A fault-tolerant and congestion-aware architecture for wireless networks-on-chip

Wireless Networks - The combination of traditional wired links for regular transmissions and express wireless paths for long distance communications is a promising solution to prevent multi-hop...     

A novel interference suppression scheme for global navigation satellite systems using antenna array

This paper considers interference suppression and multipath mitigation in Global Navigation Satellite Systems (GNSSs). In particular, a self-coherence anti-jamming scheme is introduced which relies on the unique structure of the coarse/acquisition (C/A) code of the satellite signals. Because of the repetition of the C/A-code within each navigation symbol, the satellite signals exhibit strong self-coherence between chip-rate samples separated by integer multiples of the spreading gain. The proposed scheme utilizes this inherent self-coherence property to excise interferers that have different temporal structures from that of the satellite signals. Using a multiantenna navigation receiver, the proposed approach obtains the optimal set of beamforming coefficients by maximizing the cross correlation between the output signal and a reference signal, which is generated from the received data. It is demonstrated that the proposed scheme can provide high gains toward all satellites in the field of view, while suppressing strong interferers. By imposing constraints on the beamformer, the proposed method is also capable of mitigating multipath that enters the receiver from or near the horizon. No knowledge of either the transmitted navigation symbols or the satellite positions is required.     

Reliability and Mobility Load Balancing in Next Generation Self-organized Networks: Using Stochastic Learning Automata

Self-organizing networking (SON) is an automation technology designed to make the planning, configuration, management, optimization and healing of mobile radio access networks simpler and faster. Most current self-organization networking functions apply rule-based recommended systems to control network resources which seem too complicated and time-consuming to design in practical conditions. This research proposes a cognitive cellular network empowered by an efficient self-organization networking approach which enables SON functions to separately learn and find the best configuration setting. An effective learning approach is proposed for the functions of the cognitive cellular network, which exhibits how the framework is mapped to SON functions. One of the main functions applied in this framework is mobility load balancing. In this paper, a novel Stochastic Learning Automata has been suggested as the load balancing function in which approximately the same quality level is provided for each subscriber. This framework can also be effectively extended to cloud-based systems, where adaptive approaches are needed due to unpredictability of total accessible resources, considering cooperative nature of cloud environments. The results demonstrate that the function of mobility robustness optimization not only learns to optimize HO performance, but also it learns how to distribute excess load throughout the network. The experimental results demonstrate that the proposed scheme minimizes the number of unsatisfied subscribers (N_us) by moving some of the edge users served by overloaded cells towards one or more adjacent target cells. This solution can also guarantee a more balanced network using cell load sharing approach in addition to increase cell throughput outperform the current schemes.

     

Tuning Thermal Transport Through Atomically Thin Ti3C2Tz MXene by Current Annealing in Vacuum

Heat transport across vertical interfaces of heterogeneous 2D materials is usually governed by the weak Van der Waals interactions of the surface‐terminating atoms. Such interactions play a significant role in thermal transport across transition metal carbide and nitride (MXene) atomic layers due to their hydrophilic nature and variations in surface terminations. Here, the metallicity of atomically thin Ti₃C₂T_z MXene, which is also verified by scanning tunneling spectroscopy for the first time, is exploited to develop a self‐heating/self‐sensing platform to carry out direct‐current annealing experiments in high (<10^?8 bar) vacuum, while simultaneously evaluating the interfacial heat transport across a Ti₃C₂T_z/SiO₂ interface. At room temperature, the thermal boundary conductance (TBC) of this interface is found, on average, to increase from 10 to 27 MW m^?2 K^?1 upon current annealing up to the breakdown limit. In situ heating X‐ray diffraction and X‐ray photo‐electron spectroscopy reveal that the TBC values are mainly affected by interlayer and interface spacing due to the removal of absorbents, while the effect of surface termination is negligible. This study provides key insights into understanding energy transport in MXene nanostructures and other 2D material systems.     

Differential Distributed Space-Time Modulation for Cooperative Networks

In this paper, we develop a protocol for the construction of cooperative networks when the channel state information is not available at the transmitters and the receivers. In the proposed protocol, differential space-time codewords are generated at the source terminal. In the broadcast phase, each row of the differential space-time codeword is transmitted to a different relay, whereas in the relay phase, the relaying terminals retransmit the codeword through simple amplify-and-forward algorithm. The performance of the cooperative diversity system is analyzed for a two-user case for different channel environments in. terms of the diversity gain and the diversity product. The optimization of the power allocation between source and relay terminals is considered for the maximization of the diversity product. When the same modulation scheme is used, the performance of differential detection is degraded by 3 dB noise enhancement compared with coherent detection     

