The binary quadratic knapsack problem (QKP) aims at optimizing a quadratic cost function within a single knapsack. Its applications and difficulty make it appealing for various industrial fields. In this paper we present an efficient strategy to solve the problem by modeling it as an Ising spin model using an Ising machine to search for its ground state which translates to the optimal solution of the problem. Secondly, in order to facilitate the search, we propose a novel technique to visualize the landscape of the search and demonstrate how difficult it is to solve QKP on an Ising machine. Finally, we propose two software solution improvement algorithms to efficiently solve QKP on an Ising machine.

This paper investigates an adaptive notch digital filter that employs normal state-space realization of a single-frequency second-order IIR notch digital filter. An adaptive algorithm is developed to minimize the mean-squared output error of the filter iteratively. This algorithm is based on a simplified form of the gradient-decent method. Stability and frequency estimation bias are analyzed for the adaptive iterative algorithm. Finally, a numerical example is presented to demonstrate the validity and effectiveness of the proposed adaptive notch digital filter and the frequency-estimation bias analyzed for the adaptive iterative algorithm.

In conclusion, an initialization method has been introduced and studied to improve the SET speed in PCM. Before experiment verification, a two-dimensional finite analysis is used, and the results illustrate the proposed method is feasible to improve SET speed. Next, the R-I performances of the discrete PCM device and the resistance distributions of a 64 M bits PCM test chip with and without the initialization have been studied and analyzed, which confirms that the writing speed has been greatly improved. At the same time, the resistance distribution for the repeated initialization operations suggest that a large number of PCM cells have been successfully changed to be in an intermediate state, which is thought that only a shorter current pulse can make the cells SET successfully in this case. Compared the transmission electron microscope (TEM) images before and after initialization, it is found that there are some small grains appeared after initialization, which indicates that the nucleation process of GST has been carried out, and only needs to provide energy for grain growth later.

Since HEVC intra rate control has no prior information to rely on for coding, it is a difficult work to obtain the optimal λ for every coding tree unit (CTU). In this paper, a convolutional neural network (CNN) based intra rate control is proposed. Firstly, a CNN with two last output channels is used to predict the key parameters of the CTU R-λ curve. For well training the CNN, a combining loss function is built and the balance factor γ is explored to achieve the minimum loss result. Secondly, the initial CTU λ can be calculated by the predicted results of the CNN and the allocated bit per pixel (bpp). According to the rate distortion optimization (RDO) of a frame, a spatial equation is derived between the CTU λ and the frame λ. Lastly, The CTU clipping function is used to obtain the optimal CTU λ for the intra rate control. The experimental results show that the proposed algorithm improves the intra rate control performance significantly with a good rate control accuracy.

Information exchange through a relay node is attracting attention for applying machine-to-machine communications. If the node demodulates the received signal in relay processing confidentially, the information leakage through the relay station is a problem. In wireless MIMO switching, the frequency spectrum usage efficiency can be improved owing to the completion of information exchange within a short time. This study proposes a novel wireless MIMO switching method for secure information exchange. An overloaded situation, in which the access nodes are one larger than the number of antennas in the relay node, makes the demodulation of the relay node difficult. The access schedule of nodes is required for maintaining the overload situation and the high information exchange efficiency. This study derives the equation model of the access schedule and constructs an access schedule with fewer time periods in the integer programming problem. From the computer simulation, we confirm that the secure capacity of the proposed MIMO switching is larger than that of the original one, and the constructed access schedule is as large as the ideal and minimum time period for information exchange completion.

In distributed processing for communication services, a proper server selection scheme is required to reduce delay by ensuring the event occurrence order. Although a conservative synchronization algorithm (CSA) has been used to achieve this goal, an optimistic synchronization algorithm (OSA) can be feasible for synchronizing distributed systems. In comparison with CSA, which reproduces events in occurrence order before processing applications, OSA can be feasible to realize low delay communication as the processing events arrive sequentially. This paper proposes an optimal server selection scheme that uses OSA for distributed processing systems to minimize end-to-end delay under the condition that maximum status holding time is limited. In other words, the end-to-end delay is minimized based on the allowed rollback time, which is given according to the application designing aspects and availability of computing resources. Numerical results indicate that the proposed scheme reduces the delay compared to the conventional scheme.

