Millimeter wave full-digital full-connected multibeam array can play a crucial role in meeting the massive data capacity demands for the future Internet of Things. In this paper, a novel digital predistortion (DPD) technique is designed to linearize this array for the scenario under constraint bandwidth. Two different linearization schemes, including beam-oriented DPD schemes and PA-oriented DPD schemes, are analyzed and compared, along with their respective applicable scenarios. Based on the characteristics of full-digital full-connected arrays, and by fully leveraging the alleviation of adjacent channel power ratio metrics requirement in FR2 band specification, the band-limited DPD concept can be effectively integrated into the full-digital full-connected multibeam array architecture, which allows for the array linearization with low system bandwidth requirements without introducing an analog filter for each PA. To demonstrate the effectiveness of the proposed technique, the simulations are analyzed for an array with 6-beam 64-chain configuration. Furthermore, experiments on a 2-beam 4-chain full-digital full-connected array are verified at the center frequency of 26 GHz with different modulated bandwidth scenarios. The experiment results indicate that the proposed method successfully achieves expected linearization performance for wideband multibeam array.

This paper presents a bandwidth-enhanced, low-profile dual-polarization (dual-pol) patch antenna array with a height of only 0.03λ₀ for glass-enclosed mobile platforms in 5G-enabled IoT applications at the millimeter-wave band. The bandwidth improvement and low-profile characteristic are achieved through a single-layer radiating patch fed by closely positioned lateral microstrip resonators, forming a multi-resonance structure that expands bandwidth while reducing the overall height by minimizing vertical feeding components. Dual-pol performance is accomplished via common-mode excitation of back-to-back C-shaped quarter-wavelength resonators on one side of the patch for one polarization, and differential-mode excitation of folded line-shaped resonators symmetrically placed on opposite sides for the orthogonal polarization, achieving extended bandwidth and high isolation. Building on this antenna element, a 1×4 dual-pol phased array is implemented for beam steering, fabricated using high-density interconnect (HDI) technology. Measurements reveal an operating bandwidth of 25.56–28.04 GHz (a fractional bandwidth of 9.5%) forx-pol and 24.42–28.49 GHz (15.6%) for y-pol. Beam scanning across ±45° shows a gain degradation of less than 3 dB, with cross-polarization levels remaining below -20 dB within the main lobe at each scan angle. This design is intended for integration within the camera region of a mobile terminal and has been optimized for operation in an under-glass environment. Performance tests conducted with a glass cover confirm the robustness of the design as an effective under-glass antenna for terminal applications.

This study introduces an innovative K-/Ka-band planar active shared-aperture phased array (ASAPA), advancing low earth orbit (LEO) terminal design through synergistic innovations in shared-aperture topology and three-dimensional (3D) integration. Breaking from conventional shared-aperture topologies plagued by cross-band interference and radiation pattern distortion, we propose a partial-element-reuse-based (PER) topology that strategically repurposes 50% of dual-band dual-polarized elements for simultaneous transmit and receive (STAR) operation. This configuration eliminates active element pattern distortion while achieving a reduction in element count, enabling wide-angle beam scanning essential for dynamic satellite tracking. Furthermore, a sandwich-structured printed circuit board (PCB) lamination method is further employed to streamline integration by partitioning functional modules into antenna, coupler, and active circuit layers interconnected via ball grid array (BGA) technology. This 3D integration strategy simplifies vertical interconnects, minimizes PCB layer requirements, and enhances thermal dissipation through embedded air gaps. Experimental results validate robust beamforming performance across ±60° scanning ranges for both transmitting and receiving arrays, achieving high transceiver isolation and signal integrity, which are imperative for high-capacity satellite links. By addressing key bottlenecks in LEO terminal design, the proposed innovations can accelerate the deployment of energy-efficient, cost-effective satellite networks, thus advancing the 6G vision of ubiquitous global connectivity.

In this paper, a bidirectional RF front-end based on the WIN Semiconductor’s 100 nm GaAs pHEMT process that can support the extension of E-band vector network analyzers (VNA) is proposed. For E-band VNA extensions, an amplifier multiplier chain (AMC) with high output power and high harmonic rejection characteristics is required to effectively drive the following bidirectional passive mixer. In this design, the AMC includes a 20–30 GHz frequency doubler, an E-band frequency tripler, and an E-band balanced power amplifier. The frequency doubler features a push-push architecture to effectively improve the rejection characteristics of odd harmonics. Following this stage is a frequency tripler based on an antiparallel diode pair (APDP) topology, which includes a pair of power enhancement T-lines to improve the output power of the AMC. The output of the AMC is a balanced power amplifier with source degeneration inductors to enhance the amplifier’s bandwidth and stability. To further expand the bandwidth of the AMC, driver amplifiers with an integrated RC negative feedback network are included. Additionally, a compact matching network with bandpass characteristics is integrated between the doubler and tripler to enhance harmonic rejection performance. Measurement results indicate that the peak output power of the E-band AMC is 20.9 dBm, and the harmonics rejection is better than 25 dBc. The intermediate frequency bandwidth of the bidirectional RF front-end covers 100 MHz–8 GHz, and the corresponding up-conversion gain and down-conversion gain are better than -8.2dB and -8 dB, respectively.

Millimeter-wave radar plays a vital role in 3D object detection for autonomous driving due to its all-weather and all-lighting-condition capabilities for perception. However, radar point clouds suffer from pronounced sparsity and unavoidable angle estimation errors. To address these limitations, incorporating a camera may partially help mitigate the shortcomings. Nevertheless, the direct fusion of radar and camera data can lead to negative or even opposite effects due to the lack of depth information in images and low-quality image features under adverse lighting conditions. Hence, in this paper, we present the radar-camera fusion network with Hybrid Generation and Synchronization (HGSFusion), designed to better fuse radar potentials and image features for 3D object detection. Specifically, we propose the Radar Hybrid Generation Module (RHGM), which fully considers the Direction-Of-Arrival (DOA) estimation errors in radar signal processing. This module generates denser radar points through different Probability Density Functions (PDFs) with the assistance of semantic information. Meanwhile, we introduce the Dual Sync Module (DSM), comprising spatial sync and modality sync, to enhance image features with radar positional information and facilitate the fusion of distinct characteristics in different modalities. Extensive experiments demonstrate the effectiveness of our approach, outperforming the state-of-the-art methods in the VoD and TJ4DRadSet datasets by 6.53% and 2.03% in RoI AP and BEV AP, respectively.

This paper proposes an interleaved transceiver design method for a multiple-input multiple-output (MIMO) integrated sensing and communication (ISAC) system utilizing orthogonal frequency division multiplexing (OFDM). We consider a continuous transmission system and focus on transceiver design for alternate symbols to mitigate the interference to the radar from reflections of adjacent OFDM symbols. Constructive interference (CI) is incorporated into the optimization to improve communication performance, while the integrated mainlobe-tosidelobe ratio (IMSR) of the transmission beampattern ensures directivity. A time-domain radar receive filter is designed to reduce the range sidelobes and retain loss-in-processing gain, while also mitigating the interference to the radar and eliminating spurious peaks induced by distant targets. Given the high peak-to-average power ratio (PAPR) in OFDM systems, we constrain the power of each transmitted sample. The optimization problem is addressed using alternating optimization (AO), with the subproblem of transmitted waveform design being solved via successive convex approximation (SCA). Numerical simulations validate the effectiveness of our transceiver design in achieving desirable performance in both radar sensing and communication.

