arXiv subjects

Mustafa Ozger

Publications and source records attributed to Mustafa Ozger.

27 records · Page 2Linked to original sources

arXiv2022

Energy-Efficient Transmission Range and Duration for Cognitive Radio Sensor Networks

Cognitive Radio (CR) promises an efficient utilization of radio spectrum resources by enabling dynamic spectrum access to overcome the spectrum scarcity problem. Cognitive Radio Sensor Networks (CRSNs) are one type of Wireless Sensor Networks (WSNs) equipped with CR capabilities. CRSN nodes need to operate energy-efficiently to extend network lifetime due to their limited battery capacity. In this paper, for the first time in literature, we formulate the problem of finding a common energy-efficient transmission range and transmission duration for all CRSN nodes and network deployment that would minimize the energy consumed per goodput per meter toward the sink in a greedy forwarding scenario. Results reveal non-trivial relations for energy-efficient CRSN transmission range and duration as a function of nine critical network parameters such as primary user activity levels. These relations provide valuable insights for detailed CRSN designs prior to deployment.

cs.NI↗

arXiv2022

mmWave Communications for Indoor Dense Spaces: Ray-Tracing Based Channel Characterization and Performance Comparison

In this paper, the indoor dense space (IDS) channel at 28 GHz is characterized through extensive Ray-Tracing (RT) simulations. We consider IDS as a specific type of indoor environment with confined geometry and packed with humans, such as aircraft cabins and train wagons. Based on RT simulations, we characterize path loss, shadow fading, root-mean-square delay spread, Rician K-factor, azimuth/elevation angular spread of arrival/departure considering different RT simulation scenarios of the fuselage geometry, material, and human presence. While the large-scale fading parameters are similar to the state-of-the-art channel models, the small-scale fading parameters demonstrate richer multipath scattering in IDS, resulting in poorer bit error rate performance in comparison to the 3GPP indoor channel model.

cs.IT↗

arXiv2021

A Survey of Wireless Networks for Future Aerial COMmunications (FACOM)

Electrification turned over a new leaf in aviation by introducing new types of aerial vehicles along with new means of transportation. Addressing a plethora of use cases, drones are gaining attention and increasingly appear in the sky. Emerging concepts of flying taxi enable passengers to be transported over several tens of kilometers. Therefore, unmanned traffic management systems are under development to cope with the complexity of future airspace, thereby resulting in unprecedented communication needs. Moreover, the increase in the number of commercial airplanes pushes the limits of voice-oriented communications, and future options such as single-pilot operations demand robust connectivity. In this survey, we provide a comprehensive review and vision for enabling the connectivity applications of aerial vehicles utilizing current and future communication technologies. We begin by categorizing the connectivity use cases per aerial vehicle and analyzing their connectivity requirements. By reviewing more than 500 related studies, we aim for a comprehensive approach to cover wireless communication technologies, and provide an overview of recent findings from the literature toward the possibilities and challenges of employing the wireless communication standards. After analyzing the network architectures, we list the open-source testbed platforms to facilitate future investigations. This study helped us observe that while numerous works focused on cellular technologies for aerial platforms, a single wireless technology is not sufficient to meet the stringent connectivity demands of the aerial use cases. We identified the need of further investigations on multi-technology network architectures to enable robust connectivity in the sky. Future works should consider suitable technology combinations to develop unified aerial networks that can meet the diverse quality of service demands.

cs.NI↗

arXiv2021

Noise Learning Based Denoising Autoencoder

This letter introduces a new denoiser that modifies the structure of denoising autoencoder (DAE), namely noise learning based DAE (nlDAE). The proposed nlDAE learns the noise of the input data. Then, the denoising is performed by subtracting the regenerated noise from the noisy input. Hence, nlDAE is more effective than DAE when the noise is simpler to regenerate than the original data. To validate the performance of nlDAE, we provide three case studies: signal restoration, symbol demodulation, and precise localization. Numerical results suggest that nlDAE requires smaller latent space dimension and smaller training dataset compared to DAE.

cs.LG↗

arXiv2020

Evaluation of RF Wireless Power Transfer for Low-Power Aircraft Sensors

Low-power sensors can be integrated into an aircraft for numerous use cases. Conventionally, these sensors are powered via cables, which introduces various disadvantages to the overall efficiency of the aircraft. Alternatively, batteries may be used. However, this implies the necessity of additional maintenance for battery replacement. Another option to remove power cables is to use Radio Frequency (RF) Wireless Power Transfer (WPT) systems. Although general RF WPT technology has been studied in the literature, its feasibility for aviation use cases is not fully investigated. In this paper, we study the feasibility of RF WPT to wirelessly power low-power in-cabin sensors. In a cabin mock-up we show that RF WPT techonology is capable of almost fully covering an area of 20 seats and quantitatively assess this using Received Signal Strength Indicators (up to $28$ mW) and packet interval rate (up to $5.5$ Hz). Furthermore, we perform multi-tone sinusoidal wave experiments for power transmission scheme in a lab environment and thereby present potential ways to improve receiver sensitivity and consequently increase the WPT coverage in the cabin without changing the average transmission power. The overall results show that certain low-power cabin use cases can be supported by already existing commercial RF WPT systems.

