Survivable virtual network embedding (SVNE) is one of major challenges of network virtualization. In order to improve the utilization rate of the substrate network (SN) resources with virtual network (VN) topology connectivity guarantee under link failure in SN, we first establishes an Integer Linear Programming (ILP) model for that under SN supports path splitting. Then we designs a novel survivable VN topology protection method based on particle swarm optimization (VNE-PSO), which redefines the parameters and related operations of particles with the embedding overhead as the fitness function. Simulation results show that the solution significantly improves the long-term average revenue of the SN, the acceptance rate of VN requests, and reduces the embedding time compared with the existing research results.

5G has achieved large-scale commercialization across the world and the global 6G research and development is accelerating. To support more new use cases, 6G mobile communication systems should satisfy extreme performance requirements far beyond 5G. The physical layer key technologies are the basis of the evolution of mobile communication systems of each generation, among which three key technologies, i.e., duplex, waveform and multiple access, are the iconic characteristics of mobile communication systems of each generation. In this paper, we systematically review the development history and trend of the three key technologies and define the Non-Orthogonal Physical Layer (NOPHY) concept for 6G, including Non-Orthogonal Duplex (NOD), Non-Orthogonal Multiple Access (NOMA) and Non-Orthogonal Waveform (NOW). Firstly, we analyze the necessity and feasibility of NOPHY from the perspective of capacity gain and implementation complexity. Then we discuss the recent progress of NOD, NOMA and NOW, and highlight several candidate technologies and their potential performance gain. Finally, combined with the new trend of 6G, we put forward a unified physical layer design based on NOPHY that well balances performance against flexibility, and point out the possible direction for the research and development of 6G physical layer key technologies.

In deep sub-micrometer CMOS process, owing to the thin gate oxide and small subthreshold voltage, the leakage current becomes more and more serious. The leakage current has made the impact on phase-locked loops (PLLs). In this paper, the compensation circuits are presented to reduce the leakage current on the charge pump circuit and the MOS capacitor as the loop filter. The proposed circuit has been fabricated in 0.13-µm CMOS process. The power consumption is 3 mW and the die area is 0.270.3 mm2.

In this paper, we propose a novel self-supervised learning of video representation which is capable to anticipate the video category by only reading its short clip. The key idea is that we employ the Siamese convolutional network to model the self-supervised feature learning as two different image matching problems. By using frame encoding, the proposed video representation could be extracted from different temporal scales. We refine the training process via a motion-based temporal segmentation strategy. The learned representations for videos can be not only applied to action anticipation, but also to action recognition. We verify the effectiveness of the proposed approach on both action anticipation and action recognition using two datasets namely UCF101 and HMDB51. The experiments show that we can achieve comparable results with the state-of-the-art self-supervised learning methods on both tasks.

In this paper, we present the channel estimation (CE) problem in the orthogonal frequency division multiplexing system with offset quadrature amplitude modulation (OFDM/OQAM). Most CE methods rely on the assumption of a low frequency selective channel to tackle the problem in a way similar to OFDM. However, these methods would result in a severe performance degradation of the channel estimation when the assumption is not quite inaccurate. Instead, we focus on estimating the channel impulse response (CIR) itself which makes no assumption on the degree of frequency selectivity of the channels. After describing the main idea of this technique, we present an iterative CE method that does not require zero-value guard symbols in the preamble and consequently improves the spectral efficiency. This is done by the iterative estimation of the unknown transmitted data adjacent to the preamble. Analysis and simulation results validate the efficacy of the proposed method in multipath fading channels.