Feature-level fusion shows promise in collaborative perception (CP) through balanced performance and communication bandwidth trade-off. However, its effectiveness critically relies on input feature quality. The acquisition of high-quality features faces domain gaps from hardware diversity and deployment conditions, alongside temporal misalignment from transmission delays. These challenges degrade feature quality with cumulative effects throughout the collaborative network. In this paper, we present the Domain-And-Time Alignment (DATA) network, designed to systematically align features while maximizing their semantic representations for fusion. Specifically, we propose a Consistency-preserving Domain Alignment Module (CDAM) that reduces domain gaps through proximal-region hierarchical downsampling and observability-constrained discriminator. We further propose a Progressive Temporal Alignment Module (PTAM) to handle transmission delays via multi-scale motion modeling and two-stage compensation. Building upon the aligned features, an Instance-focused Feature Aggregation Module (IFAM) is developed to enhance semantic representations. Extensive experiments demonstrate that DATA achieves state-of-the-art performance on three typical datasets, maintaining robustness with severe communication delays and pose errors. The code will be released at https://github.com/ChengchangTian/DATA.

The technique of digital twin network (DTN) has been considered as a promising technique of network management and control to accommodate disruptive applications for the sixth generation communication (6G) systems. However, it is still challenging for DTN to provide ultimate experience for the emerging services and applications, due to the lack of effective DTN model management strategies. To solve this problem, the model selection and offloading for DTN is studied in this paper. First, a framework of cooperative model selection and offloading for DTN is designed, which can adapt with the limited computation capability of users, and improve the communication efficiency of model offloading. Second, the performance metrics are proposed to effectively evaluate the accuracy loss and the privacy leakage risk of model management for DTN, and tractable expressions are provided for our studied framework. Third, a joint optimization algorithm is designed for model management and transmit power allocation, which can efficiently reduce the accuracy loss and the privacy leakage risk with low processing latency. Finally, the experiment results are provided to show the effectiveness of our introduced performance metrics, and verify the performance gains of proposed optimization algorithm for our studied framework.

An LC-VCO incorporating differentially pumped varactors for$3^{\mathrm {rd}}$order harmonic generation is demonstrated in a 65-nm bulk CMOS process with an$f_{max}$of ~250 GHz. The differentially pumped N-type Accumulation mode MOS varactor pair acting as a symmetric varactor serves as a frequency tuning element, as well as the non-linear reactive elements for harmonic generation. The measurements show that the circuit has a peak radiated power of -15.2 dBm with a frequency tuning range between 361.4 to 383.3 GHz or 5.9 %, and a peak DC-THz efficiency of 0.102 %. Among the CMOS oscillator signal generators with an on-chip antenna and output frequency greater than 300 GHz, the circuit exhibits the state-of-the-art DC-THz efficiency.

This paper proposes a 24-29.5 GHz$2\times 2$transmitter (TX) and receiver (RX) front-end chipset with streamlined intermediate frequency (IF) interfaces for digital beamforming (DBF) systems. An ultra-compact 90° coupler employing a three-dimensional (3-D) coupling structure achieves a 70% reduction in size compared to typical Lange couplers while maintaining excellent performance. This 3-D 90° coupler enables the synthesis/distribution of IF I-Q signals within the transceiver (TRX), enhancing the feasibility of mmWave DBF by reducing the demand for baseband channels and facilitating scalability for multi-beam arrays. To ensure a low-level error vector magnitude (EVM) in the TRX, the chipset employs joint package design for optimal power and noise performance. It utilizes an image-reject mixer to suppress RX image noise and introduces a DC-offset in the single-sideband up-converter for LO leakage regulation. Additionally, a frequency doubler chain is incorporated to enhance phase noise performance. Fabricated using 0.13-$\mu $m SiGe BiCMOS technology and packaged in WLCSP process, the TX achieves an average output 1-dB compression point ($OP_{1dB}$) of 20 dBm per channel. The RX chip features a minimum noise figure (NF) of 3.4 dB. Over-the-air (OTA) measurements conducted over a 1.1-m distance reveal a transmission data rate of up to 8 Gb/s using 16-QAM modulation at 25 GHz. Furthermore, when employing a 400-MHz 64-QAM 5G New Radio (NR) modulation, the system maintains EVM levels below -33 dB at 25 GHz and below -30 dB over 24-29.5 GHz.

Due to the wide bandwidth and large observation area, spaceborne synthetic aperture radar (SAR) is easily interfered by other electromagnetic signals, namely radio frequency interference (RFI), which can severely degrade SAR image quality and submerge useful information. Classic parametric and non-parametric methods are used to suppress RFI as much as possible without considering the useful information. To protect the real reflected signals, semi-parametric methods, based on low-rank and sparse recovery, are proposed to mitigate RFI, but they suffer from the singular-value over-shrinking problem when RFI is not strictly low-rank, resulting in interference residues in the recovered scene. Hence, in this paper, a robust interference self-cancellation (RISC) method is proposed to protect raw ground scenes from polluted data with better extraction accuracy of RFI. The proposed model can adaptively fit in different scenes and backgrounds by using adjacent homologous interference (HI) subregions instead of the low-rank constraints, thus better protecting SAR scenes and enhancing its robustness. Based on the alternating direction method of multipliers (ADMM), we design two different solvers for the proposed optimization model, and both are tested on four different scenes of Sentinel-1 measured data. All experiments demonstrate that the proposed method has excellent performance in RFI mitigation and SAR image recovery.

As a wideband radar system, spaceborne synthetic aperture radar (SAR) has been widely applied in multiple applications, such as maritime surveillance and terrain observation. However, with the increase of electromagnetic devices, spaceborne SAR suffers from radio frequency interference (RFI) frequently. Many previous methods have been effective in interference mitigation, among which semiparametric methods demonstrate excellent performance and high efficiency. However, as a classic low-rank recovery method, robust principal component analysis (RPCA) usually suffers from the over-penalization problem of large singular values. Although some useful schemes were proposed to address this issue, their performance may still degrade if the low-rank characteristics of interference are not prominent. To overcome these obstacles, we first investigate the characteristics and distributions of different SAR signals and leverage kurtosis to differentiate interference and real echoes. Herein, interference tends to have a low kurtosis while the real echoes tend to have a high kurtosis. Then, we improve the low-rank recovery model with kurtosis and propose the kurtosis-based reweighted nuclear norm (KRNN) model to precisely extract interference components. Then, we derive the closed-form solution of the KRNN model via the alternating direction method of multipliers (ADMM) framework. Through the proposed KRNN method, we can effectively mitigate interference, preserve real echoes, and solve the problems of over-penalization and nonideal low-rank property. Finally, we conduct numerical experiments using the measured Sentinel-1 and LT-1 data to demonstrate the effectiveness and robustness of our proposed method.

As an advanced remote sensing technology, synthetic aperture radar (SAR) generates high-resolution images by transmitting continuous electromagnetic waves toward the target area. SAR has played a pivotal role in both contemporary research and practical applications. This underscores the importance of maintaining imaging integrity. However, the performance of SAR systems is severely affected by the increasingly prevalent radio frequency interference (RFI). RFI not only degrades the quality of SAR images but also hinders the accurate interpretation of SAR data. The rapid development and widespread use of modern electromagnetic devices have led to a diversification of interference types, resulting in complex mixed-mode interference. Traditional interference mitigation techniques struggle to effectively alleviate these issues. Moreover, varying terrains add significant difficulty to mitigating interferences, often resulting in residual interference in processed images and the loss of substantial scene information. To tackle these challenges, this article proposes a novel interference mitigation method called the group-parametric method. Unlike previous semiparametric methods, the group-parametric method refines both the interference and target models and achieves more effective interference mitigation and scene preservation by applying distinct regularizations to the refined models. Based on the new model, we have designed a structured trifactorization (STF) algorithm across frequency and time domains, which achieves data recovery through regularizations of low-rank and sparsity applied to the interference. Experimental verification with Level-1 data from LuTan-1 (LT-1) and Sentinel-1 confirms the effectiveness and superiority of our proposed model and method.

