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In this letter, we propose a rate splitting sparse vector coding (RS-SVC) scheme for the next generation ultra-reliable and low-latency communication (xURLLC). The key idea is to segregate the users’ data into common and private parts. A part of users’ information is taken from each user to form the common data (CD) and the rest of the information are considered as private data (PD). Subsequently, the CD is sparse mapped into a sparse vector and each user’s PD is symbol modulated and embedded into the non-zero positions of the sparse vector forming the RS-SVC. This RS-SVC is pseudo randomly spread over a code book and transmitted via a wireless channel. Upon reception, the RS-SVC undergoes a sparse demapping process to pinpoint the non-zero positions representing the CD, concurrently demodulating the embedded symbols will extract the PD. Our numerical analyses and Monte-Carlo simulations substantiate the RS-SVC scheme’s superiority over traditional SVC and its enhanced variant, demonstrating significant improvements in block error rate, user fairness and spectral efficiency.

One of the most challenging tasks in a disaster scenario is the detection and localization of victims with high accuracy and minimum delay, especially in out-of-coverage areas. In the event of a disaster that disrupts the cellular network infrastructure, emergency calls can be relayed to the core network via multi-hop D2D communications. In this paper, a localization system is proposed that uses radio measurements obtained through such D2D multi-hop assisted emergency calls to localize in-coverage and out-of-coverage devices. To address the uncertainty and gradual reception of data in real-time in this scenario, a dynamic constraint satisfaction-based Multi Victim Localization Algorithm (MVLA) is proposed. This algorithm locates multi-hop devices in a progressive propagation manner to provide fast and accurate updates on victim locations. Additionally, three modes of ${MVLA}$MVLA, namely ${MVLA}_{recent}$MVLArecent, ${MVLA}_{seq}$MVLAseq, and ${MVLA}_{all}$MVLAall are proposed. Simulation results demonstrate that ${MVLA}_{all}$MVLAall has a lower localization error compared to ${MVLA}_{recent}$MVLArecent and ${MVLA}_{seq}$MVLAseq. Moreover, ${MVLA}_{all}$MVLAall, is compared with an existing particle filtering-based localization algorithm called RSSI Monte-Carlo Boxed Localization (RSSI-MCL) under an increasing number of emergency user devices and functional gNodeBs. Results show that ${MVLA}_{all}$MVLAall significantly outperforms the RSSI-MCL method in terms of localization accuracy and computational delay.