Integrated sensing and communication (ISAC) is expected to play a prominent role in 6G. Avoiding full duplex transceivers, bi-static sensing is free from self-interference and able to leverage ubiquitous network devices, thus considered an indispensable scenario of ISAC. However, bi-static sensing must resolve the non-ideal synchronization of the transceiver nodes. Such timing offset (TO) remains a challenging issue, especially in the non-line-of-sight (NLOS) condition with rich multipaths. This article makes the best use of all multipaths, deriving the cyclic shift relation between the delay spectrums measured at the two sides of transceivers to estimate the TO. Based on this theoretical analysis, an algorithm called enhanced round-trip measurement (eRTM) is developed to mitigate the TO. Specifically, the cyclic cross-correlation between the amplitudes of the two normalized delay spectrums measured at the two sides of the transceivers respectively is conducted to estimate the TO, which is then used for TO mitigation. Extensive simulations have verified the advantages of the eRTM algorithm, especially in NLOS conditions and with low signal-to-noise ratios (SNRs). In addition, field tests conducted with a bi-static ISAC prototype system and eRTM algorithm, have achievedcentimeter-level positioning accuracy even in NLOS conditions with rich multipaths, confirming the validities of our theoretical analysis as well as the proposed algorithm.

Foreseen as a killer application in next-generation wireless networks, integrated sensing and communication (ISAC) has gained tremendous developments in recent years, and will contribute to realize Internet of Everything (IoE). Particularly, by enabling separable sensing transceivers, bi-static sensing is free from self-interference and able to leverage ubiquitous network devices, and thus has become an indispensable scenario of ISAC. However, bi-static sensing suffers from transceiver asynchronization, which induces timing offset (TO), timing drift (TD) and carrier frequency offset (CFO). In this paper, we first give the theoretical analyses on how TO, TD and CFO impact the sensing signal, under the practical configuration following the new radio (NR) protocol. Based on this, we systematically reveal the mechanism of TD and the correspondingly resulted delay-Doppler spectrum dispersion. Specifically, the delay spectrum shifts and the phase drifts induced by TD are analyzed, which are the two main factors eventually leading to the delay-Doppler spectrum dispersion and consequent severe errors in signal detection and parameter estimation. Based on the revealed mechanisms, we develop an asynchronous delay-Doppler (ADD) algorithm for bi-static sensing, including delay spectrum alignment and phase compensation, respectively to suppress the delay spectrum shifts and phase drifts. Thanks to the revealed mechanism, the ADD algorithm does not rely on specific prerequisites. Simulation results have confirmed the revealed mechanisms and verified the effectiveness of the ADD algorithm. Particularly, field tests are conducted on an ISAC prototype, and achieve a centimeter-level positioning accuracy, which further confirms the revealed mechanisms and validates the ADD algorithm.

Recently, integrated sensing and communication (ISAC) has attracted substantial research efforts and been deemed as a major scenario of 6G. To support the study and standardization of ISAC, sensing channel modeling is rather critical. In this paper, we demonstrate the latest sensing channel measurement results, in which the sensing pathloss is particularly focused. In principle, we adopt a cascaded sensing pathloss model, inherited from pathloss model in 3GPP TR 38.901, by considering the radar cross section (RCS) of the sensing target. Further, we conduct the sensing channel measurements with an ISAC prototype in the indoor hotspots (InH) and urban micro (UMi) scenarios, in which the sensing pathloss is focused. The sensing signal in the measurements is configured based on the new radio (NR) protocols, to accord with practical application. The specific path corresponding to the sensing target is identified to obtain the sensing pathloss, through the signal processing method in the delay-doppler domain. From the results, the cascaded sensing pathloss model is shown to fit well with the measurements at acceptable deviations. This work preliminarily verifies the feasibility of sensing channel modeling based on the 3GPP TR 38.901 channel model in field test on prototype, for the first time to our best knowledge.

It is anticipated that wireless vehicular ad hoc networks (VANETs) can further enhance the safety of autonomous vehicles. Recently, two major standards for the next generation of VANET technologies have been suggested and tested: IEEE 802.11p/bd and 3GPP NR-V2X. Compared with human driving VANET, autonomous driving VANET poses big challenges for more stringent requirements on reliability and delay of message transmissions. Up to date, the quality of service (QoS) requirements of VANETs for the safety services of autonomous driving vehicles have not been investigated systematically. Whether or not the current proposed communication systems can meet such high QoS requirements is unclear. This paper reviews the communication requirements for safety use cases, and the QoS requirements for level 5 autonomous driving VANETs are deduced. Then, the QoS of IEEE 802.11-based VANETs, including 802.11p, 802.11bd, and other 802.11 cutting-edge versions, are evaluated by simulations and analyses on a few selected safety use cases and benchmarked against the corresponding QoS requirements. The numerical results reveal that the current IEEE 802.11p/bd cannot meet the QoS requirements for some selected safety applications in autonomous driving vehicles. However, IEEE 802.11 systems with new enhancements can potentially support safety services with assured QoS.