Existing deep learning-based methods for synthetic aperture radar (SAR) target recognition typically rely solely on the amplitude images without considering the complex characteristic of SAR images, making it difficult to recognize SAR targets with high visual similarity. To solve this issue, a novel dual-stream manifold multiscale network fused with electromagnetic features, i.e., EFMM-Net is proposed for target recognition in complex-valued SAR images. In EFMM-Net, the attributed scattering center (ASC) model is first utilized to reconstruct the complex-valued SAR image, thereby highlighting the electromagnetic scattering features of the target. Subsequently, the reconstructed complex-valued SAR image is combined with the original one to construct the dual-stream input. Second, a scattering-guided manifold multiscale (SGMM) backbone is proposed for parallel extraction of data features and electromagnetic scattering features of the target from the dual-stream input. During feature extraction, the SGMM backbone can effectively leverage the phase information of complex-valued SAR images and inject target scattering information into data features through scattering-guided channel-wise feature alignment, thus enhancing the awareness of data features to critical scattering characteristics. Finally, to effective fuse the data features and electromagnetic scattering features, a location awareness feature fusion (LAFF) recognition module is proposed. By exploiting coordinate attention, LAFF utilizes the target location information captured from electromagnetic scattering features to direct the feature fusion process, thereby increasing the focus of fusion features on the target region. The extensive recognition results of three-class and six-class ship targets in the OpenSARShip 2.0 dataset demonstrate the effectiveness and superiority of the proposed method.

High-resolution radar imaging with compressive sensing (CS) is significantly important and meaningful in practical applications, such as data collection burden reduction and resource allocation scheduling in a multifunctional radar. The class of matrix completion (MC) methods is a powerful tool to directly reconstruct the missing data to be applied in sparse radar imaging, which can overcome the discrete error drawback of traditional dictionary-based CS methods. In this article, we extend the MC method to tensor completion (TC) with multidimensional data representation, and a novel manifold low-rank and sparse TC (MLRSTC) radar imaging algorithm is proposed for enhanced sparse imaging performance. In the scheme, an attractive tensor radar data model is proposed, and the low-rank tensor property is discovered by capturing the latent and intrinsic data structure in high dimensions. In particular, the low-rankness superiority of the tensor model is confirmed by both the theoretical derivation and experimental analysis. Then, the Kronecker-basis-representation (KBR)-based tensor sparsity model is applied to format the proposed MLRSTC algorithm of sparse radar imaging, which can effectively promote the reconstruction of tensor data with enhanced low-rank property. Meaningfully, the proposed MLRSTC algorithm can work well under the condition of different sparse data sampling patterns. Next, the proposed MLRSTC algorithm is efficiently solved in an iterative manner under the framework of alternating direction method of multipliers (ADMMs) by updating the involved parameters in a closed-form solution. Finally, the experiments using both electromagnetic simulation and measured data are performed to confirm the effectiveness and superiority of the proposed MLRSTC algorithm beyond state-of-the-art (SOTA).

With the advancement of self-driving vehicles, autonomous driving systems depend on multimodal data to achieve a dynamic perception of the surrounding environment. Synthetic aperture radar (SAR) techniques can enhance azimuth resolution by utilizing the relative motion between the vehicle and targets, requiring a precise trajectory of the vehicle, normally without the assistance of automotive-grade navigation systems. In this case, data-driven autofocus-based algorithms are typically used to implement compensation for non-systematic motion errors. Despite demonstrating robust autofocus capabilities in numerous scenarios, their potential for application in automotive scenarios still needs to be exploited. This paper aims to provide a comprehensive automotive SAR imaging with autofocus workflow and to analyze the performance of autofocus algorithms based on phase gradient autofocus (PGA) in typical automotive scenarios. We rigorously derive the Omega-$\boldsymbol {K}$algorithm based on the system-grade waveform of frequency modulated continuous wave (FMCW) signals. Based on the analysis of motion error and phase error characteristics, a sub-aperture contrast-based weighted PGA (SAC-WPGA) method, a contrast-based selection strategy (CBSS), and a contrast-based WPGA kernel are proposed to improve the robustness of autofocus for automotive scenarios. In addition, we theoretically discuss the impact of the selection strategy, the PGA kernel, and the selection threshold in detail, highlighting the validity of the proposed method. Finally, we showcase the superiority of the proposed technique by employing experimental data in two typical automotive scenarios, i.e., a simple scenario with isolated dominant points and a complex scenario with strong clutter.

Integrated sensing and communication (ISAC) has been proved as a promising technique to further improve the performance for both the communication and the sensing tasks in wireless networks. However, due to the complicated and dynamic interference circumstances, the performance of ISAC cannot be guaranteed. To provide some insights for keeping a sophisticated balance between communication and sensing with considering co-channel interference, the theoretical performance of ISAC is studied in this paper. First, an analytical system model is provided based on stochastic geometry. Second, the success serving probability (SSP) is defined for both the communication and the sensing tasks based on mutual information, which provided a unified analysis framework for ISAC. The tractable expressions of SSP are also derived. Finally, the simulation results are shown to verify the analytical results of SSP, which can provide some insights for the tradeoff between sensing and communication of ISAC.

Lack of full use of phase information is a common problem in synthetic aperture radar (SAR) automatic target recognition (ATR). In this paper, we propose a complex manifold multi-scale feature fusion network (CMMFF-Net) for SAR image target recognition. Unlike traditional complex-valued networks, we extend SAR complex images to complex manifold space and construct a complex-valued manifold feature extraction module, which can extract manifold features from SAR amplitude and phase images. Moreover, the multiscale feature extraction and fusion module helps to further extract richer and discriminative target features by fusing multiscale information. Experimental results on SAR complex image dataset demonstrate the effectiveness of proposed method.

Automotive synthetic aperture radar (SAR) that can achieve long aperture by coherently processing chirps collected by radar mounted on moving vehicle platform shows remarkable superiority in terms of angular/azimuth resolution. To further enhance imaging performance, MIMO technology has been combined with SAR for extended signal-to-noise ratio (SNR), side-lobe level and etc. In this paper, an automotive MIMO SAR image fusion algorithm using tensor decomposition is proposed. In the scheme, the redundancy between MIMO SAR image stacks after compensating phase difference between channels is modeled as the low-rank property of tensor. Then, the low-rank tensor representation is verified and adopted to enhance the image quality of MIMO SAR imaging. Numerical experiments using measured data from an automotive MIMO radar system are carried out. The imaging results obtained using the proposed algorithm show significant improvement compared to single channel SAR and MIMO digital beamforming (DBF) results.

A low-cost millimeter-wave wideband filtenna integrating filtering and radiation performance for next-generation millimeter-wave (mmWave) Internet of Things (IoT) devices is proposed in this article. The filtering performance is achieved by utilizing the intrinsic high-pass property of a microstrip-fed magneto-electric dipole (ME-dipole) and filtering feeding mechanism. Several shorted parasitic patches are settled around the E-dipole to enhance the radiation performance within the operating band and develop suppression levels at both the lower and upper bands. A stepped cross-shaped microstrip-line structure is added in the center of the radiating aperture to generate two resonances, so that the in-band impedance performance and the selectivity can be improved. Measurement results indicate that the proposed filtenna achieves a wide operating bandwidth of 30.26% (22.8-31.4 GHz), a peak gain of 8.21 dBi and an out-of-band gain suppression better than 23 dB. Furthermore, a 2×2 filtenna subarray is constructed with some shorted parasitic patches being shared, and then, a 4×4 circularly-polarized (CP) sequential rotated (SR) array is also delicately designed using the 2×2 filtenna subarray. Remarkable operating bandwidths and appropriate out-of-band suppression performance with simplistic structures are all confirmed well through experiments. The outstanding performance of the proposed filtenna and its arrays makes them potential candidates for the B5G/6G mmWave communications.