Modern field programmable gate arrays (FPGAs) with heterogeneous resources are partially reconfigurable. Existing methods of reconfiguration-aware floorplanning have limitations with regard to homogeneous resources; they solve only a part of the reconfigurable problem. In this paper, first, a precise model for partially reconfigurable FPGAs is formulated, and then, a two-phase floorplanning approach is presented. In the proposed approach, resource distribution is taken into consideration at all times. In the first step, a resource-aware insertion-after-remove perturbation is devised on the basis of the multi-layer sequence pair constraint graphs, and resource-aware slack-based moves (RASBM) are made to satisfy resource requirements. In the second step, a resource-aware fixed-outline floorplanner is used, and RASBM are applied to pack the reconfigurable regions on the FPGAs. Experimental results show that the proposed approach is resource- and reconfiguration-aware, and facilitates stable floorplanning. In addition, it reduces the wire-length by 4–28% in the first step, and by 12% on average in the second step compared to the wire-length in previous approaches.

The capacity of optical transport networks has been increasing steadily and the networks are becoming more dynamic, complex, and transparent. Though it is common to use worst case assumptions for estimating the quality of transmission (QoT) in the physical layer, over provisioning results in high margin requirements. Accurate estimation on the QoT for to-be-established lightpaths is crucial for reducing provisioning margins. Machine learning (ML) is regarded as one of the most powerful methodological approaches to perform network data analysis and enable automated network self-configuration. In this paper, an artificial neural network (ANN) framework, a branch of ML, to estimate the optical signal-to-noise ratio (OSNR) of to-be-established lightpaths is proposed. It takes account of both nonlinear interference between spectrum neighboring channels and optical monitoring uncertainties. The link information vector of the lightpath is used as input and the OSNR of the lightpath is the target for output of the ANN. The nonlinear interference impact of the number of neighboring channels on the estimation accuracy is considered. Extensive simulation results show that the proposed OSNR estimation scheme can work with any RWA algorithm. High estimation accuracy of over 98% with estimation errors of less than 0.5dB can be achieved given enough training data. ANN model with R=4 neighboring channels should be used to achieve more accurate OSNR estimates. Based on the results, it is expected that the proposed ANN-based OSNR estimation for new lightpath provisioning can be a promising tool for margin reduction and low-cost operation of future optical transport networks.

Audio applications for mobile phone and portable devices are increasingly popular. To attract consumer interest, a multi-standard design on a single device is the trend of current audio decoder development. This paper presents a configurable common filterbank processor (CCFP) for AC-3, MP3 and AAC audio decoder. It is used as an accelerator for general purpose processors to improve performance. All the filterbank transforms are derived to even- or odd-point IFFT flows. In the architecture, a fully pipelined approach is developed which can be configured for different operation modes. This design is synthesized using UMC 0.18 µm library and takes about 26.7 K gates. By the fast algorithm and fully pipelined architecture, the operation cycles are greatly reduced. Therefore, it can be executed at a very low operation frequency with the range of 1.3 to 3.6 MHz. Besides, the power consumption is only 0.9 mW, 3.2 mW and 1 mW for AC-3, MP3 and AAC respectively. We further port our design on an ARM Integrator platform to make a real play system. On average, over 50% ARM performance loading can be saved and used for handling other applications.

A cumulant-based lattice algorithm for multichannel adaptive filtering is proposed in this paper. Proposed algorithm takes into account the advantages of higer-order statistics, that is, improvement of estimation accuracy, blindness to colored Gaussian noise and the possibility to estimate the nonminimum-phase system etc. Without invoking the Instrumental Variable () method as used in other papers [1], [2], the algorithm is derived directly from the recursive pseudo-inverse matrix. The behavior of the algorithm is illustrated by numerical examples.

In this letter, we propose a power allocation scheme to optimize the ergodic secrecy rate of multiple-input single-output (MISO) fading wiretap channels with a probabilistic constraint, using the statistical channel state information (CSI) of the eavesdropper (CSI-E). The analytical expressions of the false secrecy probability are derived and used as constraints in the rate maximization problem. Moreover, we obtain a suboptimal solution by formulating the power allocation problem as a Rayleigh quotient problem.

