Vahid Vahidi

Orthogonal frequency division multiplexing and channel models for payload communications of unmanned aerial systems

Orthogonal Frequency Division Multiplexing (OFDM) can be a good candidate for wideband communications to transmit payload data from an Unmanned Aerial Vehicle (UAV) to the ground station in an Unmanned Aerial System (UAS). However, OFDM systems are prone to inter-channel interference caused by the Doppler spread. Furthermore, because of possible high speed of UAVs, the Doppler spread can be large. In order to design a proper OFDM system for a UAS, it is essential to have an appropriate air-to-ground channel model that accurately models the multipath and Doppler properties of the wideband channel from the UAV to the ground station. Six different channel models are proposed based on various scenarios of the altitude of the UAV (very low, low, and high) and the type of the environment that they are flying over (low-density suburban areas and high-density urban areas). Since no measurement data has been published for wideband signaling from UAVs to a ground station, these models are created by combining parameters of narrowband aeronautical channel models with downlink channel models of wideband terrestrial systems, including HiperLAN, LTE and IEEE 802.16 systems. These channel models were used to evaluate the performance of an OFDM for UAV-to-ground communications. Simulation results show that for high-speed UAVs, the number of sub-channels in an OFDM should be relatively small in order to have reliable communications.

Channel estimation, equalisation, and evaluation for high-mobility airborne hyperspectral data transmission

In the past few years, unmanned aerial vehicles (UAVs) have become a primary airborne platform for hyperspectral imager for studies on precision agriculture, defence, and the environment. The ‘push-broom’ type of hyperspectral sensors require moving vehicle, and transmission and analysis of hyperspectral data by means of a UAVs high-mobility channel is challenging. While high bandwidth of hyperspectral imaging justify using orthogonal frequency division multiplexing (OFDM) for data transmission, the high speed of UAVs imposes intercarrier interference (ICI) on the transmitted OFDM signal because of the Doppler shift. This study proposes a technique for channel estimation and equalisation in order to compensate the ICI. This technique uses a complete channel matrix estimation in the frequency domain in contrast to conventional methods that only use diagonal elements when recovering the data. In order to evaluate the received data using this technique, a classification framework was designed that took into consideration both spectral and spatial information. In order to verify the robustness of the proposed model, the system was analysed using a Pavia Center hyperspectral dataset, and evaluated against speeds of 50 and 500 m/s. By using this method, improvement in both data transmission and the analysis was achieved.

ICI Mitigation for High-Speed OFDM Communications in High-Mobility Vehicular Channels

The performance of an Orthogonal Frequency Division Multiplexing (OFDM) system to transmit high bandwidth data from a vehicle to a base station can suffer from Inter-Carrier Interference (ICI) created by high Doppler shifts. In current communication systems, high Doppler shifts can happen because of the high speed of the vehicles such as fixed wings unmanned aircraft vehicles (UAVs) and high speed trains (HST). In next generation wireless systems with high carrier frequency, such as 5G cellular data systems at center frequency between 27.5–71 GHz, even a vehicle moving with moderate speed can cause Doppler shift of several kilohertz. To cancel the ICI, the time variant channel matrix should be estimated in the frequency domain. In this paper, a new channel estimation scheme is presented suitable for high Doppler scenarios. To estimate the channel in the frequency domain, a training sequence in the time domain is transmitted, and both channel amplitudes and Doppler shifts are estimated in time domain. Then, the complete frequency domain channel matrix is constructed from the estimated parameters and used for ICI mitigation. In contrast to conventional methods that only estimate diagonal elements of the frequency domain channel matrix or other partial section of the matrix to reduce the complexity, this new method estimate the complete matrix. Simulation results show significant gain in performance for the complete channel estimation as compared to conventional methods using least square and minimum mean square diagonal elements of the channel estimators in high Doppler scenarios.

Performance Enhancement of OMP Algorithm for Compressed Sensing Based Sparse Channel Estimation in OFDM Systems

Long duration of the channel impulse response along with limited number of actual paths in orthogonal frequency division multiplexing (OFDM) vehicular wireless communication systems results in a sparse discrete equivalent channel. Implementing different compressed sensing (CS) algorithms enables channel estimation with lower number of pilot subcarriers compared to conventional channel estimation. In this paper, new methods to enhance the performance of the orthogonal matching pursuit (OMP) for CS channel estimation method is proposed. In particular, in a new algorithm dubbed as linear minimum mean square error-OMP (LMMSE-OMP), the OMP is implemented twice: first using the noisy received pilot data as the input and then using a modified received pilot data processed by the outcome of the first estimator. Simulation results show that LMMSE-OMP improves the performance of the channel estimation using the same number of pilot subcarrier. The added computational complexity is studied and several methods are suggested to keep it minimal while still achieving the performance gain provided by the LMMSE-OMP including using compressive sampling matching pursuit (CoSaMP) CS algorithm for the second round and also changing the way the residue is calculated within the algorithm.