In the era of 5G and beyond, the strategic utilization of both sub-6 GHz and millimeter-wave (mmWave) spectrums supports diverse communication services. Through smartphones, consumers can conveniently access a wide range of 5G/6G-enabled Internet of Things (IoT) scenarios anytime and anywhere. In this paper, mmWave and sub-6 GHz aperture-shared antenna and array are proposed for mobile terminals. For the mmWave antenna design, a slot radiating array is integrated into the metallic frame of a smartphone. This design incorporates a differential square-ring feeder and utilizes hybrid mode operation, enabling dual-polarized radiation capability across a wide operating frequency band. Importantly, this configuration requires only two metal layers with a 1.0-mm profile. Sharing the same frame, an inverted-F antenna (IFA) and a hybrid mode antenna with loop antenna and IFA operation are designed to work in sub-6 GHz bands. With the design principle of equal clearance, the sub-6 GHz antennas can perform well with the coexistence of the mmWave array. This approach is particularly applicable for sub-6 GHz antennas of different modes and frequencies. The proposed 1×4 mmWave phased array prototype demonstrates a -10 dB bandwidth of 23.3-30.8 GHz (covering the 5G n257/258 bands), a beam scanning range of ±40∘, and an in-band realized gain above 10.3 dBi. The sub-6 GHz antennas effectively cover 5G bands n1/2/3/7/18/28. By utilizing impedance tuning technique, the lower band can be further tuned to cover the bands n8/5.

In the realm of fifth-generation (5G)-enabled Internet of Things (IoT), the smartphone plays a pivotal role in providing users access to various IoT scenarios. With the emergence of millimeter-wave (mmWave) technology in 5G mobile terminals, it is feasible to realize an ultrabroadband, ultrahigh speed, and ultralow latency communication for advanced IoT applications. However, in a smartphone, the end-fire mmWave radiation is blocked by the metal frame. To solve this problem without altering the industrial design (ID) of the smartphone, we present a new mmWave beam-tilted phased array antenna with multiple hybrid modes operation. Our approach employs a physically oblique radiating aperture to achieve a tilted and frequency-insensitive radiation pattern, effectively addressing the interference from the smartphone platform while preserving the integrity of the ID. To expand the impedance bandwidth, monopole mode, magnetic dipole mode, and stepped patch mode are generated with an effective space utilization. For experimental validation, the proposed prototype is measured in a simplified mobile terminal. The$1\times 4$phased array achieves a −10 dB impedance bandwidth of 23.5–30.5 GHz, which covers the 5G n257 and n258 bands, with an in-band realized gain higher than 9.4 dBi. Furthermore, at 27.0 GHz, a wide 3-dB scanning range of 102.5°/72.0° is obtained for vertical/horizontal polarization, along with a peak gain of 9.4/11.0 dBi. The experimental results validate the proposed beam-tilted antenna solution, indicating that it can effectively address impedance mismatching, radiation distortion, low robustness, and other practical issues in 5G smartphones.

A low-profile dual-broadband dual-circularly-polarized (dual-CP) reflectarray (RA) is proposed and demonstrated, supporting independent beamforming for right-/left-handed CP waves at both K-band and Ka-band. Such functionality is achieved by incorporating multi-layered phase shifting elements individually operating in the K- and Ka-band, which are then interleaved in a shared aperture, resulting in a cell thickness of only about 0.1 λ L . By rotating the designed K- and Ka-band elements around their own geometrical centers, the dual-CP waves in each band can be modulated separately. To reduce the overall profile, planar K-/Ka-band dual-CP feeds with a broad band are designed based on the magnetoelectric dipoles and multi-branch hybrid couplers. The planar feeds achieve bandwidths of about 32% and 26% at K- and Ka-band respectively with reflection magnitudes below −13 dB, an axial ratio smaller than 2 dB, and a gain variation of less than 1 dB. A proof-of-concept dual-band dual-CP RA integrated with the planar feeds is fabricated and characterized which is capable of generating asymmetrically distributed dual-band dual-CP beams. The measured peak gain values of the beams are around 24.3 and 27.3 dBic, with joint gain variation <1 dB and axial ratio <2 dB bandwidths wider than 20.6% and 14.6% at the lower and higher bands, respectively. The demonstrated dual-broadband dual-CP RA with four degrees of freedom of beamforming could be a promising candidate for space and satellite communications.

This article presents a fully packaged 94-GHz 16-channel local oscillator (LO) phase-shifting transmitter (TX) and a single-channel receiver (RX). The implementation is accomplished using a hybrid integration scheme, combining high-output-power 100 nm GaAs pHEMT front-end chips and highly-integrated 130 nm SiGe BiCMOS beamformer chips. High-accuracy LO phase shifting is achieved with the utilization of a commercial SiGe-based four-channel beamformer chips, offering 7-bit phase control at 24–28 GHz. A 26-to-78 GHz tripler chain using power-enhancing and harmonic-suppression techniques, a 16-to-94 GHz bi-directional mixer, and a 94-GHz power amplifier are designed in the transmitter front-end chip based on the GaAs process. The GaAs transmitter front-ends are wire-bonded to microstrip lines and then converted to low-loss substrate integrated waveguides (SIWs), which directly feed a high-gain TEM horn antenna array. The inter-element spacing of the transmitter array is optimized to 1.6 mm ($0.5 \lambda _{0}$@94 GHz) for a wide scanning range. The 16-channel transmitter achieves a wide scanning range of ±50° and a peak effective isotropic radiated power (EIRP) of 43.6 dBm at 94 GHz. The GaAs receiver chip is packaged with the WR10 waveguide RF interface and connected to a horn antenna. The packaged GaAs receiver module achieves a conversion gain (CG) of 25 dB and a noise figure (NF) of 5.8 dB. Additionally, the 16T1R over-the-air (OTA) measurement supports 5G New Radio 400-MHz 64-QAM signal between 88 and 94 GHz over a 5-meter ±48° scanning range.

Due to the strong consistency with the synthetic aperture radar (SAR) system, deceptive jamming can be well integrated with SAR images and has high concealment. Therefore, deceptive jamming suppression in SAR is an urgent problem that needs to be solved. This article proposes a slow-time group phase coding (GPC) scheme for deceptive jamming suppression. Specifically, the proposed method can be divided into three steps: first, by using the slow-time GPC, the SAR transmitted signals are encoded separately in pulses and divided into two groups. Second, based on each group of signals, we propose a new optimization problem to reconstruct the SAR images and eliminate the unmatched deceptive jamming, i.e., the deceptive jamming combined with the second kind of GPC is unmatched with the first kind of GPC. Third, due to the design of GPC, each group of signals generates a SAR image where the scene stays almost the same, while the residual matched deceptive jamming is located at different azimuths. In this context, this difference is successfully used to eliminate the remaining deceptive jamming. Finally, the RADARSAT-1 and MiniSAR datasets are used to evaluate the effectiveness of the proposed method.

Synthetic Aperture Radar (SAR) is a type of active microwave remote sensing imaging radar that can generate two-dimensional high-resolution images. Its ability to operate in all weather conditions and at all times has led to its widespread use. As a multi-parameter and multi-channel extension of SAR, polarimetric SAR (PolSAR) provides a wealth of scattering information for various applications, including topographic mapping, ocean exploration, polar observation, and target identification. Compared with single-polarization SAR, multi-polarization SAR enhances the information potential of the data by expanding its one-dimensional information, however, this potential cannot be fully realized without a clean SAR echo signal. The electromagnetic environment is becoming increasingly congested with radio frequency interference (RFI) signals, presenting a significant challenge for the subsequent tasks of PolSAR. Although there have been many related studies based on polarization information to carry out the aforementioned applications, there is a lack of research on the joint suppression of interference by using multi-polarization information, and single-polarization data alone is insufficient in effectively mitigating interference. To address these challenges, this paper presents a framework combining multi-polarization data to improve performance of low-rank based methods. Based on the proposed framework, experiments are conducted on real PolSAR data to assess the feasibility of the proposed framework in interference suppression. The results demonstrate that the framework significantly enhances the suppression performance of various low-rank based methods with clearer scene details being recovered.

