Sensing as a Service in 6G Perceptive Mobile Networks: Architecture, Advances, and the Road Ahead

Sensing-as-a-service is anticipated to be the core feature of 6G perceptive mobile networks (PMN), where high-precision real-time sensing will become an inherent capability rather than being an auxiliary function as before. With the proliferation of wireless connected devices, resource allocation in terms of the users' specific quality-of-service (QoS) requirements plays a pivotal role to enhance the interference management ability and resource utilization efficiency. In this article, we comprehensively introduce the concept of sensing service in PMN, including the types of tasks, the distinctions/advantages compared to conventional networks, and the definitions of sensing QoS. Subsequently, we provide a unified RA framework in sensing-centric PMN and elaborate on the unique challenges. Furthermore, we present a typical case study named "communication-assisted sensing" and evaluate the performance trade-off between sensing and communication procedure. Finally, we shed light on several open problems and opportunities deserving further investigation in the future

Communication-Assisted Sensing in 6G Networks

The exploration of coordination gain achieved through the synergy of sensing and communication (S&C) functions plays a vital role in improving the performance of integrated sensing and communication systems. This paper focuses on the optimal waveform design for communication-assisted sensing (CAS) systems within the context of 6G perceptive networks. In the CAS process, the base station actively senses the targets through device-free wireless sensing and simultaneously transmits the pertinent information to end-users. In our research, we establish a CAS framework grounded in the principles of rate-distortion theory and the source-channel separation theorem (SCT) in lossy data transmission. This framework provides a comprehensive understanding of the interplay between distortion, coding rate, and channel capacity. The purpose of waveform design is to minimize the sensing distortion at the user end while adhering to the SCT and power budget constraints. In the context of target response matrix estimation, we propose two distinct waveform strategies: the separated S&C and dual-functional waveform schemes. In the former strategy, we develop a simple one-dimensional search algorithm, shedding light on a notable power allocation tradeoff between the S&C waveform. In the latter scheme, we conceive a heuristic mutual information optimization algorithm for the general case, alongside a modified gradient projection algorithm tailored for the scenarios with independent sensing sub-channels. Additionally, we identify the presence of both subspace tradeoff and water-filling tradeoff. Finally, we validate the effectiveness of the proposed algorithms through numerical simulations

Sensing With Random Signals

Radar systems typically employ well-designed deterministic signals for target sensing. In contrast to that, integrated sensing and communications (ISAC) systems have to use random signals to convey useful information, potentially causing sensing performance degradation. This paper analyzes the sensing performance via random ISAC signals over a multi-antenna system. Towards this end, we define a new sensing performance metric, namely, ergodic linear minimum mean square error (ELMMSE), which characterizes the estimation error averaged over the randomness of ISAC signals. Then, we investigate a data-dependent precoding scheme to minimize the ELMMSE, which attains the {optimized} sensing performance at the price of high computational complexity. To reduce the complexity, we present an alternative data-independent precoding scheme and propose a stochastic gradient projection (SGP) algorithm for ELMMSE minimization, which can be trained offline by locally generated signal samples. Finally, we demonstrate the superiority of the proposed methods by simulations.Comment: 6 pages, 5 figures, submitted to ICASSP 202

Waveform Design for Communication-Assisted Sensing in 6G Perceptive Networks

The integrated sensing and communication (ISAC) technique has the potential to achieve coordination gain by exploiting the mutual assistance between sensing and communication (S&C) functions. While the sensing-assisted communications (SAC) technology has been extensively studied for high-mobility scenarios, the communication-assisted sensing (CAS) counterpart remains widely unexplored. This paper presents a waveform design framework for CAS in 6G perceptive networks, aiming to attain an optimal sensing quality of service (QoS) at the user after the target's parameters successively ``pass-through'' the S$\&$C channels. In particular, a pair of transmission schemes, namely, separated S&C and dual-functional waveform designs, are proposed to optimize the sensing QoS under the constraints of the rate-distortion and power budget. The first scheme reveals a power allocation trade-off, while the latter presents a water-filling trade-off. Numerical results demonstrate the effectiveness of the proposed algorithms, where the dual-functional scheme exhibits approximately 12% performance gain compared to its separated waveform design counterpart

