Accurate indoor localization information of the quadrotor plays an important role in many Internet of Things applications. To improve the estimation accuracy and robustness, a fix-lag extended finite impulse response smoother (FEFIRS) algorithm is proposed for fusing the inertial navigation system (INS) and ultra wide band (UWB) data tightly, which employs distance between the UWB reference nodes and a blind node measured by the INS and UWB. The FEFIRS algorithm consists of an extended unbiased finite impulse response (EFIR) filter and a fix-lag unbiased FIR (UFIR) smoother. The EFIR filter is employed to improve the robustness, and the fix-lag UFIR smoother is capable of improving the accuracy. Based on extensive test investigations employing real data, the proposed FEFIRS has a higher accuracy and robustness than the Kalman-based solutions in tightly integrated INS/UWB-based indoor quadrotor localization.

Power amplifier (PA) models, such as the neural network (NN) models and the multilayer NN models, have problems with high complexity. In this article, we first propose a novel behavior model for wideband PAs, using a real-valued time-delay convolutional NN (RVTDCNN). The input data of the model is sorted and arranged as a graph composed of the in-phase and quadrature ( $I/Q$ ) components and envelope-dependent terms of current and past signals. Then, we created a predesigned filter using the convolutional layer to extract the basis functions required for the PA forward or reverse modeling. Finally, the generated rich basis functions are input into a simple, fully connected layer to build the model. Due to the weight sharing characteristics of the convolutional model’s structure, the strong memory effect does not lead to a significant increase in the complexity of the model. Meanwhile, the extraction effect of the predesigned filter also reduces the training complexity of the model. The experimental results show that the performance of the RVTDCNN model is almost the same as the NN models and the multilayer NN models. Meanwhile, compared with the abovementioned models, the coefficient number and computational complexity of the RVTDCNN model are significantly reduced. This advantage is noticeable when the memory effects of the PA are increased by using wider signal bandwidths.

Centralized radio resource management method puts all of the computational burdens in an agent, which is unbearable with the increasing of data dimensionality. This paper focuses on how to schedule limited satellite-based radio resources efficiently to enhance transmission efficiency and extend broadband coverage with low complexity. We propose a cooperative multi-agent deep reinforcement learning (CMDRL) framework to achieve the radio resources management strategy. The bandwidth allocation problem is taken as an example to analyze the proposed novel method in simulation. The experimental results show that this approach is capable of enhancing transmission efficiency and be flexible to achieve the desired goal with low complexity.

This paper presents an in-depth, systematic study of the impact of input and output harmonics in the design of high-efficiency power amplifiers (PAs). The study evaluates the performance of harmonically tuned amplifiers, tackling concurrently both input and output harmonics. The proposed theory starts with deriving an altered input voltage waveform under the impact of input nonlinearity. Intrinsic drain voltage and drain current components are formulated as a function of the conduction angle α considering both source and load terminations. Output power and drain efficiency are then computed as a function of input nonlinearity, α, and output loading conditions. The derived formulations allow to investigate the design sensitivity to input nonlinearity and its impact on fundamental design space. The impact of source harmonics is evaluated using harmonic source pull under different output loading conditions. Thereafter, PA design and implementation has been carried out using NXP 1.95 mm die to confirm the distinctive behavior of class GF and GF⁻¹ amplifiers with respect to the input harmonic terminations. For practical validation, four different design cases with different second harmonic source impedances are investigated. At 2.6 GHz, drain efficiencies ranging between 76% and 83% were exhibited depending on the source and load harmonic tuning for each design case. Measurement results confirm the theoretical findings reported in this paper.

High peak-to-average power ratio (PAPR) in orthogonal frequency division multiplexing (OFDM) systems is one of the major drawbacks in wireless transmitters. In this letter, a novel low complexity PAPR reduction method based on the real-valued neural network (NN) is proposed. This method first builds the PAPR reduction module using the real-valued NN to achieve the reduction of PAPR. To reconstruct the transmission signal, the PAPR decompression module is introduced into the receiving end to recover the signal in order to minimize the bit error rate (BER) of the systems. The PAPR reduction module and the PAPR decompression module can be jointly trained offline, therefore, the reduction of PAPR and the minimization of BER can be achieved simultaneously. To reduce the process rate of the PAPR reduction, the trained model uses multiple parallel processing links to achieve the reduction of the PAPR. The extensive simulations indicate that the proposed method outperforms previously available methods in terms of PAPR and BER, while having low complexity.

Neural network (NN)-based models are perceived as being accurate models for power amplifier (PA) behavioral modeling and digital predistortion (DPD) applications. However, the complexity of NN’s models in terms of the number of coefficients increases with the memory depth, nonlinearity order, signal bandwidth, and the number of carriers. In this article, a novel augmented convolutional neural network (ACNN)-based DPD is proposed to linearize the concurrent multiband PAs. In the ACNN model, the convolution layer with a fully connected layer serves to model the intermodulation distortion (IMD), cross-modulation distortion (CMD), and nonlinearities of the concurrent multiband PA. The pooling layer in the ACNN reduces the model’s complexity. The measurement results show that the proposed DPD requires significantly fewer coefficients and floating-point operations (FLOPs) than the state-of-the-art DPDs. This model’s complexity reduction arises with the number of carriers in the multiband aggregated signal. The measurement results also show that the proposed DPD has a better linearization performance in terms of metrics, such as normalized mean square error (NMSE), adjacent channel power ratio (ACPR), and error vector magnitude (EVM).