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Channel Tracking with Flight Control System for UAV mmWave MIMO Communications
Abstract and Figures
Unmanned aerial vehicle (UAV) communications could offer flexible scheduling, improved reliability, enhanced capacity over much wider range, and has become a key part of the space-air-ground integrated network. In this paper, we consider a communication system in millimeter wave (mmWave) band, where UAV serves as an airborne base station (BS) with multiple antennas, and propose a new flight control system based channel tracking method. Specifically, the 3-dimension (3D) geometry channel model is formulated as a combination of the UAV movement state information and the channel gain information, where the former can be obtained by the sensor fusion of the flight control system, while the latter can be estimated through the pilot transmission. Simulation results are provided to verify the effectiveness of the proposed method.
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... RMSE was analyzed to prove the performance of the developed techniques. Zhao et al. [69] presented a flight control system-assisted channel tracking technique in millimeter wave MIMO system. Here, a millimetre wave band of communication system was considered in which an unmanned aerial vehicle (UAV) afforded as an airborne BS with various antennas and generated a flight control system for tracking the channels precisely. ...
... UAV position tracking[69] ...
Due to enhanced data rates and channel capacity, the Multiple-Input Multiple-Output (MIMO) antennas are mainly utilized for advanced IoT applications. The recent research carried out on MIMO antennas ensures the contribution to IoT applications. Moreover, the MIMO antennas have the ability to make communication smooth and enhance communication efficiency between both source and destination. Because of the growing performance of MIMO in IoT, the present study intends to survey the recent studies conducted on different MIMO antennas in IoT and 5G applications from the year of 2015 to 2022. In order to obtain better efficiency, improving the isolation among antenna elements is highly required. Hence, this review article covers various isolation techniques employed in MIMO systems along with performance comparison. In particular, gain enhancement methods, size reduction methods, localization, and tracking methods are surveyed in detail. Also, computational complexity is the major issue faced by the MIMO system. The methods utilized for reducing complexity issues in MIMO are reviewed. Finally, the challenges faced by existing studies and how they will be mitigated in the future are discussed clearly.
Cellular-connected unmanned aerial vehicles (UAVs) represent a promising technology for extending the coverage of 5G and 6G networks in a cost-effective manner. Additionally, Massive multiple-input multiple-output (MIMO) serves as an effective solution to interference mitigation in cellular-connected UAV communications. In this letter, we propose a fusion of wireless and sensor data to enhance beam alignment for cellular-connected UAV massive MIMO communications. We develop a predictive beamforming framework, including the frame structure and predictive beamformer. Moreover, we employ an extended Kalman filter (EKF) to integrate channel and sensor data and provide the corresponding state-space and observation models. Simulation results demonstrate that the proposed scheme can improve position/orientation estimation accuracy significantly, leading to higher spectral efficiency.
Genie aided algorithm can be used for mm wave (Millimeter wave) MIMO systems. It provides the information of AOA's (Angle of Arrival) and AOD's (Angle of Departure) in beam space channel. In this, we proposed a genie-assisted channel learning estimate for uplink transmissions with equal power allocation. In this we study that mmWave MIMO (Multiple Input Multiple Output) channel has unique property and these are not exploited by conventional techniques i.e. LS (Least Square) and MMSE (Minimum Mean Square Error). Hence we created array response vector dictionary of codebook at receiver and transmitter. While we model the mm wave MIMO channel estimation the sparse matrix can be generated with large number of non zero entries. To eliminate this non zero entries we proposed the OMP (Orthogonal matching Pursuit).
In this paper, the deployment of an unmanned aerial vehicle (UAV) as a flying
base station used to provide on the fly wireless communications to a given
geographical area is analyzed. In particular, the co-existence between the UAV,
that is transmitting data in the downlink, and an underlaid device-todevice
(D2D) communication network is considered. For this model, a tractable
analytical framework for the coverage and rate analysis is derived. Two
scenarios are considered: a static UAV and a mobile UAV. In the first scenario,
the average coverage probability and the average sum-rate for the users in the
area are derived as a function of the UAV altitude and the number of D2D users.
In the second scenario, using the disk covering problem, the minimum number of
stop points that the UAV needs to visit in order to completely cover the area
is computed. Simulation and analytical results show that, depending on the
density of D2D users, optimal values for the UAV altitude exist for which the
average sum-rate and the coverage probability are maximized. Moreover, our
results also show that, by enabling the UAV to intelligently move over the
target area, the overall communication rate and coverage probability can be
significantly improved. Finally, in order to provide a full coverage for the
area of interest, the tradeoff between the coverage and delay, in terms of the
number of stop points, is discussed.
