It is believed that the wavelength switched optical network (WSON) technology is moving towards being adopted by large-scale networks. Wavelength conversion and signal regeneration through reamplifying, reshaping, and retiming (3R) are beneficial to support the expansion of WSON. In many cases, these two functions can be technically integrated into a single shared physical component, namely the wavelength convertible 3R regenerator (WC3R). However, fully deploying such devices is infeasible due to their excessive cost. Thus, this topic serves as a motivation behind the investigation of the sparse placement issue of WC3Rs presented in this paper. A series of strategies are proposed based on knowledge of the network. Moreover, a novel adaptive routing and joint resource assignment algorithm is presented to provision the lightpaths in WSON with sparsely placed WC3Rs. Extensive simulation trials are conducted under even and uneven distribution of WC3R resource. Each strategic feature is examined for its efficiency in lowering the blocking probability. The results reveal that carefully designed sparse placement of WC3Rs can achieve performance comparable to that of full WC3R placement scenario. Furthermore, the expenditure of WC3R deployment also depends on the type of used WC3Rs characterized by the wavelength convertibility, i.e., fixed WC3R or tunable WC3R. This paper also investigates WSON from the perspective of cost and benefit by employing different types of WC3Rs in order to find the possibility of more efficient WC3R investment.

In this paper, we consider a dual-hop wireless cooperative network with amplify-and-forward (AF) relaying. The output signal-to-noise ratio (SNR) at the destination of the AF cooperative networks is in the form of the sum of harmonic mean of the source-relay channel SNR and the relay-destination channel SNR. Instead of deriving the exact probability density function (PDF) of the output SNR, we study the series expansion of this PDF around zero. This result is then applied to evaluate the performance of the AF cooperative systems over Nakagami-m fading channels, and closed-form high-SNR approximations of the average symbol error rate (SER) and the outage probability are derived. Next, we investigate the optimal power allocation (OPA) among the source node and the relays to minimize the approximate SER as well as the outage probability. It is shown that the optimal power allocation depends on the channel m parameters and the ratio of the source-relay channel gain to the relay-destination gain. In addition to the optimal power allocation, we also propose a low complexity sub-optimal power allocation (SubOPA) scheme. The performance improvement with optimal and sub-optimal power allocation is analyzed and validated by numeric results. It is shown that equal power allocation is near optimal when the relays are close to the source, while significant performance improvement is observed by both the optimal and sub-optimal power allocation schemes when the relays are close to the destination.

Scanning quantized transform coefficients is an important tool for video coding. For example, the MPEG-4 video coder adopts three different scans to get better coding efficiency. This paper proposes an adaptive zero-coefficient distribution scan in inter block coding. The proposed method attempts to improve H.264/AVC zero coefficient coding by modifying the scan operation. Since the zero-coefficient distribution is changed by the proposed scan method, new VLC tables for syntax elements used in context-adaptive variable length coding (CAVLC) are also provided. The savings in bit-rate range from 2.2% to 5.1% in the high bit-rate cases, depending on different test sequences.

The current Internet does not offer any quality of service guarantees or support to Internet multimedia applications such as Internet telephony and video-conferencing, due to the best-effort nature of the Internet. Their performance may be adversely affected by network congestion. Also, since these applications commonly employ the UDP transport protocol, which lacks congestion control mechanisms, they may severely overload the network and starve other applications. We present an overview of recent research efforts in developing adaptive delivery models for Internet multimedia applications, which dynamically adjust the transmission rate according to network conditions. We classify the approaches used to develop adaptive delivery models with brief descriptions of representative research work. We then evaluate the approaches based on important design issues and performance criteria, such as the scalability of the control mechanism, responsiveness in detecting and reacting to congestion, and ability to accommodate receiver heterogeniety. Some conclusions are developed regarding the suitability of particular design choices under various conditions.

Aiming at the problem of non-line of sight (NLOS) signal recognition for Ultra Wide Band (UWB) positioning, we utilize the concepts of Neural Network Clustering and Neural Network Pattern Recognition. We propose a classification algorithm based on self-organizing feature mapping (SOM) neural network batch processing, and a recognition algorithm based on convolutional neural network (CNN). By assigning different weights to learning, training and testing parts in the data set of UWB location signals with given known patterns, a strong NLOS signal recognizer is trained to minimize the recognition error rate. Finally, the proposed NLOS signal recognition algorithm is verified using data sets from real scenarios. The test results show that the proposed algorithm can solve the problem of UWB NLOS signal recognition under strong signal interference. The simulation results illustrate that the proposed algorithm is significantly more effective compared with other algorithms.

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.

