To deal with the reliability issue caused by soft errors, this paper proposed a low power soft error hardened latch (SHC) design using a novel Schmitt-Trigger-based C-element for reliable low power applications. Unlike state-of-the-art soft error tolerant latches that are usually based on hardware redundancy with large area overhead and high power consumption, the proposed SHC latch is implemented through double-sampling and node-checking using a novel Schmitt-Trigger-based C-element, which can help to reduce the area overhead and the corresponding power consumption as well. The evaluation results show that the total number of transistors of the proposed SHC latch is only increased by 2 when compared to the conventional unhardened C2MOS latch, while up to 20.35% and 82.96% power reduction can be achieved when compared to the conventional unhardened C2MOS latch and the existing soft error tolerant HiPeR design, respectively.

Non-volatile memories are a promising alternative to memory design but data stored in them still may be destructed due to crosstalk and radiation. The data stored in them can be restored by using error-correcting codes but they require extra bits to correct bit errors. One of the largest problems in non-volatile memories is that they consume ten to hundred times more energy than normal memories in bit-writing. It is quite necessary to reduce writing bits. Recently, a REC code (bit-write-reducing and error-correcting code) is proposed for non-volatile memories which can reduce writing bits and has a capability of error correction. The REC code is generated from a linear systematic error-correcting code but it must include the codeword of all 1's, i.e., 11…1. The codeword bit length must be longer in order to satisfy this condition. In this letter, we propose a method to generate a relaxed REC code which is generated from a relaxed error-correcting code, which does not necessarily include the codeword of all 1's and thus its codeword bit length can be shorter. We prove that the maximum flipping bits of the relaxed REC code is still limited theoretically. Experimental results show that the relaxed REC code efficiently reduce the number of writing bits.

Computing autocorrelation coefficient can effectively reduce the influence of additive white noise, thus estimation precision will be improved. In this paper, an autocorrelation-like function, different from the ordinary one, is defined, and is proven to own better linear predictive performance. Two algorithms for signal model are developed to achieve frequency estimates. We analyze the theoretical properties of the algorithms in the additive white Gaussian noise. The simulation results match with the theoretical values well in the sense of mean square error. The proposed algorithms compare with existing estimators, are closer to the Cramer-Rao bound (CRLB). In addition, computer simulations demonstrate that the proposed algorithms provide high accuracy and good anti-noise capability.

8B/10B is an encoding technique largely used in different communication protocols, with several advantages such as zero DC bias. In the last years transmission rates have grown rapidly, thus the need of encoders with better performance in terms of throughput, area and power consumption raised rapidly. In this article we will present and discuss the architecture of two symbols parallel encoder, comparing it with a classical pipelined solution.

This letter proposes a novel modulation classification method for overlapped sources named LRGP involving multinomial logistic regression (MLR) and multi-gene genetic programming (MGGP). MGGP based feature engineering is conducted to transform the cumulants of the received signals into highly discriminative features and a MLR based classifier is trained to identify the combination of the modulation formats of the overlapped sources instead of signal separation. Extensive simulations demonstrate that LRGP yields superior performance compared with existing methods.

This letter proposes a numerical method for approximating the location of and dynamics on a class of chaotic saddles. In contrast to the conventional strategy of maximizing the escape time, our proposal is to impose a zero-expansion condition along transversely repelling directions of chaotic saddles. This strategy exploits the existence of skeleton-forming unstable periodic orbits embedded in chaotic saddles, and thus can be conveniently implemented as a variant of subspace Newton-type methods. The algorithm is examined through an illustrative and another standard example.

In this letter, we study a wireless powered communication network (WPCN) with non-orthogonal multiple access (NOMA), where the user clustering scheme that groups each two users in a cluster is adopted to guarantee the system performance. The two users in a cluster transmit data simultaneously via NOMA, while time division multiple access (TDMA) is used among clusters. We aim to maximize the system throughput by finding the optimal cluster permutation and the optimal time allocation, which can be obtained by solving the optimization problems corresponding to all cluster permutations. The closed-form solution of each optimization problem is obtained by exploiting its constraint structures. However, the complexity of this exhaustive method is quite high, we further propose a sub-optimal clustering scheme with low complexity. The simulation results demonstrate the superiority of the proposed scheme.

A multi-channel cognitive radio is a powerful solution for recovering the exhaustion of frequency spectrum resources. In a cognitive radio, although master and slave terminals (which construct a communication link) have the freedom to access arbitrary channels, access channel mismatch is caused. A rendezvous scheme based on frequency hopping can compensate for this mismatch by exchanging control signals through a selected channel in accordance with a certain rule. However, conventional frequency hopping schemes do not consider an access protocol of both control signals in the rendezvous scheme and the signal caused by channel access from other systems. Further, they do not consider an information sharing method to reach a consensus between the master and slave terminals. This paper proposes a modified rendezvous scheme based on learning-based channel occupancy rate (COR) estimation and describes a specific channel-access rule in the slave terminal. On the basis of this rule, the master estimates a channel selected by the slave by considering the average COR of the other systems. Since the master can narrow down the number of channels, a fast rendezvous scheme with a few control signals is established.