Crowd flow prediction in high density urban scenes is involved in a wide range of intelligent transportation and smart city applications, and it has become a significant topic in urban computing. In this letter, a CNN-based framework called Pyramidal Spatio-Temporal Network (PSTNet) for crowd flow prediction is proposed. Spatial encoding is employed for spatial representation of external factors, while prior pyramid enhances feature dependence of spatial scale distances and temporal spans, after that, post pyramid is proposed to fuse the heterogeneous spatio-temporal features of multiple scales. Experimental results based on TaxiBJ and MobileBJ demonstrate that proposed PSTNet outperforms the state-of-the-art methods.

Beamforming with sparse constraint has shown significant performance improvement. In this letter, a least squares constant modulus blind adaptive beamforming with sparse constraint is proposed. Simulation results indicate that the proposed approach exhibits better performance than the well-known least squares constant modulus algorithm (LSCMA).

Elliptic curve cryptography (ECC), one of the asymmetric cryptography, is widely used in practical security applications, especially in the Internet of Things (IoT) applications. This paper presents a low-power reconfigurable architecture for ECC, which is capable of resisting simple power analysis attacks (SPA) and can be configured to support all of point operations and modular operations on 160/192/224/256-bit field orders over GF(p). Point multiplication (PM) is the most complex and time-consuming operation of ECC, while modular multiplication (MM) and modular division (MD) have high computational complexity among modular operations. For decreasing power dissipation and increasing reconfigurable capability, a Reconfigurable Modular Multiplication Algorithm and Reconfigurable Modular Division Algorithm are proposed, and MM and MD are implemented by two adder units. Combining with the optimization of operation scheduling of PM, on 55 nm CMOS ASIC platform, the proposed architecture takes 0.96, 1.37, 1.87, 2.44 ms and consumes 8.29, 11.86, 16.20, 21.13 uJ to perform one PM on 160-bit, 192-bit, 224-bit, 256-bit field orders. It occupies 56.03 k gate area and has a power of 8.66 mW. The implementation results demonstrate that the proposed architecture outperforms the other contemporary designs reported in the literature in terms of area and configurability.

The energy efficiency of Internet of Things (IoT) could be improved by RF energy transfer technologies.Aiming at IoT applications with a mobility-constrained mobile sink, a double-sourced energy transfer (D-ET) scheme is proposed. Based on the hierarchical routing information of network nodes, the Simultaneous Wireless Information and Power Transfer (SWIPT) method helps to improve the global data gathering performance. A genetic algorithm and graph theory are combined to analyze the node energy consumption distribution. Then dedicated charger nodes are deployed on the basis of the genetic algorithm's output. Experiments are conducted using Network Simulator-3 (NS-3) to evaluate the performance of the D-ET scheme. The simulation results show D-ET outperforms other schemes in terms of network lifetime and data gathering performance.

Abnormal behavior detection is now a widely concerned research field, especially for crowded scenes. However, most traditional unsupervised approaches often suffered from the problem when the normal events in the scenario with large visual variety. This paper proposes a self-learning probabilistic Latent Semantic Analysis, which aims at taking full advantage of the high-level abnormal information to solve problems. We select the informative observations to construct the “reference events” from the training sets as a high-level guidance cue. Specifically, the training set is randomly divided into two separate subsets. One is used to learn this model, which is defined as the initialization sequence of “reference events”. The other aims to update this model and the the infrequent samples are chosen into the “reference events”. Finally, we define anomalies using events that are least similar to “reference events”. The experimental result demonstrates that the proposed model can detect anomalies accurately and robustly in the real-world crowd environment.

