Due to limited volume, weight and power consumption, micro-satellite has to reduce data transmission and storage capacity by image compression when performs earth observation missions. However, the quality of images may be unsatisfied. This paper considers the problem of recovering sparse signals by exploiting their unknown sparsity pattern. To model structured sparsity, the prior correlation of the support is encoded by imposing a transformed Gaussian process on the spike and slab probabilities. Then, an efficient approximate message-passing algorithm with structured spike and slab prior is derived for posterior inference, which, combined with a fast direct method, reduces the computational complexity significantly. Further, a unified scheme is developed to learn the hyperparameters using expectation maximization (EM) and Bethe free energy optimization. Simulation results on both synthetic and real data demonstrate the superiority of the proposed algorithm.

This paper addresses the spectral coexistence between LEO constellation and GEO belt for global distributed earth stations. A specific method is introduced to mitigate the in-line interference by tilting the direction normal of phased array antennas of LEO satellites, and the optimal direction is found by solving a non-linear programming problem. The simulation results prove that the proposed approach leads to greater link availability while guaranteeing the desired received signal level especially for low-latitude earth stations.

To deal with the dynamic and imbalanced traffic requirements in Low Earth Orbit satellite networks, several distributed load balancing routing schemes have been proposed. However, because of the lack of global view, these schemes may lead to cascading congestion in regions with high volume of traffic. To solve this problem, a Hybrid-Traffic-Detour based Load Balancing Routing (HLBR) scheme is proposed, where a Long-Distance Traffic Detour (LTD) method is devised and coordinates with distributed traffic detour method to perform self-adaptive load balancing. The forwarding path of LTD is acquired by the Circuitous Multipath Calculation (CMC) based on prior geographical information, and activated by the LTD- Shift-Trigger (LST) through real-time congestion perception. Simulation results show that the HLBR can mitigate cascading congestion and achieve efficient traffic distribution.

With the booming development of terrestrial network, scaling terrestrial network over satellite network to build Integrated Terrestrial-Satellite Network (ITSN) and meanwhile to provide the global Internet access, has become ever more attractive. Naturally, the widely and successfully used terrestrial routing protocols are the promising protocols to integrate the terrestrial and satellite networks. However, the terrestrial routing protocols, which rely on propagating routing messages to discover New Network Topology (NNT) in the terrestrial network with rare topology changes, will suffer from overly numerous routing messages in satellite network whose topology frequently changes as satellites move. In this paper, a Topology Discovery Sub-layer for ITSN Routing Schemes (TDS-IRS) is firstly proposed to avoid the propagation of numerous routing messages by taking advantage of the movement predictability of satellite and the requirements of routing schemes to discover NNT in advance of topology change. Secondly, a Weighted Perfect Matching based Topology Discovery (WPM-TD) model is designed to conduct the NNT discovery on the ground. Thirdly, this paper builds a testbed with real network devices and meanwhile interconnect that testbed with real Internet, to validate that RS-TDS can discover NNT immediately with the less on-board overhead compared with optimized routing schemes. Finally, different network scenarios are applied to validate the WPM-TD, i.e., the core module of TDS-IRS. Extensive experiments show WPM-TD can work efficiently, avoiding the invalid NNT discovery and decreasing 20% ~ 57% of potential topology changes, which can also improve up to 47% ~ 105% of network throughput.

With the development of manufacture technology, the multi-level cell (MLC) technique dramatically increases the storage density of NAND flash memory. As the result, cell-to-cell interference (CCI) becomes more serious and hence causes an increase in the raw bit error rate of data stored in the cells. Recently, low-density parity-check (LDPC) codes have appeared to be a promising solution to combat the interference of MLC NAND flash memory. However, the decoding complexity of the sum-product algorithm (SPA) is extremely high. In this paper, to improve the accuracy of the log likelihood ratio (LLR) information of each bit in each NAND flash memory cell, we adopt a non-uniform detection (N-UD) which uses the average maximum mutual information to determine the value of the soft-decision reference voltages. Furthermore, with an aim to reduce the decoding complexity and improve the decoding performance, we propose a modified soft reliability-based iterative majority-logic decoding (MSRBI-MLGD) algorithm, which uses a non-uniform quantizer based on power function to decode LDPC codes. Simulation results show that our design can offer a desirable trade-off between the performance and complexity for high-column-weight LDPC-coded MLC NAND flash memory.