In view of the different spatial and temporal resolutions of observed multi-source heterogeneous carbon dioxide data and the uncertain quality of observations, a data fusion prediction model for observed multi-scale carbon dioxide concentration data is studied. First, a wireless carbon sensor network is created, the gross error data in the original dataset are eliminated, and remaining valid data are combined with kriging method to generate a series of continuous surfaces for expressing specific features and providing unified spatio-temporally normalized data for subsequent prediction models. Then, the long short-term memory network is used to process these continuous time- and space-normalized data to obtain the carbon dioxide concentration prediction model at any scales. Finally, the experimental results illustrate that the proposed method with spatio-temporal features is more accurate than the single sensor monitoring method without spatio-temporal features.

The sum rate performance of nonlinier quantized precoding using Gibbs sampling are evaluated in a massive multiuser multiple-input multiple-output (MU-MIMO) system in this paper. Massive MU-MIMO is a key technology to handle the growth of data traffic. In a full digital massive MU-MIMO system, however, the resolution of digital-to-analogue converters (DACs) in transmit antenna branches have to be low to yield acceptable power consumption. Thus, a combinational optimization problem is solved for the nonlinier quantized precoding to determine transmit signals from finite alphabets output from low resolution DACs. A conventional optimization criterion minimizes errors between desired signals and received signals at user equipments (UEs). However, the system sum rate may decrease as it increases the transmit power. This paper proposes two optimization criteria that take the transmit power into account in order to maximize the sum rate. Mixed Gibbs sampling is applied to obtain the suboptimal solution of the nonlinear optimization problem. Numerical results obtained through computer simulations show that the two proposed criteria achieve higher sum rates than the conventional criterion. On the other hand, the sum rate criterion achieves the largest sum rate while it leads to less throughputs than the MMSE criterion on approximately 60% of subcarriers.

Time of arrival (TOA) is a widely used wireless cellular network ranging technology. How to perform accurate TOA estimation in multi-path and non-line-of-sight (NLOS) environments and then accurately calculating mobile terminal locations are two critical issues in positioning research. NLOS identification can be performed in the TOA measurement part and the position calculation part. In this paper, for the above two steps, two schemes for mitigating NLOS errors are proposed. First, a TOA ranging method based on clustering theory is proposed to solve the problem of line-of-sight (LOS) path estimation in multi-path channels. We model the TOA range as a Gaussian mixture model and illustrate how LOS and NLOS can be measured and identified based on non-parametric Bayesian methods when the wireless transmission environment is unknown. Moreover, for NLOS propagation channels, this paper proposes a user location estimator based on the maximum a posteriori criterion. Combined with the TOA estimation and user location computation scheme proposed in this paper, the terminal's positioning accuracy is improved. Experiments showed that the TOA measurement and localization algorithms presented in this paper have good robustness in complex wireless environments.

In this paper, we present the transmission characteristics control of a 125 GHz-band split-ring resonator (SRR) bandstop filter by coupling an alignment-free lattice pattern. We demonstrate that the transmission characteristics of the SRR filter can be controlled by coupling the lattice pattern; however, the required accuracy of alignment between the SRR filter and lattice pattern was below 200 µm. Therefore, we designed an alignment-free lattice pattern whose unit cell size is different from that of the SRR unit cell. S21 of the SRR bandstop filter changes from -38.7 to -4.0 dB at 125 GHz by arranging the alignment-free lattice pattern in close proximity to the SRR stopband filter without alignment. A 10 Gbit/s data transmission can be achieved over a 125 GHz-band wireless link by setting the alignment-free lattice pattern substrate just above the SRR bandstop filter.

