Integrated sensing and communications (ISAC) has become increasingly crucial in next-generation wireless networks. Leveraging the reliable line-of-sight (LoS) links and mobility of unmanned aerial vehicles (UAVs), UAV-assisted ISAC has attracted significant attention. Different from the previous UAV-ISAC scenarios with single target or overlapping users and targets, we investigate ISAC in a more general multi-UAV network with independent multiple communication users and multiple sensing targets, where the UAVs provide downlink communications to the users while sensing the targets. Additionally, we consider the complicated interference management among the UAVs to further enhance the network's practicality. Such a scenario presents a new challenge for the joint optimization problem in terms of the UAV trajectories, the user association, the target association, and the power control. Furthermore, since the existing single-objective and weighted optimization approaches may result in potential performance loss and optimization biases, we propose a dual-objective model to further optimize ISAC, aiming for a better tradeoff between the communication and sensing performance. Specifically, we propose an efficient sensing and communication dual-objective multi-UAV optimization algorithm (SC-DO-MUOA) to maximize communication rate and simultaneously minimize sensing Cramer-Rao bound (CRB). Simulation results demonstrate that our proposed SC-DO-MUOA outperforms various baselines in both communication and sensing performance.

Integrated sensing and communication (ISAC) is expected to play a key role in future sixth-generation (6G) networks, where random data-bearing signals are reused to support both communications and sensing, thereby improving time-frequency utilization. In practice, the high peak-to-average power ratio (PAPR) of conventional multicarrier signals incurs severe power-amplifier (PA) back-off and limits both communication throughput and sensing range. Constant-envelope orthogonal frequency division multiplexing (CE-OFDM) is a promising remedy due to its 0-dB PAPR. However, its random nonlinear phase mapping produces high autocorrelation sidelobes, significantly degrading ranging performance. In this paper, we develop an expectation-oriented autocorrelation-function (ACF) design framework for pulse-shaped random CE-OFDM signals. We derive a tractable analytical approximation for the average squared ACF, which decomposes into a pulse-induced pedestal determined by the shaping filter and a phase-dependent leakage term controlled by time-domain phase rotation. Based on this characterization, we formulate an expected integrated sidelobe level (EISL) minimization problem and propose a joint design of phase rotation and Nyquist pulse shaping. We further show that coherent integration across independent transmission slots suppresses residual leakage proportionally to the integration length. Numerical results demonstrate that the proposed design achieves pronounced sidelobe suppression over the prescribed delay interval while preserving the ultra-low PAPR, favorable BER performance, and spectral efficiency of CE-OFDM.

In this paper, a novel large language model (LLM)-based pathloss map generation model (LLM4PG) is proposed for sixth-generation (6G) artificial intelligence (AI)-native communication systems via Synesthesia of Machines (SoM). To explore the mapping mechanism between sensing images and pathloss maps, a new synthetic intelligent multi-modal sensing-communication dataset for SoM in uncrewed aerial vehicle (UAV)-to-ground (U2G) scenarios, named SynthSoM-U2G, is constructed, including multiple U2G scenarios with multiple frequency bands and multiple flight altitudes. By adapting the LLM to the cross-modal pathloss map generation for the first time, a novel framework that enables effective cross-domain alignment between the multi-modal sensing-communication domain and the natural language domain is introduced. Furthermore, a task-specific adaptation of the LLM is achieved through fine-tuning, with a properly designed layer selection and activation scheme tailored to the unique demands of massive-scale and high-quality pathloss map generation. Compared with the conventional artificial intelligence generated content (AIGC) models, the proposed LLM4PG enables accurate pathloss map generation and demonstrates strong generalization across various scenarios, frequency bands, and flight altitudes under three-dimensional (3D) high-mobility U2G scenarios. The accuracy and generality of the proposed LLM4PG are validated by comparing simulation results and ray-tracing (RT)-based results. Simulation results demonstrate that the proposed LLM4PG can achieve accurate pathloss map generation with a normalized mean squared error (NMSE) of 0.0428, outperforming the conventional AIGC model by more than 3.16 dB. The generality of the proposed LLM4PG across different conditions achieves an NMSE of 0.0492, outperforming the conventional AIGC model by more than 4.52 dB.

