The channel nonlinearity and high-speed mobility in low-earth orbit (LEO) constellation communication are key bottlenecks that constrain the performance of waveform transmission. To address these challenges, we propose a universal continuous phase modulation (CPM) spread-spectrum communication method with high spectral efficiency, and a Doppler-insensitive signal detection mechanism. First, an enhanced dual-phase CPM spread-spectrum (DP-CPM-SS) waveform is investigated. By analyzing the principles and power spectrum characteristics of DP-CPM-SS, we correct the modulation index and design the optimalWiener filtering reception for CPM, improving the spectral efficiency and noise resilience. Subsequently, a CPM noncoherent detection integrating frequency estimation and phase pre-compensation is developed. The performance lower bound of the partial matched filter-fast fourier transform (PMF-FFT) frequency estimation algorithm is derived, revealing the relationship among the length and number of partial matched filters, the normalized frequency offset and the mean square error (MSE) of frequency estimation. Additionally, error probability performance and frequency offset adaptation range are analyzed. Numerical results show that the proposed method exhibits superior and stable performance under severe Doppler effects, which is a promising scheme for LEO constellation communication.

This paper investigates the problem of multidimensional resource management in multi-access mobile edge computing (MEC) networks against external dynamic jamming. The objective is to minimize the long-term computational cost of the MEC network while satisfying the task computation delay requirements of user equipment (UE) by jointly optimizing computing and communication resource allocation. To overcome challenges such as frequency conflict and dynamic jamming attacks, a distributed multi-agent hierarchical deep reinforcement learning (MAHDRL) MEC framework based on hybrid heterogeneous decision-making is proposed. Specifically, a hierarchical MEC anti-jamming data offloading optimization model is constructed, and the MEC resource management problem is formulated as a decentralized partially observable Markov decision process (Dec-POMDP). Based on this, a distributed MAHDRL algorithm based on the actor-critic (AC) model is designed to solve the multi-agent high-dimensional nonlinear hybrid integer programming NP-hard problem: the high-level network in the base station (BS) optimizes discrete channel access strategies, while the low-level network in UEs learns data offloading strategies. Additionally, the computational complexity is discussed and a theoretical proof of the algorithm convergence is presented. Simulation results demonstrate the superiority of the proposed algorithm, which reduces energy consumption and data processing delay across the network.

As a fundamental requirement for IoT communication systems the importance of highly reliable anti-jamming communication methods in safety use cases is growing. In this article, we propose a novel anti-intelligent jamming scheme called opponent awareness-based anti-jamming algorithm (OA3). The user-jammer-environment interaction is formulated as a two-player simultaneous action stochastic game where participators have the ability to update their strategies. The decision-making process of each agent is modeled as a Markov decision process (MDP). Begin with the intuition “learn how the jammer learns,” the opponent awareness-based iterative learning objective (OAL) of the user is presented by considering the learning awareness of the jammer to defeat the intelligent jamming. Finally, we introduce the framework, including offline policy learning and online policy exploiting to implement OAL and accelerate the learning. Simulations show that the OA3 outperforms the benchmark anti-jamming strategy in terms of packet success rate.

Due to the flexibility in 3-D space and high probability of line-of-sight (LoS) in air-to-ground communications, unmanned aerial vehicles (UAVs) have been considered as means to support energy-efficient data collection. However, in emergency applications, the mission completion time should be main concerns. In this article, we propose a UAV-aided data collection design to gather data from a number of ground users (GUs). The objective is to optimize the UAV’s trajectory, altitude, velocity, and data links with GUs to minimize the total mission time. However, the difficulty lies in that the formulated time minimization problem has mutual effect with trajectory variables. To tackle this issue, we first transform the original problem equivalently to the trajectory length problem and then decompose the problem into three subproblems: 1) altitude optimization; 2) trajectory optimization; and 3) velocity and link scheduling optimization. In the altitude optimization, the aim is to maximize the transmission region of GUs which can benefit trajectory designing; then, in the trajectory optimization, we propose a segment-based trajectory optimization algorithm (STOA) to avoid repeat travel; besides, we also propose a group-based trajectory optimization algorithm (GTOA) in large-scale high-density GU deployment to relieve massive computation introduced by STOA. Then, the velocity and link scheduling optimization is modeled as a mixed-integer nonlinear programming (MINLP) and block coordinate descent (BCD) is employed to solve it. Simulations show that both STOA and GTOA achieve shorter trajectory compared with the existing algorithm and GTOA has less computational complexity; besides, the proposed time minimization design is valid by comparing to the benchmark scheme.

