Recent years have witnessed growing interest in leveraging the bidirectional wave control of intelligent omni-surfaces (IOS) for integrated sensing and communication (ISAC) systems. Nevertheless, acquiring precise channel state information (CSI) is particularly challenging due to the inherent interplay between the electromagnetic properties of IOS and the dual functions of ISAC. In this paper, we propose a robust beamforming design for IOS-enabled ISAC systems. We jointly optimize the transmit beamforming, sensing waveform and IOS phase shifts to minimize the Cram´er-Rao bound (CRB) for sensing while ensuring communication reliability under an outage probability constraint. The resulting mixed-integer non-convex problem is tackled via a dual-loop penalty dual decomposition (PDD) algorithm. This framework solves the augmented Lagrangian (AL) subproblem in the inner loop, while the outer loop adjusts dual variables and penalty parameters to enforce constraint satisfaction. Simulation results demonstrate that our design substantially enhances sensing accuracy and communication reliability in scenarios with large CSI errors or fluctuating service requirements. Furthermore, it is shown that an optimal ratio between sensing and passive IOS elements must be maintained to balance energy utilization and spatial sampling capability in ISAC systems.

In this paper, we propose a joint non-line-of-sight (NLoS) predictive beamforming and power allocation (JNPB-PA) scheme to enhance the power efficiency of the road side units in the integrated sensing and communication (ISAC)-assisted vehicular networks. This scheme decouple the spatial and amplitude components in power allocation by exploiting angular domain discretization of a novel modulation technique–spatially-spread orthogonal time frequency space (SS-OTFS). Specifically, we first develop an auxiliary target method to achieve predictive beamforming in NLoS scenarios, which initially determines the power allocation vector’s non-zero positions corresponding to discrete angles of the vehicles. Then, we further refine the power allocation by solving a multi-objective optimization problem (MOP) aimed at minimizing both the age of information (AoI) for communication and the Cramér-Rao bound (CRB) for sensing. A low-complexity algorithm based on the proposed reconstruction-contraction-constraint (RCC) approach is developed to solve the formulated MOP based on its inherent features. Simulation shows that our proposed JNPB-PA scheme can achieve higher power utilization rate, lower AoI, and lower CRB in comparison with benchmark schemes. Besides, RCC solves the formulated MOP more efficiently by avoiding iterative searching of traditional methods.

In this paper, we investigate the sensing and communication (S&C) trade-off in the integrated sensing and communication (ISAC)-assisted vehicular network with spatially spread orthogonal time frequency space (SS-OTFS) modulation technique, where power allocation is the trigger leading to S&C performance shift. We tailor S&C metrics specifically for the vehicular network where information freshness and sensing accuracy are critical due to safety concerns, indicated by age of information (AoI) and Cramér-Rao bound (CRB), respectively. Then we propose an AoI-and-CRB-based power allocation (ACPA) scheme and develop a reconstruction-contraction-constraint (RCC) approach to derive the non-dominated solutions, which delineate the S&C trade-off. Simulation shows that our proposed ACPA scheme can identify the S&C performance frontier of the system, and the RCC approach is more efficient than the traditional non-dominated sorting genetic algorithm II (NSGA-II). In addition, the intrinsic mechanism of how power allocation affects S&C performances in the SS-OTFS-enabled ISAC system is analyzed.

In post-disaster scenarios, it is challenging for affected users to transmit data as quickly as possible before their residual energy (RE) is exhausted. Besides, the problem of limited users’ RE causes severe transmission delay unfairness within the network. In this paper, we propose a novel two-phase scheme, called energy-aware unmanned aerial vehicle (UAV) relay transmission (EURT), to balance transmission delay of users and network fairness. Specifically, in the first phase, we pair users two-by-two based on their RE and minimize the maximum transmission delay among all pairs by jointly optimizing the bandwidth allocation, transmit powers, and UAV altitude. In the second phase, we design a cognitive radio (CR) inspired rate-splitting multiple access (RSMA) scheduling strategy to obtain the optimal power splitting factor for each pair. This strategy considers the user with a lower RE value in each pair as the primary user (PU) and the other one as the secondary user (SU), then minimizes the transmission delay of the SU while ensuring the quality of service (QoS) of the PU. Furthermore, we propose a novel evaluation framework to explore the degree of impact of RE and channel state information (CSI) on network delay fairness. Simulation results demonstrate that: i) the proposed EURT algorithm effectively improves performance metrics of networks in terms of transmission delays, throughput, energy consumption and energy efficiency; ii) The proposed algorithm achieves a trade-off between the minimum delay and the optimal network fairness by adjusting the QoS threshold of the PU.

In this paper, we propose a novel age-of-information (AoI)-aware waveform design scheme for the cooperative joint radar-communications (JRC) system, called AoI-aware Online Prediction (A-OnP) scheme. To be specific, we optimize the power allocation of the orthogonal frequency division multiplexing (OFDM) signal. We aim to maximize the radar mutual information (RMI) with considering the communication data rate (CDR) and AoI performance. Specifically, we design a cognitive operating framework for the JRC system, with a particular emphasis on the closed-loop signal processing for online prediction of the radar target scattering coefficient (TSC). Then, considering the obtained TSC prediction result and corresponding communication performance requirement, we optimize the power allocation of the transmit waveform and the signal-to-interference-plus-noise ratio (SINR) threshold of the communication users. Accordingly, we propose a constraints-splitting coordinate descent (CS-CD) method to solve the formulated non-convex problem by strategically splitting the sum-constraints and assign a quota to each channel, where the allocation criteria is automatically decided during iteration. Simulation results demonstrate that, the cooperative radar-centric communication-constrained (RC-CC) waveform outperforms the separately optimized radar-optimal plus communication-optimal (RO-CO) waveform. Additionally, the A-OnP scheme can increase RMI while meeting the communication CDR and AoI requirements.

In this paper, we propose a novel orthogonal frequency division multiplexing (OFDM) waveform power allocation approach in the spectrum-sharing dual-functional radar-communication (DFRC) systems, with a particular emphasis on improving radar recognition performance while considering the specific communication requirements of individual users. Specifically, the radar mutual information (RMI) is maximized in terms of allocating the radar power to the OFDM subcarrier within the limits of the power constraints and meeting the age of information (AoI) expectation requirements. By considering the impact of radar interference, we measure the AoI performance of individual users using the transmission outage probability. Then, an iterative constraints-splitting (ICS) method is developed to find the optimal radar power allocation results from the formulated non-convex problem by transforming it into an equivalent convex problem using an iteratively determined factor. Simulation results demonstrate that the proposed individual user AoI preference-based approach can improve RMI performance while meeting the AoI communication requirements of individual users. Additionally, higher RMI and more stable AoI performance can be achieved while maintaining total communication performance by allocating more sub-carriers to fewer users.