Motion state sensing is crucial in wireless communication systems employing Integrated Sensing and Communication (ISAC), with accurate target velocity estimation being central to its effectiveness. While waveform design for ISAC signals can enhance the sensing capability, most existing studies focus on single-target scenarios, with limited discussion on multi-target scenarios. This paper proposes a novel waveform design approach aiming for enhancing the multi-target sensing performance, under the prior knowledge that the Doppler difference between two hardly resolvable targets lies in a certain interval. In particular, we use the Ziv-Zakai Bound (ZZB) of Doppler difference to design the waveform for multi-target sensing, which effectively captures the ambiguity phenomenon. We first derive the expression of the Doppler difference ZZB, which reveals the connection between ZZB and the widely-used ambiguity function. Based on this expression, we propose an SNR-adaptive pulse modulation strategy that significantly improves the velocity estimation accuracy for multi-target scenarios. Numerical results demonstrate that the Doppler difference ZZB effectively reflects the multi-target Doppler frequency estimation performance of maximum a posteriori estimators.

A wireless channel foundation model for pathloss map generation (WiCo-PG) via Synesthesia of Machines (SoM) is developed for the first time. Considering sixth-generation (6G) uncrewed aerial vehicle (UAV)-to-ground (U2G) scenarios, a new multi-modal sensing-communication dataset is constructed for WiCo-PG pre-training, including multiple U2G scenarios, diverse flight altitudes, and diverse frequency bands. Based on the constructed dataset, the proposed WiCo-PG enables cross-modal pathloss map generation by leveraging RGB images from different scenarios and flight altitudes. In WiCo-PG, a novel network architecture designed for cross-modal pathloss map generation based on dual vector quantized generative adversarial networks (VQGANs) and Transformer is proposed. Furthermore, a novel frequency-guided shared-routed mixture of experts (S-R MoE) architecture is designed for cross-modal pathloss map generation. Simulation results demonstrate that the proposed WiCo-PG achieves improved pathloss map generation accuracy through pre-training with a normalized mean squared error (NMSE) of 0.012, outperforming the large language model (LLM)-based scheme, i.e., LLM4PG, and the conventional deep learning-based scheme by more than 6.98 dB. The enhanced generality of the proposed WiCo-PG can further outperform the LLM4PG by at least 1.37 dB using 2.7

Accurate velocity sensing is crucial in Integrated Sensing and Communication (ISAC) systems, while most studies focus on single-target cases with limited attention to multi-target scenarios. This paper proposes a novel waveform design approach that enhances multi-target sensing performance by leveraging the Ziv-Zakai Bound (ZZB) of Doppler difference, effectively capturing the ambiguity phenomenon under the prior knowledge that the Doppler difference between two hardly resolvable targets lies in a certain interval. We first derive the ZZB for Doppler difference, which reveals the connection between ZZB and the widely-used ambiguity function. Based on this, an SNR-adaptive pulse modulation strategy is developed to enhance multi-target sensing accuracy. Numerical results confirm that the proposed Doppler difference ZZB effectively captures the estimation performance of maximum a posteriori estimators in multi-target scenarios.

Unmanned aerial vehicle (UAV) communications have emerged as a promising solution for future full coverage networks. To further meet the massive connection demands in beyond-fifth-generation (B5G) systems, in this paper, we propose to employ non-orthogonal multiple access (NOMA) in both uplink and downlink relaying hops in an amplify-and-forward (AF) based UAV relaying network with multiple source-destination (SD) user pairs. Specifically, taking NOMA design in both hops into a joint consideration in relaying networks is investigated for the first time and presents a new challenge for the joint optimization problem in terms of the deployment of the UAV relay, the two-hop NOMA user grouping, and the transmit power control for both the source users and the UAV. To maximize the system sum rate, we propose an efficient joint uplink and downlink NOMA-based relay (JUDNR) scheme to decompose the problem into three sub-problems and adopt the alternating optimization (AO) method to iteratively obtain a promising solution. In detail, we provide a joint NOMA groups design of both the uplink and downlink and propose a novel recursive two-hop NOMA grouping (RTNG) algorithm to efficiently group users. Simulation results demonstrate that our proposed JUDNR scheme outperforms various baselines in terms of sum rate.

Abstract While the terrestrial base stations (TBSs) in the fifth‐generation (5G) network provide high throughput for the conventional terrestrial users (TUs), it is still challenging for the network to support massive TUs and unmanned aerial vehicles (UAVs) simultaneously due to the complicated air–ground channel and severe interference. In this paper, the deployment of a high‐altitude platform (HAP) as a supplement for the terrestrial networks, in which the HAP and TBSs serve TUs and UAVs simultaneously in a joint manner, is studied. The novel network has two challenges. First, the deployment of the HAP, which is a new degree of freedom, should be optimized considering the terrestrial network. Second, the channel of the joint HAP and TBS network that serves multiple TUs and UAVs concurrently is complicated, and the resource allocation of the network should be designed. To tackle the above two challenges, a joint resource allocation and HAP deployment problem are formulated, and a gradient‐and‐matching‐based algorithm is proposed to solve it efficiently. Simulation results show that the HAP and the proposed algorithm enhance the sum‐rate of the network by over 30%, and the average data rate of both the TUs and the UAVs can be effectively improved.

