Satellite-assisted internet of things (S-IoT), especially the S-IoT based on low earth orbit (LEO) satellite, plays an important role in future wireless systems. However, the limited on-board communication and computing resource and high mobility of LEO satellites make it hard to provide satisfied service for IoT users. To maximize the task completion rate under latency constraints, collaborative computing and resource allocation among LEO networks are jointly investigated in this paper, and the joint task offloading, scheduling, and resource allocation is formulated as a dynamic mixed-integer problem. To tack the complex problem, we decouple it into two subproblems with low complexity. First, the max-min fairness is adopted to minimize the maximum latency via optimal resource allocation with fixed task assignment. Then, the joint task offloading and scheduling is formulated as a Markov decision process with optimal communication and computing resource allocation, and deep reinforcement learning is utilized to obtain long-term benefits. Simulation results show that the proposed scheme has superior performance compared with other referred schemes.

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Summary Hybrid geosynchronous earth orbit (GEO) and low earth orbit (LEO) satellite networks play an important role in future satellite‐assisted internet of things (S‐IoT). However, the limited satellite on‐board communication and computing resource poses a large challenge for the task offloading in the hybrid GEO‐LEO satellite networks. In this paper, the problem of task offloading is formulated as a cooperative user association and resource allocation problem. To tackle the problem, we model it as a Markov decision process and decompose it into two sub‐problems, which are sequential decisions for user association and resource allocation with fixed user association conditions. Deep reinforcement learning (DRL) is adopted to make sequential decisions to achieve long‐term benefits, and convex optimization method is utilized to obtain optimal communication and computing resource allocation. Simulation results show that the proposed method is superior to other referred schemes.

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This paper presents a new method of suppressing in tersymbol interference (lSI) for indQor wireless communications in Rayleigh fading channel. In this system, we introduce a 16ary Quadrature Amplitude Modulation (16QAM) digital model which is spread spectrum with orthogonal Hadamard matrix row vector inserted zeros. This method not only can reduce the lSI resulting from multi path fading channel, but also can lower the peak to average-power ratio (PAPR) problem. On the other hand, the Least Square (LS) channel estimator has been introduced in order to achieve the hetter performance. Furthermore, the system performance has been analyzed and simulated with computer.