The unforeseen mobile data explosion as well as the scarce of spectrum resource pose a major challenge to the performance of today’s cellular networks which are in urgent need of novel solutions to handle such voluminous mobile data. Long term evolution-unlicensed (LTE-U), which extends the LTE standard operating on the unlicensed band, has been proposed to improve system throughput. In LTE-U system, arriving users will contend the unlicensed spectrum resource with wireless fidelity (WiFi) users to transmit data information. Nevertheless, there is no clear consensus as to the benefits of transmission using unlicensed bands for LTE users. To this end, in this paper an analytical model is presented based on a queue system to understand the performance achieved by unlicensed based LTE system taking quality of services (QoS) and LTE-U users’ behaviors into account. To obtain the stead-state solutions of the queue system, a matrix geometric method is used to solve it. Then, the average delay and utilization of unlicensed band for the LTE-U users is derived by using the queuing model. The performance of LTE-U coexistence is evaluated with WiFi using the proposed model and provide some initial insights as to the advantage of LTE-U in practice.

Aiming at making full use of analog to digital converter (ADC) digitalizing bit without oversaturation while keeping peak to average ratio (PAR) stable, this paper puts forward a new segmented full-digital (SFD)-automatic gain control (AGC) algorithm for a new long term evolution (LTE) communication system. Segmented digital gain control strategy is adopted to adjust the gain by only one step based on detected power status. Whether the gain needs to be adjusted is determined by current signal state derived from the change ranges of adjacent root mean square (RMS) of input signal, but not the difference between the power level of current signal and target signal. Software simulation and hardware implementing had been conducted with LTE frequency division dual (FDD) uplink signal and the results indicated that the proposed AGC algorithm can judge power status accurately and hence adjust the gain precisely in one step with a short delay, further, it can make full use of ADC digitalizing bit without oversaturation as well as keeping stable PAR. In addition, the mean error vector magnitude (EVM) was confined less than 1.6% to meet the 3rd generation partnership project (3GPP) standard well.

To achieve secure communication in wireless sensor networks (WSNs), where sensor nodes with limited computation capability are randomly scattered over a hostile territory, various key pre-distribution schemes (KPSs) have been proposed. In this paper, a new KPS is proposed based on symplectic geometry over finite fields. A fixed dimensional subspace in a symplectic space represents a node, all 1-dimensional subspaces represent keys and every pair of nodes has shared keys. But this naive mapping does not guarantee a good network resiliency. Therefore, it is proposed an enhanced KPS where two nodes have to compute a pairwise key, only if they share at least q common keys. This approach enhances the resilience against nodes capture attacks. Compared with the existence of solution, the results show that new approach enhances the network scalability considerably, and achieves good connectivity and good overall performance.

Ciphertext-policy attribute-based searchable encryption (CP-ABSE) can achieve fine-grained access control for data sharing and retrieval, and secure deduplication can save storage space by eliminating duplicate copies. However, there are seldom schemes supporting both searchable encryption and secure deduplication. In this paper, a large universe CP-ABSE scheme supporting secure block-level deduplication are proposed under a hybrid cloud mechanism. In the proposed scheme, after the ciphertext is inserted into bloom filter tree (BFT), private cloud can perform fine-grained deduplication efficiently by matching tags, and public cloud can search efficiently using homomorphic searchable method and keywords matching. Finally, the proposed scheme can achieve privacy under chosen distribution attacks block-level (PRV-CDA-B) secure deduplication and match-concealing (MC) searchable security. Compared with existing schemes, the proposed scheme has the advantage in supporting fine-grained access control, block-level deduplication and efficient search, simultaneously.

Owing to the unreliability of wireless link and the resource constraints of embedded devices in terms of energy, processing power, and memory size in low power and lossy networks (LLNs), network congestion may occur in an emergency and lead to significant packet loss and end-to-end delay. To mitigate the effect of network congestion, this paper proposes a centralized congestion control routing protocol based on multi-metrics (CCRPM). It combines the residual energy of a node, buffer occupancy rate, wireless link quality, and the current number of sub-nodes for the candidate parent to reduce the probability of network congestion in the process of network construction. In addition, it adopts a centralized way to determine whether the sub-nodes of the congested node need to be switched based on the traffic analysis when network congestion occurs. Theoretical analysis and extensive simulation results show that compared with the existing routing protocol, the performance of CCRPM is improved significantly in reducing the probability of network congestion, prolonging average network lifetime, increasing network throughput, and decreasing end-to-end delay.

