A secure optical communication requires both high transmission efficiency and high authentication performance, while existing cryptographic key distribution protocols based on ghost imaging have many shortcomings. Here, based on computational ghost imaging, we propose an interactive protocol that enables multi-party cryptographic key distribution over a public network and self-authentication by setting an intermediary that shares partial roles of the server. This fragment-synthesis-based authentication method may facilitate the remote distribution of cryptographic keys.

Single-pixel imaging (SPI) is very popular in subsampling applications, but the random measurement matrices it typically uses will lead to measurement blindness as well as difficulties in calculation and storage, and will also limit the further reduction in sampling rate. The deterministic Hadamard basis has become an alternative choice due to its orthogonality and structural characteristics. There is evidence that sorting the Hadamard basis is beneficial to further reduce the sampling rate, thus many orderings have emerged, but their relations remain unclear and lack a unified theory. Given this, here we specially propose a concept named selection history, which can record the Hadamard spatial folding process, and build a model based on it to reveal the formation mechanisms of different orderings and to deduce the mutual conversion relationship among them. Then, a weight ordering of the Hadamard basis is proposed. Both numerical simulation and experimental results have demonstrated that with this weight sort technique, the sampling rate, reconstruction time and matrix memory consumption are greatly reduced in comparison to traditional sorting methods. Therefore, we believe that this method may pave the way for real-time single-pixel imaging.

With the increasing cloud-trend of high-performance computing (HPC), more users submit their applications simultaneously to the platform and wish they could finish before the deadline. Moreover, due to the severe holistic performance degradation caused by I/O contention, a deadline-sensitive I/O scheduler is needed to allocate storage resources according to the requirements of applications and resultantly guarantee the quality of service (QoS) of concurrently running applications. In this paper, we first explore the bandwidth allocation phenomenon caused by interference in applications through the modeling of historical data, and then we quote a metric called random percentage that can represent the random degree of the applications and be used to guide I/O scheduling in the later stage. We design a dynamic I/O scheduler named DDL-QoS that uses solid state drives(SSDs) as QoS guarantee to minimize interference and ensure applications meet their deadline. The potential of our design is that the greater the I/O interference, the greater the performance improvement, but this performance improvement will be limited by the physical properties of the storage hardware.

BACKGROUND:The treatment of post-traumatic stress disorder (PTSD) has long been a challenge because the symptoms of PTSD are multifaceted. PTSD is primarily treated with psychotherapy and medication, or a combination of psychotherapy and medication. The present study was designed to analyze the literature on medications for PTSD and explore high-frequency common drugs and low-frequency burst drugs by burst detection algorithm combined with Unified Medical Language System (UMLS) and provide references for developing new drugs for PTSD.METHODS:Publications related to medications for PTSD from 2010 to 2019 were identified through PubMed, Web of Science Core Collection, and BIOSIS Previews. SemRep and SemRep semantic result processing system were performed to extract the set of drug concepts with therapeutic relationship according to the semantic relationship of UMLS. Kleinberg's burst detection algorithm was applied to calculate the burst weight index of drug concepts by a Java-based program. These concepts were sorted according to the frequency and the burst weight index.RESULTS:Four hundred and fifty-nine treatment-related drug concepts were extracted. The drug with the highest burst weight index was "Psilocybine", a hallucinogen, which was more likely to be a hotspot for the pharmacotherapy of PTSD. The highest frequency concept was "prazosin", which was more likely to be the focus of research in the medications for PTSD.CONCLUSION:The present study assessed the medication-related literature on PTSD treatment, providing a framework of burst words detection-based method, a baseline of information for future research and the new attempt for the discovery of textual knowledge. The bibliometric analysis based on the burst detection algorithm combined with UMLS has shown certain feasibility in amplifying the microscopic changes of a specific research direction in a field, it can also be used in other aspects of disease and to explore the trends of various disciplines.