In optical frequency domain reflectometry (OFDR) system, the hardware limitations result in poor performance in spatial resolution and measurement distance, which fails to meet practical expectations. Based on the hardware foundation of an auxiliary interferometer, this paper proposed a novel joint optimization scheme of equal sampling and Gaussian fitting. During the transmission process, a fixed threshold wavelet denoising method is used for sub-band synchronous denoising to reduce the influence of phase noise. Meanwhile at the demodulation end, frequency-domain compensation is achieved by sliding window zero padding and cross-correlation Gaussian fitting. Simulation result showed that the proposed scheme can achieve 0.12mm spatial resolution within a measurement range of 420m. Compared with the existing method, the performance ratio, namely the measurement range divided by the spatial resolution, is improved at least 1.5 times, which can effectively not only improve the spatial resolution, but also expand the measurement range of the system while ensuring the detection speed.

The ant colony algorithm has been widely used in the field of data analysis of smart cities. However, the research of the traditional ant colony algorithm is more focused on one-to-one scenarios and there is insufficient research on many-to-one scenarios. Therefore, for the many-to-one topology mapping problem, this paper proposes a mapping method based on the ant colony algorithm. The design purpose of the mapping algorithm is to study the optimal mapping scheme, which can effectively reduce the cost of solving the problem. The core of the mapping algorithm is to design the objective function of the algorithm optimization. The commonly used optimization objective function and evaluation index is the average hop count; the average hop count is the most important indicator to measure the entire system. The smaller the average hop count, the less the pulse data needs to be forwarded, which can reduce the communication pressure of the system, reduce congestion, reduce the energy consumption caused by communication, and reduce the delay from the generation of pulse data to the response, etc. Therefore, this paper chooses the average hop count as the optimization objective and reduces the average hop count by designing a mapping algorithm. Through the simulation and verification of the improved ant colony algorithm in the scenario of many-to-one topology mapping, it is concluded that the final convergence result and convergence speed of the improved ant colony algorithm are significantly better than those of the traditional ant colony algorithm.

In this letter, we consider a high altitude platform (HAP) system with perturbation caused by inevitable short-term airflow. However, such a HAP perturbation will lead to a decrease in the Quality-of-Service (QoS). To address this issue, we maximize the system sum-rate by jointly optimizing the location of HAP and the beamformer in the presence of HAP perturbation. We formulate a distributionally robust optimization problem under an information transmission chance constraint to tackle the perturbation. To solve this non-convex problem, we decouple it into two subproblems: location optimization subproblem and beamformer design subproblem, and then employ the Conditional Value-at-Risk (CVaR) based method to find the solutions. Simulation results show that the proposed scheme can achieve a better performance than the non-robust scheme.

Within the UAV network, the UAV first receives signals from multiple remote mobile devices (MDs) and then amplifies and forwards the transmitted signals to the base station (BS) with different amplification coefficients to form a UAV relay multiuser network. In this paper, we propose a new method to solve the problem of maximizing the throughput of the relay network. The proposed problem is decoupled into two subproblems, UAV deployment design and amplification coefficient optimization, to be solved iteratively, respectively. We solve the UAV deployment problem by adjusting its trajectory with a gradient descent-based method and solve the amplification coefficient subproblem with a convex optimization-based method iteratively. Simulation shows that the proposed UAV deployment and amplification coefficient of the UAV optimization design algorithm significantly improves the sum-rate compared with existing fixed relay and equal power allocation schemes. Finally, we discuss future potential performance enhancing methods including multiple UAV cooperation, massive multi-input multioutput (MIMO) communications, and nonorthogonal multiple access (NOMA) communications.

In this paper, we aim at the characteristics of the large-scale dynamic beamforming system model, and design an overall scheme for beam-dominated resource allocation and beam scheduling. We first propose a channel capacity fairness carrier allocation algorithm based on the beam as the basic unit to allocate frequency resources between beams, and then perform carrier allocation between users according to the channel demand ratio. Simulation results show that, under the condition of sufficient hardware resources, this scheme can achieve higher user communication satisfaction than the fixed beam resource allocation scheme.