A novel jitter-robust integrated sensing and communication (ISAC)-enabled beam tracking framework is proposed for unmanned aerial vehicle (UAV)-to-UAV (U2U) communication systems. In particular, a tethered UAV base station (UAV-B) serves a maneuvering UAV user (UAV-U), where jitter disrupts beam alignment and degrades communication rates. To enable beam alignment, a UAV-U angle tracking method is first proposed to estimate the relative angles between UAV-B and UAV-U. Specifically, the tethered UAV-B leverages sensing echo signals to obtain motion information of UAV-U, and integrates them with UAV-U’s prior motion models to enhance the accuracy of angle prediction. Meanwhile, through the communication link, UAV-U can receive the direction of UAV-B. Building on the obtained angle information, a jitter compensation algorithm is further developed to maximize the achievable rate of the U2U communication systems. In particular, UAV-B employs adaptive beamwidth control (ABC) to adjust its beamwidth and thus ensures robust coverage for UAV-U. Meanwhile, UAV-U employs beam steering refinement (BSR) to optimize the direction based on predicted angle information, enhancing the received signal-to-noise ratio (SNR) and reducing beam misalignment caused by jitter. Finally, our numerical results unveil that: 1) higher angle prediction accuracy is achieved by the proposed UAV-U angle tracking scheme compared to conventional Kalman filter-based angle tracking methods; and 2) a higher achievable rate in the U2U communication systems is achieved by the proposed jitter compensation scheme compared with traditional beamwidth control-based methods.

As one of the key enabling technologies for the sixth-generation (6G) mobile communication system, near-field communication can significantly enhance the spectral efficiency of the communication system through the beam focusing effect. In order to effectively extend the near-field coverage so that more users can benefit from the near-field effect, the concept of modular array architecture is introduced. However, the traditional uniform modular array (UMA) has a significant grating lobe effect, leading to severe inter-user interference (IUI) problems. To effectively suppress the grating lobe interference and optimize the sum-rate of multi-user communication, a joint optimization algorithm for module position and beamforming based on the movable modular array is proposed in this paper. By establishing a joint optimization model for the module position and beamforming, a modified minimum mean square error beamforming algorithm and a dynamic particle swarm position optimization algorithm (MMSE-DPSO) are proposed. Simulation results show that the proposed algorithm can effectively suppress the grating lobes while improving the sum-rate of multi-user communication.

Integrated sensing and communication (ISAC), which can exploit the wireless spectrum for concurrent sensing and communication functions, is regarded as a promising technology for the future sixth generation (6G) wireless communication networks. This paper proposes a robust beam tracking method for maneuverable unmanned aerial vehicles (UAVs) with jittering effect within the ISAC framework. By utilizing reflected echoes, the kinematic parameters are measured and the interacting multiple model with extended Kalman filter (IMM-EKF) is designed for robust beam tracking of maneuverable UAVs. Furthermore, due to the jittering effect, the UAV may not point to the optimal alignment direction. To this end, we propose the coordinate descent particle swarm optimization (CDPSO) algorithm to balance the jittering effect by maximizing the received signal-to-noise ratio (SNR) of the UAV. The effectiveness of the proposed scheme is verified via simulation results.

This article introduces a novel concept of near-field integrated sensing and communication (ISAC) within a cognitive radio (CR) framework. The secondary ISAC transmitter, equipped with an extremely large-scale array, aims to minimize transmit power while meeting the requirements for both communication and sensing, as well as satisfying an interference constraint for the primary receiver. We initially solve the formulated nonconvex problem using a semidefinite relaxation approach to achieve a globally optimal solution. To reduce computational complexity, we propose two suboptimal beamforming strategies: 1) zero-forcing (ZF)-based and 2) maximum ratio transmission (MRT)-based beamforming designs. These approaches provide a practical tradeoff between performance and complexity by fixing beam directions according to the principles of ZF and MRT. Additionally, we develop a robust beamforming design under imperfect channel state information in the communication and interference channels, ensuring reliable performance across all possible channel realizations within the uncertainty bounds. Simulation results confirm the effectiveness of the proposed methods, demonstrating power-efficient joint communication and sensing capabilities within the CR scenario.

Orthogonal time frequency space (OTFS) has attracted a lot of attention as a feasible waveform applied in joint communication and radar (JCR) systems in contrast to orthogonal frequency division multiplexing (OFDM) waveform. To explore the advantages of OTFS waveform, first, a unified matrix (UM) expression is summarized by utilizing discrete fractional Fourier transform (DFrFT), and then a novel OTFS waveform based on UM expression is investigated in this article. The fractional order parameters of the proposed UM-OTFS waveform is set to the same values during preprocessing and Heisenberg transformation stages, and the UM-OTFS waveform can be converted into other waveform forms by undergoing different fractional order parameters. In addition, a three-stage sensing parameter estimation algorithm is developed for target velocity and range estimation through grid partitioning, coarse and fine estimation. Meanwhile, a low-complexity fractional zero force (ZF) or minimum mean square error (MMSE) equalizer based on lower-upper (LU) decomposition (LU-ZF/MMSE) is presented, which results in a log-linear order of complexity without any performance degradation of bite error ratio (BER) by analyzing sparsity and quasi-banded structure of the equivalent matrix. The simulation results indicate the superiority of the proposed UM-OTFS waveform in terms of sensing parameter estimation and BER performance compared with several advanced waveforms.

Dual-function radar and communication (DFRC) has recently drawn significant attention due to its enormous potential. This letter deals with waveform design of DFRC to improve target detectability embedded in clutter environment while guaranteeing the service quality of communication users. Our design objective is to maximize the output signal-to-clutter-plus-noise ratio (SCNR) of multiple-input multiple-output (MIMO) radar, subject to worst-case received symbol errors at communication users. Coordinate descent (CD) as an efficient iteration algorithm is proposed to solve above optimization problem, which splits high-dimensional problem into multiple one-dimensional problem. Furthermore, we introduce Dinkelbach algorithm (DA) to increase rate of convergence, which is an efficient way to reduce complexity. Finally, simulation results are presented to illustrate the effectiveness of the proposed techniques.