Integrated sensing and communication (ISAC) is one of the main usage scenarios for 6G wireless networks. To most efficiently utilize the limited wireless resources, integrated super-resolution sensing and communication (ISSAC) has been recently proposed to significantly improve sensing performance with super-resolution algorithms for ISAC systems, such as the Multiple Signal Classification (MUSIC) algorithm. However, traditional super-resolution sensing algorithms suffer from prohibitive computational complexity of orthogonal-frequency division multiplexing (OFDM) systems due to the large dimensions of the signals in the subcarrier and symbol domains. To address such issues, we propose a novel two-stage approach to reduce the computational complexity for super-resolution range estimation significantly. The key idea of the proposed scheme is to first uniformly decimate signals in the subcarrier domain so that the computational complexity is significantly reduced without missing any target in the range domain. However, the decimation operation may result in range ambiguity due to pseudo peaks, which is addressed by the second stage where the total collocated subcarrier data are used to verify the detected peaks. Compared with traditional MUSIC algorithms, the proposed scheme reduces computational complexity by two orders of magnitude, while maintaining the range resolution and unambiguity. Simulation results verify the effectiveness of the proposed scheme.

Benefited from the design of separating control plane and data plane, software defined networking (SDN) is widely concerned and applied. Its quick response capability to network events with changes in network policies enables more dynamic management of data center networks. Although the SDN controller architecture is increasingly optimized for swift policy updates, the data plane, especially the prevailing ternary content-addressable memory (TCAM) based flow tables on physical SDN switches, remains unoptimized for fast rule updates, and is gradually becoming the primary bottleneck along the policy update pipeline. In this paper, we present RuleTris, the first SDN update optimization framework that minimizes rule update latency for TCAM-based switches. RuleTris employs the dependency graph (DAG) as the key abstraction to minimize the update latency. RuleTris efficiently obtains the DAGs with novel dependency preserving algorithms that incrementally build rule dependency along with the compilation process. Then, in the guidance of the DAG, RuleTris calculates the TCAM update schedules that minimize TCAM entry moves, which are the main cause of TCAM update inefficiency. In evaluation, RuleTris achieves a median of <12 ms and 90-percentile of < 15ms the end-to-end perrule update latency on our hardware prototype, outperforming the state-of-the-art composition compiler CoVisor by ~ 20 times.