PSK modulation/demodulation and performance evaluation in FM band using USRP

Rapid changes in the wireless systems require reconfigurability. So, an efficient and highly configurable hardware is feasible for such kind of systems. Reconfigurability in fixed hardware systems increases complexity. Thus, it does not cater the problem of frequent changes. A Software Defined Radio (SDR) is a platform which is viable in terms of reconfigurability to implement different types of communication system requirements with same sort of software and hardware such as Lab View and USRP. This paper presents the USRP based communication system, which implements the PSK modulation and demodulation. This system transmits the PSK modulated signal in FM band to examine the channel. Further in the paper, Bandwidth and Bit Error Rate (BER) performance are compared for two different types of PSK modulations based on multiple pulse shaping techniques

UAV-X Communication: Empirical Characterization and Performance Optimization

In emerging wireless networks, the scalability of deploying drones presents an opportunity to design extensive aerial networks. These networks could effectively monitor large agricultural fields from the air and soil for food production with efficient resource utilization. On the one hand, unmanned aerial vehicles (UAVs) have gained interest in agricultural aerial inspection due to their ubiquity and observation scale. On the other hand, agricultural internet-of-thing devices, including buried soil sensors, have gained interest in improving natural resource efficiency in crop production. In this work, we investigate the natural interaction of these two phenomena, where UAVs can be leveraged as flying nodes to collect information from above-ground (AG) and underground (UG) nodes. Additionally, UAVs play a crucial role in bridging farmlands with the core network, facilitating the provision of essential network services. In such a scenario, the UAV can be easily imagined to communicate with AG nodes, UG nodes, neighboring UAVs, and the primary base station (BS) that connects the aerial network to the core network. Therefore, we consider several communication links in a UAV-X communication scenario in a multi-UAV network that is imagined to be deployed by a primary BS to monitor a large field, where X refers to the ground node, UG node, BS, or neighboring UAV. We also consider that the primary BS is equipped with a mobile edge computing server (MEC) that not only assists the aerial network but also serves the communication and computational needs of the respective ground users. Following that, four links can be defined: the Air-to-Air (A2A) link, the Air-to-Ground (A2G) link, the Air-to-Underground (A2UG) link, and the Ground-to-Ground (G2G) link, all of which are considered in this work to study various research problems. First, we investigate the multi-antenna channels between two UAVs in terms of antenna correlation and system capacity in the A2A link scenario. The effect of 3D position on multi-antenna channel characteristics is investigated, and significant variation in the channel is observed in relation to the azimuth and elevation angles between the UAV nodes. Based on the findings, we propose an effective machine learning-based technique for estimating the direction of a transmitting node in an A2A link. Second, we study a UAV-based full-duplex (FD) multi-user communication network in the A2G link scenario, where a UAV is deployed as a multiple-input--multiple-output (MIMO) FD BS to serve multiple FD users on the ground. A novel multi-objective resource allocation problem is designed and solved, which maximizes the sum uplink (UL) and downlink (DL) rates while optimizing the DL beamformer, beamwidth angle, 3D position of the UAV, and UL power of the FD users. Third, we investigate path loss and fading characteristics between UAV and UG nodes using outdoor measurements, aiming to facilitate energy-efficient data collection to and from A2UG wireless links. A novel model is developed that estimates path loss with reduced errors across various UAV 3D positions than prior models. Accordingly, an energy-efficient aerial data collection strategy is designed. Last, in the G2G link scenario, we consider a network in which a BS associated with an MEC server provides computing services to uplink user equipment (UUE) and downlink user equipment (DUE). By leveraging FD at the BS, we design a novel time-slotted computational task completion protocol that can efficiently use computation and communication resources in the network. In this setup, we jointly optimize the BS transmitter precoding vector, UUE uplink transmit power, MEC computational resources, and time-slotted computational task shares to minimize the sum weighted energy at the UUE and server while satisfying a completion-time threshold for each user\u27s task