In the community of automotive millimeter wave radar, the recently developed concept of four-dimensional (4D) radar can provide high-resolution point clouds image with enhanced imaging performance. Currently, the density of point clouds for single-frame image is usually too sparse to satisfy the demands of target classification and recognition due to the limitation of Doppler and angle resolutions. To address the aforementioned issues, a novel algorithm is proposed for 4D high-resolution imagery generation of point clouds with extremely high Doppler and angle resolutions in this paper. For high Doppler resolution with high-dynamic, a novel velocity ambiguity resolution algorithm is proposed using a dual pulse repetition frequency (dual-PRF) waveform design embedded in an innovative time-division multiplexing & Doppler-division multiplexing MIMO (TDM-DDM-MIMO) framework. Meanwhile, an attractive complex-valued deep convolutional network (CV-DCN) of super-resolution direction-of-arrival (DOA) estimation is proposed only using single-frame data. To be specific, a spatial smoothing operator on array data is applied as input of the network, and a CV-DCN is designed to learn the transformation of the spatial spectrum from the end-to-end to effectively protect the spectrum extraction. Furthermore, experimental analysis is performed to confirm the effectiveness of the proposed super-resolution DOA estimation algorithm. Finally, the 4D high-resolution imagery of point clouds is obtained by experiments in the parking lot.

With the surge of vehicles and transportation, sensing obstacles and warning drivers to avoid accidents have become a great concern in recent years. In the current roadworthy electromagnetic environment, the number of frequency modulated continuous wave (FMCW) millimeter-wave (MMW) automotive radars has exploded due to their unique advantages in environmental sensing. However, the frequency band of the automotive radars is limited from 77 to 81 GHz, hence the burgeoning of radars on the road is bound to cause mutual interference and jeopardize further target detection and parameter estimation. In this paper, two basic schemes are considered to mitigate mutual interference of automotive radars. First, we consider the sparse characteristics of the mutual interference in the two-dimensional (2-D) time domain and employ a sparse interference extraction (SIE) method to tackle the mutual interference. Next, we further consider the low-rank property of the useful echoes across multiple channels and propose a novel three-dimensional (3-D) tensor decomposition (TD) method to decompose the received signals into mutual interference and useful echoes. Several numerical simulations are fulfilled to test the robustness of the proposed TD method, especially for multiple input and multiple output (MIMO) systems under complex electromagnetic circumstances. Furthermore, more experiments are implemented to demonstrate its feasibility in practical applications in comparison to multiple state-of-the-art methods.

In order to play the key role of automotive millimeter wave radar in intelligent vehicle systems, direction-of-arrival (DOA) estimation is an essential problem to be solved. For practical intelligent driving applications, DOA estimation requires both real-time performance and high accuracy. Due to unique advantages, deep learning (DL) based methods have attracted more attention. Most of the existing DL-based methods require a large number of snapshots, but only a few snapshots can be guaranteed in practical applications. Moreover, they usually model DOA estimation as a multi-label classification task. The output represents the position of signal DOA on the discrete grid, and the resolution will be limited by the grid. In this paper, a new space-time Network (STNet) is proposed, which models DOA estimation as a regression task to achieve the effect of gridless estimation. We design a space correlation extraction module (SCEM) and a time correlation extraction module (TCEM), using the covariance matrix of the received signal and the original received signal as inputs respectively, treat them as different types of data. In these two modules, skip connection dense blocks (SCDBs) and long short-term memory (LSTM) networks are adopted to process two different forms of data. Through such processing, we retain sufficient information, obtain more features for the regression task, and ensure the estimation effect of using a small number of snapshots. The experimental results indicate that the STNet shows obvious performance gain in the case of small snapshots, achieves gridless estimation effect, and demonstrates excellent adaptability in situations where target DOAs are closely positioned.

Federated learning is a promising technique to implement network intelligence for the sixth generation (6G) communication systems. However, the collected data in wireless networks is non-independent and identically distributed (non-IID), which leads to severe deterioration of model performance. Although various enhanced schemes are proposed, it is still challenging to balance the communication cost and the model performance, due to the scarcity of radio resource for model update in wireless networks. In this paper, an ensemble federated learning paradigm is proposed for handling non-IID data, which is also optimized for its deployment in wireless networks in a cost efficient way. First, the framework of ensemble federated learning is designed. By formulating individual user clusters, intra-cluster federated learning models can be generated to reduce the impact of non-IID data, which can be integrated to adapt to various learning data via model ensemble. Second, the optimization of user cluster formation is studied to improve the performance of ensemble federated learning, which is modeled as a coalition formation game to design a Nash-stable algorithm. Finally, the simulation results on the public data sets are provided to verify the performance gains of our proposed schemes for deploying federated learning with non-IID data in wireless networks.

As a wideband radar system, synthetic aperture radar (SAR) may conflict with several electromagnetic systems. These signals may severely interfere with SAR image quality. Numerous previous researches focused on the interference suppression problem, among which semiparametric methods, such as low-rank recovery methods, have been verified to have state-of-the-art (SOTA) performance. However, semiparametric methods are restricted by extremely strong interferences when the signal-to-interference-and-noise ratio (SINR) exceeds the ability upper bound of semiparametric methods. In recent years, multichannel SAR (MC-SAR) systems have been widely used for more applications, where multiple antennas are mounted along the azimuth or in elevation. Adaptive digital beamforming (DBF) is a classic spatial filtering method to focus energy in the expected direction and suppress unexpected interferences. Its performance is determined by the array manifold and the interference-impinging angle. In this article, we propose a novel space-time-combined method that takes advantage of both low-rank recovery methods in the 2-D time domain and the adaptive DBF method in the spatial domain. Specifically, we construct a single optimization problem to unify both kinds of methods. The alternating direction of the multiple multiplier (ADMM) framework is leveraged with a closed-form solution for each step. Multiple experiments are provided to demonstrate the effectiveness of the proposed method.

Synthetic aperture radar (SAR) is a kind of active imaging radar, which can obtain high-resolution wide-swath SAR images, especially for spaceborne SAR systems. In practical electromagnetic environment, due to the overlap of same frequency bands, spaceborne SAR is extremely vulnerable to interferences from other electromagnetic systems, called radio frequency interference (RFI) to SAR systems. RFI seriously reduces the imaging quality of the SAR system and causes resolution reduction and scene occluded. To mitigate RFI in SAR systems, researchers have proposed many methods, in which semi-parametric methods, such as robust principal component analysis (RPCA)-based methods, played important roles in strong RFI mitigation in recent years. However, it is observed that they may be hard to recover the true scene well under extremely strong RFIs since the strong scatterers are also mixed in the extracted low-rank interferences. Therefore, in this paper, we propose a novel adaptive method, which combines the advantages of both semi-parametric method and frequency domain notched filter (FNF) method, called adaptive notch semi-parametric (ANSP) method, where the FNF method, as a non-parametric method, can retain more true scenes when mitigating interferences. As a result, the proposed method can not only effectively deal with strong RFIs but also protect the strong scatterers better with an adaptive threshold. This method can recover the true scene under extremely strong RFI and be applied to both Level-0 and Level-1 SAR data. Finally, we conduct experiments on several real SAR data and demonstrate the effectiveness of the proposed method.

