This paper presents an adaptive modulation and power allocation method for uplink multiuser multiple-input multiple-output systems under the assumption that there is perfect channel information at the receiver but not at the transmitter. The receiver jointly optimizes the power level and modulation order for all users under constraints of transmit power and error requirements, and returns these information to each user via a low-rate feedback channel. Power and modulation optimization maximizes the total throughput under a tolerable bit error ratio for each user. The ellipsoid method is used to design efficient algorithms for optimal power and modulation level.

Precoding techniques can be introduced into relay systems due to the similarity between relay systems and traditional multi-input-multi-output (MIMO) systems. A channel state information feedback scheme is firstly presented for the MIMO relay system in this letter, where the zero-forcing relaying protocol is proposed to be used so that the information of the equivalent channel and the relaying noise can be compressed into two coefficients. With the proposed feedback scheme, the distributed precoding is presented to be applied through two continuous transmitted vectors of the source node while the co-channel interference cancellation equalizer is used in the destination node. The system outage probability can be improved with the precoding in the source node. Furthermore, various spatial data rates can be conveniently supported by the proposed distributed spatial-temporal precoding method.

Simple half-duplex repetition-based relaying protocols can achieve spatial diversity at the expense of additional relaying signals in the time domain. In this paper, a linear unitary precoder based on a singular vector for cooperative systems with the amplify-and-forward (AF) relaying protocol is proposed in order to improve spectral efficiency. An exact expression of the precoder design is first derived for the case of equal power allocation. Then, water-filling power allocation is used in conjunction with the precoder to further increase the system capacity, where the precoder matrix is generated with an iterative process. From the implementation point of view, the channel state information (CSI) has to be estimated and quantized in systems, the detail of which is described in the sequel. The adaptive modulation and coding (AMC) technique with the proposed precoder is also discussed to achieve high throughput performance. Finally, numerical and simulation results are presented to demonstrate the effectiveness of the proposed technique in improving capacity and throughput.

A major challenge in wideband spectrum sensing, in cognitive radio system for example, is the requirement of a high sampling rate which may exceed today's best analog-to-digital converters (ADCs) front-end bandwidths. Compressive sampling is an attractive way to reduce the sampling rate. The modulated wideband converter (MWC) proposed recently is one of the most successful compressive sampling hardware architectures, but it has high hardware complexity owing to its parallel channels structure. In this paper, we design a single channel sub-Nyquist sampling scheme to bring substantial savings in terms of not only sampling rate but also hardware complexity, and we also present a wideband power spectrum sensing and reconstruction method for bandlimited wide-sense stationary (WSS) signals. The total sampling rate is only one channel rate of the MWC's. We evaluate the performance of the sensing model by computing the probability of detecting signal occupancy in terms of the signal-to-noise ratio (SNR) and other practical parameters. Simulation results underline the promising performance of proposed approach.

In this paper, the multicell distributed beamforming (MDBF) design problem of suppressing intra-cell interference (InCI) and inter-cell interference (ICI) is studied. To start with, in order to decrease the InCI and ICI caused by a user, we propose a gradient-iteration altruistic algorithm to derive the beamforming vectors. The convergence of the proposed iterative algorithm is proved. Second, a metric function is established to restrict the ICI and maximize cell rate. This function depends on only local channel state information (CSI) and does not need additional CSIs. Moreover, an MDBF algorithm with the metric function is proposed. This proposed algorithm utilizes gradient iteration to maximize the metric function to improve sum rate of the cell. Finally, simulation results demonstrate that the proposed algorithm can achieve higher cell rates while offering more advantages to suppress InCI and ICI than the traditional ones.

The conventional covariance matrix technique based subspace methods, such as the 2-D Capon algorithm and computationally efficient ESPRIT-type algorithms, are invalid with a single snapshot in a bistatic MIMO radar. A novel matrix pencil method is proposed for the direction of departures (DODs) and direction of arrivals estimation (DOAs) estimation. The proposed method constructs an enhanced matrix from the direct sampled data, and then utilizes the matrix pencil approach to estimate DOAs and DODs, which are paired automatically. The proposed method is able to provide favorable and unambiguous angle estimation performance with a single snapshot. Simulation results are presented to verify the effectiveness of the proposed method.

Linear complexity and the k-error linear complexity of periodic sequences are the important security indices of stream cipher systems. This paper focuses on the distribution of p-error linear complexity of p-ary sequences with period pn. For p-ary sequences of period pn with linear complexity pn-p+1, n≥1, we present all possible values of the p-error linear complexity, and derive the exact formulas to count the number of the sequences with any given p-error linear complexity.