Due to the non-ideality of analog components, transmitters (TXs) and receivers (RXs) suffer from hardware impairments such as in-phase/quadrature (I/Q) imbalance, which manifests itself as the mismatches of amplitude, phase and frequency response between I/Q branches. Without compensation, I/Q imbalance can severely degrade the system performance. This paper addresses the joint compensation of TX and RX I/Q imbalances for single carrier frequency domain equalization (SC-FDE) systems. Specifically, we develop an expectation maximization (EM) based approach to separately estimate TX and RX I/Q imbalances, and the multipath channel. With the obtained estimates, we can readily fulfill the compensation of I/Q imbalances. Unlike previous methods, which only estimate the combined effect of I/Q imbalances and the multipath channel, we are able to obtain the separate estimates of the TX I/Q imbalance, the RX I/Q imbalance and the multipath channel. This will significantly alleviate the computation burden of the proposed compensation scheme in the long run. Simulation results show that the proposed scheme outperforms the counterparts, and can significantly mitigate the adverse effects of I/Q imbalances.

This paper addresses the channel estimation issue for millimeter wave (mmWave) massive MIMO systems with lens antenna array. Firstly, we formulate the channel estimation problem into the framework of sparse signal recovery. The formulation takes into account of the energy focusing property of the lens antenna array as well as the mutipath sparsity in mmWave MIMO channels. Then we develop an expectation maximization (EM) based algorithm to learn the parameters in the formulation, thereby fulfilling the channel estimation. Finally, we conduct simulations to verify the advantages of the proposed scheme over the state-of-the-art counterparts.

Gossiping of a single source with multiple messages (by splitting information into pieces) has been treated only for complete graphs, shown to considerably reduce the completion time, that is, the first time at which all network nodes are informed, compared with single-message gossiping. In this paper, gossiping of a single source with multiple messages is treated, for networks modeled as certain structured graphs, wherein upper bounds of the high-probability completion time are established through a novel “dependency graph” technique. The results shed useful insights into the behavior of multiple-message gossiping and can be useful for data dissemination in sensor networks, multihopping content distribution, and file downloading in peer-to-peer networks.

Cellular networks are overloaded due to the mobile traffic surge,and mobile social networks(MSNets) can be leveraged for traffic offloading.In this paper,we study the issue of choosing seed users for maximizing the mobile traffic offloaded from cellular networks.We introduce a gossip-style social cascade(GSC) model to model the epidemic-like information diffusion process in MSNets.For static-case and mobile-case networks,we establish an equivalent view and a temporal mapping of the information diffusion process,respectively.We further prove the submodularity in the information diffusion and propose a greedy algorithm to choose the seed users for traffic offloading,yielding a sub-optimal solution to the NP-hard traffic offloading maximization(TOM) problem.Experiments are carried out to study the offloading performance,illustrating that the greedy algorithm significantly outperforms the heuristic and random algorithms,and user mobility can help further reduce cellular load.

To decrease potentially harmful effects to the environment and reduce costs of cellular network operators, the exploration of energy saving is significant. In this paper, we shed light on the following energy consumption minimization (ECM) problem: "how to select the smallest set of active base stations (BSs) that can preserve the quality of service (the minimum data rate) required by the users." Since the ECM problem belongs to the field of integer programming and is NP-hard, we propose two low-complexity approximation algorithms that yield performance guarantees: the iterative minimal set cover algorithm and the iterative maximal coverage (IMC) algorithm. Moreover, by leveraging the submodularity theory, we establish the performance bound of the IMC algorithm. Finally, the computational complexity of our proposed algorithms is analyzed, and a simulation platform is developed to evaluate the number of active BSs of proposed approaches. The numerical results demonstrate that our proposed algorithms outperform the conventional counterpart.

