Mutual Information Metrics for Uplink MIMO-OFDM Integrated Sensing and Communication System

As the uplink sensing has the advantage of easy implementation, it attracts great attention in integrated sensing and communication (ISAC) system. This paper presents an uplink ISAC system based on multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) technology. The mutual information (MI) is introduced as a unified metric to evaluate the performance of communication and sensing. In this paper, firstly, the upper and lower bounds of communication and sensing MI are derived in details based on the interaction between communication and sensing. And the ISAC waveform is optimized by maximizing the weighted sum of sensing and communication MI. The Monte Carlo simulation results show that, compared with other waveform optimization schemes, the proposed ISAC scheme has the best overall performance.

Waveform Design for MIMO-OFDM Integrated Sensing and Communication System: An Information Theoretical Approach

Integrated sensing and communication (ISAC) is regarded as the enabling technology in the future 5th-Generation-Advanced (5G-A) and 6th-Generation (6G) mobile communication system. ISAC waveform design is critical in ISAC system. However, the difference of the performance metrics between sensing and communication brings challenges for the ISAC waveform design. This paper applies the unified performance metrics in information theory, namely mutual information (MI), to measure the communication and sensing performance in multicarrier ISAC system. In multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) ISAC system, we first derive the sensing and communication MI with subcarrier correlation and spatial correlation. Then, we propose optimal waveform designs for maximizing the sensing MI, communication MI and the weighted sum of sensing and communication MI, respectively. The optimization results are validated by Monte Carlo simulations. Our work provides effective closed-form expressions for waveform design, enabling the realization of MIMO-OFDM ISAC system with balanced performance in communication and sensing.

ISAC Signal Processing Over Unlicensed Spectrum Bands

As a promising key technology of 6th-Generation (6G) mobile communication systems, integrated sensing and communication (ISAC) technology aims to make full use of spectrum resources to enable the functional integration of communication and sensing. The ISAC-enabled mobile communication systems regularly operate in non-continuous spectrum bands due to crowded licensed frequency bands. However, the conventional sensing algorithms over non-continuous spectrum bands have disadvantages such as reduced peak-to-sidelobe ratio (PSR) and degraded anti-noise performance. Facing this challenge, we propose a high-precision ISAC signal processing algorithm based on compressed sensing (CS) in this paper. By integrating the resource block group (RBG) configuration information in 5th-Generation new radio (5G NR) and channel information matrices, we can dynamically and accurately obtain power estimation spectra. Moreover, we employ the fast iterative shrinkage-thresholding algorithm (FISTA) to address the reconstruction problem and utilize K-fold cross validation (KCV) to obtain optimal parameters. Simulation results show that the proposed algorithm has lower sidelobes or even zero sidelobes and high anti-noise performance compared with conventional sensing algorithms.

Carrier Aggregation Enabled Integrated Sensing and Communication Signal Design and Processing

The future mobile communication systems will support intelligent applications such as Internet of Vehicles (IoV) and Extended Reality (XR). Integrated Sensing and Communication (ISAC) is regarded as one of the key technologies satisfying the high data rate communication and highly accurate sensing for these intelligent applications in future mobile communication systems. With the explosive growth of wireless devices and services, the shortage of spectrum resources leads to the fragmentation of available frequency bands for ISAC systems, which degrades sensing performance. Facing the above challenges, this paper proposes a Carrier Aggregation (CA)-based ISAC signal aggregating high and low-frequency bands to improve the sensing performance, where the CA-based ISAC signal can use four different aggregated pilot structures for sensing. Then, an ISAC signal processing algorithm with Compressed Sensing (CS) is proposed and the Fast Iterative Shrinkage-Thresholding Algorithm (FISTA) is used to solve the reconfiguration convex optimization problem. Finally, the Cram'er-Rao Lower Bounds (CRLBs) are derived for the CA-based ISAC signal. Simulation results show that CA efficiently improves the accuracy of range and velocity estimation.

Symbol-level Integrated Sensing and Communication enabled Multiple Base Stations Cooperative Sensing

With the support of integrated sensing and communication (ISAC) technology, mobile communication system will integrate the function of wireless sensing, thereby facilitating new intelligent applications such as smart city and intelligent transportation. Due to the limited sensing accuracy and sensing range of single base station (BS), multi-BS cooperative sensing can be applied to realize high-accurate, long-range and continuous sensing, exploiting the specific advantages of large-scale networked mobile communication system. This paper proposes a cooperative sensing method suitable to mobile communication systems, which applies symbol-level sensing information fusion to estimate the location and velocity of target. With the demodulation symbols obtained from the echo signals of multiple BSs, the phase features contained in the demodulation symbols are used in the fusion procedure, which realizes cooperative sensing with the synchronization level of mobile communication system. Compared with the signal-level fusion in the area of distributed aperture coherence-synthetic radars, the requirement of synchronization is much lower. When signal-to-noise ratio (SNR) is -5 dB, it is evaluated that symbol-level multi-BS cooperative sensing effectively improves the accuracy of distance and velocity estimation of target. Compared with single-BS sensing, the accuracy of distance and velocity estimation is improved by 40% and 72%, respectively. Compared with data-level multi-BS cooperative sensing based on maximum likelihood (ML) estimation, the accuracy of location and velocity estimation is improved by 12% and 63%, respectively. This work may provide a guideline for the design of multi-BS cooperative sensing system to exploit the widely deployed networked mobile communication system.