Advanced metering infrastructure (AMI) is a kind of wireless sensor network that provides two-way communication between smart meters and city utilities in the neighborhood area of the smart grid. And the routing protocol for low-power and lossy network (RPL) is being considered for use in AMI networks. However, there still exist several problems that need to be solved, especially with respect to QoS guarantees. To address these problems, an improved algorithm of RPL based on triangle module operator named as TMO is proposed. TMO comprehensively evaluates routing metrics: end-to-end delay, number of hops, expected transmission count, node remaining energy, and child node count. Moreover, TMO uses triangle module operator to fuse membership functions of these routing metrics. Then, the node with minimum rank value will be selected as preferred parent (the next hop). Consequently, the QoS of RPL-based AMI networks can be guaranteed effectively. Simulation results show that TMO offers a great improvement over several the most popular schemes for RPL like ETXOF, OF-FL and additive composition metric manners in terms of network lifetime, average end-to-end delay, average packet loss ratio, average hop count from nodes to root, etc.

Throughput prediction is one of the promising techniques to improve the quality of service (QoS) and quality of experience (QoE) of mobile applications. To address the problem of predicting future throughput distribution accurately during the whole session, which can exhibit large throughput fluctuations in different scenarios (especially scenarios of moving user), we propose a history-based throughput prediction method that utilizes time series analysis and machine learning techniques for mobile network communication. This method is called the Hybrid Prediction with the Autoregressive Model and Hidden Markov Model (HOAH). Different from existing methods, HOAH uses Support Vector Machine (SVM) to classify the throughput transition into two classes, and predicts the transmission control protocol (TCP) throughput by switching between the Autoregressive Model (AR Model) and the Gaussian Mixture Model-Hidden Markov Model (GMM-HMM). We conduct field experiments to evaluate the proposed method in seven different scenarios. The results show that HOAH can predict future throughput effectively and decreases the prediction error by a maximum of 55.95% compared with other methods.

This paper presents a 1024-QAM OFDM signal capable WLAN receiver in 65nm CMOS technology. Thermal noise-based IQ frequency-independent mismatch correction and IQ frequency-dependent mismatch correction with baseband loopback are proposed for the self-calibration in the receiver. The measured image rejection ratio of the self-calibration is -56.3dB. The receiver achieves the extremely low EVM of -37.1dB even with wide channel bandwidth of 80MHz and has the ability to receive the 1024-QAM signal. The result indicates that the receiver is extendable for the 802.11ax compliant receiver that supports a higher density modulation scheme of MIMO.

With the rapid increase in demand for mobile data, mobile network operators are trying to expand wireless network capacity by deploying wireless local area network (LAN) hotspots on to which they can offload their mobile traffic. However, these network-centric methods usually do not fulfill the interests of mobile users (MUs). Taking into consideration many issues such as different applications' deadlines, monetary cost and energy consumption, how the MU decides whether to offload their traffic to a complementary wireless LAN is an important issue. Previous studies assume the MU's mobility pattern is known in advance, which is not always true. In this paper, we study the MU's policy to minimize his monetary cost and energy consumption without known MU mobility pattern. We propose to use a kind of reinforcement learning technique called deep Q-network (DQN) for MU to learn the optimal offloading policy from past experiences. In the proposed DQN based offloading algorithm, MU's mobility pattern is no longer needed. Furthermore, MU's state of remaining data is directly fed into the convolution neural network in DQN without discretization. Therefore, not only does the discretization error present in previous work disappear, but also it makes the proposed algorithm has the ability to generalize the past experiences, which is especially effective when the number of states is large. Extensive simulations are conducted to validate our proposed offloading algorithms.

By focusing on the recent swing to the centralized approach by the software defined network (SDN), this paper presents a novel network architecture for refactoring the current distributed Internet protocol (IP) by not only utilizing the SDN itself but also implementing its cooperation with the optical transport layer. The first IP refactoring is for flexible network topology reconfiguration: the global routing and explicit routing functions are transferred from the distributed routers to the centralized SDN. The second IP refactoring is for cost-efficient maintenance migration: we introduce a resource portable IP router that can behave as a shared backup router by cooperating with the optical transport path switching. Extensive evaluations show that our architecture makes the current IP network easier to configure and more scalable. We also validate the feasibility of our proposal.

The energy consumption of network virtualization environments (NVEs) has become a critical issue. In this paper, we focus on reducing the data switching energy consumption of NVE. We first analyze the data switching energy of NVE. Then, we propose a dynamic energy efficient virtual link resource reallocation (eEVLRR) approach for NVE. eEVLRR dynamically reallocates the energy efficient substrate resources (s-resources) for virtual links with dynamic changes of embeddable s-resources to save the data switching energy. In order to avoid traffic interruptions while reallocating, we design a cross layer application-session-based forwarding model for eEVLRR that can identify and forward each data transmission flow along the initial specified substrate data transport path until end without traffic interruptions. The results of performance evaluations show that eEVLRR not only guarantees the allocated s-resources of virtual links are continuously energy efficient to save data switching energy but also has positive impacts on virtual network acceptance rate, revenues and s-resources utilization.