This paper presents the prototype validation of integrated coordinated multi-point (CoMP) sensing and commu-nication. Specifically, multiple user equipments (UEs) at different locations work collaboratively to achieve human trajectory tracking, while simultaneously conducting communications with the base station (BS). This scenario is well-suited for the cellular-based integrated sensing and communication (ISAC) since there are large numbers of available UEs distributed in the network. The trajectory estimation can be realized in any one of the sensing nodes, by fusing the measurement results from all the nodes. To deal with the clock asynchrony between BS and UEs, the channel state information (CSI) ratio is used. Moreover, to combat with the environmental non-ideal factors (i.e., target's radar cross section (RCS) fluctuation and the channel fadings), a sensing schedule strategy which is comprised of reliable estimates decision and dynamic switchover of sensing nodes, is proposed. The prototype experiments confirm the feasibility of CoMP sensing, and demonstrate that our design can achieve accurate human trajectory tracking, while conducting reliable communications.

IEEE 802.11 communication systems have been extensively investigated for improving vehicular road safety. However, dynamic vehicular environments and various safety applications with different quality of service (QoS) requirements cannot be accommodated by a fixed set of communication parameters and network configuration. This paper proposes and exploits a real-time constrained optimization platform that leverages the combination of a fast stochastic model with a carefully configured regression deep learning neural network (DLNN) to achieve an optimal balance between the QoS and the channel spectrum efficiency. The stochastic model is utilized to predict the QoS of IEEE 802.11 broadcast vehicular ad hoc networks given a selected group of communication parameters and analytical equations or measured data about the communication channels. The data provided by the stochastic model is sorted based on the QoS requirements for a given safety service and is preprocessed to keep enough distance between the training data patterns. The DLNN is trained by a randomly sampled data set to accomplish the inverse mapping (from the QoS to the corresponding parameter sets) to facilitate real-time optimization. In the process of optimization, by working in tandem, the DLNN and stochastic model synergistically identify the optimal parameter set that maximizes channel efficiency while adhering to QoS constraints in a fast and precise manner. The computation complexity of the optimization is analyzed and estimated. The effectiveness and robustness of the proposed optimization system have been demonstrated through experiments conducted on Google Colab using TensorFlow and Python, showcasing its superiority over the alternative optimization algorithms.

IEEE 802.11p/bd driven Vehicular Ad Hoc Networks (VANETs) have been investigated for safety-critical applications with high reliability and low transmission latency. However, due to dynamic vehicular environment and various safety applications requiring different quality of service (QoS), a fixed configuration of the communication network parameters performs poorly in terms of balance between QoS and channel spectrum efficiency. This paper proposes a new real-time optimization scheme based on a designated deep learning neural network (DLNN) working with a stochastic model. In the scheme, the stochastic model is adopted to predict the QoS of VANET given a set of communication parameters. The DLNN is trained to approach the inverse maps from the parameter sets to the corresponding QoS by a sampled data set from running the stochastic model. In the process of optimization, for a given safety service, the DLNN and the stochastic model complement each other to find an optimal solution of the parameters that maximize the channel efficiency under the constraints of QoS requirements in a fast and precise way. The experiments on Google Colab with TensorFlow and Python demonstrate the effectiveness of the scheme.

The safety-critical applications of Vehicular Ad Hoc Networks (VANETs) call for wireless communication systems with high reliability and low transmission latency. Recently, a very promising communication system standardized as IEEE 802.11 bd was proposed and studied to address the reliability deficiency of IEEE 802.11 P based Dedicated Short Range Communications (DSRC) system in the adverse vehicular situations. Seamless evolution of radio access technology from IEEE 802.11p to IEEE 802.11bd have undergone performance improvement evaluations in the physical layer. However, the impact of the enhancements on the network layer and safety services in V ANETs remain unknown. In this paper, we conduct analysis of IEEE 802.11bd beyond the physical layer. First, the performance gains of IEEE 802.11bd comparing to IEEE 802.11p in the physical layer are summarized. Then, packet loss rate (PLR) curves as a function of signal-to-noise ratio (SNR) in the physical layer are quantified and connected to the quality of service (QoS) metrics in the MAC layer, while considering the effect of channel access and interference among network nodes. Furthermore, application-level analysis is conducted to identify if the proposed IEEE 802.11bd can meet the QoS requirements of the selected safety applications where IEEE 802.11p previously failed. Finally, constructive conclusions are presented on IEEE 802.11bd's suitability for V ANET critical safety services and future developments.