Joint Optimization of Source Power Allocation and Relay Beamforming in Multiuser Cooperative Wireless Networks

Relay beamforming techniques have been shown to significantly enhance the sum capacity of a multiuser cooperative wireless network through the optimization of the relay weights, where concurrent communications of multiple source-destination pairs are achieved via spatial multiplexing. Further optimization of the transmit power allocation over the source nodes is expected to improve the network throughput as well. In this paper, we maximize the sum capacity of a multiuser cooperative wireless network through the joint optimization of power allocation among source nodes and relay beamforming weights across the relay nodes. We consider a two-hop cooperative wireless network, consisting of single-antenna nodes, in which multiple concurrent links are relayed by a number of cooperative nodes. When a large number of relay nodes are available, the channels of different source-destination pairs can be orthogonalized, yielding enhanced sum network capacity. Such cooperative advantage is particularly significant in high signal-to-noise ratio (SNR) regime, in which the capacity follows a logarithm law with the SNR, whereas exploiting spatial multiplexing of multiple links yields capacity increment linear to the number of users. However, the capacity performance is compromised when the input SNR is low and/or when the number of relay nodes is limited. Joint optimization of source power allocation and relay beamforming is important when the input SNR and/or the number of relay nodes are moderate or the wireless channels experience different channel variances. In these cases, joint optimization of source power and distributed beamforming weights achieves significant capacity increment over both source selection and equal source power spatial multiplexing schemes. With consideration of the needs to deliver data from each source node, we further examine the optimization of global sum capacity in the presence of individual capacity requirements by maximizing sum capacity of the network subject to a minimum capacity constraint over each individual user.     

Optical Burst Switching Router With 40-,10-Gb/s Bit-Rate Transparent Contention Resolution

We report a bit-rate transparent optical burst switching (OBS) router prototype using a fast 5 times 5 PLZT [(Pb,La)(Zr,Ti)O₃ ] optical matrix switch. Dynamic switching in a two-wavelength, 2 times 2 OBS switch is demonstrated. Contention resolution using a tunable Mach-Zehnder interferometer wavelength converter for both 40- and 10-Gb/s bursts is demonstrated for the first time. Error-free operation was achieved for both bit rates under the same settings, as required in autonomous networks     

A Survey on Traffic Management in Software-Defined Networks: Challenges,Effective Approaches,and Potential Measures

Software-defined networks (SDNs), as an emerging paradigm by separating the control plane from the data plane, increases flexibility and network utilization and reduces redundancy and operational cost. Traffic management of software-defined networks can be defined as network traffic monitoring and analyzing measures to improve network performance and quality of service metrics. Traffic management as an effective instrument for optimizing network traffic can offer the appropriate services according to network situation. Due to the inherent characteristics of SDN, special techniques are required to analyze, predict, and adapt the network traffic in order to achieve an efficient traffic management mechanism. This paper surveys traffic management techniques of SDN in four distinct categories including, routing, load balancing, congestion control, and flow control to cover the impressible issues. Moreover, the differences between SDN and traditional networks are analyzed in terms of traffic management necessities across the various groups to further determine the dimensions affecting research in this area. Furthermore, the available algorithms in each group and their role in traffic management are reviewed as well as the research challenges and future trends.

     

Time-frequency kernel design by the two-dimensional frequencytransformation method

A subclass of Cohen's class of time-frequency (TF) distributions is introduced in which the TF distribution kernels are provided via the frequency transformation method (FTM), used in two dimensional (2-D) filter design. The FTM kernels have finite extent in time and frequency and allow the TF distribution to be efficiently implemented in all four domains     

Speech frame recognition based on less shift sensitive wavelet filter banks

The wavelet transform possesses multi-resolution property and high localization performance; hence, it can be optimized for speech recognition. In our previous work, we show that redundant wavelet filter bank parameters work better in speech recognition task, because they are much less shift sensitive than those of critically sampled discrete wavelet transform (DWT). In this paper, three types of wavelet representations are introduced, including features based on dual-tree complex wavelet transform (DT-CWT), perceptual dual-tree complex wavelet transform, and four-channel double-density discrete wavelet transform (FCDDDWT). Then, appropriate filter values for DT-CWT and FCDDDWT are proposed. The performances of the proposed wavelet representations are compared in a phoneme recognition task using special form of the time-delay neural networks. Performance evaluations confirm that dual-tree complex wavelet filter banks outperform conventional DWT in speech recognition systems. The proposed perceptual dual-tree complex wavelet filter bank results in up to approximately 9.82 % recognition rate increase, compared to the critically sampled two-channel wavelet filter bank.