This paper investigates the air-to-air channel model for Unmanned Aerial Vehicle (UAV) communication links. Although the 3-D ellipsoid model is widely used for characterization of wireless channels, present studies only include the angular distribution of scatterers while the power distribution is absent. Besides, all scatterers are assumed homogeneous in existing work, which is inaccurate for low-altitude UAVs. For above-mentioned problems, our work has two main contributions. First, a precise description of statistical characteristics of receiving power in both delay and direction of arrival (DoA) is provided based on the current 3-D ellipsoid model. Second, we extend the original model to a composite model including two independent ellipsoid models. Apart from surrounding scatterers, obtrusive objects like skyscrapers are specially considered in our model as far clusters. These far clusters could cause distinctive rays with excessive delay even from a distance and influence the statistical characteristics of the channel evidently. Numerical results show that the occurrence of far clusters increases the spreads of delay and DoA.

Different from conventional proper Gaussian signalling (PGS), whose achievable rate depends on the input signals' covariances only, the capacity of channels with improper Gaussian signalling (IGS) is a function with the signals' covariances and pseudo‐covariances. Thus, the additional degree of freedom provided by pseudo‐covariance is available to improve the achievable rate. In this study, the authors investigate the achievable rate region of two‐users X‐interference channel (IC) in the condition of applying IGS. By treating the interference as additive Gaussian noise, the authors analyze the mathematical expression of capacity based on Shannon's theorem. Specifically, for the two‐users simple input, simple output (SISO)‐IC, they propose two optimal signalling schemes to achieve the Pareto boundary in situations of employing PGS and IGS, respectively. In these optimal signalling schemes, the transparent geometric model which takes less computational complexity is applied to calculate the optimal transmission parameters for the Pareto boundary. Numerical simulation results show that the proposed IGS strategies can achieve larger achievable rate region and a greater sum rate. Finally, the authors give an in‐depth discussion about the structure of the Pareto boundary, which is characterised by the degree of impropriety measured by the covariance and the pseudo‐covariance of signals.

Deep Neural Networks (DNNs) have recently shown state of the art performance on semantic segmentation tasks, however, they still suffer from problems of poor boundary localization and spatial fragmented predictions. The difficulties lie in the requirement of making dense predictions from a long path model all at once since details are hard to keep when data goes through deeper layers. Instead, in this work, we decompose this difficult task into two relative simple sub-tasks: seed detection which is required to predict initial predictions without the need of wholeness and preciseness, and similarity estimation which measures the possibility of any two nodes belong to the same class without the need of knowing which class they are. We use one branch network for one sub-task each, and apply a cascade of random walks base on hierarchical semantics to approximate a complex diffusion process which propagates seed information to the whole image according to the estimated similarities. The proposed DifNet consistently produces improvements over the baseline models with the same depth and with the equivalent number of parameters, and also achieves promising performance on Pascal VOC and Pascal Context dataset. OurDifNet is trained end-to-end without complex loss functions.

The problem of quantization for Log-likelihood ratios in the presence of a practical automatic-gain-control (AGC) is elaborated in this paper. A median based quantization approach is proposed, in which the median of the absolute value of loglikelihood ratios (LLRs) is set as the quantized midpoint. This approach can diminish the mutual information loss caused by quantization. When applied to bit-interleaved coded modulation scheme, the proposed quantizer proves to be robust to different transmission schemes in both AWGN and rayleigh channels. The implementation issue of median estimate using subset averaged median estimator is also covered in details.

Direct-conversion transceivers are gaining increasing attention due to their low power consumption. However, they suffer from a serious in- and quadrature-phase (I/Q) imbalance problem. The I/Q imbalance can severely limit the achievable operating signal-to-noise ratio (SNR) at the receiver and, consequently, the supported constellation sizes and data rates. In this paper, we first investigate the effects of I/Q imbalance on orthogonal frequency division multiplexing (OFDM) receivers, and then propose a new I/Q imbalance compensation scheme. In the proposed method, a new statistic, which is robust against channel distortion, is used to estimate the I/Q imbalance parameters, and then the I/Q imbalance is corrected in the frequency domain. Simulations are performed to verify the effectiveness of the proposed method for I/Q imbalance compensation. The results show that the proposed I/Q imbalance compensation method can achieve bit error rate (BER) performance close to that in the ideal case without I/Q imbalance in additive white Gaussian noise (AWGN) or multipath environments. Furthermore, because no pilot information is required, this method can be applied in various standard communication systems.