In this paper, the uplink performance of Multi-User Multiple Input Multiple Output (MU-MIMO) Zero Forcing (ZF) receiver is investigated over correlated Rayleigh fading channels with channel estimation error. A mathematical expression for the sub-streams' output Signal to Noise Ratio (SNR) with transmit and receive-correlation is derived in the presence of erroneous channel estimates. Besides, an approximate and accurate expression for the Bit Error Rate (BER) of ZF receiver for 16-Quadrature Amplitude Modulation (QAM) with transmit-correlation is deduced in terms of the hypergeometric function. Subsequently, the developed analytical BER is verified by Monte-Carlo trails accounting various system parameters. The simulation results indicate that ZF receiver's BER relies solely on the transmit-correlation for the same number of transmit and receive-antennas at higher average SNR values per transmitted symbol (Es/N0). Also, a logarithmic and exponential growth in the BER is observed with an increase in the Mean Square estimation Error (MSE) and correlation coefficient, respectively.

Long-range radars (LRRs) for higher level autonomous driving (AD) will require more antennas than simple driving assistance. The point at issue here is 50-60% of the LRR module area is used for antennas. To miniaturize LRR modules, we use horn and lens antenna with highly efficient gain. In this paper, we propose two high-density implementation techniques for radio-frequency (RF) front-end using horn and lens antennas. In the first technique, the gap between antennas was eliminated by taking advantage of the high isolation performance of horn and lens antennas. In the second technique, the RF front-end including micro-strip-lines, monolithic microwave integrated circuits, and peripheral parts is placed in the valley area of each horn. We fabricated a prototype LRR operating at 77 GHz with only one printed circuit board (PCB). To detect vehicles horizontally and vertically, this LRR has a minimum antenna configuration of one Tx antenna and four Rx antennas placed in 2×2 array, and 30 mm thickness. Evaluation results revealed that vehicles could be detected up to 320 m away and that the horizontal and vertical angle error was less than +/- 0.2 degrees, which is equivalent to the vehicle width over 280 m. Thus, horn and lens antennas implemented using the proposed techniques are very suitable for higher level AD LRRs.

Service composition optimization is a classic NP-hard problem. How to quickly select high-quality services that meet user needs from a large number of candidate services is a hot topic in cloud service composition research. An efficient second-order beetle swarm optimization is proposed with a global search ability to solve the problem of cloud service composition optimization in this study. First, the beetle antennae search algorithm is introduced into the modified particle swarm optimization algorithm, initialize the population bying using a chaotic sequence, and the modified nonlinear dynamic trigonometric learning factors are adopted to control the expanding capacity of particles and global convergence capability. Second, modified secondary oscillation factors are incorporated, increasing the search precision of the algorithm and global searching ability. An adaptive step adjustment is utilized to improve the stability of the algorithm. Experimental results founded on a real data set indicated that the proposed global optimization algorithm can solve web service composition optimization problems in a cloud environment. It exhibits excellent global searching ability, has comparatively fast convergence speed, favorable stability, and requires less time cost.

Heterogeneous networks (HetNets) are emerging as an inevitable method to tackle the capacity crunch of the cellular networks. Due to the complicated network environment and a large number of configured parameters, coverage and capacity optimization (CCO) is a challenging issue in heterogeneous cellular networks. By combining the self-optimizing algorithm for radio frequency (RF) parameters with the power control mechanism of small cells, the CCO problem of self-organizing network is addressed in this paper. First, the optimization of RF parameters is solved based on reinforcement learning (RL), where the base station is modeled as an agent that can learn effective strategies to control the tunable parameters by interacting with the surrounding environment. Second, the small cell can autonomously change the state of wireless transmission by comparing its distance from the user equipment with the virtual cell size. Simulation results show that the proposed algorithm can achieve better performance on user throughput compared to different conventional methods.

In an amusement park, an attraction-visiting route considering the waiting time and traveling time improves visitors' satisfaction and experience. We focus on Ising machines to solve the problem, which are recently expected to solve combinatorial optimization problems at high speed by mapping the problems to Ising models or quadratic unconstrained binary optimization (QUBO) models. We propose a mapping of the visiting-route recommendation problem in amusement parks to a QUBO model for solving it using Ising machines. By using an actual Ising machine, we could obtain feasible solutions one order of magnitude faster with almost the same accuracy as the simulated annealing method for the visiting-route recommendation problem.

