The increasing demand for integrated sensing and communication (ISAC) in sixth generation (6 G) mobile networks calls for advancements in sensing parameter estimation technologies. Orthogonal frequency division multiplexing (OFDM) is the core technology of the fifth generation (5G) system with excellent resilience to multi-path fading and is gaining popularity as an ISAC waveform. However, the performance of traditional range/velocity (r/v) estimation algorithms, e.g., multiple signal classification (MUSIC), is restricted by the resolution defined by the bandwidth and symbol duration of OFDM, especially in multi-target scenarios. To overcome this issue, we proposed a REsidual network based SPEctra Calibration (RESPEC) algorithm. It improved the multi-target sensing accuracy by residual network that resists gradient vanishment to calibrate the spectra generated with the two-dimensional MUSIC (2D-MUSIC). Simulation results demonstrated the superiority in r/v estimation accuracy of RESPEC in multi-target scenarios, compared to the traditional 2D-MUSIC algorithm and other benchmarks. The results also exposed a trade-off between neural network depth and communication bandwidth for range estimation.

The growing demand for edge computing is driving the proliferation of wireless computing power infrastructures and poses significant challenges to network energy efficiency (EE). Traditional offloading schemes rely solely on multi-access edge computing (MEC) servers. High-quality communication links and adequate distributed resources are expected to improve EE. Inspired by reconfigurable intelligent surface (RIS) and device-to-device communication technologies, this paper first proposed an edge-end collaborative computing system in RIS-empowered heterogeneous wireless computing power networks. Through resource virtualization, heterogeneous computing powers on MEC servers and nearby devices are unified into resource pools for efficient utilization. In this wireless system, task offloading is closely coupled with channel allocation, power coordination, RIS phase shift design, and base station receive beamforming. To tackle it, this paper suggested a block coordinate descent (BCD)-based framework that decouples the problem into three sub-problems. For each sub-problem, specialized solutions are applied: the Rayleigh quotient maximization and concave-convex procedure for the beamforming and power allocation co-design sub-problem, dimensionality reduction for 3-dimensional task offloading and channel allocation co-pairing sub-problem, and convex optimization for the RIS phase shift control sub-problem. Numerical results showed that the proposed method can outperform benchmark approaches in terms of EE and delay by up to 28.5% and 22.9%, respectively.