This paper presents a modal-expansion analysis of the electromagnetically coupled coaxial dipole antenna. The analysis of the antenna problem is initially simplified using the even-odd mode excitation and then the resultant half structure is divided into two parts; one is the characterization of a coaxial feeding network and the other is the modeling of a sleeve monopole antenna driven by a coaxial line. The formally exact modal-expansion method is employed to analyze both parts. The analysis of the sleeve monopole antenna is facilitated by introducing a perfectly conducting boundary at a distance from the monopole's top end. The current distribution and input impedance of the electromagnetically coupled coaxial dipole antenna are obtained by finding expansion coefficients through enforcing the continuity of tangential field components across regional interfaces and cascading the two parts together. Numerical results for the coaxial dipole antenna's radiation characteristics are presented and discussed.

One of the patterns that the design of parallel file systems has to solve stems from the difficulty of handling the metadata-intensive I/O generated by parallel applications accessing a single large directory. We demonstrate a middleware design called SFS to support existing parallel file systems for distributed and scalable directory service. SFS distributes directory entries over data servers instead of metadata servers to offer increased scalability and performance. Firstly, SFS exploits an adaptive directory partitioning based on extendible hashing to support concurrent and unsynchronized partition splitting. Secondly, SFS describes an optimization based on recursive split-ordering that emphasizes speeding up the splitting process. Thirdly, SFS applies a write-optimized index structure to convert slow, small, random metadata updates into fast, large, sequential writes. Finally, SFS gracefully tolerates stale mapping at the clients while maintaining the correctness and consistency of the system. Our performance results on a cluster of 32-servers show our implementation can deliver more than 250,000 file creations per second on average.

File system workloads are increasing write-heavy. The growing capacity of RAM in modern nodes allows many reads to be satisfied from memory while writes must be persisted to disk. Today's sophisticated local file systems like Ext4, XFS and Btrfs optimize for reads but suffer from workloads dominated by microdata (including metadata and tiny files). In this paper we present an LSM-tree-based file system, RFS, which aims to take advantages of the write optimization of LSM-tree to provide enhanced microdata performance, while offering matching performance for large files. RFS incrementally partitions the namespace into several metadata columns on a per-directory basis, preserving disk locality for directories and reducing the write amplification of LSM-trees. A write-ordered log-structured layout is used to store small files efficiently, rather than embedding the contents of small files into inodes. We also propose an optimization of global bloom filters for efficient point lookups. Experiments show our library version of RFS can handle microwrite-intensive workloads 2-10 times faster than existing solutions such as Ext4, Btrfs and XFS.

In this paper we describe a multicast routing algorithm, which builds upon mobile multicast agents of an ad-hoc network. Mobile multicast agents (MMAs) form a virtual backbone of an ad-hoc network and they provide multicast tree discovery, multicast tree maintenance and datagram delivery. First, we construct a cluster-spine hierarchy structure for an ad-hoc network. Second, we propose a multicast routing algorithm, which is inspired by Ad-hoc On-Demand Distance Vector (AODV) routing protocol. The results show that the MMA multicast algorithm can simplify the multicast tree discovery, reduce control overhead of the network, and increase the total network throughput, in comparison with general AODV multicast operation. We also overcome the deficiency of CBRP multicast routing, which places much burden on cluster heads.

In the paper, we propose a new method for chromatic dispersion measurement of WDM components in both transmission and reflection, employing photonic microwave technology. The dispersion can be determined by measuring the frequency spectrum range change of the microwave notch filter. The method features the advantages of low-cost and simplicity. Experimental results demonstrate that our setup is capable of measuring relative group delay with better than 1 ps time resolution and the measurement results show a good agreement with that measured by the conventional phase-shift technique.

Building high-quality text-to-speech (TTS) systems without expert knowledge of the target language and/or time-consuming manual annotation of speech and text data is an important yet challenging research topic. In this kind of TTS system, it is vital to find representation of the input text that is both effective and easy to acquire. Recently, the continuous representation of raw word inputs, called “word embedding”, has been successfully used in various natural language processing tasks. It has also been used as the additional or alternative linguistic input features to a neural-network-based acoustic model for TTS systems. In this paper, we further investigate the use of this embedding technique to represent phonemes, syllables and phrases for the acoustic model based on the recurrent and feed-forward neural network. Results of the experiments show that most of these continuous representations cannot significantly improve the system's performance when they are fed into the acoustic model either as additional component or as a replacement of the conventional prosodic context. However, subjective evaluation shows that the continuous representation of phrases can achieve significant improvement when it is combined with the prosodic context as input to the acoustic model based on the feed-forward neural network.

Recently, the GMPLS controlled WSON has emerged as a promising optical transport network. In order to guarantee the optical signal transmission feature without deformation, the optoelectronic 3R regenerators still need to be sparsely placed in the network, termed as translucent networks. The growing size and complexity of the translucent network requires a transition of control plane to move from the traditional centralized model to a fully distributed architecture in the future. However, centrally designed routing, wavelength assignment, and 3R regenerator allocation approaches become unfeasible under the distributed paradigm due to the outdated and inconsistent network state information. A common solution is to accelerate the update frequency of network state, but the fundamental problem remains that the inaccurate state information is still inevitable. Furthermore, it adds a significant increase to the control traffic volume which adversely degrades the performance and scalability of the network control system. In order to mitigate the impact of having inaccurate state information on network performance in the distributed systems, a novel RWA approach is proposed in this paper, termed as routing and distributed wavelength assignment with top ranked probing wavelength set computation. In our proposal, the wavelength assignment is performed by signalling process with a set of carefully preselected probing wavelengths. This set is dynamically computed based on the resource utilization each time the network state is refreshed. The PCE module is adopted in WSON control plane to be responsible for the computation of RWA and 3R allocation. The performance of the proposed approach is studied by extensive simulations. The experiment results reveal that by employing the proposed scheme, without loss on the blocking performance the inaccuracy of the wavelength availability information can be well tolerated, and the set-up delay in lightpath provisioning can be kept at a low level.

