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Long-term evolution (LTE) deployment in the unlicensed band has been a promising solution to handle the ever-increasing data traffic growth. However, spectrum sharing on unlicensed band poses a significant challenge regarding the interaction between LTE licensed assisted access (LAA) and Wi-Fi. In this paper, a radio access technology (RAT) heterogeneous network that consists of an LAA tier and a Wi-Fi tier is constructed, to achieve the coexistence and alleviate the intra-RAT and inter-RAT interference, a listen-and-talk (LAT) scheme is utilized in Wi-Fi while LAA adopts the listen-before-talk (LBT). By leveraging the full-duplex (FD) techniques in Wi-Fi, the collision can be avoided and the utilization of unlicensed spectrum can be improved. Furthermore, the sensing errors are derived based on the FD strategy and an enhanced Markov model is presented to analyze the performance of the heterogeneous network with consideration of the residual self-interference (RSI). The fairness between LAA and Wi-Fi is also investigated. At last, to ensure the performance of Wi-Fi when serious RSI exists, a switched MAC (S-MAC) that can adaptively switch between the FD mode and half-duplex (HD) mode is presented.

The existing sparse imaging observation error estimation methods are to usually estimate the error of each observation position by substituting the error parameters into the iterative reconstruction process, which has a huge calculation cost. In this paper, by analysing the relationship between imaging results of single-observation sampling data and error parameters, a SAR observation error estimation method based on maximum relative projection matching is proposed. First, the method estimates the precise position parameters of the reference position by the sparse reconstruction method of joint error parameters. Second, a relative error estimation model is constructed based on the maximum correlation of base-space projection. Finally, the accurate error parameters are estimated by the Broyden–Fletcher–Goldfarb–Shanno method. Simulation and measured data of microwave anechoic chambers show that, compared to the existing methods, the proposed method has higher estimation accuracy, lower noise sensitivity, and higher computational efficiency.

For muti-scatter point target and undersampled data, tradintional imaging method has a poor precision for imaging result and spin angular velocity. In view of the above problems, this paper proposes a narrow-band Interferometric inverse synthetic aperture radar(InISAR) 3-dimensional(3D) Compressed sensing imaging method based on the joint spin angle velocity error parameter estimation. First, a sparse model of narrow-band spin target is constructed. Then, the image entropy is used as the criterion function to search the optimal angle velocity by the joint reconstruction iteration. Finally, the image of the target is obtained by interference processing. The simulation results show that compared with the traditional method, the estimation of spin angular velocity and the imaging result are more accurate in proposed method.

It is known that the eigenvalue decomposition and singular value decomposition (SVD) of multi-static data matrices (MDMs) are the basis of time-reversal (TR) imaging. However, the computational loads of the SVD can be significantly high when the MDMs are large. The computational cost of the propagator method (PM) without any SVD of MDMs is much lower than that of SVD-based methods. In this paper, to reduce the computational complexity in TR imaging, first a novel space-frequency propagator method (SF-PM) for TR imaging in large MDMs is proposed. The computational complexity of the proposed SF-PM is then analyzed. Moreover, the theoretical analysis is presented to show that the SF-PM has a significantly lower computational complexity than the conventional SVD-based approach. Finally, the reasonable imaging results and superior computational efficiency of the SF-PM are verified by simulation.

This letter studies the problem of estimating direction-of-departure (DOD) and direction-of-arrival (DOA) for bistatic multiple-input multiple-output (MIMO) radar with unknown source number, and a modified multiple signal classification (MUSIC) algorithm for weak scattering targets is proposed. We utilize the weighting coefficients to weight the corresponding eigenvectors to reduce the influence of signal subspace vectors that are erroneously classified as noise subspace vectors on angle estimation, which can effectively avoid ignoring weak scatterers by incorrect source number estimation. Some simulations are conducted to validate the effectiveness and robustness of the improved MUSIC method.