Information diffusion is efficient via gossip or rumor spreading in many of the next generation networks. It is of great importance to select some seed nodes as information sources in a network so as to maximize the gossip spreading. In this paper, we deal with the issue of the selection of information sources, which are initially informed nodes (i.e., seed nodes) in a network, for pull-based gossip protocol. We prove that the gossip spreading maximization problem (GSMP) is NP-hard. We establish a temporal mapping of the gossip spreading process using virtual coupon collectors by leveraging the concept of temporal network, further prove that the gossip spreading process has the property of submodularity, and consequently propose a greedy algorithm for selecting the information sources, which yields a suboptimal solution within 1 - 1 / e of the optimal value for GSMP. Experiments are carried out to study the spreading performance, illustrating the significant superiority of the greedy algorithm over heuristic and random algorithms.

Mobility load balancing (MLB) is a key technology for self-organization networks (SONs). In this paper, we explore the mobility load balancing problem and propose a unified cell specific offset adjusting algorithm (UCSOA) which more accurately adjusts the largely uneven load between neighboring cells and is easily implemented in practice with low computing complexity and signal overhead. Moreover, we evaluate the UCSOA algorithm in two different traffic conditions and prove that the UCSOA algorithm can get the lower call blocking rates and handover failure rates. Furthermore, the interdependency of the proposed UCSOA algorithm's performance and that of the inter-cell interference coordination (ICIC) algorithm is explored. A self-organization soft frequency reuse scheme is proposed. It demonstrates UCSOA algorithm and ICIC algorithm can obtain a positive effect for each other and improve the network performance in LTE system.

We study the uplink Multi-Cell Processing (MCP) model where each base station (BS) is connected to a centralized processor (CP) via a noiseless backhaul link with fixed and finite capacity. User data is decoded in the CP. In this paper, a fixed-order Successive Interference Cancellation (SIC) decoding scheme is adopted in the CP and the rate region is also derived. Further, we extend the MCP model with fixed-order SIC scheme to include the constraint that each user has the requirement of signal-to- interference-and-noise ratio (SINR). We analyze the option of quantized levels and give the theoretical lower bound and upper bound of the quantized levels. The analysis reveals that the quantized levels can affect whether each user uses its own maximized power to achieve the SINR requirement or not. Finally, numerical simulation shows the lower bound and the upper bound are very closed to the theoretical results.

This paper considers the model that two mobile users communicate with a central processer (CP) via two base stations (BSs). The BSs are connected to the CP via orthogonal finite-capacity links. The theoretical capacity of this model is still an open problem. For given capacity of backhaul links, we use private message, common message and joint decoding message to explore a new achievable rate. We also propose an optimization method to obtain the boundary. A better achievable rate region is acquired by our proposed scheme, and optimal power allocation for private message, common message and joint decoding message is derived. In our simulations, the results illustrate how the capacity of backhaul links determines the power allocation of private message, common message and joint message.

SUMMARYMobility load balance is becoming increasingly crucial in the design of self‐organization networks for LTE (long‐term evolution), especially under dynamic scenarios. In this paper, we study the problem of load balance with guarantee of users' rate and formulate a general optimization model, where the weighted sum of the first‐order and second‐order statistics of load distribution (load variance and total load level) are considered directly. We prove the convexity of the model and derive the optimal user‐station association scheme in different weights. Based on analysis of the optimization model, a dynamic admission and handover scheme is proposed. Mobile users with rate requirement select a base station based on the load utility in a distributed way and handover to the selected station (if handover is triggered). Furthermore, a simulation platform with seven cells is developed to evaluate the dynamic admission and handover scheme. It is demonstrated that our proposed scheme outperforms the conventional counterpart. Copyright © 2013 John Wiley & Sons, Ltd.

We propose an energy-efficient adaptive transmission scheme for multiple-input-multiple-output (MIMO) beamforming systems with orthogonal space-time block coding (OSTBC) based on imperfect channel state information at the transmitter (CSIT). The transmission rate and transmit power are adapted to maximize the energy efficiency (EE) subject to an instantaneous bit-error-rate (I-BER) constraint. We solve this nonconcave problem based on the generalized convexity. Closed-form expressions of the energy-efficient transmission rate and transmit power for both modulations with constant power amplifier (PA) inefficiency and M -ary quadratic-amplitude modulation (MQAM) with nonconstant PA inefficiency are obtained. Based on these results, we also analyze four special cases: The circuit power dissipation is negligible, the CSIT is perfect, the CSIT is only channel distribution information (CDI), and the discrete rate adaptation policy is employed. Numerical results are also given to further illustrate the impact of CSIT imperfection to the transmitter EE.

