Due to the high speed mobile environment, the aeronautical Cognitive Radio (CR) communications base on the channel with the time-variant stochastic non-continuous spectrum. The traditional fading channel models, such as Rayleigh, Rice, Nakagami-m and multipath channel models, can not describe the whole property of the channels of CR communications. In this letter, the statistical channel modeling scheme for aeronautical CR communications is proposed with a M/M/s(1) queuing model, which properly describes the random spectrum occupation of the primary users, i.e. aircrafts in aeronautical communications. The proposed channel model can be easily utilized in the channel simulation to testify the validity and efficiency of the aeronautical CR communications.

A narrowband interference (NBI) estimation and mitigation method based on compressive sensing (CS) for communication systems with repeated training sequences is investigated in this letter. The proposed CS-based differential measuring method is performed through the differential operation on the inter-block-interference-free regions of the received adjacent training sequences. The sparse NBI signal can be accurately recovered from a time-domain measurement vector of small size under the CS framework, without requiring channel information or dedicated resources. Theoretical analysis and simulation results show that the proposed method is robust to NBI under multi-path fading channels.

In this letter, we enhance Complementary Code Keying (CCK) modulation with Pseudo-Periodic Sequence. It has been proved that the new Pseudo-Periodic CCK modulation is more efficient and robust fighting against multi-path interference. In order to support our new scheme, we design and implement the corresponding simulation. The in-depth analysis of the reason why Pseudo-Periodic Sequence can do a favor to CCK is also presented and emphasized.

Large Area Synchronized (LAS)-CDMA, actually composed of LA codes and pulse compressing LS codes, has been proposed as a most promising scheme in 3G and 4G wireless communications. LS codes are famous for the Zero Correlation Zone (ZCZ) in the auto-correlation and cross-correlation functions, which endows the codes with the capability to perfectly reduce the Multiple Access Interference (MAI) and Inter Symbol Interference (ISI) if the maximum transmission delay is less than the length of ZCZ. In this letter, we provide a general and systematic method to construct LS codes from the set of complete complementary sequences. Our method is much more general than the ordinary LS construction method revealed previously.

Rotate magnetic field can be used for ranging, especially the environment where electronic filed suffers a deep fading and attenuation, such as drilling underground. However, magnetic field is still affected by the ferromagnetic materials, e.g., oil casing pipe. The measurement error is not endurable for single measurement. In this paper, the Geometric Predicted Unscented Kalman Filtering (GP-UKF) algorithm is developed for rotate magnetic ranging system underground. With GP-UKF, the Root Mean Square Error (RMSE) can be suppressed. It is really important in a long range detection by magnetic field, i.e., more than 50 meters.

Convolutional neural network (CNN) based vehicle re-identificatioin (ReID) inevitably has many disadvantages, such as information loss caused by downsampling operation. Therefore we propose a vision transformer (Vit) based vehicle ReID method to solve this problem. To improve the feature representation of vision transformer and make full use of additional vehicle information, the following methods are presented. (I) We propose a Quadratic Split Architecture (QSA) to learn both global and local features. More precisely, we split an image into many patches as “global part” and further split them into smaller sub-patches as “local part”. Features of both global and local part will be aggregated to enhance the representation ability. (II) The Auxiliary Information Embedding (AIE) is proposed to improve the robustness of the model by plugging a learnable camera/viewpoint embedding into Vit. Experimental results on several benchmarks indicate that our method is superior to many advanced vehicle ReID methods.

In this letter, a path dynamics assessment asynchronous advantage actor-critic scheduling algorithm (PDAA3C) is proposed to solve the MPTCP scheduling problem by using deep reinforcement learning Actor-Critic framework. The algorithm picks out the optimal transmitting path faster by multi-core asynchronous updating and also guarantee the network fairness. Compared with the existing algorithms, the proposed algorithm achieves 8.6% throughput gain over RLDS algorithm, and approaches the theoretic upper bound in the NS3 simulation.

Trellis coded modulation (TCM) concept is applied to the mode constellation points of orbital angular momentum (OAM) modulation. OAM modulation considers the multiple OAM modes as additional constellation points and maps a first part of a block of information bits to the transmitting OAM modes. Therefore, spatial multiplexing gains are retained and spectral efficiency is boosted. The second part of the block of information bits is mapped to a complex symbol using conventional digital modulation schemes. At any particular time instant, only one OAM mode is active. The receiver estimates the transmitted symbol and the active OAM mode, then uses the two estimates to retrieve the original block of data bits. Simulation reveals that with the TCM employed both for the OAM constellation points and the signal constellation points, a considerable bit error rate (BER) gain can be obtained under all turbulence conditions, compared with that of the no coding scheme.