Device-to-device (D2D) multicast communication is a useful way to improve the communication efficiency of local services. This study considers a scenario of D2D multicast communication in a single frequency network (SFN) system and investigates the frequency resource allocation problem. Firstly, we propose that D2D user equipments (DUEs) do not share frequency with cellular user equipments (CUEs) in the same SFN, but reuse frequency with CUEs in other SFNs, by which the interference between D2D and cellular communications can be avoided. Then, under the principle that two nearest D2D multicast groups cannot reuse the same frequency, the study develops a distance-based fair frequency resource allocation (DFRA) algorithm. The DFRA algorithm ensures control of the interference within a reasonable range and fairly allocate the available frequency resources to the D2D multicast groups. Numerical simulation results show that the proposed resource allocation algorithm is effective in improving the data rate and reducing the outage probability for D2D communications.

A novel approach for fast traffic classification for the high speed networks is proposed, which bases on the protocol behavior statistical features. The packet size and a new parameter named "Estimated Protocol Processing Time" are collected from the real data flows. Then a set of joint probability distributions is obtained to describe the protocol behaviors and classify the traffic. Comparing the parameters of an unknown flow with the pre-obtained joint distributions, we can judge which application protocol the unknown flow belongs to. Distinct from other methods based on traditional inter-arrival time, we use the "Estimated Protocol Processing Time" to reduce the location dependence and time dependence and obtain better results than traditional traffic classification method. Since there is no need for character string searching and parallel feature for hardware implementation with pipeline-mode data processing, the proposed approach can be easily deployed in the hardware for real-time classification in the high speed networks.

In the letter, we propose an improved histogram shifting (HS) based reversible data hiding scheme for small payload embedding. Conventional HS based schemes are not suitable for low capacity embedding with relatively large distortion due to the inflexible side information selection. From an analysis of the whole HS process, we develop a rate-distortion model and provide an optimal adaptive searching approach for side information selection according to the given payload. Experiments demonstrate the superior performance of the proposed scheme in terms of performance curve for low payload embedding.

In this letter, we study the performance of multi-antenna relay networks with limited feedback beamforming in decode-and-forward (DF) relaying. Closed-form expression for both outage probability and symbol error rate are derived by using the moment generation function (MGF) of the combined signal-to-noise ratio (SNR) at the destination. Subjected to a total power constraint, we also explore adaptive power allocation between source and relay to optimize the performance. Simulations are given to verify the correctness of our theoretical derivations. Results show that the proposed adaptive power allocation solution significantly outperforms the uniform power allocation method.

Network Slicing (NS) is recognized as a key technology for the 5G network in providing tailored network services towards various types of verticals over a shared physical infrastructure. It offers the flexibility of on-demand provisioning of diverse services based on tenants' requirements in a dynamic environment. In this work, we focus on two important issues related to 5G Core slices: the deployment and the reconfiguration of 5G Core NSs. Firstly, for slice deployment, balancing the workloads of the underlying network is beneficial in mitigating resource fragmentation for accommodating the future unknown network slice requests. In this vein, we formulate a load-balancing oriented 5G Core NS deployment problem through an Integer Linear Program (ILP) formulation. Further, for slice reconfiguration, we propose a reactive strategy to accommodate a rejected NS request by reorganizing the already-deployed NSs. Typically, the NS deployment algorithm is reutilized with slacked physical resources to find out the congested part of the network, due to which the NS is rejected. Then, these congested physical nodes and links are reconfigured by migrating virtual network functions and virtual links, to re-balance the utilization of the whole physical network. To evaluate the performance of deployment and reconfiguration algorithms we proposed, extensive simulations have been conducted. The results show that our deployment algorithm performs better in resource balancing, hence achieves higher acceptance ratio by comparing to existing works. Moreover, our reconfiguration algorithm improves resource utilization by accommodating more NSs in a dynamic environment.

Text categorization, especially short text categorization, is a difficult and challenging task since the text data is sparse and multidimensional. In traditional text classification methods, document texts are represented with “Bag of Words (BOW)” text representation schema, which is based on word co-occurrence and has many limitations. In this paper, we mapped document texts to Wikipedia concepts and used the Wikipedia-concept-based document representation method to take the place of traditional BOW model for text classification. In order to overcome the weakness of ignoring the semantic relationships among terms in document representation model and utilize rich semantic knowledge in Wikipedia, we constructed a semantic matrix to enrich Wikipedia-concept-based document representation. Experimental evaluation on five real datasets of long and short text shows that our approach outperforms the traditional BOW method.