Channel state information (CSI) acquisition at the transmitter side is a major challenge in massive MIMO systems for enabling high-efficiency transmissions. To address this issue, various CSI feedback schemes have been proposed, including limited feedback schemes with codebook-based vector quantization and explicit channel matrix feedback. Owing to the limitations of feedback channel capacity, a common issue in these schemes is the efficient representation of the CSI with a limited number of bits at the receiver side, and its accurate reconstruction based on the feedback bits from the receiver at the transmitter side. Recently, inspired by successful applications in many fields, deep learning (DL) technologies for CSI acquisition have received considerable research interest from both academia and industry. Considering the practical feedback mechanism of 5th generation (5G) New radio (NR) networks, we propose two implementation schemes for artificial intelligence for CSI (AI4CSI), the DL-based receiver and end-to-end design, respectively. The proposed AI4CSI schemes were evaluated in 5G NR networks in terms of spectrum efficiency (SE), feedback overhead, and computational complexity, and compared with legacy schemes. To demonstrate whether these schemes can be used in real-life scenarios, both the modeled-based channel data and practically measured channels were used in our investigations. When DL-based CSI acquisition is applied to the receiver only, which has little air interface impact, it provides approximately 25% SE gain at a moderate feedback overhead level. It is feasible to deploy it in current 5G networks during 5G evolutions. For the end-to-end DL-based CSI enhancements, the evaluations also demonstrated their additional performance gain on SE, which is 6%-26% compared with DL-based receivers and 33%-58% compared with legacy CSI schemes. Considering its large impact on air-interface design, it will be a candidate technology for 6th generation (6G) networks, in which an air interface designed by artificial intelligence can be used.

In data center networks, Quantized Congestion Notification (QCN) has been ratified as the standard congestion management mechanism in the link layer. Since QCN nonlinearly switches between the rate increase and decrease stages, it is very difficult to understand QCN in depth and provide theoretical guidelines on setting the buffer size of the QCN switch. This paper gives an explicit formula for the boundary of buffer occupancy of the QCN switch. Specifically, based on the fluid model of QCN, we first derive the uniformly asymptotic stability condition of the QCN system. Then, under the condition that QCN is uniformly asymptotically stable, we analyze the buffer occupancy of the QCN switch theoretically and show that the classic rule-of-thumb for buffer sizing is not suitable for QCN. Finally, simulations validate the accuracy of our theoretical results.

Selection relaying is a promising technique for practical implementation of cooperative systems with multiple relay nodes. However, to select the best relay, global channel knowledge is required at the selecting entity, which may result in considerable signaling overhead. In this paper, we consider the relay selection problem in dual-hop amplify-and-forward (AF) communication systems with partial channel state information (CSI). Relay selection strategies aiming at minimizing either the outage probability or the bit error rate (BER) with quantized CSI available are presented. We also propose a target rate based quantizer to efficiently partition the SNR range for outage minimized relay selection, and a target BER based quantizer for BER minimized relay selection. Simulation results show that near optimal performance is achievable with a few bits feedback to the selecting entity.

Like many processors, GPGPU suffers from memory wall. The traditional solution for this issue is to use efficient schedulers to hide long memory access latency or use data prefetch mech-anism to reduce the latency caused by data transfer. In this paper, we study the instruction fetch stage of GPU's pipeline and analyze the relationship between the capacity of GPU kernel and instruction miss rate. We improve the next line prefetch mechanism to fit the SIMT model of GPU and determine the optimal parameters of prefetch mechanism on GPU through experiments. The experimental result shows that the prefetch mechanism can achieve 12.17% performance improvement on average. Compared with the solution of enlarging I-Cache, prefetch mechanism has the advantages of more beneficiaries and lower cost.

This letter studies the asymptotic bit error rate (BER) performance of multihop communication systems with amplify-and-forward relaying over Nakagami-m fading channels. Since it is difficult to find the exact probability density function (PDF) of the output signal-to-noise ratio (SNR) at the destination, we resort to the series expansion of this PDF in the neighborhood of zero. Building upon this result, a closed-form expression for the average BER in the high SNR region is derived. Numeric results show that the derived asymptotic BER expression is accurate at medium and high SNR for both independent identically distributed (i.i.d.) and independent non-identically distributed (i.n.i.d.) channels.