Tomographic synthetic aperture radar (TomoSAR), which can provide three-dimensional (3-D) image of the observed scenes, has become an important technology for topographic mapping, forest parameter estimation, urban buildings modeling and etc. Recently, the developed compressed sensing (CS) and other similar methods have been widely applied for the achievement of super-resolution SAR tomography. However, there always exists inevitable model errors during the mining of scene information, such as discrete gridding on used dictionary and outliers among independent identically distribution (IID) samples, which tends to dramatically degrade the TomoSAR inversion. In this paper, a novel robust gridless CS (RGLCS) algorithm is proposed for high-resolution 3-D imaging of array TomoSAR. In the scheme, the atomic norm minimization (ANM) is used to model the joint-sparsity pattern on elevation distribution between adjacent pixels, which can be treated as gridless CS to avoid the discrete error of the dictionary. Meanwhile, the outliers and disturbances not satisfying the IID elevation distribution are modelled as sparsely distributed spike-noise in the image domain. The proposed RGLCS algorithm has the capability of perfectly separating the outliers and maintaining high-precision height resolution. For efficient solution, a fast alternative optimization is used to solve the objective function to effectively reduce the computational complexity. Next, the post-processing, including point cloud clustering and double-bounce scattering detection & eliminating, are studied to obtain high-resolution 3-D point cloud image. Finally, the experimental analysis using both simulated and measured data are performed to verify the effectiveness of the proposed algorithm. In particular, a practical demonstration using measured airborne array TomoSAR data is presented for urban mapping.

Driven by big data in wireless networks, the meta-learner-based scheme provides a promising paradigm to make full use of big data at the base stations to improve the per-formance of few-shot learning tasks, which plays an important role in facilitating network edge intelligence. However, it is a dilemma to balance the few-shot learning performance and the communication costs of meta-learner transmission. In this paper, we studied the fast adaptation of few-shot learning in wireless networks. First, a user grouping-based meta-learner generation scheme is designed, and a multicasting-based model transmission scheme is proposed. Second, a learning task selection scheme is designed to facilitate the fast adaptation to few-shot learning tasks at the users. Finally, the simulation results are provided to show that our proposed scheme can achieve model accuracy performance gains with low communication costs.

Analog over-the-air computing enables a swarm of end-user devices to efficiently conduct distributed learning, where the intermediate parameters of users, such as gradients, are modulated and transmitted via a group of orthogonal waveforms, and can be mixed directly at a server without individually detecting the feedback parameters of each user. Nonetheless, the scarcity of orthogonal waveforms, as well as communication resources of the end-user devices, are throttling this paradigm in adopting complex deep learning models. To balance the tradeoff between communication efficiency and accuracy performance, we study model pruning for analog over-the-air distributed learning in this paper. First, a model pruning scheme is proposed to improve the communication efficiency of analog over-the-air training. An importance measure for model parameter pruning is also designed based on the analog over-the-air aggregated gradient, which can characterize the contribution of each parameter without removing channel fading and electromagnetic interference. Second, an analytical expression of the training error upper bound is derived, which shows the proposed scheme is able to converge even when the aggregated gradient is corrupted by heavy-tailed electromagnetic interference with an infinite variance. Finally, several experimental results are provided to show the performance gains achieved by our proposed scheme, and also verify the correctness of analytical results.

In the above article[1], the corresponding authors should be Yan Huang and Jie Li.

As a wideband radar system, the synthetic aperture radar (SAR) usually conflicts with several electromagnetic systems, such as frequency modulation (FM), TV, and other communication systems. These signals, which are radio frequency interference (RFI) for radar systems, severely interfere with SAR systems to generate a high-resolution image. Some previous parametric methods focused on the time-varying RFI model; however, they cannot realize the comparable effectiveness and efficiency against semi-parametric methods. However, previous semiparametric methods did not focus on the time-varying RFI case. Hence, in this letter, a graph Laplacian clustering (GLC) semiparametric algorithm is proposed to suppress RFIs by constructing the Laplacian embedding connections between different pulses of signals. As a result, locally time-varying interferences are clustered in a nonlinear low-dimensional manifold and can be effectively mitigated. The real SAR data with measured RFIs are provided to demonstrate the effectiveness and efficiency of the proposed algorithm.

This paper presents a monolithically integrated E-band amplifier multiplier chain (AMC) developed in 130 nm SiGe BiCMOS process. This E-band AMC is composed of a 25 GHz 1:1 power divider, two 25 GHz driver amplifiers (DA1,2), a 75 GHz passive frequency tripler, and a 75 GHz power amplifier (PA). By applying a bypass tuning capacitor based power enhancing technique in the single-ended DA and PA, the output power and power-added-efficiency (PAE) of the AMC have been effectively improved. Benefiting from the proposed passive tripler core with second harmonic suppression function, and the impedance matching network with frequency selection characteristics, the AMC presents better harmonic suppression performance compared with the conventional topology. The bias circuits with temperature compensation are applied to the DA and PA to ensure the performance of the AMC when the temperature changes. The AMC has a measured output power exceeding 0 dBm in the entire E-band frequency range with a peak output power of 10.9 dBm at 77 GHz, and exhibits a record PAE of 8.25 %. Within the 3 dB operating frequency range from 69 to 87 GHz, the rejection of fundamental and second harmonics are better than 33.5 dB. The AMC can work properly between −40°C and 125 °C with the proposed temperature compensation bias circuits.

As a wideband radar system, a synthetic aperture radar (SAR) may conflict with several electromagnetic systems, such as frequency modulation (FM), TV, and other communication systems. These signals, termed as radio frequency interference (RFI), may severely interfere SAR systems from generating a high-resolution image. Numerous previous researches focused on the RFI suppression problem, among which the semiparametric methods have been verified to have the state-of-the-art performance. However, most of the semiparametric methods are computationally expensive and can hardly be used on wide-swath SAR imaging processing. In this article, an efficient semiparametric algorithm is proposed to suppress RFIs via alternating projections. It has comparable performance as the other methods but significantly improves the computational efficiency a lot. It is able to remove both narrowband and wideband RFIs and can be used directly on the Level-1 SAR data. Finally, multiple real SAR data are provided to demonstrate the effectiveness and efficiency of the proposed algorithm.

Synthetic aperture radar (SAR), as a wideband radar system, may be subject to strong interferences with a variety of signals. The narrowband interference (NBI) exemplifies the most typical of its kind, such as the form of radio frequency interference (RFI). With the development of SAR imaging technology, the high-resolution wide-swath (HRWS) imagery technology now reaches its maturity to finally take shape in current SAR systems. To obtain HRWS images, the multichannel SAR (MC-SAR) system has been employed to tackle the contradictory requirements for both high resolution and low pulse repetition frequency (PRF). Previous interference methods focused on single-channel SAR systems and few research works for MC-SAR systems. In this article, we first derive a new interference-mitigation model for HRWS SAR systems and conclude that the low-rank property of the NBI is suitable for MC-SAR systems. Then we employ an image-domain sparse regularization to protect the real echoes of the SAR system and mitigate the NBIs by solving the low-rank recovery problems of NBIs. Also, the MC-SAR system errors are further taken into account as a measure for our method’s practical applicability. Finally, the real SAR data is used to demonstrate the effectiveness of the proposed method.

There has been an increasing interest in addressing the issue of high-resolution inverse synthetic aperture radar (ISAR) imaging from sparse sampling data. Traditional compressed sensing (CS) and matrix completion (MC) methods are based on sparse and low-rank constraints, respectively, which do not make full use of the structure of ISAR data. In this article, a sparse ISAR imaging algorithm using a structured low-rank approach is proposed for enhanced imaging performance. Based on the observation that the structured Hankel matrix has better low-rank property, the proposed algorithm can outperform the group of conventional MC methods in terms of accuracy to data quality and quantity. Rather than using the traditional singular value decomposition (SVD) solution of nuclear norm minimization, the proposed algorithm restates the nuclear norm via an equivalent reformulation that the structured Hankel matrix can be decomposed into two disjointed parts to avoid the dimensional expansion of the Hankel matrix. Meanwhile, the alternative direction method of multipliers (ADMMs) is applied to effectively reduce the computational complexity. Finally, the effectiveness of the proposed algorithm is further validated using the experiments on simulated and measured data.

