage. Using stochastic geometry, we obtain the Laplace transform of the aggregate received power at the TX to calculate the power coverage probability. We approximate and discretize the transmit power of each TX into a finite number of discrete power levels in log scale to compute the channel and total coverage probability. We compare our analytical predictions to simulations and observe good accuracy. The proposed model allows insights into effect of system parameters, such as transmit power of PBs, PB density, main lobe beam-width and power circuit activation threshold on the overall coverage probability. The results confirm that it is feasible and safe to power TXs in a mmWave ad hoc network using PBs.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Highly efficient impulse-radio ultra-wideband cavity-backed slot antenna in stacked air-filled substrate integrated waveguide technology

An impulse-radio ultra-wideband (IR-UWB) cavity-backed slot antenna covering the [5.9803; 6.9989] GHz frequency band of the IEEE 802.15.4a-2011 standard is designed and implemented in an air-filled substrate integrated waveguide (AFSIW) technology for localization applications with an accuracy of at least 3 cm. By relying on both frequency and time-domain optimization, the antenna achieves excellent IR-UWB characteristics. In free-space conditions, an impedance bandwidth of 1.92 GHz (or 29.4%), a total efficiency higher than 89%, a front-to-back ratio of at least 12.1 dB, and a gain higher than 6.3 dBi are measured in the frequency domain. Furthermore, a system fidelity factor larger than 98% and a relative group delay smaller than 100 ps are measured in the time domain within the 3 dB beamwidth of the antenna. As a result, the measured time-of-arrival of a transmitted Gaussian pulse, for different angles of arrival, exhibits variations smaller than 100 ps, corresponding to a maximum distance estimation error of 3 cm. Additionally, the antenna is validated in a real-life worst-case deployment scenario, showing that its characteristics remain stable in a large variety of deployment scenarios. Finally, the difference in frequency-and time-domain performance is studied between the antenna implemented in AFSIW and in dielectric filled substrate integrated waveguide (DFSIW) technology. We conclude that DFSIW technology is less suitable for the envisaged precision IR-UWB localization application

Performance Analysis and Learning Algorithms in Advanced Wireless Networks

Over the past decade, wireless data traffic has experienced an exponential growth, especially with multimedia traffic becoming the dominant traffic, and such growth is expected to continue in the near future. This unprecedented growth has led to an increasing demand for high-rate wireless communications.Key solutions for addressing such demand include extreme network densification with more small-cells, the utilization of high frequency bands, such as the millimeter wave (mmWave) bands and terahertz (THz) bands, where more bandwidth is available, and unmanned aerial vehicle (UAV)-enabled cellular networks. With this motivation, different types of advanced wireless networks are considered in this thesis. In particular, mmWave cellular networks, networks with hybrid THz, mmWave and microwave transmissions, and UAV-enabled networks are studied, and performance metrics such as the signal-to-interference-plus-noise ratio (SINR) coverage, energy coverage, and area spectral efficiency are analyzed. In addition, UAV path planning in cellular networks are investigated, and deep reinforcement learning (DRL) based algorithms are proposed to find collision-free UAV trajectory to accomplish different missions. In the first part of this thesis, mmWave cellular networks are considered. First, K-tier heterogeneous mmWave cellular networks with user-centric small-cell deployments are studied. Particularly, a heterogeneous network model with user equipments (UEs) being distributed according to Poisson cluster processes (PCPs) is considered. Distinguishing features of mmWave communications including directional beamforming and a detailed path loss model are taken into account. General expressions for the association probabilities of different tier base stations (BSs) are determined. Using tools from stochastic geometry, the Laplace transform of the interference is characterized and general expressions for the SINR coverage probability and area spectral efficiency are derived. Second, a distributed multi-agent learning-based algorithm for beamforming in mmWave multiple input multiple output (MIMO) networks is proposed to maximize the sum-rate of all UEs. Following the analysis of mmWave cellular networks, a three-tier heterogeneous network is considered, where access points (APs), small-cell BSs (SBSs) and macrocell BSs (MBSs) transmit in THz, mmWave, microwave frequency bands, respectively. By using tools from stochastic geometry, the complementary cumulative distribution function (CCDF) of the received signal power, the Laplace transform of the aggregate interference, and the SINR coverage probability are determined. Next, system-level performance of UAV-enabled cellular networks is studied. More specifically, in the first part, UAV-assisted mmWave cellular networks are addressed, in which the UE locations are modeled using PCPs. In the downlink phase, simultaneous wireless information and power transfer (SWIPT) technique is considered. The association probability, energy coverages and a successful transmission probability to jointly determine the energy and SINR coverages are derived. In the uplink phase, a scenario that each UAV receives information from its own cluster member UEs is taken into account. The Laplace transform of the interference components and the uplink SINR coverage are characterized. In the second part, cellular-connected UAV networks is investigated, in which the UAVs are aerial UEs served by the ground base stations (GBSs). 3D antenna radiation combing the vertical and horizontal patterns is taken into account. In the final part of this thesis, deep reinforcement learning based algorithms are proposed for UAV path planning in cellular networks. Particularly, in the first part, multi-UAV non-cooperative scenarios is considered, where multiple UAVs need to fly from initial locations to destinations, while satisfying collision avoidance, wireless connectivity and kinematic constraints. The goal is to find trajectories for the cellular-connected UAVs to minimize their mission completion time. The multi-UAV trajectory optimization problem is formulated as a sequential decision making problem, and a decentralized DRL approach is proposed to solve the problem. Moreover, multiple UAV trajectory design in cellular networks with a dynamic jammer is studied, and a learning-based algorithm is proposed. Subsequently, a UAV trajectory optimization problem is considered to maximize the collected data from multiple Internet of things (IoT) nodes under realistic constraints. The problem is translated into a Markov decision process (MDP) and dueling double deep Q-network (D3QN) is proposed to learn the decision making policy