Millimeter-Wave (mmWave) massive multiple-input multiple-output (MIMO) system has gained much attention for its considerable improvement in system throughput. However, the cost of the complex hardware, e.g., the radio frequency (RF) chains, hinders it from the practical deployment. In this paper, we propose an angle domain hybrid precoding and channel tracking method by exploring the spatial features of mmWave massive MIMO channel. The number of the effective spatial beams, or equivalently the RF chains, is enormously decreased via the operation of spatial rotation. The users are then scheduled by the angle division multiple access (ADMA) scheme, which groups users according to their direction of arrivals (DOAs). Meanwhile, a channel tracking method is designed for the subsequent data transmission through a small number of pilot symbols. Specifically, the channel information is divided into the DOA information and the gain information, where the DOA information is tracked by a modified unscented Kalman filter (MUKF) and the gain information is estimated from beam training. Numerical results are provided to corroborate our studies.
To support high data rate urgent or ad hoc communications, we consider mmWave UAV cellular networks and the associated challenges and solutions. To enable fast beamforming training and tracking, we first investigate a hierarchical structure of beamforming codebooks and design of hierarchical codebooks with different beam widths via the sub-array techniques. We next examine the Doppler effect as a result of UAV movement and find that the Doppler effect may not be catastrophic when high gain directional transmission is used. We further explore the use of millimeter wave spatial division multiple access and demonstrate its clear advantage in improving the cellular network capacity. We also explore different ways of dealing with signal blockage and point out that possible adaptive UAV cruising algorithms would be necessary to counteract signal blockage. Finally, we identify a close relationship between UAV positioning and directional millimeter wave user discovery, where update of the former may directly impact the latter and vice versa.
Wireless communication systems that include unmanned aerial vehicles (UAVs) promise to provide cost-effective wireless connectivity for devices without infrastructure coverage. Compared to terrestrial communications or those based on high-altitude platforms (HAPs), on-demand wireless systems with low-altitude UAVs are in general faster to deploy, more flexibly re-configured, and are likely to have better communication channels due to the presence of short-range line-of-sight (LoS) links. However, the utilization of highly mobile and energy-constrained UAVs for wireless communications also introduces many new challenges. In this article, we provide an overview of UAV-aided wireless communications, by introducing the basic networking architecture and main channel characteristics, highlighting the key design considerations as well as the new opportunities to be exploited.
Almost all mobile communication systems today use spectrum in the range of 300 MHz-3 GHz. In this article, we reason why the wireless community should start looking at the 3-300 GHz spectrum for mobile broadband applications. We discuss propagation and device technology challenges associated with this band as well as its unique advantages for mobile communication. We introduce a millimeter-wave mobile broadband (MMB) system as a candidate next-generation mobile communication system. We demonstrate the feasibility for MMB to achieve gigabit-per-second data rates at a distance up to 1 km in an urban mobile environment. A few key concepts in MMB network architecture such as the MMB base station grid, MMB inter-BS backhaul link, and a hybrid MMB + 4G system are described. We also discuss beamforming techniques and the frame structure of the MMB air interface.
The relatively high cost of inertial navigation systems (INSs) has been preventing their integration with global positioning systems (GPSs) for land-vehicle applications. Inertial sensors based on microelectromechanical system (MEMS) technology have recently become commercially available at lower costs. These relatively lower cost inertial sensors have the potential to allow the development of an affordable GPS-aided INS (INS/GPS) vehicular navigation system. While MEMS-based INS is inherently immune to signal jamming, spoofing, and blockage vulnerabilities (as opposed to GPS), the performance of MEMS-based gyroscopes and accelerometers is significantly affected by complex error characteristics that are stochastic in nature. To improve the overall performance of MEMS-based INS/GPS, this paper proposes the following two-tier approach at different levels: (1) improving the stochastic modeling of MEMS-based inertial sensor errors using autoregressive processes at the raw measurement level and (2) enhancing the positioning accuracy during GPS outages by nonlinear modeling of INS position errors at the information fusion level using neuro-fuzzy (NF) modules, which are augmented in the Kalman filtering INS/GPS integration. Experimental road tests involving a MEMS-based INS were performed, which validated the efficacy of the proposed methods on several trajectories.
Almost all mobile communication systems today use spectrum in the range of 300 MHz-3 GHz. In this article, we reason why the wireless community should start looking at the 3-300 GHz spectrum for mobile broadband applications. We discuss propagation and device technology challenges associated with this band as well as its unique advantages for mobile communication. We introduce a millimeter-wave mobile broadband (MMB) system as a candidate next generation mobile communication system. We demonstrate the feasibility for MMB to achieve gigabit-per-second data rates at a distance up to 1 km in an urban mobile environment. A few key concepts in MMB network architecture such as the MMB base station grid, MMB interBS backhaul link, and a hybrid MMB + 4G system are described. We also discuss beamforming techniques and the frame structure of the MMB air interface.
The extended Kalman filter (EKF) is probably the most widely used estimation algorithm for nonlinear systems. However, more than 35 years of experience in the estimation community has shown that is difficult to implement, difficult to tune, and only reliable for systems that are almost linear on the time scale of the updates. Many of these difficulties arise from its use of linearization. To overcome this limitation, the unscented transformation (UT) was developed as a method to propagate mean and covariance information through nonlinear transformations. It is more accurate, easier to implement, and uses the same order of calculations as linearization. This paper reviews the motivation, development, use, and implications of the UT.