Due to the difficulty of deploying Internet protocol (IP) multicast on the Internet on a large scale, overlay multicast has been considered as a promising alternative to develop the multicast communication in recent years. However, the existing overlay multicast solutions suffer from high costs to maintain the state information of nodes in the multicast forwarding tree. A stateless overlay multicast scheme is proposed, in which the multicast routing information is encoded by a bloom filter (BF) and encapsulated into the packet header without any need for maintaining the multicast forwarding tree. Our scheme leverages the node heterogeneity and proximity information in the physical topology and hierarchically constructs the transit-stub overlay topology by assigning geometric coordinates to all overlay nodes. More importantly, the scheme uses BF technology to identify the nodes and links of the multicast forwarding tree, which improves the forwarding efficiency and decreases the false-positive forwarding loop. The analytical and simulation results show that the proposal can achieve high forwarding efficiency and good scalability.

Cloud download service, as a new application which downloads the requested content offline and reserves it in cloud storage until users retrieve it, has recently become a trend attracting millions of users in China. In the face of the dilemma between the growth of download requests and the limitation of storage resource, the cloud servers have to design an efficient resource allocation scheme to enhance the utilization of storage as well as to satisfy users’ needs like a short download time. When a user’s churn behavior is considered as a Markov chain process, it is found that a proper allocation of download speed can optimize the storage resource utilization. Accordingly, two dynamic resource allocation schemes including a speed switching (SS) scheme and a speed increasing (SI) scheme are proposed. Both theoretical analysis and simulation results prove that our schemes can effectively reduce the consumption of storage resource and keep the download time short enough for a good user experience.

Voice conversion (VC) based on Gaussian mixture model (GMM) is the most classic and common method which converts the source spectrum to target spectrum. However this method is prone to over-fitting because of its frame-by-frame conversion. The VC with non-negative matrix factorization (NMF) is presented in this paper, which can keep spectrum from over-fitting by adjusting the size of basis vector (dictionary). In order to realize the non-linear mapping better, kernel NMF (KNMF) is adopted to achieve spectrum mapping. In addition, to increase the accuracy of conversion, KNMF combined with GMM (GKNMF) is also introduced into VC. In the end, KNMF, GKNMF, GMM, principal component regression (PCR), PCR combined with GMM (GPCR), partial least square regression (PLSR), NMF correlation-based frequency warping (NMF-CFW) and deep neural network (DNN) methods are compared with each other. The proposed GKNMF gets better performance in both objective evaluation and subjective evaluation.

A feature fusion approach is presented to extract the region of interest (ROI) from the stereoscopic video. Based on human vision system (HVS), the depth feature, the color feature and the motion feature are chosen as vision features. The algorithm is shown as follows. Firstly, color saliency is calculated on superpixel scale. Color space distribution of the superpixel and the color difference between the superpixel and background pixel are used to describe color saliency and color salient region is detected. Then, the classic visual background extractor (Vibe) algorithm is improved from the update interval and update region of background model. The update interval is adjusted according to the image content. The update region is determined through non-obvious movement region and background point detection. So the motion region of stereoscopic video is extracted using improved Vibe algorithm. The depth salient region is detected by selecting the region with the highest gray value. Finally, three regions are fused into final ROI. Experiment results show that the proposed method can extract ROI from stereoscopic video effectively. In order to further verify the proposed method, stereoscopic video coding application is also carried out on the joint model (JM) encoder with different bit allocation in ROI and the background region.

An ant colony optimization (ACO) based load balancing routing and wavelength assignment (RWA) algorithm (ALRWA) was put forward for the sake of achieving a fairy load balancing over the entire optical satellite networks. A multi-objective optimization model is established considering the characteristic of global traffic distribution. This not only employs the traffic intensity to modify the light path cost, but also monitors the wavelength utilization of optical inter-satellite links (ISLs). Then an ACO algorithm is utilized to solve this model, leading to finding an optimal light path for every connection request. The optimal light path has the minimum light path cost under satisfying the constraints of wavelength utilization, transmission delay and wavelength-continuity. Simulation results show that ALRWA performs well in blocking probability and realizes efficient load balancing. Meanwhile, the average transmission delay can meet the basic requirement of real-time business transmission.