This paper proposes an analytical model to demonstrate the benefit of data service in wireless networks using dynamic multi-channel scheme with channel de-allocation. The performance of a system providing buffers to voice calls to reduce the raised voice blocking probability caused by data contention is investigated. The effect of the cell dwell time and overlap area with adjacent cells on system performance are studied. All free channels are allocated to data users dynamically. For those data users using more than one channel, channels would be de-allocated for new requests, voice or data. Buffers are provided for voice calls to reduce the voice blocking probability caused by data packets contention. Handoff calls are given priority to be queued in the front of the buffer instead of providing guard channels to reduce their dropping probability. Meanwhile, the reneging time for new calls and the handoff dwell time for handoff calls are considered in our analysis to obtain an appropriate amount of buffer to voice. To compensate the blocking probability in data, guard channels are provided for data traffic. Numerical results show that the dynamic multi-channel scheme with possible de-allocation, compared with the single channel scheme, can enhance data traffic performance significantly in terms of the mean transmission time and blocking probability. A system providing an appropriate amount of buffer to voice traffic and giving priority to queued handoff calls can indeed reduce new call blocking probability and handoff call dropping probability. In addition, the proposed scheme can reduce the incomplete transmission probability of data packets.

In this paper, we analyzed the performance of dynamic resource allocation with channel de-allocation and buffering in cellular networks. Buffers are applied for data traffic to reduce the packet loss probability while channel de-allocation is exploited to reduce the voice blocking probability. The results show that while buffering data traffic can reduce the packet loss probability, it has negative impact on the voice performance even if channel de-allocation is exploited. Although the voice blocking probability can be reduced with large slot capacity, the improvement decreases as the slot capacity increases. On the contrary, the packet loss probability increases as the slot capacity increases. In addition to the mean value analysis, the delay distribution and the 95% delay of data packets are provided.

To overcome the false lock or detection missing problems caused by the multiple peaks of the auto-correlation function (ACF) of Binary Offset Carrier (BOC) modulated signal, an acquisition algorithm based on unit correlation for BOC(n,n) signal is proposed in this paper. The local BOC signal is separated into two unit signals, an odd one and an even one. Then a reconstruction of the unit correlation functions between the unit signals and the received BOC signal is performed and M sections of reconstructed correlation function are accumulated according to the non-coherent method, so that this novel acquisition algorithm can not only eliminate the multiple secondary peaks, but also retain the advantage of the narrow correlation main peak. Simulation results show that the acquisition sensitivity of the proposed algorithm is increased 3dBHz compared with the ASPeCT method, and the computation cost is only 41.46% of the ASPeCT method when M=2.

Multi-rate capabilities are supported by the physical layers of most 802.11 devices. To enhance the network throughput of MANETs, transfer rate adaptation schemes at MAC layer should employ the multi-rate capability at physical and the information of previous transmissions provided by MAC and physical layers. In this paper, we propose a transfer rate adaptation scheme plus back-to-back frame transmissions, and fragmentation at MAC layer, named TRAF. TRAF adopts a bi-direction-based approach with an extended option to select an appropriate rate for frame transmission under fast changing channel conditions. Consecutive back-to-back frame transmissions to fully utilize good channel quality during a coherent time interval and fragmentation algorithm to maintain high throughput under worse channel conditions are recommended in TRAF. Extensive simulation is experimented to evaluate the performance of TRAF. Regarding simulation results, frame delivery ratio, network throughput, and fairness of TRAF are significantly improved by comparing to that of fix rate, ARF, RBAR, OAR, and AAR protocols.

In this paper, we focus on the adaptive resource allocation issue for uplink OFDMA systems. The resources are allocated according to a proportional fairness criterion, which can strike an alterable balance between fairness and efficiency. Optimization theory is used to analyze the multi-constraint resource allocation problem and some heuristic characteristics about the optimal solution are obtained. To deal with the cohesiveness of the necessary conditions, we resort to bargaining theory that has been deeply investigated in game theory. Firstly, we summarize some assumptions about bargaining theory and show their similarities with the resource allocation process. Then we propose a priority-ranked bargaining model, whose primary contribution is applying the economic thought to the resource allocation process. A priority-ranked bargaining algorithm (PRBA) is subsequently proposed to permit the base station to auction the subcarriers one by one according to the users' current priority. By adjusting the predefined rate ratio flexibly, PRBA can achieve different degrees of fairness among the users' capacity. Simulation results show that PRBA can achieve similar performance of the max-min scheme and the NBS scheme in the case of appropriate predefined rate ratio.