Interference detection is an important part of the electronic defense system. It is difficult to detect interference with the traditional method of extracting characteristic parameters for interference generated at the same frequency as the original signal. Aiming at this special time-frequency overlapping interference signal, this paper proposes an interference detection algorithm based on the long short-term memory-support vector machines (LSTM-SVM) model. LSTM is used for the time series prediction of the received signal. The difference between the predicted signal and the received signal is used as the feature sample, and the SVM algorithm is used to classify the feature samples to obtain the recognition rate of whether the sample has interference. The LSTM-SVM model is compared with the gate recurrent unit-support vector machines (GRU-SVM) model, and the comparison results are visualized using a confusion matrix. The simulation results show that this LSTM-SVM model algorithm cannot only detect the existence of the interference signal but also can determine the specific position of the interference signal in the received waveform, and the detection performance is better than the GRU-SVM model.

There are several advantages of compressive sensing (CS) technologies for spectrum sensing in wideband cognitive radios (CRs), especially in reducing the sampling rate. However, in CS, the number of measurements needed to reconstruct the sparse signal is determined by the actual sparsity order of the signal, which, in practice, is often unknown and dynamically varying. Thus, in practice, an excessive number of measurements has to be chosen, and choosing such a number degrades the sensing performance. To solve this problem, this article proposes a two-step adaptive compressive spectrum sensing (TS-ACSS) method to estimate the sparsity order. By using a simple statistical analysis of the compressive measurements, a coarse estimation of the sparsity order can be obtained, and an accurate estimation can be acquired by comparing multiple iterations. Compared with existing CS approaches, the proposed TS-ACSS method can not only adaptively adopt an optimal number of measurements without a priori knowledge about current sparsity order but can also detect a sensing failure that can happen if the wideband signal is not sparse in the frequency domain and if the noise level is too large to sense successfully. Furthermore, we verify the effectiveness of our method in dynamic spectrum changing scenarios. The results show that our method can work in both low signal-to-noise ratios (SNRs) and high SNRs environments, and can also be applied in the condition of different energy coefficients and positions of channel occupancy.

Considering the neighbour discovery problem in mobile ad hoc networks (MANETS) with omnidirectional antennas, all nodes want to discover the nodes within a single hop and obtain their identities. However, many existing algorithms spend an extra overhead for neighbour discovery and there is a need of response from neighbours. In this study, a slot allocation scheme is introduced for duplex communication, making all nodes recognisable to each other with a unique codeword. Then, a novel neighbour discovery algorithm based on compressed sensing to apply in this scheme is proposed; it enables all nodes to update neighbour information through the received signal after one or several frames and spends no extra overhead. Furthermore, many algorithms cannot be applied in the overload networks, but the algorithm proposed here is suited for overload networks; the upper bound for its active neighbours is limited to the performance of the network. Finally, the algorithm is validated through simulation and the effect of the three main factors on the performance of the algorithm is researched. The result implies that this algorithm can adapt to various scenarios of the MANETS.

To address the problem of imaging instability when space-frequency (SF) time-reversal (TR) imaging algorithm chooses different ranks of signal subspaces in TR imaging for multiple targets, a modified SF-TR is proposed in this letter, termed W-SF-TR, in which the vectors of noise subspace are weighted to provide the imaging stability. The simulation results show that W-SF-TR significantly reduces the sensitivity of imaging to different ranks, and improves the image resolution as well.

Limited by the Shannon&ndash, Nyquist sampling law, the number of antenna elements and echo signal data of the traditional microwave three-dimensional (3D) imaging system are extremely high. Compressed sensing imaging methods based on sparse representation of target scene can reduce the data sampling rate, but the dictionary matrix of these methods takes a lot of memory, and the imaging has poor quality for continuously distributed targets. For the above problems, a microwave 3D imaging method based on optimal wave spectrum reconstruction and optimization with target reflectance gradient is proposed in this paper. Based on the analysis of the spatial distribution characteristics of the target echo in the frequency domain, this method constructs an orthogonal projection reconstruction model for the wavefront to realize the optimal reconstruction of the target wave spectrum. Then, the inverse Fourier transform of the optimal target wave spectrum is optimized according to the law of the target reflectance gradient distribution. The proposed method has the advantages of less memory space and less computation time. What is more, the method has a better imaging quality for the continuously distributed target. The computer simulation experiment and microwave anechoic chamber measurement experiment verify the effectiveness of the proposed method.