Admission control is a key mechanism to manage the increasing number of the simultaneous demanding services, requiring a desired Quality of Service (QoS) in a spectrum efficient manner. To address this issue, we investigate in this work, the use of the superposition coding technique to increase the system capacity through multiuser diversity exploitation. We propose a novel joint admission control and superposition coding formalism based on different utility functions: Opportunistic (OPSC), Great Fairness (GFSC) and Proportional Fairness (PFSC). Simulation results show the superiority of our proposed approaches over other by providing higher mean of served VoIP users and higher throughput while maintaining an average VoIP packet transmission delay lower than 0.6 ms.

As the increasing demand for mobile communications and the shrinking of the coverage of cells, handover mechanism will play an important role in future wireless networks to provide users with seamless mobile communication services. In order to guarantee the user experience, the handover decision should be made timely and reasonably. To achieve this goal, this paper presents a hybrid handover forecasting mechanism, which contains long-term and short-term forecasting models. The proposed mechanism could cooperate with the standard mechanisms, and improve the performance of standard handover decision mechanisms. Since most of the parameters involved are imprecise, fuzzy forecasting model is applied for dealing with predictions of them. The numerical results indicate that the mechanism could significantly decrease the rate of ping-pong handover and the rate of handover failure.

Interference alignment (IA) is suitable for cognitive radio networks (CRNs). However, in IA spectrum sharing (SS) process of general underlay CRNs, transmit power of cognitive radio transmitters usually should be reduced to satisfy interference constraint of primary user (PU), which may lead to low signal-to-noise-ratio at cognitive radio receivers (CRRs). Consequently, sum rate of cognitive users (CUs) may fall short of the theoretical maximum through IA. To solve this problem, we propose an adaptive IA SS method for general distributed multi-user multi-antenna CRNs. The relationship between interference and noise power at each CRR is analyzed according to channel state information, interference requirement of PU, and power budget of CUs. Based on the analysis, scenarios of the CRN are classified into 4 cases, and corresponding IA SS algorithms are properly designed. Transmit power adjustment, CU access control and adjusted spatial projection are used to realize IA among CUs. Compared with existing methods, the proposed method is more general because of breaking the restriction that CUs can only transmit on the idle sub-channels. Moreover, in comparison to other five IA SS methods applicable in general CRN, the proposed method leads to improved achievable sum rate of CUs while guarantees transmission of PU.

Named Data Networking (NDN) improves the data delivery efficiency by caching contents in routers. To prevent corrupted and faked contents be spread in the network, NDN routers should verify the digital signature of each published content. Since the verification scheme in NDN applies the asymmetric encryption algorithm to sign contents, the content verification overhead is too high to satisfy wire-speed packet forwarding. In this paper, we propose two schemes to improve the verification performance of NDN routers to prevent content poisoning. The first content verification scheme, called “user-assisted”, leads to the best performance, but can be bypassed if the clients and the content producer collude. A second scheme, named ``Router-Cooperation ‘’, prevents the aforementioned collusion attack by making edge routers verify the contents independently without the assistance of users and the core routers no longer verify the contents. The Router-Cooperation verification scheme reduces the computing complexity of cryptographic operation by replacing the asymmetric encryption algorithm with symmetric encryption algorithm. The simulation results demonstrate that this Router-Cooperation scheme can speed up 18.85 times of the original content verification scheme with merely extra 80 Bytes transmission overhead.

Shortest-path calculation on weighted graphs are an essential operation in computer networks. The performance of such algorithms has become a critical challenge in emerging software-defined networks (SDN), since SDN controllers need to centralizedly perform a shortest-path query for every flow, usually on large-scale network. Unfortunately, one of the challenges is that current algorithms will become incalculable as the network size increases. Therefore, inspired by the compression graph in the field of compute visualization, we propose an efficient shortest path algorithm by compressing the original big network graph into a small one, but the important graph properties used to calculate path is reserved. We implement a centralized version of our approach in SDN-enabled network, and the evaluations validate the improvement compared with the well-known algorithms.

