Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userDFT-spread-OFDM Based Chirp Transmission
129
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
In this study, we propose a framework for chirp-based communications by exploiting discrete Fourier transform-spread orthogonal frequency division multiplexing (DFT-s-OFDM). We show that a well-designed frequency-domain spectral shaping (FDSS) filter for DFT-s-OFDM can convert its single-carrier nature to a linear combination of chirps circularly translated in the time domain. Also, by exploiting the properties of the Fourier series and Bessel function of the first kind, we analytically obtain the FDSS filter for an arbitrary chirp. We theoretically show that the chirps with low ripples in the frequency domain result in a lower bit-error ratio (BER) via less noise enhancement. We also address the noise enhancement by exploiting the repetitions in the frequency. The proposed framework offers a new way to efficiently synthesize chirps that can be used in Internet-of-Things (IoT), dual-function radar and communication (DFRC) or wireless sensing applications with existing DFT-s-OFDM transceivers.
rate research
Read More
We present a deep learning based joint source channel coding (JSCC) scheme for wireless image transmission over multipath fading channels with non-linear signal clipping. The proposed encoder and decoder use convolutional neural networks (CNN) and directly map the source images to complex-valued baseband samples for orthogonal frequency division multiplexing (OFDM) transmission. The proposed model-driven machine learning approach eliminates the need for separate source and channel coding while integrating an OFDM datapath to cope with multipath fading channels. The end-to-end JSCC communication system combines trainable CNN layers with non-trainable but differentiable layers representing the multipath channel model and OFDM signal processing blocks. Our results show that injecting domain expert knowledge by incorporating OFDM baseband processing blocks into the machine learning framework significantly enhances the overall performance compared to an unstructured CNN. Our method outperforms conventional schemes that employ state-of-the-art but separate source and channel coding such as BPG and LDPC with OFDM. Moreover, our method is shown to be robust against non-linear signal clipping in OFDM for various channel conditions that do not match the model parameter used during the training.
In this study, we propose an approach to constructing on-off keying (OOK) symbols for wake-up radios (WURs) by using sequences in the frequency domain. The proposed method enables orthogonal multiplexing of wake-up signals (WUSs) and orthogonal frequency division multiplexing (OFDM) waveforms. We optimize the sequences with a tractable algorithm by considering the reliability of WUSs in fading channels. The proposed algorithm relies on an alternating minimization technique, i.e. cyclic algorithm-new (CAN), which was originally proposed for obtaining a unimodular sequence with good aperiodic correlation properties. In this study, we extend CAN to generate OOK waveforms with Manchester coding. We demonstrate the performance of four optimized sequences and compare with state-of-the-art approaches. We show that the proposed scheme improves the wake-up radio receiver (WURx) performance by controlling the energy distribution in frequency domain while removing the interference-floor at the OFDM receiver.
A novel intercarrier interference (ICI)-aware orthogonal frequency division multiplexing (OFDM) channel estimation network ICINet is presented for rapidly time-varying channels. ICINet consists of two components: a preprocessing deep neural subnetwork (PreDNN) and a cascaded residual learning-based neural subnetwork (CasResNet). By fully taking into account the impact of ICI, the proposed PreDNN first refines the initial channel estimates in a subcarrier-wise fashion. In addition, the CasResNet is designed to further enhance the estimation accuracy. The proposed cascaded network is compatible with any pilot patterns and robust against mismatched system configurations. Simulation results verify the superiority of ICINet over existing networks in terms of better performance and much less complexity.
The fifth generation (5G) wireless standard will support several new use cases and 10 to 100 times the performance of fourth generation (4G) systems. Because of the diverse applications for 5G, flexible solutions which can address conflicting requirements will be needed. In this paper, we propose a solution which enables the use of discrete Fourier transform spread orthogonal frequency division multiplexing (DFT-s-OFDM) and OFDM, which address different requirements, using a common reference symbol (RS) design. In this solution, the DFT-s-OFDM symbol contains RSs in the frequency domain that may be shared by a subsequent OFDM symbol. The proposed scheme is generated by puncturing the output of a DFT-spread block and replacing the punctured samples with RSs in frequency. We prove that puncturing the interleaved samples at the output of the DFT-spread operation equivalently introduces a periodic interference to the data symbols at the input of the DFT-spread operation. We show that the interference due to the puncturing can be removed with a low-complexity receiver by exploiting the zeros inserted to certain locations before the DFT-spread block at the transmitter. Simulation results support that the proposed scheme removes the error floor caused by the puncturing and achieves lower peak-to-average-power ratio than OFDM.
This paper considers device-free sensing in an orthogonal frequency division multiplexing (OFDM) cellular network to enable integrated sensing and communication (ISAC). A novel two-phase sensing framework is proposed to localize the passive targets that cannot transmit/receive reference signals to/from the base stations (BSs), where the ranges of the targets are estimated based on their reflected OFDM signals to the BSs in Phase I, and the location of each target is estimated based on its values of distance to the BSs in Phase II. Specifically, in Phase I, we design a model-free range estimation approach by leveraging the OFDM channel estimation technique for determining the delay values of all the BS-target-BS paths, which does not rely on any BS-target channel model. In Phase II, we reveal that ghost targets may be falsely detected in some cases as all the targets reflect the same signals to the BSs, which thus do not know how to match each estimated range with the right target. Interestingly, we show that the above issue is not a fundamental limitation for device-free sensing: under the ideal case of perfect range estimation in Phase I, the probability for ghost targets to exist is proved to be negligible when the targets are randomly located. Moreover, under the practical case of imperfect range estimation, we propose an efficient algorithm for joint range matching and target localization. Numerical results show that our proposed framework can achieve very high accuracy in the localization of passive targets, which increases with the system bandwidth.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
