Distributed beamforming has emerged as a pivotal technology in modern communication systems, radar applications, and collaborative electronic warfare. Through distributed collaboration, these jamming nodes composed of unmanned aerial vehicles (UAVs) can execute flexible electronic jamming toward wireless communication systems despite energy limitations. However, they encounter challenges such as time and positioning inaccuracies, which can diminish the synthesis power of jamming beams and reduce jamming effectiveness. This paper examines the impact of angle and distance errors on jamming effectiveness, deriving upper error bounds under various suppression coefficients. The findings offer practical guidelines for enhancing the precision and effectiveness of distributed beamforming systems in electronic warfare applications.

In optical communication systems, coherent detection is a standard method. The received signal enters the digital domain after passing through a time-interleaved analog-to-digital converter (TI-ADC). However, the time delay of the ADC brings noise into the signal, which decreases the signal quality; therefore, ADC calibration is essential. At present, there are many calibration methods for time delay, but their performances are not satisfactory at a high sampling frequency. This paper presents a method of time delay estimation and calibration in a coherent optical communication system. First, the expected maximum (EM) method is used to roughly estimate the time delay and then transfer the estimated value into the trained back propagation (BP) neural network to generate more accurate results. Second, the sampled signal is reconstructed, and then a finite impulse response (FIR) filter is designed to compensate for the time delay. There are several advantages of the proposed method compared with previous works: the convergence with a BP network is faster, the estimation accuracy is higher, and the calibration does not affect the sample operation of the ADC working in the background mode. In addition, the proposed calibration method does not need additional circuits and its low power consumption provides more sources for dispersion compensation, error correction, and other subsequent operations in the coherent optical communication system. Based on the quadrature phase shift keying (QPSK) system, the proposed method was implemented in a 16-channel/8-bit, 40-GS/s ADC. After estimation and calibration, the relative error of estimation was below 1%, the signal noise distortion rate (SNDR) reached 55.9 dB, the spurious free dynamic range (SFDR) improved to 61.2 dB, and the effective number of bits (ENOB) was 6.7 bits. The results demonstrate that the proposed method has a better calibration performance than other methods.

Chromatic dispersion equalization (CDE) in coherent optical communication systems is extremely critical for subsequent digital signal processing (such as frequency offset estimation and carrier phase recovery). Various methods mentioned in the published literature are not satisfactory when the signal bandwidth is limited. This paper proposes a way of using singular value decomposition least square (SVDLS) to obtain the optimal tap weight of the CDE filter and a method to introduce the adaptive mutation particle swarm optimizer (AMPSO) algorithm into the CDE. We show that the two proposed approaches are based on the best approximation of the frequency domain response of the designed and ideal CDE filter. Compared with the traditional CDE method, which needs to be implemented in the full frequency band, the two methods can be implemented in the narrow frequency band. The simulation shows that the effective bandwidth of the baseband signal is limited by squared-root-raised-cosine (SRRC) pulse shaping with a roll-off factor of 0.25 in different modulation formats (DP-QPSK, DP-16 QAM, DP-64 QAM) when the number of taps of the filter is 131, which is 37.5% less than the full frequency band. The designed filter is superior to the existing filter in terms of filtering effect and implementation complexity.

This paper proposes a novel and efficient low-complexity chromatic dispersion equalizer (CDE) based on finite impulse response (FIR) filter architecture for polarization-multiplexed coherent optical communication systems. The FIR filter coefficients are optimized by weights to reduce the energy leakage caused by the truncation effect, and then quantization is used uniformly to reduce the number of real number additions and real number multiplications by utilizing the diversity of the quantized coefficients. Using Optisystem 15 to build a coherent optical communication system for simulation and experimental demonstration, the results show that after the filter coefficients are optimized by weights. Compared with the time-domain chromatic dispersion equalizer (TD-CDE), the proposed design has a lower bit error rate (BER) and better equalization effect. When the transmission distance is 4000 km and the system quantization stages M = 16, the multiplication operation and addition operations reduce computing resources by 99% and 43%, and the BER only increases by 5%. Compared with frequency-domain chromatic dispersion equalizer (FD-CDE), widely used in long-distance communication, the multiplication operation reduces computing resources by 30%. The proposed method provides a new idea for high-performance CDE in long-distance coherent optical communication systems.

Unmanned Aerial Vehicles (UAVs) are widely used in defense applications. Multiple UAVs equipped with jamming sources can form a sparse array. The sparse array can be quickly deployed and achieve an extensive range of effective jamming. At present, the jamming power in the target area is mainly calculated through the superposition of node waveforms. The algorithm needs to sequentially calculate the angle of the target position relative to each node to obtain the corresponding gain and then calculate the path loss by the transmission model to obtain the total jamming power, which has high algorithmic complexity and needs to recount the power every time after adjusting the position of the node. An equivalent transmitted power estimation algorithm based on the pattern multiplication theorem is proposed in this paper, which regards the sparse array as a whole directional jammer. The power in the target area can be estimated according to the jammer gain, the equivalent transmitted power, and the jamming distance. In the calculation of jamming power, the proposed array-based algorithm reduces the complexity by 50% compared with the waveform superposition algorithm, and the estimation variance of the jamming power is less than 1.4%.

Signal-to-noise ratio (SNR) estimation plays a crucial role in communication systems, directly impacting the quality and reliability of signal transmission. This paper proposes a novel deep learning framework aimed at enhancing the accuracy and robustness of SNR estimation. The framework converts received signals into time–frequency matrices as feature inputs, effectively capturing both temporal and spectral characteristics through time–frequency analysis. Extensive experimental results across an SNR range of −5 dB to 15 dB demonstrate that our method achieves a mean squared error (MSE) that closely approaches the theoretical Cramér–Rao bound (CRB), comparable to data-aided (DA) maximum likelihood methods. A quantitative analysis reveals that, even under challenging conditions, such as a low SNR of −5 dB, the model maintains superior accuracy with a mean absolute error (MAE) as low as 0.352, significantly outperforming traditional M2M4 and NDA estimators. The model’s performance was systematically evaluated in a wide range of scenarios, encompassing various signal modulation formats, upsampling factors, multipath fading channels, frequency offsets, phase shifts, and roll-off factors. The evaluation highlights its exceptional generalization capability and robustness, with high performance and stability maintained even in challenging and dynamic environments.