The spectrum sculpting precoder (SSP) is a precoding scheme for sidelobe suppression of frequency division multiplexing (OFDM) signals. It can form deep spectral notches at chosen frequencies and is suitable for cognitive radio systems. However, the SSP degrades the error rate as the number of notched frequencies increases. Orthogonal precoding that improves the SSP can achieve both spectrum notching and the ideal error rate, but its computational complexity is very high since the precoder matrix is large in size. This paper proposes an effective and equivalent decomposition of the precoder matrix by QR-decomposition in order to reduce the computational complexity of orthogonal precoding. Numerical experiments show that the proposed method can drastically reduce the computational complexity with no performance degradation.

We introduce a Welch FFT segment size selection method for FFT-based wide band spectrum measurement in the context of smart spectrum access (SSA), in which statistical spectrum usage information of primary users (PUs), such as duty cycle (DC), will be exploited by secondary users (SUs). Energy detectors (EDs) based on Welch FFT can detect the presence of PU signals in a broadband environment efficiently, and DC can be estimated properly if a Welch FFT segment size is set suitably. There is a trade-off between detection performance and frequency resolution in terms of the Welch FFT segment size. The optimum segment size depends on signal-to-noise ratio (SNR) which makes practical and optimum segment size setting difficult. For this issue, we previously proposed a segment size selection method employing a relationship between noise floor (NF) estimation output and the segment size without SNR information. It can achieve accurate spectrum awareness at the expense of relatively high computational complexity since it employs exhaustive search to select a proper segment size. In this paper, we propose a segment size selection method that offers reasonable spectrum awareness performance with low computational complexity since limited search is used. Numerical evaluations show that the proposed method can match the spectrum awareness performance of the conventional method with 70% lower complexity or less.

A frequently occurring subcircuit consists of a loop of a resistor (R), a field-effect transistor (FET), and a capacitor (C). The FET acts as a switch, controlled at its gate terminal by a clock voltage. This subcircuit may be acting as a sample-and-hold (S/H), as a passive mixer (P-M), or as a bandpass filter or bandpass impedance. In this work, we will present a useful analysis that leads to a simple signal flow graph (SFG), which captures the FET-R-C circuit's action completely across a wide range of design parameters. The SFG dissects the circuit into three filtering functions and ideal sampling. This greatly simplifies analysis of frequency response, noise, input impedance, and conversion gain, and leads to guidelines for optimum design. This paper focuses on the analysis of a single-path FET-R-C circuit's signal transfer characteristics including the reconstruction of the complete waveform from the discrete-time sampled voltage.

This paper reviews architectures and topologies for column-parallel analog-to-digital converters (ADCs) used for CMOS image sensors (CISs) and discusses the performance of CISs using column-parallel ADCs based on figures-of-merit (FoM) with considering noise models which behave differently at low/middle and high pixel-rate regions. Various FoM considering different performance factors are defined. The defined FoM are applied to surveyed data on reported CISs using column-parallel ADCs which are categorized into 4 types; single slope, SAR, cyclic and delta-sigma ADCs. The FoM defined by (noise)2(power)/(pixel-rate) separately for low/middle and high pixel-rate regions well explains the frontline of the CIS' performance in all the pixel rates. Using the FoM defined by (noise)2(power)/(intrascene dynamic range)(pixel-rate), the effectiveness of recently-reported techniques for extended-dynamic-range CISs is clarified.

This paper presents low-noise amplifier (LNA)-less 300-GHz CMOS receivers that operate above the NMOS unity-power-gain frequency, fmax. The receivers consist of a down-conversion mixer with a doubler- or tripler-last multiplier chain that upconverts an LO1/n signal into 300 GHz. The conversion gain of the receiver with the doubler-last multiplier is -19.5 dB and its noise figure, 3-dB bandwidth, and power consumption are 27 dB, 27 GHz, and 0.65 W, respectively. The conversion gain of the receiver with the tripler-last multiplier is -18 dB and its noise figure, 3-dB bandwidth, and power consumption are 25.5 dB, 33 GHz, and 0.41 W, respectively. The receivers achieve a wireless data rate of 32 Gb/s with 16QAM. This shows the potential of the moderate-fmax CMOS technology for ultrahigh-speed THz wireless communications.

This paper proposes Code-Modulated Synchronized (CMS) -OOK modulation scheme for normally-off wireless sensor networks, and demonstrates the operation of the transmitter for the CMS-OOK using 65nm SOTB (Silicon-On Thin Buried Oxide) CMOS technology. Based on investigating RF characteristics of SOTB CMOS, analog part of a CMS-OOK transmitter was designed, fabricated and evaluated in combination with based-FPGA digital part. With code modulation and controlling the carrier frequency by body bias of the SOTB devices, the spectrum of a CMS-OOK transmitter output is widen to achieve -62dBm/MHz peak power spectrum density at 15 MHz bandwidth. Chip of analog part on-board is supplied by 1V for power amplifier and 0.75V for the rest. It consumes average 83µW according to 83nJ/bit at 1kbps data transmission.