Integrated sensing and communications (ISAC) is emerging as a transformative 6G technology. By employing bistatic passive sensing between base stations (BSs), ISAC shows significant potential for intelligent transport systems (ITS), enabling real-time measurements of multiple vehicles across large areas without extensive hardware modifications. However, bistatic sensing faces significant challenges due to clock asynchronism. Existing phase offset elimination schemes are often limited by insufficient resolution or the introduction of undesired interference. To address these challenges, we propose a novel joint Doppler-delay (DD) estimation scheme that accounts for clock asynchronism, comprising four key components: (1) snapshot augmentation for covariance matrix estimation; (2) a spectrumweighted integrated periodogram-multiple signal classification (SWIP-MUSIC) algorithm for composite timing offset (CTO) estimation; (3) residual phase offset estimation and alignment; and (4) a periodogram-based joint DD estimation algorithm. Simulation results validate the robustness of the proposed method in multi-target scenarios, highlighting its potential to advance traffic monitoring and other sensing applications in the 6G era.

In the paper, we consider the general semantic transmission in wireless networks based on probability distribution. Firstly, we extract a multidimensional semantic probability distribution function, independent of any a specific wireless channel model, by using the variational inference technique. Secondly, we propose a new semantic similarity metric for measuring the difference between the received semantics and the expected semantics based on Kullback-Leibler divergence. Then, we formulate the semantic transmission problem as an optimization problem of transmission symbol adjustment with the aim to maximize the semantic similarity. Finally, we develop an optimal semantic transformation and transmission (STT) algorithm to obtain the optimal transmission symbol adjustment decision. This decision makes the closed-form expression of semantic transmission symbol available, which can realize lossless semantic transmission with energy constraint. Simulation results verify the effectiveness and robustness of the proposed STT algorithm.

Perceptive mobile networks (PMNs), integrating ubiquitous sensing capabilities into mobile networks, represent an important application of integrated sensing and communication (ISAC) in 6G. In this paper, we propose a practical framework for uplink sensing of angle-of-arrival (AoA), Doppler, and delay in millimeter-wave (mmWave) communication systems, which addresses challenges posed by clock asynchrony and hybrid arrays, while being compatible with existing communication protocols. We first introduce a beam scanning method and a corresponding AoA estimation algorithm, which utilizes frequency smoothing to effectively estimate AoAs for both static and dynamic paths. We then propose several methods for constructing a “clean” reference signal, which is subsequently used to cancel the effect caused by the clock asynchrony. We further develop a signal ratio-based joint AoA-Doppler-delay estimator and propose an AoA-based 2D-FFT-MUSIC (AB2FM) algorithm that applies 2D-FFT operations on the signal subspace, which accelerates the computation process with low complexity. Our proposed framework can estimate parameters in pairs, removing the complicated parameter association process. Simulation results validate the effectiveness of our proposed framework and demonstrate its robustness in both low and high signal-to-noise ratio (SNR) conditions.

Joint communications and sensing (JCAS) have the potential to improve the overall energy, cost and frequency efficiency of Internet-of-Things (IoT) systems. As a first effort, we propose to optimize the MIMO-OFDM data symbols carried by sub-carriers for better time-and spatial-domain signal orthogonality. This can reduce inter-target and inter-antenna interference, enabling high-quality sensing. We establish an optimization problem that modifies data symbols on sub-carriers to enhance the above-mentioned signal orthogonality. We also develop an efficient algorithm to solve the problem based on the majorization-minimization framework. Moreover, we discover unique signal structures and features from the newly modeled problem, which substantially reduce the complexity of majorizing the objective function. We also develop new projectors to enforce the feasibility of the obtained solution. Simulations show that to achieve the same sensing performance, the optimized waveform can reduce the signal-to-noise ratio (SNR) requirement by 3~4.5 dB compared with the original waveform, while the SNR loss for the uncoded bit error rate is only 1~1.5 dB.

Full-duplex (FD) integrated sensing and communication (ISAC) has great potential in future vehicular networks. However, the FD requirement and the ISAC functions make the receiver processing extremely complicated, particularly when multiple transmissions are uncoordinated. In this paper, we study frequency-hopping (FH) based receivers in an FD ISAC system, where the arrivals of backscattered signals from one node may overlap with those of signals from another node. To mitigate the interferences caused by the overlapping signals, we consider two receiver options based on either conventional communications or frequency-modulated continuous-wave radars, and two signal modulations based on either fast FH or un-slotted ALOHA FH. Based on the different signal modulations, we develop two parameter estimation schemes via using FH-decoding and de-chirp operations, respectively. To further improve the sensing accuracy, we proceed to propose an iterative algorithm, which refines the estimates of all parameters via using short-time-Fourier transform and maximizing the received power in desired frequency bands. After obtaining all channel parameters in sensing, bilateral communications between two nodes are realized by differential phase-shift keying. Finally, simulation results are provided and verify that the proposed FD ISAC can obtain parameters in high resolution and realize robust communication links.

Joint communication and radar sensing (JCAS) integrates the two functions into one system, sharing one transmitted signal. In this paper, we investigate JCAS waveform optimization in communication-centric systems, where a base station (BS) detects radar targets and communicates with mobile users simultaneously. Different from existing works, we study multi-metric optimizations for a practical low-cost system and establish their connections. To relax the requirement of full-duplex technology, we add a single receive antenna for sensing at the BS, which is synchronized with and spatially separated from the JCAS transmit array. We first optimize precoders for communications and radar, individually. Then, we formulate a JCAS waveform optimization problem that constrains either mutual information (MI) or Cramér-Rao bound (CRB) of radar and maximizes the relaxed signal-to-interference-plus-noise rate (SINR) of communications. Exploiting the geometric characteristic of the relaxed SINR, we provide a closed-form solution under certain conditions and propose a numerical iteration algorithm that works in all situations. We also disclose the connections between optimizations with constraining MI and CRB, using numerical results. Finally, simulation results are provided and validate the proposed optimization solutions.

Angle of Arrival (AoA) estimation with localized hybrid arrays is challenging in millimeter-wave (mmWave) communication systems. Most existing solutions quantize AoAs into limited values with relatively low accuracy. This paper presents a multi-AoA estimation scheme which is capable of estimating multiple AoAs from multiple users with low complexity. Specifically, we design a path filter via combining the received signals for each subarray. Each path filter enables a certain range of AoAs to pass through while suppressing the rest. Then we can use low-complexity cross-correlation operations to obtain continuous AoA estimates. Association of paths to users is further achieved by a follow-up pseudo-random codes based correlation operation. The scheme is first presented for a narrowband system and then extended to wideband with frequency selectivity. We also introduce new metrics and derive the lower bound of mean square error for evaluating the accuracy of AoA estimates, as conventional metrics face difficulties in the presence of multiple closely located AoAs. Extensive simulation results are provided and validate the effectiveness of the proposed multi-AoA estimation scheme.