Average Error Probability Analysis in mmWave Cellular Networks

In this paper, a mathematical framework for the analysis of average symbol error probability (ASEP) in millimeter wave (mmWave) cellular networks with Poisson Point Process (PPP) distributed base stations (BSs) is developed using tools from stochastic geometry. The distinguishing features of mmWave communications such as directional beamforming and having different path loss laws for line-of-sight (LOS) and non-line-of-sight (NLOS) links are incorporated in the average error probability analysis. First, average pairwise error probability (APEP) expression is obtained by averaging pairwise error probability (PEP) over fading and random shortest distance from mobile user (MU) to its serving BS. Subsequently, average symbol error probability is approximated from APEP using the nearest neighbor (NN) approximation. ASEP is analyzed for different antenna gains and base station densities. Finally, the effect of beamforming alignment errors on ASEP is investigated to get insight on more realistic cases.Comment: Presented at IEEE VTC2015-Fal

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

Restricted Neyman-Pearson approach based spectrum sensing in cognitive radio systems

Ankara : The Department of Electrical and Electronics Engineering and the Graduate School of Engineering and Science of Bilkent University, 2012.Thesis (Master's) -- Bilkent University, 2012.Includes bibliographical refences.Over the past decade, the demand for wireless technologies has increased enormously, which leads to a perceived scarcity of the frequency spectrum. Meanwhile, static allocation of the frequency spectrum leads to under-utilization of the spectral resources. Therefore, dynamic spectrum access has become a necessity. Cognitive radio has emerged as a key technology to solve the conflicts between spectrum scarcity and spectrum under-utilization. It is an intelligent wireless communication system that is aware of its operating environment and can adjust its parameters in order to allow unlicensed (secondary) users to access and communicate over the frequency bands assigned to licensed (primary) users when they are inactive. Therefore, cognitive radio requires reliable spectrum sensing techniques in order to avoid interference to primary users. In this thesis, the spectrum sensing problem in cognitive radio is studied. Specifically, the restricted Neyman-Pearson (NP) approach, which maximizes the average detection probability under the constraints on the minimum detection and false alarm probabilities, is applied to the spectrum sensing problem in cognitive radio systems in the presence of uncertainty in the prior probability distribution of primary users’ signals. First, we study this problem in the presence of Gaussian noise and assume that primary users’ signals are Gaussian. Then, the problem is reconsidered for non-Gaussian noise channels. Simulation results are obtained in order to compare the performance of the restricted NP approach with the existing methods such as the generalized likelihood ratio test (GLRT) and energy detection. The restricted NP approach outperforms energy detection in all cases. It is also shown that the restricted NP approach can provide important advantages over the GLRT in terms of the worst-case detection probability, and sometimes in terms of the average detection probability depending on the situation in the presence of imperfect prior information for Gaussian mixture noise channels.Turgut, EsmaM.S