In a real-time system, tasks are required to be completed before their deadlines. Under normal workload conditions, a scheduler with a proper scheduling policy can make all the tasks meet their deadlines. However, in practical environment, system workload may vary widely. Once system workload becomes too heavy, so that there does not exist a feasible schedule can make all the tasks meet their deadlines, we say the system is overloaded under which some tasks will miss their deadlines. To alleviate the degrees of system performance degradation caused by the missed deadline tasks, the design of scheduling is crucial. Many design objectives can be considered. In this paper, we first focus on maximizing the total number of tasks that can be completed before their deadlines. A scheduling method based on satisfiability modulo theories (SMT) is proposed. In the method, the problem of scheduling is treated as a satisfiability problem. The key work is to formalize the satisfiability problem using first-order language. After the formalization, a SMT solver (e.g., Z3, Yices) is employed to solve this satisfiability problem. An optimal schedule can be generated based on the solution model returned by the SMT solver. The correctness of this method and the optimality of the generated schedule can be verified in a straightforward manner. The time efficiency of the proposed method is demonstrated through various simulations. Moreover, in the proposed SMT-based scheduling method, we define the scheduling constraints as system constraints and target constraints. This means if we want to design scheduling to achieve other objectives, only the target constraints need to be modified. To demonstrate this advantage, we adapt the SMT-based scheduling method to other design objectives: maximizing effective processor utilization and maximizing obtained values of completed tasks. Only very little changes are needed in the adaption procedure, which means the proposed SMT-based scheduling method is flexible and sufficiently general.

In recent years, many variants of key point based image descriptors have been designed for the image matching, and they have achieved remarkable performances. However, to some images, local features appear to be inapplicable. Since theses images usually have many local changes around key points compared with a normal image, we define this special image category as the image with local changes (IL). An IL pair (ILP) refers to an image pair which contains a normal image and its IL. ILP usually loses local visual similarities between two images while still holding global visual similarity. When an IL is given as a query image, the purpose of this work is to match the corresponding ILP in a large scale image set. As a solution, we use a compressed HOG feature descriptor to extract global visual similarity. For the nearest neighbor search problem, we propose random projection indexed KD-tree forests (rKDFs) to match ILP efficiently instead of exhaustive linear search. rKDFs is built with large scale low-dimensional KD-trees. Each KD-tree is built in a random projection indexed subspace and contributes to the final result equally through a voting mechanism. We evaluated our method by a benchmark which contains 35,000 candidate images and 5,000 query images. The results show that our method is efficient for solving local-changes invariant image matching problems.

The success of heuristic search in AI planning largely depends on the design of the heuristic. On the other hand, previous experience contains potential domain information that can assist the planning process. In this context, we have studied dynamic macro-based heuristic planning through action relationship analysis. We present an approach for analyzing the action relationship and design an algorithm that learns macros in solved cases. We then propose a dynamic macro-based heuristic that appropriately reuses the macros rather than immediately assigning them to domains. The above ideas are incorporated into a working planning system called Dynamic Macro-based Fast Forward planner. Finally, we evaluate our method in a series of experiments. Our method effectively optimizes planning since it reduces the result length by an average of 10% relative to the FF, in a time-economic manner. The efficiency is especially improved when invoking an action consumes time.

In a two-way half-duplex system where source nodes are equipped with multiple antennas and a relay with a single antenna, we study the diversity multiplexing tradeoff (DMT) achieved by orthogonal decode-and-forward (ODF), orthogonal amplify-and-forward (OAF), non-orthogonal decode-and-forward (NDF) and non-orthogonal amplify-and-forward (NAF), respectively. Their closed-form DMT are derived with given transmission time of each terminal, and optimized by allocating the transmission time. From these analyses, NDF achieves the best performance in terms of DMT in low spectral efficiency scenarios, while NAF outperforms other protocols in high spectral efficiency scenarios.

The pilot contamination is a serious problem which hinders the capacity increasing in the massive MIMO system. Similar to Fractional Frequency Reuse (FFR) in the OFDMA system, Fractional Pilot Reuse (FPR) is proposed for the massive MIMO system. The FPR can be further classified as the strict FPR and soft FPR. Meanwhile, the detailed FPR schemes with pilot assignment and the mathematical models are provided. With FPR, the capacity and the transmission quality can be improved with metrics such as the higher Signal to Interference and Noise Ratio (SINR) of the pilots, the higher coverage probability, and the higher system capacity.

In this paper, we present a transmission scheme for a multiple-input multiple-output (MIMO) quasi-static fading channel with imperfect channel state information at the transmitter (CSIT). In this scheme, we develop a precoder structure to exploit the available CSIT and apply spatial coupling for further performance enhancement. We derive an analytical evaluation method based on extrinsic information transfer (EXIT) functions, which provides convenience for our precoder design. Furthermore, we observe an area property indicating that, for a spatially coupled system, the iterative receiver can perform error-free decoding even the original uncoupled system has multiple fixed points in its EXIT chart. This observation implies that spatial coupling is useful to alleviate the uncertainty in CSIT which causes difficulty in designing LDPC code based on the EXIT curve matching technique. Numerical results are presented, showing an excellent performance of the proposed scheme in MIMO fading channels with imperfect CSIT.