This paper demonstrates 300GHz terahertz wireless communication using CMOS transmitter (TX) and receiver (RX) modules targeting sixth-generation (6G). To extend communication distance, CMOS modules with WR-3.4 waveguide interface and a high-gain antenna of 40dBi Cassegrain antenna are designed, achieving 36Gbps throughput at a 1m communication distance. Besides, in order to support orthogonal frequency-division multiplexing (OFDM), a self-heterodyne architecture is introduced, which effectively cancels the phase noise in multi-carrier modulation. As a proof-of-concept (PoC), the paper successfully demonstrates real-time video transfer at a 10m communication distance using fifth-generation (5G) based OFDM at the 300GHz frequency band.

In general, since the in-band noise of phase-locked loops (PLLs) is mainly caused by charge pumps (CPs), large-size transistors that occupy a large area are used to improve in-band noise of CPs. With the high demand for low phase noise in recent high-performance communication systems, the issue of the trade-off between occupied area and noise in conventional CPs has become significant. A noise-canceling CP circuit is presented in this paper to mitigate the trade-off between occupied area and noise. The proposed CP can achieve lower noise performance than conventional CPs by performing additional noise cancelation. According to the simulation results, the proposed CP can reduce the current noise to 57% with the same occupied area, or can reduce the occupied area to 22% compared with that of the conventional CPs at the same noise performance. We fabricated a prototype of the proposed CP embedded in a 28-GHz LC-PLL using a 16-nm FinFET process, and 1.2-dB improvement in single sideband integrated phase noise is achieved.

Applications of continuous-time (CT) comparator include relaxation oscillators, pulse width modulators, and so on. CT comparator receives a differential input and outputs a strobe ideally when the differential input crosses zero. Unlike the DT comparators with positive feedback circuit, amplifiers consuming static power must be employed in CT comparators to amplify the input signal. Therefore, minimization of comparator delay under the constraint of power consumption often becomes an issue. This paper analyzes transient behavior of a CT comparator. Using “constant delay approximation”, the comparator delay is derived as a function of input slew rate, number of stages of the preamplifier, and device parameters in each block. This paper also discusses optimum design of the CT comparator. The condition for minimum comparator delay is derived with keeping power consumption constant. The results include that the optimum DC gain of the preamplifier is e∼e3 per stage depending on the element which dominates load capacitance of the preamplifier.

Communication systems operating in the millimeter-wave (mmWave) band have the potential to realize ultra-high throughput and ultra-low latency vehicle-to-vehicle (V2V) communications in 5G and beyond wireless networks. Moreover, because of the weak penetration nature of mmWave, one mmWave channel can be reused in all V2V links, which improves the spectrum efficiency. Although the outstanding performance of the mmWave above has been widely acknowledged, there are still some shortcomings. One of the unavoidable defects is multipath interference. Even though the direct interference link cannot penetrate vehicle bodies, other interference degrades the throughput of the mmWave V2V communication. In this paper, we focus on the multipath interference caused by signal reflections from roads and surroundings, where the interference strength varies in road scenarios. Firstly, we analyze the multipath channel models of mmWave V2V with relay in three typical road scenarios (single straight roads, horizontal curves, and slopes). Their interference differences are clarified. Based on the analysis, a novel method of ZigZag antenna configuration is proposed to guarantee the required data rate. Secondly, the performance of the proposed method is evaluated by simulation. It proves that the ZigZag antenna configuration with an optimal antenna height can significantly suppress the destructive interference, and ensure a throughput over 1Gbps comparing to the conventional antenna configuration at 60GHz band. Furthermore, the effectiveness of ZigZag antenna configuration is demonstrated on a single straight road by outdoor experiments.

We consider an asymptotic stabilization problem for a chain of integrators by using an event-triggered controller. The times required between event-triggered executions and controller updates are uncertain, time-varying, and not necessarily small. We show that the considered system can be asymptotically stabilized by an event-triggered gain-scaling controller. Also, we show that the interexecution times are lower bounded and their lower bounds can be manipulated by a gain-scaling factor. Some future extensions are also discussed. An example is given for illustration.