REMUS-II (REconfigurable MUltimedia System 2) is a coarse-grained dynamically reconfigurable computing system for multimedia and communication baseband processing. This paper proposes a real-time H.264 baseline profile encoder on REMUS-II. First, we propose an overall mapping flow for mapping algorithms onto the platform of REMUS-II system and then illustrate it by implementing the H.264 encoder. Second, parallel and pipelining techniques are considered for fully exploiting the abundant computing resources of REMUS-II, thus increasing total computing throughput and solving high computational complexity of H.264 encoder. Besides, some data-reuse schemes are also used to increase data-reuse ratio and therefore reduce the required data bandwidth. Third, we propose a scheduling scheme to manage run-time reconfiguration of the system. The scheduling is also responsible for synchronizing the data communication between tasks and handling conflict between hardware resources. Experimental results prove that the REMUS-MB (REMUS-II version for mobile applications) system can perform a real-time H.264/AVC baseline profile encoder. The encoder can encode CIF@30 fps video sequences with two reference frames and maximum search range of [-16,15]. The implementation, thereby, can be applied to handheld devices targeted at mobile multimedia applications. The platform of REMUS-MB system is designed and synthesized by using TSMC 65 nm low power technology. The die size of REMUS-MB is 13.97 mm2. REMUS-MB consumes, on average, about 100 mW while working at 166 MHz. To my knowledge, in the literature this is the first implementation of H.264 encoding algorithm on a coarse-grained dynamically reconfigurable computing system.

In this paper, a multicast concept for Device-to-Device (D2D) communication underlaying a cellular infrastructure is investigated. To increase the overall capacity and improve resource utilization, a novel interference coordination scheme is proposed. The proposed scheme includes three steps. First, in order to mitigate the interference from D2D multicast transmission to cellular networks (CNs), a dynamic power control scheme is proposed that can determine the upper bound of D2D transmitter power based on the location of Base Station (BS) and areas of adjacent cells from the coverage area of D2D multicast group. Next, an interference limited area control scheme that reduces the interference from CNs to each D2D multicast receiver is proposed. The proposed scheme does not allow cellular equipment (CUE) located in the interference limited area to reuse the same resources as the D2D multicast group. Then two resource block (RB) allocation rules are proposed to select the appropriate RBs from a candidate RB set for D2D multicast group. From the simulation results, it is confirmed that the proposed schemes improve the performance of the hybrid system compared to the conventional ways.

Leveraging on Network Function Virtualization (NFV) and Software Defined Networking (SDN), network slicing (NS) is recognized as a key technology that enables the 5G Infrastructure Provider (InP) to support diversified vertical services over a shared common physical infrastructure. 5G end-to-end (E2E) NS is a logical virtual network that spans across the 5G network. Existing works on improving the reliability of the 5G mainly focus on reliable wireless communications, on the other hand, the reliability of an NS also refers to the ability of the NS system to provide continued service. Hence, in this work, we focus on enhancing the reliability of the NS to cope with physical network node failures, and we investigate the NS deployment problem to improve the reliability of the system represented by the NS. The reliability of an NS is enhanced by two means: firstly, by considering the topology information of an NS, critical virtual nodes are backed up to allow failure recovery; secondly, the embedding of the augmented NS virtual network is optimized for failure avoidance. We formulate the embedding of the augmented virtual network (AVN) to maximize the survivability of the NS system as the survivable AVN embedding (S-AVNE) problem through an Integer Linear Program (ILP) formulation. Due to the complexity of the problem, a heuristic algorithm is introduced. Finally, we conduct intensive simulations to evaluate the performance of our algorithm with regard to improving the reliability of the NS system.

Network virtualization has become a promising paradigm for supporting diverse vertical services in Software Defined Networks (SDNs). Each vertical service is carried by a virtual network (VN), which normally has a chaining structure. In this way, a Service Function Chain (SFC) is composed by an ordered set of virtual network functions (VNFs) to provide tailored network services. Such new programmable flexibilities for future networks also bring new network management challenges: how to collect and analyze network measurement data, and further predict and diagnose the performance of SFCs? This is a fundamental problem for the management of SFCs, because the VNFs could be migrated in case of SFC performance degradation to avoid Service Level Agreement (SLA) violation. Despite the importance of the problem, SFC performance analysis has not attracted much research attention in the literature. In this current paper, enabled by a novel detailed network debugging technology, In-band Network Telemetry (INT), we propose a learning based framework for early SFC fault prediction and diagnosis. Based on the SFC traffic flow measurement data provided by INT, the framework firstly extracts SFC performance features. Then, Long Short-Term Memory (LSTM) networks are utilized to predict the upcoming values for these features in the next time slot. Finally, Support Vector Machine (SVM) is utilized as network fault classifier to predict possible SFC faults. We also discuss the practical utilization relevance of the proposed framework, and conduct a set of network emulations to validate the performance of the proposed framework.