Federated learning provides a promising paradigm to enable network edge intelligence in the future sixth generation (6G) systems. However, due to the high dynamics of wireless circumstances and user behavior, the collected training data is non-independent and identically distributed (non-IID), which causes severe performance degradation of federated learning. To solve this problem, federated learning with non-IID data in wireless networks is studied in this paper. Firstly, based on the derived upper bound of expected weight divergence, a federated averaging scheme is proposed to reduce the distribution divergence of non-IID data. Secondly, to further harmonize the distribution divergence, data sharing is associated with federated learning in wireless networks, and a joint optimization algorithm is designed to keep a sophisticated balance between the model accuracy and the cost. Finally, the simulation results based on a common-used image data set are provided to evaluate the performance of our proposed schemes, which can achieve significant performance gains with a small price of latency and energy consumption.

Due to the distributed characteristics of Federated Learning (FL), the vulnerability of global model and coordination of devices are the main obstacle. As a promising solution of decentralization, scalability and security, leveraging blockchain in FL has attracted much attention in recent years. However, the traditional consensus mechanisms designed for blockchain like Proof of Work (PoW) would cause extreme resource consumption, which reduces the efficiency of FL greatly, especially when the participating devices are wireless and resource-limited. In order to address device asynchrony and anomaly detection in FL while avoiding the extra resource consumption caused by blockchain, this paper introduces a framework for empowering FL using Direct Acyclic Graph (DAG)-based blockchain systematically (DAG-FL). Accordingly, DAG-FL is first introduced from a three-layer architecture in details, and then two algorithms DAG-FL Controlling and DAG-FL Updating are designed running on different nodes to elaborate the operation of DAG-FL consensus mechanism. The extensive simulations show that DAG-FL can achieve the better performance in terms of training efficiency and model accuracy compared with the typical existing on-device federated learning systems as the benchmarks.

Abstract The fifth generation (5G) wireless communication networks are being deployed worldwide from 2020 and more capabilities are in the process of being standardized, such as mass connectivity, ultra-reliability, and guaranteed low latency. However, 5G will not meet all requirements of the future in 2030 and beyond, and sixth generation (6G) wireless communication networks are expected to provide global coverage, enhanced spectral/energy/cost efficiency, better intelligence level and security, etc. To meet these requirements, 6G networks will rely on new enabling technologies, i.e., air interface and transmission technologies and novel network architecture, such as waveform design, multiple access, channel coding schemes, multi-antenna technologies, network slicing, cell-free architecture, and cloud/fog/edge computing. Our vision on 6G is that it will have four new paradigm shifts. First, to satisfy the requirement of global coverage, 6G will not be limited to terrestrial communication networks, which will need to be complemented with non-terrestrial networks such as satellite and unmanned aerial vehicle (UAV) communication networks, thus achieving a space-air-ground-sea integrated communication network. Second, all spectra will be fully explored to further increase data rates and connection density, including the sub-6 GHz, millimeter wave (mmWave), terahertz (THz), and optical frequency bands. Third, facing the big datasets generated by the use of extremely heterogeneous networks, diverse communication scenarios, large numbers of antennas, wide bandwidths, and new service requirements, 6G networks will enable a new range of smart applications with the aid of artificial intelligence (AI) and big data technologies. Fourth, network security will have to be strengthened when developing 6G networks. This article provides a comprehensive survey of recent advances and future trends in these four aspects. Clearly, 6G with additional technical requirements beyond those of 5G will enable faster and further communications to the extent that the boundary between physical and cyber worlds disappears.

The deployment of millimeter-wave (mmWave) cellular systems in dense urban environments with an acceptable coverage and cost-efficient transmission scheme is essential for the rollout of fifth-generation and beyond technology. In this paper, cluster-based analysis of mmWave channel characteristics in two typical dense urban environments is performed. First, radio propagation measurement campaigns are conducted in two identified mmWave bands of 28 and 39 GHz in a central business district and a dense residential area. The custom-designed channel sounder supports high-efficiency directional scanning sounding, which helps collect sufficient data for statistical channel modeling. Next, using an improved auto-clustering algorithm, multipath clusters and their scattering sources are identified. An appropriate measure for inter- and intra-cluster characteristics is provided, which includes the cluster number, the Ricean K -factor, root-mean-squared (RMS) delay spread, RMS angular spread, and their correlations. Comparisons of these parameters across two mmWave bands for both line-of-sight (LoS) and non-light-of-sight (NLoS) links are given. To shed light on the blockage effects, detailed analysis of the propagation mechanisms corresponding to each NLoS cluster is provided, including reflection from exterior walls and diffraction over building corners and rooftops. Finally, the results show that the cluster-based analysis takes full advantage of mmWave beamspace channel characteristics and has further implications for the design and deployment of mmWave wireless networks.

Unmanned aerial vehicle (UAV) synthetic aperture radar (SAR) is usually sensitive to trajectory deviations that cause severe motion error in the recorded data. Because of the small size of the UAV, it is difficult to carry a high-accuracy inertial navigation system. Therefore, in order to obtain a precise SAR imagery, autofocus algorithms, such as phase gradient autofocus (PGA) method and map-drift (MD) algorithm, were proposed to compensate the motion error based on the received signal, but most of them worked on range-invariant motion error and abundant prominent scatterers. In this letter, an improved MD algorithm is proposed to compensate the range-variant motion error compared to the existed MD algorithm. In this context, in order to solve the outliers caused by homogeneous scenes or absent prominent scatterers, a random sample consensus (RANSAC) algorithm is employed to mitigate the influence resulting from the outliers, realizing robust performance for different cases. Finally, real SAR data are applied to demonstrate the effectiveness of the proposed method.

Synthetic aperture radar (SAR) as a wideband radar system is subject to complicated interferences, such as radio frequency interference or other narrowband interferences (NBIs). In order to suppress the NBI, voluminous literature focused on its signal models and characteristics, such as the sinusoidal model and relatively constant frequencies. However, in practice, the interference environment is commonly complicated. It is hard to model the interferences accurately and mitigate them clearly in an easy way, especially for the time-varying interferences. In this article, a novel graph-based algorithm is proposed to mitigate the time-varying NBIs by using graph theory, which constructs the connections between different azimuth samples of NBIs. As a result, the locally time-varying interferences can be clustered in a nonlinear low-dimensional manifold and effectively removed by the proposed algorithm. In addition, the case of the globally time-varying interference is also analyzed in detail with strict derivations to demonstrate its low-rank property. Furthermore, the matrix factorization scheme is introduced to improve the efficiency of the proposed algorithm, and the closed-form solutions are derived for each iteration. The real SAR data with measured NBIs are provided to demonstrate the effectiveness and efficiency of the proposed algorithm.

Nowadays, in the electromagnetism environment, the complex interferences, including the narrowband interferences (NBIs) and wideband interferences (WBIs), may severely affect the imaging quality of synthetic aperture radar (SAR) systems. Most traditional methods can only tackle with one kind of isolated interferences, NBIs or WBIs, which are widely distributed in the 1-D range frequency domain or 2-D range time-frequency domain. In this paper, we propose a complex tensor robust principal component analysis (CT-RPCA) method based on a novel 3-D range-azimuth-space tensor model to mitigate continuously distributed NBIs and WBIs simultaneously. The main contributions of this paper are summarized in three aspects. First, we strictly prove the low-rank property of the isolated NBIs and WBIs in the range-azimuth domain. Second, we use multiple views of the signal to construct a novel 3-D range-azimuth-space tensor model, where both the NBI tensor and the WBI tensor have spatial low-rank property due to the approximately stable frequency bands along the spatial dimension. Third, the CT-RPCA method is employed to efficiently suppress NBIs and WBIs simultaneously by solving the tensor RPCA problem. Finally, the real SAR data with simulated complex interferences are employed to demonstrate the effectiveness of the proposed method.