Machine Learning (ML) techniques have been widely applied in recent traffic classification. However, the problems of both discriminator bias and class imbalance decrease the accuracies of ML based traffic classifier. In this paper, we propose an accurate and extensible traffic classifier. Specifically, to address the discriminator bias issue, our classifier is built by making an optimal cascade of binary sub-classifiers, where each binary sub-classifier is trained independently with the discriminators used for identifying application specific traffic. Moreover, to balance a training dataset, we apply SMOTE algorithm in generating artificial training samples for minority classes. We evaluate our classifier on two datasets collected from different network border routers. Compared with the previous multi-class traffic classifiers built in one-time training process, our classifier achieves much higher F-Measure and AUC for each application.

Due to the rapid development of mobile Internet services, such as Skype and WeChat, traditional telecom services have suffered a large decline in the business volumes in recent years. Thus, telecom operators pay much attention to analyzing the changes of the enterprise performance, in order to adjust market strategies in time. In this paper, we propose a new methodology to analyze the operation data of telecom operators dynamically, which can characterize the changing process of the operating states and predict the developing trends. In particular, the proposed methodology contains two steps: migration pattern analysis and trajectory pattern analysis. Firstly, migration analysis is based on the changing of operating states between two points-in-time, which is referred to as the migration paths. Applying the clustering analysis to the migration paths can obtain the migration patterns, which characterize the changing feature of telecom operators during a short period, e.g. one month. Secondly, we combine a sequence of consecutive migration paths to obtain the migration trajectory. Through analyzing different trajectories based on the hierarchical cluster method and the Markov chain model, we obtain the trajectory patterns, which describe the changing progress during a relatively long period, e.g. one year. Based on the trajectory patterns, we can predict the possible performance changes. Finally, we apply the proposed method to a Chinese telecom operator for an empirical research, and has obtained a lot of development rules which provides insights into current telecom.

Many improved authentication solutions were put forward, on purpose of authenticating more quickly and securely. However, neither the overuse of hash function, or additional symmetric encryption, can truly increase the overall security. Instead, extra computation cost degraded the performance. They were still vulnerable to a variety of threats, such as smart card loss attack and impersonation attack, due to hidden loopholes and flaws. Even worse, user’s identity can be parsed in insecure environment, even became traceable. Aiming to protect identity, a lightweight mutual authentication scheme is proposed. Redundant operations are removed, which make the verification process more explicit. It gains better performance with average cost compared to other similar schemes. Cryptanalysis shows the proposed scheme can resist common attacks and achieve user anonymity. Formal security is further verified by using the widely accepted Automated Validation of Internet Security Protocols and Applications (AVISPA) tool.

In this paper, we investigate the weighted iterative decoding to improve the performance of turbo-polar code. First of all, a minimum weighted mean square error criterion is proposed to optimize the scaling factors (SFs). Secondly, for two typical iterative algorithms, such as soft cancellation (SCAN) and belief propagation (BP) decoding, genie-aided decoders are proposed as the ideal reference of the practical decoding. Guided by this optimization framework, the optimal SFs of SCAN or BP decoders are obtained. The bit error rate performance of turbo-polar code with the optimal SFs can achieve 0.3 dB or 0.7 dB performance gains over the standard SCAN or BP decoding respectively.

The uplink of mobile satellite communication (MSC) system with hundreds of spot beams is essentially a multiple-input multiple-output (MIMO) channel. Dual-turbo iterative detection and decoding as a kind of MIMO receiver, which exchanges soft extrinsic information between a soft-in soft-out (SISO) detector and an SISO decoder in an iterative fashion, is an efficient method to reduce the uplink inter-beam-interference (IBI), and so the receiving bit error rate (BER).We propose to replace the linear SISO detector of traditional dual-turbo iterative detection and decoding with the AMP detector for the low-density parity-check (LDPC) coded multibeam MSC uplink. This improvement can reduce the computational complexity and achieve much lower BER.

In the Davey-MacKay (DM) construction, the inner decoder treats unknown transmitted bits as random independent substitution errors. It limits the synchronization capability of the inner decoder, and thus weakens the error-correcting capability of the DM construction. In order to improve the performance of the DM construction, an iterative decoding scheme is proposed, which iteratively utilizes the more accurate estimates of transmitted codewords. In the proposed scheme, the estimated average bit error rates and the estimated low-density parity-check (LDPC) codewords from the outer decoder are fed back into the inner decoder to update the synchronization process. Simulation results show that the proposed iterative decoding scheme significantly outperforms the traditional DM construction.