eess.SY↗

arXiv2020

Quality of Service Aware Traffic Management for Aircraft Communications

In-flight Internet connectivity is a necessity for aircraft passengers as well as aircraft systems. It is challenging to satisfy required quality of service (QoS) levels for flows within aircraft due to the large number of users and the highly varying air to ground (A2G) link capacities composed of satellite and direct air to ground communication (DA2GC). To represent service quality variations, we propose models for the generated traffic flows from aircraft and variations in A2G links. We present three different forwarding schemes based on priority, delay requirements and history of the dropped flows metrics. Forwarding schemes schedule the flows in real time by choosing either satellite or direct air to ground link depending on the delay and capacity requirements of flows to maximize the number of accepted flows with required QoS guarantees in terms of dropped packets and delay. Also, the effect of local caching is studied to fully satisfy the QoS requirement of flows in simulated flights. We implement the forwarding procedures and caching in ns-3 and test their performance in a current connectivity scenario of 100 Mbps capacity for both the satellite spot and ground base station in a one-hour flight. Our study shows that although the forwarding procedure based on a combination of priority and delay requirement has relatively better performance than the other schemes, which are based on priority only and weighted average of all metrics, in dropped packet percentage and delay, the current connectivity setup is not able to satisfy all QoS requirements. Furthermore, at least 0.9 cache hit rate is required to satisfy all flows for at least 50% of simulated flights.

eess.SY↗

arXiv2020

On the Localization of Unmanned Aerial Vehicles with Cellular Networks

Localization plays a key role for safe operation of UAVs enabling beyond visual line of sight applications. Compared to GPS based localization, cellular networks can reduce the positioning error and cost since cellular connectivity is becoming a prominent solution as a communication system for UAVs. As a first step towards localization, UAV needs to receive sufficient number of localization signals each having a signal to interference plus noise ratio (SINR) greater than a threshold, which is defined as localizability. On the other hand, three-dimensional mobility of UAVs, altitude dependent channel characteristics between base stations (BSs) and UAVs, its line of sight and non-line of sight conditions, and resulting interference from the neighboring BSs pose challenges to receive usable signals from the required number of BSs. In this paper, we utilize a tractable approach to calculate localizability, probability of successfully receiving usable signals from a certain number of BSs, in a two-tier hexagonal cellular network. Localizability has an impact on overall localization performance regardless of the localization technique to be used. In our simulation study, we investigate the relation between the probability of localizability with respect to number of participating BSs, post-processing SINR requirement, air-to-ground channel characteristics, and network coordination, which are shown to be the most important factors for the localizability performance of UAVs.

eess.SP↗

arXiv2020

Machine Learning assisted Handover and Resource Management for Cellular Connected Drones

Enabling cellular connectivity for drones introduces a wide set of challenges and opportunities. Communication of cellular-connected drones is influenced by 3-dimensional mobility and line-of-sight channel characteristics which results in higher number of handovers with increasing altitude. Our cell planning simulations in coexistence of aerial and terrestrial users indicate that the severe interference from drones to base stations is a major challenge for uplink communications of terrestrial users. Here, we first present the major challenges in co-existence of terrestrial and drone communications by considering real geographical network data for Stockholm. Then, we derive analytical models for the key performance indicators (KPIs), including communications delay and interference over cellular networks, and formulate the handover and radio resource management (H-RRM) optimization problem. Afterwards, we transform this problem into a machine learning problem, and propose a deep reinforcement learning solution to solve H-RRM problem. Finally, using simulation results, we present how the speed and altitude of drones, and the tolerable level of interference, shape the optimal H-RRM policy in the network. Especially, the heat-maps of handover decisions in different drone's altitudes/speeds have been presented, which promote a revision of the legacy handover schemes and redefining the boundaries of cells in the sky.

eess.SP↗

arXiv2018

Risk-Aware Resource Allocation for URLLC: Challenges and Strategies with Machine Learning

Supporting ultra-reliable low-latency communications (URLLC) is a major challenge of 5G wireless networks. Stringent delay and reliability requirements need to be satisfied for both scheduled and non-scheduled URLLC traffic to enable a diverse set of 5G applications. Although physical and media access control layer solutions have been investigated to satisfy only scheduled URLLC traffic, there is a lack of study on enabling transmission of non-scheduled URLLC traffic, especially in coexistence with the scheduled URLLC traffic. Machine learning (ML) is an important enabler for such a co-existence scenario due to its ability to exploit spatial/temporal correlation in user behaviors and use of radio resources. Hence, in this paper, we first study the coexistence design challenges, especially the radio resource management (RRM) problem and propose a distributed risk-aware ML solution for RRM. The proposed solution benefits from hybrid orthogonal/non-orthogonal radio resource slicing, and proactively regulates the spectrum needed for satisfying delay/reliability requirement of each URLLC traffic type. A case study is introduced to investigate the potential of the proposed RRM in serving coexisting URLLC traffic types. The results further provide insights on the benefits of leveraging intelligent RRM, e.g. a 75% increase in data rate with respect to the conservative design approach for the scheduled traffic is achieved, while the 99.99% reliability of both scheduled and nonscheduled traffic types is satisfied.

cs.NI↗