Mobile edge caching network (MEN), which enables popular or reusable content caching and sharing among adjacent mobile edge devices, has become a promising solution to reduce the traffic and burden over backhaul links. Network coding (NC), represented by classical random linear network coding (RLNC), is utilized to facilitate content delivery and increase throughput in MEN. However, as the harsh decoding condition results in unacceptable time and storage overhead, classical RLNC schemes struggle to be widely deployed in practice. In this work, we propose a cost-effective NC-based content-sharing scheme based on binary sparse network coding (BSNC), called Bison, for D2D-enabled MEN. Based on the shared relationship between the binary sparse coded block (BSCB), Bison first designs a caching maintenance module to characterize the sharing progress and maintain the caching state of each edge node. Then, Bison defines a matching metric named neighbor utility to evaluate neighbors’ matching values by considering nodes’ demand and content decodability. Guiding by the metric, Bison achieves the most beneficial matching relationship among edge nodes through a proposed online matching policy. Finally, Bison devises a coded block delivery strategy to enable the sharing of valuable content between two matched edge nodes. Extensive experiments in simulations and real-world Android testbeds demonstrate its effectiveness and efficiency, wherein Bison is at least 30% less than the RLNC-based scheme on time consumption and at least 10% less than the classical BSNC-based scheme on storage overhead. The results also show that our matching policy and coded block delivery strategy can perform with a low response latency on edge and mobile devices.

With the roaring demands of network access, mobile edge caching (MEC) networks are bringing the popular cloud storage services closer to the general public. Currently, a hierarchical framework with static edge nodes (ENs, e.g., small base stations, Wi-Fi hotspots, etc.) is employed to establish an efficient and user-friendly MEC network to continuously provide content publishing/subscription services to mobile user devices (UDs). However, factors like frequent user movement and different access patterns can lead to load imbalances in ENs and thus poor user experience. This paper aims to comprehensively model and discuss the load balancing for ENs in MEC networks. We first prove that this problem is NP-complete. Then we propose a heuristic solution, called Salango, which simplifies the original load balancing problem to equalizing the number of users within the coverage of each EN. We also theoretically prove the near-optimal property of Salango. In the end, the trace-driven simulation experiments confirm that the proposed scheme improves the standard deviation of ENs' load by 21.4% on average compared with existing methods.

In space communications, the signal suffers a long-distance transmission and a high dynamic movement. The long-distance transmission causes a very low signal-to-noise ratio (SNR) and the high dynamic movement causes a high-accelerating Doppler-shift. To address the low SNR, the conventional strategy is long-time accumulation. However, during the long-time accumulation period, the high-accelerating Doppler-shift brings about the serious energy dispersion problem. In this paper, we propose one joint-comb-acquisition (JCA) scheme to address the energy dispersion problem. The proposed JCA scheme, which uses multiple accumulation periods to jointly construct a narrower comb-search-range, excludes most noise elements. Also, the theoretical analysis derives the closed-form expression for acquisition probability. Moreover, the simulation results demonstrate the significant increase of acquisition probability varies from 4.6 to 47.2 percents, as compared with the existing schemes.

For space communications, the transmitted signal generally faces a long-distance transmission and a high-accelerating movement. The long-distance transmission causes a very low signal-to-noise ratio (SNR) and the high-accelerating movement causes a dynamic Doppler-shift. Under a very large acceleration, the long-time accumulation which aims to solve the very low SNR, brings about the serious energy dispersion problem, thus posing a great challenge for Doppler-shift acquisition. Introducing the clustering idea in machine learning, we propose one clustering-fast-Fourier-transform (CFFT) based Doppler-shift acquisition scheme with an affordable computational complexity, to address the energy dispersion problem for space communications. The proposed CFFT scheme includes two algorithms called the Generic CFFT algorithm and the High-order CFFT algorithm, respectively. First, the Generic CFFT algorithm clusters multiple density-reachable signal elements into one signal cluster, thus accumulating the dispersed energy again. Second, the High-order CFFT algorithm, which conducts several rounds of clustering, clusters more density-reachable signal elements into one larger signal cluster. Simulations results show that our proposed CFFT scheme achieves a higher acquisition probability than the existing FFT based schemes and consumes a lower computational complexity than the FRFT scheme.