In this paper, we propose a distributed deep reinforcement learning (DRL) based medium access control (MAC) protocol, termed DRL-MAC, which is used to assist the backscatter communications for Internet-of-Things (IoT) networks. By leveraging the current Wi-Fi infrastructure, the backscatter communications can be reserved in advance to avoid the interference from Wi-Fi communications. In the proposed MAC protocol, the deep reinforcement learning is further introduced to learn the reserved information and make decisions such as 1) which backscatter device (TAG) will be serviced, and 2) the reservation step for the serviced TAG. In addition, the utility function is defined and the optimization problem is formulated to balance the backscatter communications and Wi-Fi communications. Moreover, a DRL algorithm is proposed to obtain the optimal strategy. The numerical results show the effectiveness of the proposed MAC for backscatter communications.

A multi-channel medium access control (MAC) is proposed for dense wireless ad-hoc networks, called MSR-MMAC, which aims to enhance throughput by introducing multi-slot reservation into multiple channels environment. Based on the integration of carrier sense multiple access with collision avoidance and time division multiple access, MSR-MMAC alternately implements contention-based channel access and multiple reservation-based data transmission on a certain frequency channel. Besides, the authors build an enhanced Markov model to analyse the throughput performance of the proposed MSR-MMAC protocol, which is further validated by extensive simulations using ns-2. Simulation results show that the proposed MSR-MMAC protocol reduces the average collision probability and improves throughput significantly.

The computational complexity and memory requirements of large-scale data seriously affect the application of compressed sensing (CS) in near-field three-dimensional (3-D) imaging system. In addition, as influenced by the measurement environment, the error in echo phase results in imaging defocusing. This paper proposes a CS near-field 3-D imaging approach based on nonuniform fast Fourier transform and phase error correction. It applies the fast Gaussian gridding nonuniform fast Fourier transform technique and Separable Surrogate Functionals with only matrix and vector multiplied to accelerate imaging speed and reduce memory requirements; it adopts the phase error correction technique to realize highly-focused imaging; in addition, a sparse observation approach based on Logistic sequence is proposed in this paper for easy availability of engineering realization for CS imaging. As indicated by numerical analysis and actual measurement in anechoic chamber, the approach proposed in this paper, compared with traditional imaging approaches, has the following advantages: accurate high resolution 3-D image of target can be obtained by applying small amount of observation data (10%); the computational complexity falls from O(LN) to O(3N) and memory occupation quantity drops from O(LN) to O(N); it can effectively perform highly-focused imaging for echo signal with phase error; the measurement matrix designed has better non-coherence and easy availability for engineering realization.

Dense wireless ad-hoc networks (DWAN) is developed with explosive growth of users and traffics, which means that the future network will bear more frequent and intensive communication services under the condition of limited spectrum resource. To meet the requirement of DWAN, an efficient medium access control (MAC) design has been a hot research topic. Towards the current MAC protocols, we mainly focus on addressing two issues in DWAN: (1) serious collisions; (2) low efficiency of spectrum utilization. In this paper, we propose a hybrid MAC with multi-slot reservation for DWAN, called MRPMAC, which is based upon the architecture of the carrier sense multiple access/collision avoidance and time division multiples access. In the proposed MRPMAC protocol, the network collisions are alleviated by using multi-slot reservation (MSR) scheme, and the frequency spectrum efficiency is improved through power control mechanism. Concretely, just with one handshake in the proposed MRPMAC protocol, multiple slots are reserved and multiple concurrent data transmissions are periodically initiated over single channel without collisions. Therefore, spectrum reusing can be achieved, the network throughput is greatly improved as well as quality of service can be ensured in DWAN. Furthermore, the upper bound of slot utilization in MRPMAC is analyzed. Through simulations using ns-2 simulator, we validate that the upper bound of throughput gain brought by power control scheme in MRPMAC is twice as high as pure MSR scheme, and demonstrate that system throughput of MRPMAC outperforms IEEE 802.11 DCF and POWMAC about 150 and $$67\%$$ , respectively.