Employing the low bitwidth, e.g. 1 bit or 2 bit analog-to-digital converter (ADC) can remarkably reduce the high power dissipation of ADC in the orthogonal frequency division multiplexing (OFDM) communications. However, the biterror-rate (BER) performance will be considerably undermined. In the paper, through deliberately reserving the subcarrier at the transmitter, the quantization distortion can be suppressed to achieve the BER gain. With the definition of the signal-to-distortion noise ratio (SDNR), the optimal reservation scheme achieving the maximum BER gain is also reported through formulating it as a single-variable optimization problem. Simulation results show that the proposed method can effectively mitigate the quantization distortion.

Network designers expect a highly mobile, highly responsive, and quickly deployable network structure to enable communication in emergency situations. For this reason, a major research goal for emerging mobile network (mobile sensor network, mobile ad hoc network, etc.)is to achieve good coverage and robust communication performance by coordinated management of base stations or nodes. In this paper, we extend current methods to balancing cell load by taking base station mobility into account.We propose a novel distributed base station mobility strategy (DMS). We focus on a recently attractive network model where localized communication is supported by using mobile base stations (MBSs) placed on moving vehicles. In order to minimize the blocking rate in high traffic density area, the proposed strategy will lead the MBSs to suitable locations and re-share the whole traffic load. We demonstrate the convergence of this distributed mobility strategy by both theoretical analysis and numerical evaluations of realistic networks. The strategy enables each MBS to select destination efficiently and locally with limited information exchange. Compared with the traditional load balancing method based on transmit power control, the proposed algorithm has obvious advantages in reducing the dropped calls, especially with extremely uneven load distribution.

MIMO energy efficiency has attracted a lot of attention recently. In this paper, energy efficiency of two different transmit diversity schemes (transmit antenna selection and transmit beamforming) is analyzed in correlated channel. We derive the relationship between spatial correlation and energy efficiency of two transmit diversity schemes and find that spatial correlation has different effects on them. In order to improve the energy efficiency of the system, we calculate the boundary spatial correlation coefficient based on which transmit diversity schemes can be adopted adaptively. Finally, a simulation is developed to prove our analysis.

In this paper, a channel allocation algorithm is proposed with low admission delay. It is designed for mobile based networks in which fixed base stations are replaced by mobile base stations(MBSs) placed on moving vehicles. Considering the high dynamic characteristic of MBSs network, the channel admission delay is a critical indicator. In order to minimize the admission delay of each MBSs fairly, an min-max optimization model for reserve channel number of each MBS is constructed. Also, the Particle Swarm Optimization(PSO) algorithm is adopted to solve the problem due to its low complexity which is crucial in mobile scenario. Finally, we evaluate the performances of proposed algorithm by simulation. Compared with former algorithm, the proposed algorithm can effectively reduce the admission delay and maintain fairness.

To cope with the ever increasing demand for bandwidth and increasing number of users, future wireless networks will be designed with the radio resource allocation techniques able to get good performance with low complexity and feedback. In this paper we study an allocation problem for OFDMA networks formulated with the objective of minimizing the load of each cell in the system or maximizing the number of accessing users subject to the constraint that each user meets its target rate. We formulate the two problem as one framework of finding the maximum weighted independent set (MWIS) in graph theory. In addition, we propose a minimal weighted-degree greedy (MWDG) algorithm. The control information requested to perform the allocation is limited and the computational burden is shared between the base station and the user equipments. Simulations have been carried out under constant bit rate traffic model and the results show MWDG has excellent performance and outperforms all other techniques.