Random ISAC Signals Deserve Dedicated Precoding

Radar systems typically employ well-designed deterministic signals for target sensing, while integrated sensing and communications (ISAC) systems have to adopt random signals to convey useful information. This paper analyzes the sensing and ISAC performance relying on random signaling in a multi-antenna system. Towards this end, we define a new sensing performance metric, namely, ergodic linear minimum mean square error (ELMMSE), which characterizes the estimation error averaged over random ISAC signals. Then, we investigate a data-dependent precoding (DDP) scheme to minimize the ELMMSE in sensing-only scenarios, which attains the optimized performance at the cost of high implementation overhead. To reduce the cost, we present an alternative data-independent precoding (DIP) scheme by stochastic gradient projection (SGP). Moreover, we shed light on the optimal structures of both sensing-only DDP and DIP precoders. As a further step, we extend the proposed DDP and DIP approaches to ISAC scenarios, which are solved via a tailored penalty-based alternating optimization algorithm. Our numerical results demonstrate that the proposed DDP and DIP methods achieve substantial performance gains over conventional ISAC signaling schemes that treat the signal sample covariance matrix as deterministic, which proves that random ISAC signals deserve dedicated precoding designs.Comment: 15 pages, 12 figure

Integrated Sensing and Communications: Recent Advances and Ten Open Challenges

It is anticipated that integrated sensing and communications (ISAC) would be one of the key enablers of next-generation wireless networks (such as beyond 5G (B5G) and 6G) for supporting a variety of emerging applications. In this paper, we provide a comprehensive review of the recent advances in ISAC systems, with a particular focus on their foundations, system design, networking aspects and ISAC applications. Furthermore, we discuss the corresponding open questions of the above that emerged in each issue. Hence, we commence with the information theory of sensing and communications (S$\&$C), followed by the information-theoretic limits of ISAC systems by shedding light on the fundamental performance metrics. Next, we discuss their clock synchronization and phase offset problems, the associated Pareto-optimal signaling strategies, as well as the associated super-resolution ISAC system design. Moreover, we envision that ISAC ushers in a paradigm shift for the future cellular networks relying on network sensing, transforming the classic cellular architecture, cross-layer resource management methods, and transmission protocols. In ISAC applications, we further highlight the security and privacy issues of wireless sensing. Finally, we close by studying the recent advances in a representative ISAC use case, namely the multi-object multi-task (MOMT) recognition problem using wireless signals.Comment: 26 pages, 22 figures, resubmitted to IEEE Journal. Appreciation for the outstanding contributions of coauthors in the paper

Transcriptome profiling analysis of Mactra veneriformis by deep sequencing after exposure to 2,2',4,4'-tetrabromodiphenyl ether

Polybrominated diphenyl ethers (PBDEs) are ubiquitous global pollutants, which are known to have immune, development, reproduction, and endocrine toxicity in aquatic organisms, including bivalves. 2,2',4,4'-Tetrabromodiphenyl ether (BDE-47) is the predominant PBDE congener detected in environmental samples and the tissues of organisms. However, the mechanism of its toxicity remains unclear. In this study, high-throughput sequencing was performed using the clam Mactra veneriformis, a good model for toxicological research, to clarify the transcriptomic response to BDE-47 and the mechanism responsible for the toxicity of BDE-47. The clams were exposed to 5 mu g/L BDE-47 for 3 days and the digestive glands were sampled for high-throughput sequencing analysis. We obtained 127 648, 154 225, and 124 985 unigenes by de novo assembly of the control group reads (CG), BDE-47 group reads (BDEG), and control and BDE-47 reads (CG & BDEG), respectively. We annotated 32 176 unigenes from the CG & BDEG reads using the NR database. We categorized 24 401 unigenes into 25 functional COG clusters and 21 749 unigenes were assigned to 259 KEGG pathways. Moreover, 17 625 differentially expressed genes (DEGs) were detected, with 10 028 upregulated DEGs and 7 597 downregulated DEGs. Functional enrichment analysis showed that the DEGs were involved with detoxification, antioxidant defense, immune response, apoptosis, and other functions. The mRNA expression levels of 26 DEGs were verified by quantitative real-time PCR, which demonstrated the high agreement between the two methods. These results provide a good basis for future research using the M. veneriformis model into the mechanism of PBDEs toxicity and molecular biomarkers for BDE-47 pollution. The regulation and interaction of the DEGs would be studied in the future for clarifying the mechanism of PBDEs toxicity