Sequence set with Three Zero Correlation Zones (T-ZCZ) can efficiently mitigate the Multiple Access Interference (MAI) and Inter Symbol Interference (ISI) caused by multi-path in CDMA system. In this paper, an algorithm for generating T-ZCZ sequence set is presented. Moreover, in order to study the restrictions among the parameters such as the length of the sequence, the number of the sequences and the length of the T-ZCZ etc., the corresponding theoretical bound is investigated and proved. Additionally, the performance of T-ZCZ sequence in MC-CDMA system is evaluated to confirm the capability of interference cancellation as well as system capacity improvement.

OSEK/VDX, a standard for an automobile OS, has been widely adopted by many manufacturers to design and develop a vehicle-mounted OS. With the increasing functionalities in vehicles, more and more complex applications are be developed based on the OSEK/VDX OS. However, how to ensure the reliability of developed applications is becoming a challenge for developers. To ensure the reliability of developed applications, model checking as an exhaustive technique can be applied to discover subtle errors in the development process. Many model checkers have been successfully applied to verify sequential software and general multi-threaded software. However, it is hard to directly use existing model checkers to precisely verify OSEK/VDX applications, since the execution characteristics of OSEK/VDX applications are different from the sequential software and general multi-threaded software. In this paper, we describe and develop an approach to translate OSEK/VDX applications into sequential programs in order to employ existing model checkers to precisely verify OSEK/VDX applications. The value of our approach is that it can be considered as a front-end translator for enabling existing model checkers to verify OSEK/VDX applications.

An unsupervised adaptive signal processing method of principal components analysis (PCA) neural networks (NN) based on signal eigen-analysis is proposed to permit the eigenstructure analysis of lower signal to noise ratios (SNR) direct sequence spread spectrum (DS) signals. The objective of eigenstructure analysis is to estimate the pseudo noise (PN) of DS signals blindly. The received signal is firstly sampled and divided into non-overlapping signal vectors according to a temporal window, which duration is two periods of PN sequence. Then an autocorrelation matrix is computed and accumulated by these signal vectors one by one. Lastly, the PN sequence can be estimated by the principal eigenvector of autocorrelation matrix. Since the duration of temporal window is two periods of PN sequence, the PN sequence can be reconstructed by the first principal eigenvector only. Additionally, the eigen-analysis method becomes inefficient when the estimated PN sequence is long. We can use an unsupervised adaptive method of PCA NN to realize the PN sequence estimation from lower SNR input DS-SS signals effectively.

In a periodic spectrum sensing framework where each frame consists of a sensing block and a data transmitting block, increasing sensing duration decreases the probabilities of both missed opportunity and interference with primary users, but increasing sensing duration also decreases the energy efficiency and the transmitting efficiency of the cognitive network. Therefore, the sensing duration to use is a trade-off between sensing performance and system efficiencies. The relationships between sensing duration and state transition probability are analyzed firstly, when the licensed channel stays in the idle and busy states respectively. Then a state transition probability based sensing duration optimization algorithm is proposed, which can dynamically optimize the sensing duration of each frame in the current idle/busy state by predicting each frame's state transition probability at the beginning of the current state. Analysis and simulation results reveal that the time-varying optimal sensing duration increases as the state transition probability increases and compared to the existing method, the proposed algorithm can use as little sensing duration in each frame as possible to satisfy the sensing performance constraints so as to maximize the energy and transmitting efficiencies of the cognitive networks.

A novel design technique for two-dimensional (2-D) waveguide slot arrays is proposed in this paper that combines a full-wave method of moments (MoM) analysis and an equivalent circuit with the explicit restraint of input matching. The admittance and slot spacing are determined first in an equivalent circuit to realize the desired distribution of power dissipation and phase, with the explicit restraint of input matching. Secondly by applying a full-wave MoM analysis to the finite 2-D array, slot parameters are iteratively determined to realize the active admittance designed above where slot mutual coupling and wall thickness are fully taken into account. The admittance, treated as the key parameter in the equivalent circuit corresponds to the power dissipation of the slots but not to the slot voltage, which is directly synthesized from the radiation pattern. The initial value of the power dissipation is assumed to be proportional to the square of the amplitude of the desired slot voltage. This assumption leads to a feedback procedure, because the resultant slot voltage distribution generally differs from the desired ones due to the effect of non-uniformity in the characteristic impedance on slot apertures. This slot voltage error is used to renew the initial distribution of power dissipation in the equivalent circuit. Generally, only one feedback cycle is needed. Two 2427-element arrays with uniform and Taylor distributions were designed and fabricated at 25.3 GHz. The measured overall reflections for both antennas were suppressed below -18 dB over the 24.3-26.3 GHz frequency range. High aperture efficiencies of 86.8% and 55.1% were realized for the antennas with uniform and Taylor distributions, the latter of which has very low sidelobes below -33 dB in both the E- and H-planes.

Sequence set with Three Zero Correlation Zones (T-ZCZ) is applied in Quasi-Synchronized CDMA communication system to reduce the Multiple Access Interference (MAI) and Inter Symbol Interference (ISI). In this letter, we present a class of sequence set with Three Low Correlation Zones (T-LCZ), which has more sequences and flexibility than T-ZCZ sequence set. Moreover, the theoretical bound on T-LCZ sequences is derived for estimating the performance of such sequence set.