Digital Self-Interference Canceler with Joint Channel Estimator for Simultaneous Transmit and Receive System.

Simultaneous transmit and receive wireless communications have been highlighted for their potential to double the spectral efficiency. However, it is necessary to mitigate self-interference (SI). Considering both the SI channel and remote transmission (RT) channel need to be estimated before equalizing the received signal, we propose two adaptive algorithms for linear and nonlinear self-interference cancellation (SIC), based on a multi-layered joint channel estimator structure. The proposed algorithms estimate the RT channel while performing SIC, and the multi-layered structure ensures improved performance across various interference-to-signal ratios. The M-estimate function enhances the robustness of the algorithm, allowing it to converge even when affected by impulsive noise. For nonlinear SIC, this paper introduces an adaptive algorithm based on generalized Hammerstein polynomial basis functions. The simulation results indicate that this approach achieves a better convergence speed and normalized mean squared difference compared to existing SIC methods, leading to a lower system bit error rate.

Transmit Beamforming Design Based on Multi-Receiver Power Suppression for STAR Digital Array.

The simultaneous transmit and receive (STAR) array system provides higher radiation gain and data rate compared to traditional radio system. Because of the various mutual couplings between each pair of transmit and receive elements, it is a great challenge to suppress the incident self-interference power at multiple receive elements, which is usually much higher than the desired signal of interest (SoI) power and causes the saturation of receive links and the distortion of the digital SoI. In this paper, we propose an optimized method for transmit beamforming based on radiation power constraints and transmit power control. Through adaptive transmit beamforming, high isolation between the transmit array and each receive link is achieved, minimizing the self-interference power at each receiving element. This method effectively reduces the self-interference power, avoiding distortion of the SoI digital signal caused by limited-bit analog-to-digital converters (ADCs). Simulation results demonstrate that this optimized transmit beamforming method can achieve more than 100 dB effective isotropic isolation (EII) on a 32-element two-dimensional phased array designed in HFSS, reducing the maximum incident self-interference power at the receive channels by approximately 35 dB, while effectively controlling the attenuation of the transmit gain. We also present the advantages in receive subarray isolation and lower ADCs digits under the transmit ABF method.

A Sparse Shared Aperture Design for Simultaneous Transmit and Receive Arrays with Beam Constraints.

The utilization of efficient digital self-interference cancellation technology enables the simultaneous transmit and receive (STAR) phased array system to meet most application requirements through STAR capabilities. However, the development of application scenario requirements makes array configuration technology for STAR phased arrays increasingly important. Thus, this paper proposes a sparse shared aperture STAR reconfigurable phased array design based on beam constraints which are achieved by a genetic algorithm. Firstly, a design scheme for transmit and receive arrays with symmetrical shared apertures is adopted to improve the aperture efficiency of both transmit and receive arrays. Then, on the basis of shared aperture, sparse array design is introduced to further reduce system complexity and hardware costs. Finally, the shape of the transmit and receive arrays is determined by constraining the side lobe level (SLL), main lobe gain, and beam width. The simulated results indicate that the SLL of the transmit and receive patterns under beam-constrained design have been reduced by 4.1 dBi and 7.1 dBi, respectively. The cost of SLL improvement is a reduction in transmit gain, receive gain, and EII of 1.9 dBi, 2.1 dBi, and 3.9 dB, respectively. When the sparsity ratio is greater than 0.78, the SLL suppression effect is also significant, and the attenuation of EII, transmit, and receive gains do not exceed 3 dB and 2 dB, respectively. Overall, the results demonstrate the effectiveness of a sparse shared aperture design based on beam constraints in producing high gain, low SLL, and low-cost transmit and receive arrays.

Research on Interrupted Sampling Repeater Jamming Performance Based on Joint Frequency Shift/Phase Modulation.

Interrupted sampling repeater jamming (ISRJ) is a classic active coherent jamming. Due to its structural limitations, it has inherent defects such as a discontinuous time-frequency (TF) distribution, strong distribution regularity of pulse compression results, limited jamming amplitude, and strong false targets lagging behind the real target. These defects have not been fully resolved yet due to the limitation of the theoretical analysis system. Based on the analysis of the influence factors of ISRJ on the interference performance for linear-frequency-modulated (LFM) and phase-coded signals, this paper proposes an improved ISRJ method based on the joint subsection frequency shift and two-phase modulation. The coherent superposition of jamming signals at different positions for LFM signals is achieved by controlling the frequency shift matrix and the phase modulation parameters to form a strong pre-lead false target or multiple positions and ranges of blanket jamming areas. For the phase-coded signal, the pre-lead false targets are generated through code prediction and the two-phase modulation of the code sequence, resulting in similar noise interference. The simulation results show that this method can overcome the inherent defects of ISRJ.