We propose a thoroughly optimal signal design strategy to achieve the Pareto boundary (boundary of the achievable rate region) with improper Gaussian signaling (IGS) on the Z-interference channel (Z-IC) under the assumption that the interference is treated as additive Gaussian noise. Specifically, we show that the Pareto boundary has two different schemes determined by the two paths manifesting the characteristic of improperly transmitted signals. In each scheme, we derive several concise closed-form expressions to calculate each user’s optimally transmitted power, covariance, and pseudo-covariance of improperly transmitted signals. The effectiveness of the proposed optimal signal design strategy is supported by simulations, and the results clearly show the superiority of IGS. The proposed optimal signal design strategy also provides a simple way to achieve the required rate region, with which we also derive a closed-form solution to quickly find the circularity coefficient that maximizes the sum rate. Finally, we provide an in-depth discussion of the structure of the Pareto boundary, characterized by the channel coefficient, the degree of impropriety measured by the covariance, and the pseudo-covariance of signals transmitted by two users.

Relay-aided device-to-device (D2D) communication is a promising technology for the next-generation cellular network. We study the transmission schemes for an amplify-and-forward relay-aided D2D system which has multiple antennas. To circumvent the prohibitive complexity problem of traditional maximum likelihood (ML) detection for full-rate space-time block code (FSTBC) transmission, two low-complexity detection methods are proposed, i.e., the detection methods with the ML-combining (MLC) algorithm and the joint conditional ML (JCML) detector. Particularly, the method with the JCML detector reduces detection delay at the cost of more storage and performs well with parallel implementation. Simulation results indicate that the proposed detection methods achieve a symbol error probability similar to that of the traditional ML detector for FSTBC transmission but with less complexity, and the performance of FSTBC transmission is significantly better than that of spatial multiplexing transmission. Diversity analysis for the proposed detection methods is also demonstrated by simulations.

Jointly detecting the signals from the source and relay in a spatial multiplexing (SM) multiple-input multiple-output (MIMO) relaying system improves the transmit reliability significantly. However, the existing joint detection schemes for SM MIMO relaying systems, which achieve full diversity, such as the near maximum likelihood (ML) decoder, suffer from high complexity. In this paper, we propose a weighted combining (WC) algorithm, which is applied before the detector in the SM MIMO decode-and-forward relaying system. The proposed algorithm merges the received signal vectors from the source and relay into a combined signal without expanding their dimension, and formulates an equivalent MIMO channel matrix for the combined signal, resulting in a lower complexity for the subsequent detection. We analyze the performance of the proposed WC algorithm with ML detection in terms of the diversity order and computational complexity. An approximate upper bound on the symbol error probability (SEP) for the proposed algorithm is also derived. Simulation results show that in symmetric networks, the proposed WC algorithm achieves substantially lower complexity, while maintaining an SEP performance similar to that of the benchmark NML decoder. The consistency of the derived upper bound on the SEP is also verified by simulations.

In-phase and quadrature-phase (I/Q) imbalance is a critical issue limit the achievable operating signal-to-noise ratio (SNR) at the receiver in direct conversion architecture. In recent literatures, the second-and fourth-order circularity property of communication signals have been used for designing compensator to eliminate the I/Q imbalance. In this paper, we investigate whether moment circularity of an order higher than four can be used in receiver I/Q imbalance compensation. It is shown that the sixth-order moment E[z4z*2] is a suitable statistic for measuring the sixth-order circularity of representative communication signals such as M-QAM and M-PSK with M > 2. Two blind algorithms are then proposed to update the coefficients of I/Q imbalance compensator by restoring the sixth-order circularity of the compensator output signal. Simulation results show that the new proposed methods based on sixth-order statistic converges faster or gives lower steady-state variance than the reference methods that are based on second-and fourth-order statistics.

We develop a new kind of "space-filling" curves, connected Fermat spirals , and show their compelling properties as a tool path fill pattern for layered fabrication. Unlike classical space-filling curves such as the Peano or Hilbert curves, which constantly wind and bind to preserve locality, connected Fermat spirals are formed mostly by long, low-curvature paths. This geometric property, along with continuity, influences the quality and efficiency of layered fabrication. Given a connected 2D region, we first decompose it into a set of sub-regions, each of which can be filled with a single continuous Fermat spiral. We show that it is always possible to start and end a Fermat spiral fill at approximately the same location on the outer boundary of the filled region. This special property allows the Fermat spiral fills to be joined systematically along a graph traversal of the decomposed sub-regions. The result is a globally continuous curve. We demonstrate that printing 2D layers following tool paths as connected Fermat spirals leads to efficient and quality fabrication, compared to conventional fill patterns.

We propose a new blind separation approach based on the Generalized Generating Function (GGF) of observations for complex sources by generalizing the definition of generating function. A new core equation is obtained and an approximate joint diagonalization scheme is used to estimate the mixing matrix by diagonalizing the Hessian matrix of the second GGF of the observations. Simulation results show that the GGF approach has superior performance to the existing classical algorithms when the SNR of observations is low and the data block is short.