For a single-channel synthetic aperture radar (SAR), finite-pulse repetition frequency and nonideal antenna pattern cause azimuth ambiguities, i.e., ghosts in image domain. In this paper, a novel algorithm of locating processing weighted group lasso SAR image formation for maritime targets is proposed to effectively mitigate the ambiguities, which can work on a single-look complex SAR image. In the scheme, the ambiguous signal model using the conventional SAR focusing processor is first explicitly derived, showing the analytical expression of SAR image formulation. The weighted sparse group lasso algorithm is then employed to group-sparsely reconstruct the subimages of nonambiguous and ambiguous Doppler components. In particular, we introduce adaptively weighted sparsity constraint, obtained from a priori azimuth antenna pattern, and clutter clustering during sparse imaging. It should be emphasized that the proposed algorithm can effectively improve the azimuth resolution by coherently integrating the ambiguous signal components, which greatly helps the target detection and recognition in maritime surveillance. Finally, experiments based on simulated and measured data are performed to confirm the effectiveness of the proposed algorithm.

Recently, sparse radar imaging has drawn more and more attentions, which has the superiority of feature enhancement, super-resolution and so on. In this paper, we focus on sparse moving target imaging (MTIm) using a SAR sensor from sparse aperture (SA) data. For maneuvering targets, their strong motion tends to introduce migration through range cell (MTRC), which increases the difficulty of SA imaging. In this paper, a novel algorithm of moving target imaging is proposed to deal with both the MTRC and SA. In the scheme, a scaled Fourier dictionary is employed to represent the MTRC. A hierarchical model of statistics is used to encode the sparsity of image. Then, SA imaging is treated as a problem of sparse Bayesian learning, which is solved by expectation maximization (EM) method. The scaled Fourier dictionary is modified to resolve the velocity ambiguity. Finally, experimental analysis is performed to confirm the effectiveness of the proposed method.

Wireless networks face the challenge of increasing energy consumption while satisfying the unprecedented demand for higher data rates. Energy-efficient transmission has been regarded as a key technology for the next-generation wireless system. Meanwhile, to reduce the cost, in practice, a base station usually has less radio chains than the antennas, which makes antenna selection an appealing transmission strategy. This letter addresses the problem of joint optimization of energy-efficient beamforming and antenna selection for downlink multiuser systems. The nonconvexity arising from both the nonlinear fractional programming and the ℓ0-(quasi)norm presents the main difficulty in solving the joint optimization problem. Nevertheless, we develop an effective algorithm to address this problem. Numerical results are given to validate the effectiveness and the performance of the developed algorithm.

In this paper, we investigate joint beamforming and power allocation in multicell multiple-input single-output (MISO) downlink networks. Our goal is to maximize the utility function defined as the ratio between the system weighted sum rate and the total power consumption subject to the users’ quality of service requirements and per-base-station (BS) power constraints. The considered problem is nonconvex and its objective is in a fractional form. To circumvent this problem, we first resort to an virtual uplink formulations of the the primal problem by introducing an auxiliary variable and applying the uplink-downlink duality theory. By exploiting the analytic structure of the optimal beamformers in the dual uplink problem, an efficient algorithm is then developed to solve the considered problem. Furthermore, to reduce further the exchange overhead between coordinated BSs in a large-scale antenna system, an effective coordinated power allocation solution only based on statistical channel state information is reached by deriving the asymptotic optimization problem, which is used to obtain the power allocation in a long-term timescale. Numerical results validate the effectiveness of our proposed schemes and show that both the spectral efficiency and the energy efficiency can be simultaneously improved over traditional downlink coordinated schemes, especially in the middle-high transmit power region.

Capacity optimization of line-of-sight (LoS) 3×2 multiple-input-multiple-output (MIMO) channels in indoor environments is investigated in this paper. First, the capacity fluctuation number (CFN) which reflects the change of capacity is proposed. Next, the expression of capacity against the CFN is derived. The CFN is used as a criterion for optimization of capacity by changing inter-element spacings of transmit and receive antenna arrays. The capacity sensitivity to the orientation is studied and compared with that of 2×2 MIMO channel. A small sensitivity of 3 × 2 MIMO channel is achieved and verified by both simulation and measurement results. Furthermore, the CFN can also be used as a criterion for optimization of average capacity and the proposed optimization method is validated through numerical results.

Significant advances in the development of millimeter-wave and terahertz (30–10 000 GHz) technologies have been made to cope with the increasing interest in this still not fully explored electromagnetic spectrum. The nature of electromagnetic waves over this frequency range is well suited for the development of high-resolution imaging applications, molecular-sensitive spectroscopic devices, and ultrabroadband wireless communications. In this paper, millimeter-wave and terahertz antenna technologies are overviewed including the conventional and nonconventional planar/nonplanar antenna structures based on different platforms. As a promising technological platform, substrate-integrated circuits (SICs) attract more and more attention. Various substrate-integrated waveguide (SIW) schemes and other synthesized guide techniques have been widely employed in the design of antennas and arrays. Different types of substrate-integrated antennas and beamforming networks are discussed with respect to theoretical and experimental results in connection with electrical and mechanical performances.

We consider a two-hop relaying network in which orthogonal frequency division multiplexing (OFDM) is employed for the source-to-destination, the source-to-relay and the relay- to-destination links. Amplify-and-forward (AF) and decode-and- forward (DF) policies are both discussed with or without two-hop diversity, respectively, for the relaying network with a sum-power constraint. An unified approach is used for optimal power allocation in the four different relaying scenarios. First, equivalent channel gains are developed for any given subcarrier pair in each scenario, and then optimal power allocation can be obtained by applying the classic water-filling method. Moreover, we provide the proof to the optimality of sorted subcarrier pairing for AF and DF relaying without diversity, which, combined with optimal power allocation, can offer further performance gain.

In this paper, the results of the quality of service for VoIP service over TD-SCDMA HSDPA system are presented. The performances of the system with different scheduling algorithms and different retransmission times are simulated and analyzed. Dynamic scheduling, adaptive modulation and code (AMC), dynamic HARQ are implemented in the system-level simulation and some recommends for HARQ channel number and retransmission time and optimality are given.

Low complexity channel estimation for single-carrier block transmission systems over multiple-input multiple-output time varying frequency-selective channels is investigated. A time slot structure that uses Golay complementary sequences with perfect periodic autocorrelations as two-sided pilot blocks is presented. Employing this strucutre, optimal least square estimate in the minimum mean square error (MMSE) sense is achieved. Furthermore, a computationally efficient algorithm which is named as fast periodic Golay correlation is proposed based on the specific generator and the properties related to circulant matrices. Finally, the simulation results show the MMSE performance of the proposed scheme and algorithm.

New frequency domain training sequences are proposed sedfor carrier frequency offset (CFO) estimation in multipleinput multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system over frequency-selective fading channels. By exploiting frequency domain orthogonality of the training sequences, integer CFO (ICFO) can be estimated without matrix inversion operation. With the non-zero pilots in the training sequences uniformly spaced, fractional CFO (FCFO) can be estimated through the roots of a complex polynomial. Moreover, a simplified CFO estimator is also proposed which exploits a geometric mapping to transform the complex polynomial to a real one. Simulation results illustrate the good performances of the CFO estimators assisted by the proposed training sequences.

In this paper we present a low-cost direct-sequence spread-spectrum scheme for data communication over the 2.4 GHz ISM band. A prototype peer to peer system has been developed for evaluation, which includes complete short range microwave transceivers. Data rate up to 1 Mbit/s has been achieved. The RF front end is developed with commercially available MMIC chip set. The data-link MAC controller is realized with a commercial DSP chip, which highly improves the system versatility in various applications. The prototype system has the potential to support point-to-multi-point connections, it can be easily expanded to a compact low cost data exchange network without any modification in system hardware.