TVBB bantlarında ölçüme dayalı kablosuz kanal modellerinin çıkarımı

In this thesis, we study TV white space (TVWS) bands, which can be used in next generation networks, in indoor-indoor and indoor-outdoor environments. Path loss measurements are taken at the beginning, middle, and at the end of TVWS band. In addition, ray tracing simulation results are obtained in air-ground TVWS channel environment. Indoor-indoor measurement results are compared with different indoor propagation models in the literature and a new indoor propagation model for TVWS frequencies is proposed, which concatenates the effects of frequency dependent path loss with penetration losses due to floors, walls, and windows. Performance comparison with existing models show that the proposed model achieves superior performance in terms of standard deviation of estimation error (STD). Similarly, for indoor-outdoor environment, measurements results are fitted to log normal shadowing model. An important observation is that the standard deviation of the shadowing coefficient varies greatly at short and long distances. A distance threshold is determined to differentiate between different short distance and long distance shadowing zones. The results indicate that the shadowing standard deviation is reduced at all transmission frequencies distance zones. For air-ground channels, ray tracing simulations are performed. Channel parameters are calculated using log normal shadowing model in two different types of scenarios (high rise buildings and sub-urban). It is found that high rise buildings scenario has high shadowing and high path loss coefficient compared to sub-urban which has low shadowing and low path loss coefficient.Bu tezde, Televizyon beyaz bandlarında binaiçi ve binaiçinden binadışına yapılan kanal ölçüm sonuçları sunulmuştur. Ölçümler TV bandının başında, ortasında ve sonunda bulunan frekanslarda yapılmıştır. Ayrıca hava yer kanalları için ışın takip yöntemi kullanılarak benzetim sonuçları elde edilmiştir. Binaiçi ölçüm sonuçları literatürde bulunan diğer yol kaybı modelleri ile karşılaştırılmış ve TVWS frekansları için yeni bir model önerilmiştir. Yeni model frekansa bağlı olan yol kaybı değerine ek olarak kapı, pencere ve katlar arasında bulunan kayıpları göz önüne almaktadır. Diğer modellerle karşılaştırıldağında yeni önerdiğimiz modelin daha iyi STD performansına sahip olduğu gözlşenmiştir. Benzer olarak bina içinden bina dışına olan yol kaybı modeli için log normal gölgeleme modeli seçilmiştir. Kısa ve uzun mesafelerde yapılan ölçümlerde gölgeleme standart sapma değerleri arasında fark olduğu gözlemlenmiştir. Kısa ve uzun mesafe ayrımı için bir eşik değer tanımlanmıştır. Alınan sonuçlar her frekans bandından hata oranının azaldığını göstermiştir. Hava-yer kanallarında, ışın izleme yöntemi kullanılarak benzetim sonuçları elde edilmiştir. Yüksek katlı binalar ve banliyöler için yapılan çalışmalarda yüksek katlı binalarda hem yol kaybı hem de gölgeleme paramatrelerinin daha yüksek olduğu belirlenmiştir

Path loss exponent and shadowing factor prediction from satellite images using deep learning

Optimal network planning for wireless communication systems requires the detailed knowledge of the channel parameters of the target coverage area. Channel parameters can be estimated through extensive measurements in the environment. Alternatively, ray tracing simulations can be done if the 3D model of the environment is available. One drawback of ray tracing simulations is the high computational complexity; therefore, ray tracing is not suitable for real-time coverage optimization. In this paper, we present a deep convolutional neural network-based approach to estimate channel parameters (specifically, path loss exponent and standard deviation of shadowing) directly from 2D satellite images. While deep learning methods require high computational resources for training and large amount of training data, once trained, the network can make predictions fast. Also, unlike the ray tracing simulations, there is no need for 3D model generation, and therefore, it can be applied easily using the images obtained from satellites or aerial vehicles. These make the proposed method a computationally efficient and reliable alternative to ray tracing simulations. The experimental results show that path loss exponent and large-scale shadowing factor at 900 MHz can be correctly classified by 88% and 76% accuracy, respectively

A novel indoor channel model for tvws communications based on measurements

In this paper, we present an indoor measurement campaign for TV white space bands inside a university building. The measurement results are compared with different indoor propagation models in the literature. We observed large estimation errors for the total path loss value from all existing models. Consequently, we are proposing a new indoor propagation model for TVWS frequencies, which concatenates the effects of frequency dependent path loss with penetration losses due to walls and windows. Performance comparison with existing models show that the proposed model achieves superior performance compared to existing models in terms of Root-Mean-Squared Error (RMSE)

Rate Maximization in a UAV Based Full-Duplex Multi-User Communication Network Using Multi-Objective Optimization

In this paper, we study an unmanned-aerial-vehicle (UAV) based full-duplex (FD) multi-user communication network, where a UAV is deployed as a multiple-input–multiple-output (MIMO) FD base station (BS) to serve multiple FD users on the ground. We propose a multi-objective optimization framework which considers two desirable objective functions, namely sum uplink (UL) rate maximization and sum downlink (DL) rate maximization while providing quality-of-service to all the users in the communication network. A novel resource allocation multi-objective-optimization-problem (MOOP) is designed which optimizes the downlink beamformer, the beamwidth angle, and the 3D position of the UAV, and also the UL power of the FD users. The formulated MOOP is a non-convex problem which is generally intractable. To handle the MOOP, a weighted Tchebycheff method is proposed, which converts the problem to the single-objective-optimization-problem (SOOP). Further, an alternative optimization approach is used, where SOOP is converted in to multiple sub-problems and optimization variables are operated alternatively. The numerical results show a trade-off region between sum UL and sum DL rate, and also validate that the considered FD system provides substantial improvement over traditional HD systems

An Experiment-Based Comparison between Fully Digital and Hybrid Beamforming Radio Architectures for Many-Antenna Full-Duplex Wireless Communication