The multi-slot reservation-based hybrid medium access control (MAC) protocol with collision prevention is analysed under a high-density deployment scenario. Extensive simulations are conducted to validate the accuracy of the proposed model (i.e. within 4% maximum relative error), which plays a theoretical guiding role in the MAC layer design and optimisation of a heterogeneous network system.

Due to the spinning target’s distribution on a continuous scene, it is impossible to guarantee that all scatterers are located exactly on the pre-discretized grid. Off-grid problem will lead to the mismatch of sensing matrix, which severely affect the performance of conventional narrow-band radar imaging based on compressed sensing. By reformatting the signal model and improving the sparse recovery algorithm, a robust narrow-band radar imaging method for off-grid spinning targets is proposed. Firstly, an imaging model, considering the gridding error, is developed, which is more close to the distribution of real target. Secondly, we put forward an improved orthogonal matching pursuit algorithm for the optimization of reconstruction, and introduce the nonlinear least squares method to further improve the reconstruction accuracy of scatterers. Finally, the effectiveness of the proposed method is verified by simulation and real data, and the selection rule of grid size is presented by quantitative analysis.

This paper proposes a new interferometric near-field 3-D imaging approach based on multi-channel joint sparse reconstruction to solve the problems of conventional methods, i.e., the irrespective correlation of different channels in single-channel independent imaging which may lead to deviated positions of scattering points, and the low accuracy of imaging azimuth angle for real anisotropic targets. Firstly, two full-apertures are divided into several sub-apertures by the same standard, secondly, the joint sparse metric function is constructed based on scattering characteristics of the target in multi-channel status, and the improved Orthogonal Matching Pursuit (OMP) method is used for imaging solving, so as to obtain high-precision 3-D image of each sub-aperture, thirdly, comprehensive sub-aperture processing is performed using all sub-aperture 3-D images to obtain the final 3-D images, finally, validity of the proposed approach is verified by using simulation electromagnetic data and data measured in the anechoic chamber. Experimental results show that, compared with traditional interferometric ISAR imaging approaches, the algorithm proposed in this paper is able to provide a higher accuracy in scattering center reconstruction, and can effectively maintain relative phase information of channels.

Long-term evolution in unlicensed band (LTE-U) has been an increasing interest for exploiting more spectrum resources. However, the spectrum sharing poses a significant challenge especially regarding the coexistence between LTE-U and Wi-Fi systems. In case of LTE-U acquires the unlicensed spectrum by sharing with the Wi-Fi system, the performance of the Wi-Fi system will be degraded. How to protect Wi-Fi communications and to achieve harmonious coexistence need to be addressed. In this paper, a listen-and-talk access mechanism featuring carrier sensing medium access/collision detection (CSMA/CD) for Wi-Fi is introduced into the LTE-U/Wi-Fi coexistence networks. Specifically, Wi-Fi user can transmit and sense simultaneously by leveraging full-duplex (FD) techniques to avoid long collision and improve spectrum utilization on unlicensed band. In addition, we analyze the system throughput with regard to false alarm and miss detection which are existence due to the residual self-interference (RSI). We further investigate the fairness between Wi-Fi and LTE-U, and for Wi-Fi traffic protection we derive the constriction of false alarm and miss detection. Based on the fairness-throughput trade off, we propose a FD/HD mode switching scheme. Finally, simulation results are conducted using ns-2 simulator, which verify the analysis of throughput in LTE-U/Wi-Fi coexistence networks.

Traditional range-instantaneous Doppler (RID) imaging methods for maneuvering targets have low resolution and poor capability of noise suppression. In this paper, a novel ISAR imaging method of maneuvering targets based on sparse reconstruction is proposed, which can provide a high-resolution and focused map of the spatial distribution of scatterers on the target. The proposed method exploits the sparse spatial feature of target scene to characterize the sparsity of the echo signal in its time-frequency domain, constructs the instantaneous Fourier-transform-based dictionary that can reflect the time-varying Doppler frequency and then transforms RID imaging into the problem of sparse reconstruction. After discriminating noise range cells from the range cells containing signal components, noise range cells are used for noise level estimation and signal range cells are used for signal reconstruction. Furthermore, the iteration weighted strategy for ISAR image generation is applied to enhance the signal components and suppress noise. Both simulated and real data experiments are performed to evaluate the proposed method, and the experimental results are presented to demonstrate the superiority of the proposed method over traditional imaging methods. HighlightsThe sparsity of the echo signal is characterized in its time-frequency domain.We propose a high-resolution ISAR imaging method for maneuvering targets.The separation method of signal range cells and noise range cells is proposed.An iteration weighted model of double parameters is introduced to improve imaging performance.