A Robust Design for Aperture-Level Simultaneous Transmit and Receive with Digital Phased Array.

Aperture-level simultaneous transmit and receive (ALSTAR) attempts to utilize adaptive digital transmit and receive beamforming and digital self-interference cancellation methods to establish isolation between the transmit and receive apertures of the single-phase array. However, the existing methods only discuss the isolation of ALSTAR and ignore the radiation efficiency of the transmitter and the sensitivity of the receiver. The ALSTAR array design lacks perfect theoretical support and simplified engineering implementation. This paper proposes an adaptive random group quantum brainstorming optimization (ARGQBSO) algorithm to simplify the array design and improve the overall performance. ARGQBSO is derived from BSO and has been ameliorated in four aspects of the ALSTAR array, including random grouping, initial value presets, dynamic probability functions, and quantum computing. The transmit and receive beamforming carried out by ARGQBSO is robust to all elevation angles, which reduces complexity and is conducive to engineering applications. The simulated results indicate that the ARGQBSO algorithm has an excellent performance, and achieves 166.8 dB of peak EII, 47.1 dBW of peak EIRP, and -94.6 dBm of peak EIS with 1000 W of transmit power in the scenario of an 8-element array.

Closed-Form Algorithm for 3-D Near-Field OFDM Signal Localization under Uniform Circular Array.

Due to its widespread application in communications, radar, etc., the orthogonal frequency division multiplexing (OFDM) signal has become increasingly urgent in the field of localization. Under uniform circular array (UCA) and near-field conditions, this paper presents a closed-form algorithm based on phase difference for estimating the three-dimensional (3-D) location (azimuth angle, elevation angle, and range) of the OFDM signal. In the algorithm, considering that it is difficult to distinguish the frequency of the OFDM signal's subcarriers and the phase-based method is always affected by errors of the frequency estimation, this paper employs sparse representation (SR) to obtain the super-resolution frequencies and the corresponding phases of subcarriers. Further, as the phase differences of the adjacent sensors including azimuth angle, elevation angle and range parameters can be expressed as indefinite equations, the near-field OFDM signal's 3-D location is obtained by employing the least square method, where the phase differences are based on the average of the estimated subcarriers. Finally, the performance of the proposed algorithm is demonstrated by several simulations.

Ambiguity Resolution for Phase-Based 3-D Source Localization under Fixed Uniform Circular Array.

Under fixed uniform circular array (UCA), 3-D parameter estimation of a source whose half-wavelength is smaller than the array aperture would suffer from a serious phase ambiguity problem, which also appears in a recently proposed phase-based algorithm. In this paper, by using the centro-symmetry of UCA with an even number of sensors, the source's angles and range can be decoupled and a novel algorithm named subarray grouping and ambiguity searching (SGAS) is addressed to resolve angle ambiguity. In the SGAS algorithm, each subarray formed by two couples of centro-symmetry sensors can obtain a batch of results under different ambiguities, and by searching the nearest value among subarrays, which is always corresponding to correct ambiguity, rough angle estimation with no ambiguity is realized. Then, the unambiguous angles are employed to resolve phase ambiguity in a phase-based 3-D parameter estimation algorithm, and the source's range, as well as more precise angles, can be achieved. Moreover, to improve the practical performance of SGAS, the optimal structure of subarrays and subarray selection criteria are further investigated. Simulation results demonstrate the satisfying performance of the proposed method in 3-D source localization.

Dynamic compressed HRRP generation for random stepped-frequency radar based on complex-valued fast sequential homotopy.

Compressed sensing has been applied to achieve high resolution range profiles (HRRPs) using a stepped-frequency radar. In this new scheme, much fewer pulses are required to recover the target's strong scattering centers, which can greatly reduce the coherent processing interval (CPI) and improve the anti-jamming capability. For practical applications, however, the required number of pulses is difficult to determine in advance and any reduction of the transmitted pulses is attractive. In this paper, a dynamic compressed sensing strategy for HRRP generation is proposed, in which the estimated HRRP is updated with sequentially transmitted and received pulses until the proper stopping rules are satisfied. To efficiently implement the sequential update, a complex-valued fast sequential homotopy (CV-FSH) algorithm is developed based on group sparse recovery. This algorithm performs as an efficient recursive procedure of sparse recovery, thus avoiding solving a new optimization problem from scratch. Furthermore, the proper stopping rules are presented according to the special characteristics of HRRP. Therefore, the optimal number of pulses required in each CPI can be sought adapting to the echo signal. The results using simulated and real data show the effectiveness of the proposed approach and demonstrate that the established dynamic strategy is more suitable for uncooperative targets.