Full-duplex (FD) communication in many-antenna base stations (BSs) is hampered by self-interference (SI). This is because a FD node’s transmitting signal generates significant interference to its own receiver. Recent works have shown that it is possible to reduce/eliminate this SI in fully digital many-antenna systems, e.g., through transmit beamforming by using some spatial degrees of freedom to reduce SI instead of increasing the beamforming gain. On a parallel front, hybrid beamforming has recently emerged as a radio architecture that uses multiple antennas per FR chain. This can significantly reduce the cost of the end device (e.g., BS) but may also reduce the capacity or SI reduction gains of a fully digital radio system. This is because a fully digital radio architecture can change both the amplitude and phase of the wireless signal and send different data streams from each antenna element. Our goal in this paper is to quantify the performance gap between these two radio architectures in terms of SI cancellation and system capacity, particularly in multi-user MIMO setups. To do so, we experimentally compare the performance of a state-of-the-art fully digital many antenna FD solution to a hybrid beamforming architecture and compare the corresponding performance metrics leveraging a fully programmable many-antenna testbed and collecting over-the-air wireless channel data. We show that SI cancellation through beam design on a hybrid beamforming radio architecture can achieve capacity within 16% of that of a fully digital architecture. The performance gap further shrinks with a higher number of quantization bits in the hybrid beamforming system

An Outdoor Experimental Study of Many Antenna Full-Duplex Wireless

Full-duplex (FD) wireless communication refers to a communication system in which both ends of a wireless link transmit and receive data simultaneously and on the same frequency band. One of the major challenges of FD communication is self-interference (SI), which refers to the interference caused by transmitting elements of a radio to its own receiving elements. Fully digital beamforming is a technique used to conduct beamforming and has been recently repurposed to also reduce SI. However, the cost of fully digital systems (e.g., base stations) dramatically increases with the increase in the number of antennas as these systems use a separate Tx-Rx RF chain for each antenna element. Hybrid beamforming systems use a much smaller number of RF chains to feed the same number of antennas, and hence can significantly reduce the deployment cost. In this paper, we aim to quantify the performance gap between these two radio architectures in terms of SI cancellation and system capacity in FD multi-user MIMO setups. We first obtained over-the-air channel measurement data on two outdoor massive MIMO deployments over the course of three months. We next study two state-of-the-art transmit beamforming based FD systems for fully digital and hybrid architectures. We show that the hybrid beamforming system can achieve 80-97% of the fully digital system capacity, depending on the number of clients

Rate Maximization in a UAV Based Full-Duplex Multi-User Communication Network Using Multi-Objective Optimization

In this paper, we study an unmanned-aerial-vehicle (UAV) based full-duplex (FD) multi-user communication network, where a UAV is deployed as a multiple-input–multiple-output (MIMO) FD base station (BS) to serve multiple FD users on the ground. We propose a multi-objective optimization framework which considers two desirable objective functions, namely sum uplink (UL) rate maximization and sum downlink (DL) rate maximization while providing quality-of-service to all the users in the communication network. A novel resource allocation multi-objective-optimization-problem (MOOP) is designed which optimizes the downlink beamformer, the beamwidth angle, and the 3D position of the UAV, and also the UL power of the FD users. The formulated MOOP is a non-convex problem which is generally intractable. To handle the MOOP, a weighted Tchebycheff method is proposed, which converts the problem to the single-objective-optimization-problem (SOOP). Further, an alternative optimization approach is used, where SOOP is converted in to multiple sub-problems and optimization variables are operated alternatively. The numerical results show a trade-off region between sum UL and sum DL rate, and also validate that the considered FD system provides substantial improvement over traditional HD systems

An Experiment-Based Comparison between Fully Digital and Hybrid Beamforming Radio Architectures for Many-Antenna Full-Duplex Wireless Communication

Full-duplex (FD) communication in many-antenna base stations (BSs) is hampered by self-interference (SI). This is because a FD node’s transmitting signal generates significant interference to its own receiver. Recent works have shown that it is possible to reduce/eliminate this SI in fully digital many-antenna systems, e.g., through transmit beamforming by using some spatial degrees of freedom to reduce SI instead of increasing the beamforming gain. On a parallel front, hybrid beamforming has recently emerged as a radio architecture that uses multiple antennas per FR chain. This can significantly reduce the cost of the end device (e.g., BS) but may also reduce the capacity or SI reduction gains of a fully digital radio system. This is because a fully digital radio architecture can change both the amplitude and phase of the wireless signal and send different data streams from each antenna element. Our goal in this paper is to quantify the performance gap between these two radio architectures in terms of SI cancellation and system capacity, particularly in multi-user MIMO setups. To do so, we experimentally compare the performance of a state-of-the-art fully digital many antenna FD solution to a hybrid beamforming architecture and compare the corresponding performance metrics leveraging a fully programmable many-antenna testbed and collecting over-the-air wireless channel data. We show that SI cancellation through beam design on a hybrid beamforming radio architecture can achieve capacity within 16% of that of a fully digital architecture. The performance gap further shrinks with a higher number of quantization bits in the hybrid beamforming system