Analysis and design of energy harvesting wireless communication systems

Wireless-powered communication is an emerging technology for powering the large number of miniature devices of the future. In a wireless-powered communication system, low-power sensors extract energy from the incident wireless signals to power their operations such as information transmission, sensing or reception. Due to sporadic energy availability, however, such a system is fundamentally different from a traditionally-powered communication system. This dissertation investigates three distinct aspects of wireless-powered communications to get insights on the system operation. First, leveraging concepts from finite-length information theory, an analytical framework is developed for examining wireless-powered communications with short packets, i.e., in the finite blocklength regime. This is relevant as remotely-powered communications may entail short packets due to small payloads, low-latency requirements, or limited energy to support a longer transmission. Second, using a stochastic geometry framework, an analytical model is developed for characterizing the performance of wireless-powered communications in the millimeter wave (mmWave) band. The proposed model incorporates the key features of mmWave systems such as directional beamforming and sensitivity to building blockages. Finally, the power transfer efficiency and the energy efficiency of a wireless-powered communication system aided by massive MIMO is characterized. The broad goal of this dissertation is to better understand wireless-powered communications in the context of the emerging technologies for 5G.Electrical and Computer Engineerin

Performance Analysis for 5G cellular networks: Millimeter Wave and UAV Assisted Communications

Recent years have witnessed exponential growth in mobile data and traffic. Limited available spectrum in microwave ($\mu$Wave) bands does not seem to be capable of meeting this demand in the near future, motivating the move to new frequency bands. Therefore, operating with large available bandwidth at millimeter wave (mmWave) frequency bands, between 30 and 300 GHz, has become an appealing choice for the fifth generation (5G) cellular networks. In addition to mmWave cellular networks, the deployment of unmanned aerial vehicle (UAV) base stations (BSs), also known as drone BSs, has attracted considerable attention recently as a possible solution to meet the increasing data demand. UAV BSs are expected to be deployed in a variety of scenarios including public safety communications, data collection in Internet of Things (IoT) applications, disasters, accidents, and other emergencies and also temporary events requiring substantial network resources in the short-term. In these scenarios, UAVs can provide wireless connectivity rapidly. In this thesis, analytical frameworks are developed to analyze and evaluate the performance of mmWave cellular networks and UAV assisted cellular networks. First, the analysis of average symbol error probability (ASEP) in mmWave cellular networks with Poisson Point Process (PPP) distributed BSs is conducted using tools from stochastic geometry. Secondly, we analyze the energy efficiency of relay-assisted downlink mmWave cellular networks. Then, we provide an stochastic geometry framework to study heterogeneous downlink mmWave cellular networks consisting of $K$ tiers of randomly located BSs, assuming that each tier operates in a mmWave frequency band. We further study the uplink performance of the mmWave cellular networks by considering the coexistence of cellular and potential D2D user equipments (UEs) in the same band. In addition to mmWave cellular networks, the performance of UAV assisted cellular networks is also studied. Signal-to-interference-plus-noise ratio (SINR) coverage performance analysis for UAV assisted networks with clustered users is provided. Finally, we study the energy coverage performance of UAV energy harvesting networks with clustered users

Integrating Drones and Wireless Power Transfer into Beyond 5G Networks

As fifth generation (5G) standards have been established and 5G commercial products are just around the corner, both academia and industry have started to look at requirements for beyond 5G networks. Network flexibility and long battery life are among the key requirements for beyond 5G wireless communication systems. These critical requirements, which have not been sufficiently addressed in the previous generations, are the focus of this thesis. The first half of this thesis explores two important use cases of drones to provide flexible communication networks. First, the performance of a cellular network with underlay drone cell for temporary events inside a stadium is studied. Using stochastic geometry, a general analytical framework is proposed to analyze the uplink and the downlink coverage probabilities for both the aerial and the terrestrial systems. Our results show that for urban environment and dense urban environment, the drone is best deployed at a low height (e.g., 200 m or lower), regardless of the distance between the center of the stadium and the terrestrial base station. However, for suburban environment and high-rise urban environment, the best drone altitude varies. Second, the performance of emergency information dissemination in public safety scenarios using drone is studied. A drone-assisted multihop multicast device-to-device (D2D) network is considered, where an emergency alert message broadcasted by a drone at the first time slot is multicasted by the D2D users that have successfully received the message through multihop. The impact of different system parameters on the link and the network performance is investigated. Our results demonstrate that a higher drone altitude provides better link and network coverage probabilities and lower mean local delay. Under practical setups, the cell edge user located 2 km from the ground projection of the drone has a link coverage probability around 90% after 5 time slots and a mean local delay of 2.32 time slots with a drone height as low as 200 m. The second half of this thesis investigates wireless power transfer networks. Specifically, the use of power beacons in a millimeter wave wireless ad hoc network is considered, where transmitters adopt the harvest-then-transmit protocol. First, the characteristic of the aggregate received power from power beacons is analyzed and the lognormal distribution is found to provide the best complementary cumulative distribution function approximation compared to other distributions considered in the literature. Then, a tractable model with discrete transmit power for each transmitter is proposed to compute the channel coverage probability and the total coverage probability. Our results show that our model provides a good accuracy and reveal the impact of different system parameters on the total coverage probability. Our results also illustrate that under practical setups, for power beacon transmit power of 50 dBm and transmitters with maximum transmit power between 20 - 40 dBm, which are safe for human exposure, the total coverage probability is around 90%. Thus, it is feasible and safe to power transmitters in a millimeter wave ad hoc network using power beacons