A design method of a local oscillator operated on C band was proposed in this paper for the high stability, high precision and light weight of the Unmanned Aerial Vehicle (UAV) data link radio frequency transmitting unit. Based on high integrated broadband phase-locked loop chip (HMC834), the system was designed by adopting the internal programming models for the fractional-N control, and the high phase noise and small frequency step of the output could be realized. The simulation and testing method were combined to optimize and select the parameters of the filter circuit, and realize high precision phase-locked frequency synthesis output in the hardware design process. The theoretical analysis and engineering prototype measurement results showed that the local oscillator had stable output frequency, accurate frequency step, high spurious suppression and low phase noise, and met the design requirements. The proposed design idea and research method have theoretical guiding sense for engineering practice.

The hybrid rib-slot-rib surface plasmon waveguide where a combined rib-slot-rib structure is added on the metal substrate within a low-index gap region with asymmetric structures is proposed using finite element method. Two types of asymmetric structures are introduced and their modal properties are discussed and compared to their traditional long range surface plasmon waveguide. Our designed structures can provide enhanced confinement energy within the gap region at proper parameters. Our simulation result is a guide for turning properties of plasmonic waveguide and providing ways for improved electromagnetic energy confinement in surface plasmon waveguide.

A model of novel periodical structural scatterer is proposed in this paper. After the operation of wire connection, the Radar Cross Section (RCS) of the diamond patch array has been decreased dramatically from the simulation and experimental results. When the impedance controllable network composed of varactor diodes is loaded between the unit cells, the RCS of scatterer could be tuned by different biasing voltage at L-band frequencies. The experimental results demonstrate the validity of this technology that the RCS of the active scatterer can be reduced more than 10 dB in a wide angle range.

In stepped-frequency radar systems, target motions produce range-profile distortions. The velocity of the target induces range shift, signal-to-noise ratio loss, range profile spreading, so it should be compensated. The conventional technology requires velocity estimation. In this paper, we propose a new way to eliminate or suppress the distortion of the synthetic range profile. This technique combines two pulse bursts which satisfy certain mathematical relationship and is also easier to implement. The simulation results of this paper show the effectiveness of the method.

In terms of the problems of the traditional near-field measurement and imaging system, such as low testing efficiency, low imaging resolution, an approach of near-field scattering measurement and imaging based on compressed sensing is proposed in this paper. Based on the theory, an ascertained measurement matrix with higher correlation is designed, which can be used to control the signal acquisition system to realize down-sampling; besides, an orthogonal dictionary with all information of imaging scenes included is also designed. Finally, separable surrogate functionals combining iterative shrinkage and proximal point optimization reconstruction algorithm are adopted to realize the inversion images. The real test experiment in microwave anechoic chamber verifies the effectiveness of the proposed measurement and imaging approach. The results indicate that, the proposed approach has higher measurement efficiency and resolution ratio compared with the traditional approach.

In this paper, a novel full polarization high-resolution ISAR imaging method based on multichannel joint sparse recovery is proposed. First, a definition of joint sparsity is given by exploiting the scattering characteristics of target in fully polarimetric channels. Then, a multichannel joint sparse recovery algorithm is proposed to reconstruct fully polarimetric radar images under the constraint of the joint sparsity. The proposed imaging method can not only provide high-resolution radar images with limited measurements, but also preserve the true polarimetric scattering characteristics of the target, which are beneficial for further applications. Finally, simulated experiments of the backhoe data verify the effectiveness of the proposed method.