SLAM for Multiple Extended Targets using 5G Signal

5th Generation (5G) mobile communication systems operating at around 28 GHz have the potential to be applied to simultaneous localization and mapping (SLAM). Most existing 5G SLAM studies estimate environment as many point targets, instead of extended targets. In this paper, we focus on the performance analysis of 5G SLAM for multiple extended targets. To evaluate the mapping performance of multiple extended targets, a new mapping error metric, named extended targets generalized optimal sub-pattern assignment (ET-GOPSA), is proposed in this paper. Compared with the existing metrics, ET-GOPSA not only considers the accuracy error of target estimation, the cost of missing detection, the cost of false detection, but also the cost of matching the estimated point with the extended target. To evaluate the performance of 5G signal in SLAM, we analyze and simulate the mapping error of 5G signal sensing by ET-GOPSA. Simulation results show that, under the condition of SNR = 10 dB, 5G signal sensing can barely meet to meet the requirements of SLAM for multiple extended targets with the carrier frequency of 28 GHz, the bandwidth of 1.23 GHz, and the antenna size of 32.

ISAC-NET: Model-driven Deep Learning for Integrated Passive Sensing and Communication

Recent advances in wireless communication with the enormous demands of sensing ability have given rise to the integrated sensing and communication (ISAC) technology, among which passive sensing plays an important role. The main challenge of passive sensing is how to achieve high sensing performance in the condition of communication demodulation errors. In this paper, we propose an ISAC network (ISAC-NET) that combines passive sensing with communication signal detection by using model-driven deep learning (DL). Dissimilar to existing passive sensing algorithms that first demodulate the transmitted symbols and then obtain passive sensing results from the demodulated symbols, ISAC-NET obtains passive sensing results and communication demodulated symbols simultaneously. Different from the data-driven DL method, we adopt the block-by-block signal processing method that divides the ISAC-NET into the passive sensing module, signal detection module and channel reconstruction module. From the simulation results, ISAC-NET obtains better communication performance than the traditional signal demodulation algorithm, which is close to OAMP-Net2. Compared to the 2D-DFT algorithm, ISAC-NET demonstrates significantly enhanced sensing performance. In summary, ISAC-NET is a promising tool for passive sensing and communication in wireless communications.

Coherent Compensation based ISAC Signal Processing for Long-range Sensing

Integrated sensing and communication (ISAC) will greatly enhance the efficiency of physical resource utilization. The design of ISAC signal based on the orthogonal frequency division multiplex (OFDM) signal is the mainstream. However, when detecting the long-range target, the delay of echo signal exceeds CP duration, which will result in inter-symbol interference (ISI) and inter-carrier interference (ICI), limiting the sensing range. Facing the above problem, we propose to increase useful signal power through coherent compensation and improve the signal to interference plus noise power ratio (SINR) of each OFDM block. Compared with the traditional 2D-FFT algorithm, the improvement of SINR of range-doppler map (RDM) is verified by simulation, which will expand the sensing range.

Topology-Aware Resilient Routing Protocol for FANETs: An Adaptive Q-Learning Approach

Flying ad hoc networks (FANETs) play a crucial role in numerous military and civil applications since it shortens mission duration and enhances coverage significantly compared with a single unmanned aerial vehicle (UAV). Whereas, designing an energy-efficient FANET routing protocol with a high packet delivery rate (PDR) and low delay is challenging owing to the dynamic topology changes. In this article, we propose a topology-aware resilient routing strategy based on adaptive Q-learning (TARRAQ) to accurately capture topology changes with low overhead and make routing decisions in a distributed and autonomous way. First, we analyze the dynamic behavior of UAV nodes via the queuing theory, and then the closed-form solutions of neighbors' change rate (NCR) and neighbors' change interarrival time (NCIT) distribution are derived. Based on the real-time NCR and NCIT, a resilient sensing interval (SI) is determined by defining the expected sensing delay of network events. Besides, we also present an adaptive Q-learning approach that enables UAVs to make distributed, autonomous, and adaptive routing decisions, where the above SI ensures that the action space can be updated in time at a low cost. The simulation results verify the accuracy of the topology dynamic analysis model and also prove that our TARRAQ outperforms the Q-learning-based topology-aware routing (QTAR), mobility prediction-based virtual routing (MPVR), and greedy perimeter stateless routing based on energy-efficient hello (EE-Hello) in terms of 25.23%, 20.24%, and 13.73% lower overhead, 9.41%, 14.77%, and 16.70% higher PDR, and 5.12%, 15.65%, and 11.31% lower energy consumption, respectively.

Toward Trusted and Swift UAV Communication: ISAC-Enabled Dual Identity Mapping

The UAV network has recently emerged as a capable carrier for ubiquitous wireless intelligent communication in the B5G/6G era. Nevertheless, the separation of dual identity raises challenges from the perspective of communication efficiency and security, including tedious communication feedback and malicious Sybil attacks. Meanwhile, thanks to the emerging integrated sensing and communication (ISAC) technology, the sensing ability incorporated in communication advances crucial opportunities for accurately and efficiently mapping identity from dual domains. This tutorial discusses the exciting intersection of ISAC and the future intelligent and efficient UAV network. We first describe the motivation scenario and present the framework of the proposed novel ISAC-enabled dual identity solution. The detailed modules of identity production, mapping, management, and authentication are discussed. By endowing UAVs with an advanced capability: opening their eyes when communicating with each other, we detail three typical applications and the advantages of our proposal. Finally, a series of key enabling techniques, open challenges, and potential solutions for ISAC-enabled dual-domain identity are discussed. This tutorial for the intelligent and efficient UAV network brings new insight on providing dual-domain identity via ISAC technology, with an eye on trusted and swift communication research tailored for the 6G UAV network.