Radio interference sources mounted on high-speed mobile platforms present a considerable challenge to accurate localization and tracking due to their wide-ranging and dynamic characteristics. This article tackles the intricate problem of tracking erratically maneuvering interference sources and introduces a dynamic tracking scheme for the spatial decomposition of motion states. Firstly, the proposed scheme decomposes the complex three-dimensional motion into three independent one-dimensional linear movements. This facilitates the concurrent application of interacting multiple model matching filters across all three dimensions, enabling precise characterization of the complex motion states. Secondly, an enhancement is made to the update process of the noise covariance matrix within the real-time noise estimator. Specifically, a time-varying exponential forgetting factor is incorporated to ensure the robustness of the adaptive cubature Kalman filter algorithm when combined with the noise estimator. Simulation results show that the proposed tracking scheme outperforms traditional methods in terms of both the speed of tracking error convergence and the fluctuations reduction. The improved accuracy and reliability of the proposed scheme position it as a promising approach for addressing the challenges associated with tracking dynamic radio interference sources on high-speed mobile platforms.

Abstract In this work, the signal recovery problem regarding incomplete and noisy spatio‐temporal signals is studied. A spatio‐temporal signal is considered as a time‐varying graph signal and a diffusion‐induced first‐order Markov signal model is developed to incorporate both the spatial structure and temporal correlation into the underlying graph. With this model, prior knowledge on spatial smoothness and temporal correlation is revisited, and the connections between the graph structure and differential temporal smoothness are revealed. The authors then accordingly formulate a spatio‐temporal signal recovery method by jointly exploiting the spatial smoothness, low rank and refined differential temporal smoothness. The formulated recovery problem is solved by a block coordinate descent‐based algorithm, which iteratively optimises the recovery accuracy and temporal correlation matrix. The experiments on three real‐world datasets reveal the high signal recovery accuracy of the proposed algorithm.

In this paper the topology learning for time-varying graph signals with incremental data is studied. In order to learn the topology which is slowly time-varying during data collection, we separate the data of observation into multiple groups by time, and for each group we model the topology learning as a sparse optimization problem, in which the penalty function is designed to consider both the incremental data and previous topology information for graph learning. Moreover, a correction function for dynamic topology is developed by considering a priori information of topology changes. Based on that, by solving the optimization problem we then propose a dynamic topology learning and tracking algorithm to learn as well as track the varying graph topology. Simulations on synthetic and real-world dataset are performed to reveal the performance gain of the proposed algorithm.

In this paper the massive access problem in IoT networks is studied from the perspective of graph signal processing (GSP). First, we reveal the connections of massive access in IoT networks and the sampling of a graph signal, and model the massive access problem as a graph-based random sampling problem. Second, inspired by the restricted isometry property (RIP) condition in compressed sensing, we derive the RIP condition for random sampling on band-limited graph signals, showing at the first time that band-limited graph signals can be recovered from randomly-selected noisy samples in a given probability. Based on the proposed RIP condition, the sampling probability of each sensing device is optimized through minimizing the Chebyshev or Gaussian approximations of mean square error between the original and the recovered signals. Experiments on the Bunny and Community graphs verify the stability of random sampling, and show the performance gain of the proposed random sampling solutions.

In this paper the transmission security for multiuser multi-input single-output (MU-MISO)- Non-orthogonal multiple access (NOMA) downlink transmission is studied for untrusted users. We propose a security-enhanced user pairing scheme under the constraints of weak security to achieve high spectrum efficiency as well as weak system secrecy. We first formulate a sum-rate maximization problem for NOMA subject to weak security constraints, and reformulate the weak security requirement into constraints of the average bit error rate (BER) at any undesired user. Then we decompose the original optimization problem into a security-guaranteed user pairing problem and a conventional sum-rate maximization problem for NOMA, which is eventually solved by jointly downlink beamforming and power allocation. Simulation results compare the proposed NOMA scheme with zero-forcing (ZF) and the conventional MU-MISO beamforming, and reveal the performance gain of the proposed NOMA scheme in secrecy sum-rate.

In this paper, we study the optimal transmitter beamforming design in a coordinated multicell multicast network under the wiretap channel model, where each cell serves a group of users and a single-antenna eavesdropper tries to overhear the confidential information. We study the problem of finding the optimal beamforming strategy at each base station to minimize the total transmit power of the multicell network while guaranteeing secrecy and QoS requirements of each user. The resulting non-convex optimization problem is solved through a sequence of convex semi-definite relaxations. The effectiveness of the proposed algorithm is validated via numerical simulations.

A Simplified Optimal Scheduling (SOS) scheme for network coded wireless multicast is studied in this paper. Specifically, a complexity reduction method proposed to make the existing algorithms more practical. Our objective is to remove redundancy while maintaining the maximum throughput. We formulate it as a Mixed Integer Non Linear Programming (MINLP) problem and propose a network coding based packet scheduling scheme that finds the optimal solution. Finally, the simulation results corroborate our method.

In three dimensional (3D) multiuser multi-input multi- output (MU-MIMO) systems with limited feedback, the high computing complexity and the large storage requirement at terminals are the significant challenges due to a large 3D codebook used with the massive antenna array setting in the base station (BS). In this paper, we study a limited feedback strategy based on the separate horizontal and vertical double codebook to reduce the storage space and the codeword search complexity. Especially, two user scheduling schemes based on the double codebook are proposed. In order to schedule more users, the first scheme under the relaxed condition distinguishes users only in horizontal dimension and the scheduled users may have the same quantified vertical channel vectors. In the second scheme with the strict condition, the scheduled users have not only the orthogonal horizontal channel vectors but also the different vertical channel vectors to suppress the inter-user interference as much as possible. The simulation results demonstrate that the first scheme can achieve better system total performance for more users selected to transmit simultaneously, while the second scheme can achieve better per-user performance due to the lower inter-user interference.

Massive Multi-input Multi-output (MIMO) technique shows great potentials in improving energy and spectral efficiency while suffers high costs of the requirement for large amount of radio frequency (RF) chains. In this paper, we develop an l1/2-regularity based downlink transmit antenna selection scheme for massive MIMO systems with limited RF chains. With the objective to minimize the transmission power at the given number of RF-chains and transmission quality, we formulate and decompose the original l1/2-norm optimization problem into two separated problems with l1/2-sparsity and signal-interference-to-noise-ratio (SINR) requirements, respectively. An iterative algorithm based on coordinate descent and feasible set projection are then developed. This scheme provides an efficient way to evaluate the minimum required RF chains for the deployment of a massive MIMO system. Simulation results show that the proposed scheme achieves higher energy efficiency compared with the lp(p > 1/2)-norm based antenna selection methods, especially for large numbers of correlated antennas.

A full frequency diversity COFDM (FD-COFDM) scheme based on sub-channel multiplexing is proposed to improve the performance of COFDM without sacrificing spectral efficiency. Since the channel suffers the frequency selective fading, the received signal contains strong inter-symbol interference (ISI). To obtain the diversity- and code-gains, an iterative receiving algorithm is presented. By jointly using diversity combining, interference cancellation and soft decoding, the proposed iterative receiving algorithm properly decodes the information bits in the strong ISI environment. Consequently, all the subcarriers of FD-COFDM participate in diversity so that the system can achieve full frequency diversity in most practical channels. The new receiving algorithm is relatively simple compared to the maximum likelihood estimation algorithm, and is therefore a better alternative to balance the algorithm complexity and system performance.