Integrated sensing and communication (ISAC) with orthogonal frequency division multiplexing (OFDM) waveform has been expected to be a key technique in the future 6th generation ( 6 G ) communication networks. In OFDM ISAC systems, joint range-angle estimation (JRAE) of targets is a essential requirement. Current works focus on far-field target sensing, while this paper studies JRAE for both near-field and far-field targets. First, the signal model is established and the Cramér-Rao bounds on JRAE and localization are derived. Then, an auto-paired high-accuracy JRAE method that applies to both near-field and far-field targets is proposed. Specifically, the proposed method consists of two stages. First, it performs frequency smoothing on the observation matrix to obtain multiple observation submatrices. Then, range estimation is performed by using the translational invariance of the submatrices, while angle estimation is achieved by utilizing the orthogonality between the noise subspace and the steering vectors. Under the 5G New Radio standard parameter setup, simulation results demonstrate that the proposed method achieves superior estimation performance, with its root mean square error approaching the root of CRB compared to the conventional methods. Specifically, at a signal-to-noise ratio (SNR) of 0 dB, the proposed method reduces the root mean square error in terms of range, angle and location estimation by 65.8%, 62.8% and 65.9%, respectively, compared to the benchmark schemes.

Orthogonal frequency division multiplexing (OFDM)-based integrated sensing and communication (ISAC) is promising for the future sixth-generation mobile communication systems. The joint sensing of target range and velocity is crucial in OFDM-based ISAC systems. When the targets are highly correlated with similar range and velocity, it is challenging for the conventional two-dimensional subspace-based sensing methods to achieve accurate joint range-velocity estimation (JRVE), particularly in the low signal-to-noise ratio (SNR) region. As such, this paper proposes a high-resolution JRVE method. Specifically, the proposed method first applies equal interval sampling smoothing to the observation signal, introducing ambiguity in range-velocity-induced phase pairs. It then utilizes the translation invariance of the signal subspace to extract these ambiguous phase pairs. Finally, it leverages the orthogonality between the constructed steering vector pair and the noise subspace to resolve the ambiguity and achieve accurate estimation. Under a 5G New Radio parameter setup, simulation results demonstrate that the proposed method significantly outperforms conventional methods in terms of both resolution and accuracy. At an SNR of -10 dB, the proposed method reduces the root mean square error of range and velocity estimation by 83.7 % and 87.3 %, respectively, compared to the benchmark scheme, while its computational cost is less than 50 % of that of the benchmark.

For 5G NR-based integrated sensing and communication (ISAC) systems, using the reference signal for sensing has attracted fast-growing attention, as it does not degrade communication performance. Existing works focus on utilizing pilot or positioning reference signal for sensing. In contrast, this paper concentrates on sensing exploiting demodulation reference signal (DM-RS) in physical downlink shared channel (PDSCH). First, we establish a framework in 5G NR-based ISAC systems using DM-RS in PDSCH for wireless sensing. However, a ghost target phenomenon will occur, due to the inherent periodic time-frequency structure and sparsity of DM-RS. Then, to address this challenge, a linear interpolation-based method is proposed, which applied linear interpolation on the target information matrix. Finally, simulation results demonstrate that leveraging DM-RS for sensing is a effective scheme, and the proposed interpolation method can mitigate ghost target phenomenon efficiently.

Integrated sensing and communication (ISAC) utilizing orthogonal frequency division multiplexing (OFDM) wave-forms is emerging as a critical technology for forthcoming sixth-generation mobile communication networks. The joint sensing of target range, velocity and azimuth is essential for OFDM-based ISAC systems. When the targets are highly correlated with similar range, velocity and azimuth, it is challenging for the conventional three dimensional subspace-based sensing methods to achieve accurate joint range-velocity-azimuth estimation (JRVAE), particularly in the low signal-to-noise ratio (SNR) region. Thus, this paper focuses on high-resolution JRVAE for highly correlated targets. First, a signal model is established, and the Cramér–Rao bounds for JRVAE are derived, considering communication symbols belonging to an arbitrary-order quadrature amplitude modulation constellation. Then, a high-resolution JRVAE method is proposed. Specifically, it first performs equal interval sampling smoothing on the observation signal, resulting in ambiguity in range-velocity-azimuth-induced phases, then uses the translation invariance of the signal subspace to extract the ambiguous phases, finally utilizes the orthogonality between the constructed steering vector pair and the corresponding noise subspaces to resolve ambiguity and obtaining accurate estimation. With 5G New Radio parameters setup, simulation results shows that the proposed method achieves higher resolution and accuracy compared to the conventional methods. At an SNR of -10 dB, the proposed method reduces the root mean square error in range, velocity, and azimuth by 76.0%, 82.7%, and 73.2%, respectively, compared to the benchmark scheme, while its computation cost is less than 1/3 of that of the benchmark.

Integrated sensing and communication (ISAC) is a main application scenario of the sixth-generation mobile communication systems. Due to the fast-growing number of antennas and subcarriers in cellular systems, the computational complexity of joint azimuth-range-velocity estimation (JARVE) in ISAC systems is extremely high. This paper studies the JARVE problem for a monostatic ISAC system with orthogonal frequency division multiplexing (OFDM) waveform, in which a base station receives the echoes of its transmitted cellular OFDM signals to sense multiple targets. The Cramér-Rao bounds are first derived for JARVE. A low-complexity algorithm is further designed for super-resolution JARVE, which utilizes the proposed iterative subspace update scheme and Levenberg-Marquardt optimization method to replace the exhaustive search of spatial spectrum in multiple-signal-classification (MUSIC) algorithm. Finally, with the practical parameters of 5G New Radio, simulation results verify that the proposed algorithm can reduce the computational complexity by three orders of magnitude and two orders of magnitude compared to the existing three-dimensional MUSIC algorithm and estimation-of-signal-parameters-using-rotational-invariance-techniques (ESPRIT) algorithm, respectively, and also improve the estimation performance.

Orthogonal frequency division multiplexing (OFDM)-based integrated sensing and communication (ISAC) is promising for future sixth-generation mobile communication systems. For OFDM-based ISAC systems, it is important to accurately sense the target’s parameters. This paper studies the three-dimensional joint estimation (3DJE) of range, velocity, and azimuth for OFDM-based ISAC systems with multiple receive antennas. First, we establish the signal model and derive the Cramér–Rao bounds (CRBs) on the 3DJE. CRBs are widely used benchmarks that provide the theoretical lower bounds of the variances for unbiased estimation. Furthermore, an auto-paired super-resolution 3DJE algorithm is proposed by exploiting the reconstructed observation sub-signal’s translational invariance property in the delay, Doppler, and angle domains. Finally, with the 5G New Radio parameter setup, simulation results show that the proposed algorithm achieves better estimation performance and its root mean square error is closer to the square root of CRBs than existing methods.

Wireless power transfer (WPT) and mobile edge computing (MEC) are two prospective technologies to enhance the computing power and endurance of mobile devices. Integrating unmanned aerial vehicle (UAV) into wireless powered MEC system, the energy collection efficiency can be effectively improved with the short‐distance line‐of‐sight path power transfer. However, WPT is susceptible to the ‘double near‐far’ effect. Therefore, in this study, the authors study power optimisation in UAV‐assisted wireless powered cooperative MEC system, which utilises the user cooperation to make the mobile device which is closer to the UAV acting as a relay for offloading. They aim to minimise the total transmission energy of the UAV through the joint power optimisation while satisfying the delay and size of the computational task. Simulation results demonstrate the performance of the proposed scheme.