The Directions of Arrivals (DOAs), speeds and distances of targets are all required for array signal processing. Based on the periodic phase shift of coherent pulse sequence waveform, a new estimation of multi-targets’ 2-Dimentional (2-D) DOA angle, Doppler frequency shift and relative time-delay is proposed. Based on a virtual sensor array constructed by pulse cumulating, the estimations of azimuth, elevation, Doppler frequency shift and time-delay can be obtained simultaneously, and the least number of pulses could be two. This method is computationally efficient even in heavier noised environment, and all estimations are automatically paired in calculation process with no spectrum searching. Further more, this algorithm can be used to any plane sensor array and deal with many targets at the same time only by few sensors. The targets number that can deal with simultaneously is several times to the sensor number, which is the upper limit for normal algorithms such as ESPRIT and MUSIC. These characteristics would be very useful, especially, for aerial systems. Simulations demonstrate the capabilities of this method efficiently.

The multichannel or wide-angle imaging performance of synthetic aperture radar (SAR) can be improved by applying the compressed sensing (CS) theory to each channel or sub-aperture image formation independently. However, this not only neglects the complementary information between signals of each channel or sub-aperture, but also may lead to failure in guaranteeing the consistency of the position of a scatterer in different channel or sub-aperture images which will make the extraction of some scattering information become difficult. By exploiting the joint sparsity of the signal ensemble, this paper proposes a novel CS-based method for joint sparse recovery of all channel or sub-aperture images. Solving the joint sparse recovery problem with a modified orthogonal matching pursuit algorithm, the recovery precision of scatterers is effectively improved and the scattering information is also preserved during the image formation process. Finally, the simulation and real data is used for verifying the effectiveness of the proposed method. Compared with single channel or sub-aperture independent CS processing, the proposed method can not only obtain better imaging performance with fewer measurements, but also preserve more valuable scattering information for target recognition.

This paper combines compressed sensing (CS) imaging theory and range migration algorithm (RMA), and then proposes a near-field three-dimensional (3-D) imaging approach for joint high-resolution imaging and phase error correction. Firstly, a sparse measurement matrix construction method based on a logistic sequence is proposed, which conducts nonlinear transformation for the determined logistic sequence, making it obey uniform distribution, then conducts sign function mapping, and generates the pseudorandom sequence with Bernoulli distribution, thus leading to good signal recovery under down-sampling and easy availability for engineering realization. Secondly, in combination with the RMA imaging approach, the dictionary with all scene information and phase error correction is constructed for CS signal recovery and error correction. Finally, the non-quadratic solution model jointing imaging and phase error correction based on regularization is built, and it is solved by two steps - the separable surrogate functionals (SSF) iterative shrinkage algorithm is adopted to realize target scattering estimate; the iteration mode is adopted for the correction of the dictionary model, so as to achieve the goal of error correction and highly-focused imaging. The proposed approach proves to be effective through numerical simulation and real measurement in anechoic chamber. The results show that, the proposed approach can realize high-resolution imaging in the case of less data; the designed measurement matrix has better non-coherence and easy availability for engineering realization. The proposed approach can effectively correct the phase error, and achieve highly-focused target image.

We propose a hybrid plasmonic waveguide where a combined rib-slot-rib structure is added on the metal substrate within a low-index gap region. The optical properties of the quasi-TM fundamental mode are numerically calculated using the finite element method. Compared to the traditional hybrid plasmonic waveguide, our designed waveguiding structure can support modes with tighter confinement and longer propagation length, by properly adjusting the geometry of the rib-slot-rib structure and the gap height. In details, it can provide the hybrid mode with mode area λ2/12000 and reasonable propagation distance 23.78 μm, simultaneously. Its excellent optical performance can facilitate potential applications in ultra-compact nanophotonic devices and circuits.

This is a copy of an article published in the Journal of Nanophotonics © 2016 Society of Photo-optical Instrumentation Engineers Publications. http://nanophotonics.spiedigitallibrary.org/

From the spectrum characters of triangular FMCW radar and the comparability of the frequency envelope of fixed clutters, an algorithm on restraining fixed clutters and MTD is proposed by calculating the spectrum energy centre. And distance and speed error are corrected by partner algorithm in frequency domain. Computer simulation indicated that the detection probability is greater than 95% in the case of SNR is −25db and SCR is above −75db.