Compressed Channel Estimation Methods for High Mobility Doubly Selective Channels in OFDM Systems

In this study, four channel-estimation (CE) methods are proposed for sparse high mobility doubly selective channels. These methods are designed to take advantage of the sparsity of the channel to decrease the training sequence size and the computational complexity of the CE. Two of the new methods use time domain training sequences. In the first method, the pilot is designed such that the time domain channel can be estimated tap-by-tap using an autoregressive procedure. In the second method, a truncated Taylor expansion of the channel phase is applied to linearise the equations for CE and a pseudorandom training sequence is utilised to solve those equations. The last two proposed channel estimators employ frequency domain pilots and are built upon a previously proposed estimator that exploits both the delay and Doppler shift sparsity of the channel. The computational complexity and the spectral efficiency of the methods are compared. In addition, simulations are performed to compare the performance of these estimators in a sparse channel. Simulation results indicate up to 30 dB improvement in the bit error rate calculation for our proposed CE methods compared to the conventional CE methods.

Channel estimation for wideband doubly selective UAS channels

Reliable high-speed wireless communication is essential for abundant new unmanned aircraft systems (UAS) applications. While orthogonal frequency division multiplexing (OFDM) has been widely used for wideband communications because of its efficiency and its robustness to multipath propagation, high Doppler shift of UAS channels makes the implementation of OFDM challenging for UAS applications. Doppler shift makes the communication channel to vary with time and therefore, destroys the orthogonality between the subcarriers in OFDM and results in inter carrier interference (ICI). To mitigate ICI, channel state information (CSI) is needed at the receiver. In numerous scenarios, UAS wideband wireless channel is sparse. Therefore, compressed sensing (CS) methods can be implemented for the channel estimation. In this paper, both the sparsity of the delay spread and Doppler shift of the UAS payload communication channel are considered for channel estimation and three major adjustments to a regular CS method are proposed in order to enhance the channel estimation performance for high Doppler shift scenarios of UAS. The proposed modifications take into account the precise frequency domain channel model, the Doppler shift statistics of UAS wireless channels, and the ICI effect of the data on the channel estimation precision. Simulation results indicate that the proposed modifications enhance the channel estimation accuracy considerably for high Doppler shift scenarios.

Bandwidth Efficient Channel Estimation Method for Airborne Hyperspectral Data Transmission in Sparse Doubly Selective Communication Channels

Abstract. A channel estimation (CE) method based on compressed sensing (CS) is proposed to estimate the sparse and doubly selective (DS) channel for hyperspectral image transmission from unmanned aircraft vehicles to ground stations. The proposed method contains three steps: (1) the priori estimate of the channel by orthogonal matching pursuit (OMP), (2) calculation of the linear minimum mean square error (LMMSE) estimate of the received pilots given the estimated channel, and (3) estimate of the complex amplitudes and Doppler shifts of the channel using the enhanced received pilot data applying a second round of a CS algorithm. The proposed method is named DS-LMMSE-OMP, and its performance is evaluated by simulating transmission of AVIRIS hyperspectral data via the communication channel and assessing their fidelity for the automated analysis after demodulation. The performance of the DS-LMMSE-OMP approach is compared with that of two other state-of-the-art CE methods. The simulation results exhibit up to 8-dB figure of merit in the bit error rate and 50% improvement in the hyperspectral image classification accuracy.

OFDM for payload communications of UAS: channel estimation and ICI mitigation

For real-time transmission of high-bandwidth sensor data from an unmanned aircraft vehicle (UAV) to the ground station (GS) in an unmanned aircraft system, a high-speed payload communication system is essential. While orthogonal frequency-division multiplexing (OFDM) is very effective for terrestrial wideband systems, possible higher speed of UAVs or higher carrier frequencies can increase the Doppler shift, and therefore increase the inter-carrier interference (ICI) in OFDM systems. In these scenarios, the complete frequency-domain channel matrix should be estimated and used for ICI mitigation and data recovery. In this study, a time-domain pilot-based channel estimation scheme is described to estimate the entire frequency-domain channel matrix for moderate-to-high Doppler OFDM systems. Simulation results indicate that the new scheme provides better performance compared with channel estimation schemes designed for low Doppler scenarios that partially estimate the frequency-domain channel matrix. The comparison is also presented with the other full channel estimation scheme, named autoregressive scheme, showing a comparable performance with much lower complexity for the new scheme.