Transcriptome profiling analysis of Mactra veneriformis by deep sequencing after exposure to 2,2',4,4'-tetrabromodiphenyl ether

Polybrominated diphenyl ethers (PBDEs) are ubiquitous global pollutants, which are known to have immune, development, reproduction, and endocrine toxicity in aquatic organisms, including bivalves. 2,2',4,4'-Tetrabromodiphenyl ether (BDE-47) is the predominant PBDE congener detected in environmental samples and the tissues of organisms. However, the mechanism of its toxicity remains unclear. In this study, high-throughput sequencing was performed using the clam Mactra veneriformis, a good model for toxicological research, to clarify the transcriptomic response to BDE-47 and the mechanism responsible for the toxicity of BDE-47. The clams were exposed to 5 mu g/L BDE-47 for 3 days and the digestive glands were sampled for high-throughput sequencing analysis. We obtained 127 648, 154 225, and 124 985 unigenes by de novo assembly of the control group reads (CG), BDE-47 group reads (BDEG), and control and BDE-47 reads (CG & BDEG), respectively. We annotated 32 176 unigenes from the CG & BDEG reads using the NR database. We categorized 24 401 unigenes into 25 functional COG clusters and 21 749 unigenes were assigned to 259 KEGG pathways. Moreover, 17 625 differentially expressed genes (DEGs) were detected, with 10 028 upregulated DEGs and 7 597 downregulated DEGs. Functional enrichment analysis showed that the DEGs were involved with detoxification, antioxidant defense, immune response, apoptosis, and other functions. The mRNA expression levels of 26 DEGs were verified by quantitative real-time PCR, which demonstrated the high agreement between the two methods. These results provide a good basis for future research using the M. veneriformis model into the mechanism of PBDEs toxicity and molecular biomarkers for BDE-47 pollution. The regulation and interaction of the DEGs would be studied in the future for clarifying the mechanism of PBDEs toxicity

The Diagnostic Value of PI-RADS v2.1 in Patients with a History of Transurethral Resection of the Prostate (TURP)

To explore the diagnostic value of the Prostate Imaging–Reporting and Data System version 2.1 (PI-RADS v2.1) for clinically significant prostate cancer (CSPCa) in patients with a history of transurethral resection of the prostate (TURP), we conducted a retrospective study of 102 patients who underwent systematic prostate biopsies with TURP history. ROC analyses and logistic regression analyses were performed to demonstrate the diagnostic value of PI-RADS v2.1 and other clinical characteristics, including PSA and free/total PSA (F/T PSA). Of 102 patients, 43 were diagnosed with CSPCa. In ROC analysis, PSA, F/T PSA, and PI-RADS v2.1 demonstrated significant diagnostic value in detecting CSPCa in our cohort (AUC 0.710 (95%CI 0.608–0.812), AUC 0.768 (95%CI 0.676–0.860), AUC 0.777 (95%CI 0.688–0.867), respectively). Further, PI-RADS v2.1 scores of the peripheral and transitional zones were analyzed separately. In ROC analysis, PI-RADS v2.1 remained valuable in identifying peripheral-zone CSPCa (AUC 0.780 (95%CI 0.665–0.854; p p = 0.594)). PSA and F/T PSA retain significant diagnostic value for CSPCa in patients with TURP history. PI-RADS v2.1 is reliable for detecting peripheral-zone CSPCa but has limited diagnostic value when assessing transitional zone lesions