Low Implementation Complexity Research Articles

Low Complexity Keystone Transform and Radon Fourier Transform Utilizing Chirp-Z Transform

Keystone Transform (KT) and Radon Fourier Transform (RFT) are two popular methods proposed to overcome range migration in radars. A major concern in these methods is the computational complexity for real time operations. In this paper, a low complexity implementation of recurrent chirp-z transform (CZT) is offered in order to be employed in fast KT with no loss in performance. Additionally, a novel RFT implementation utilizing recurrent CZT is proposed to take advantage of the fast execution of repeated CZT. A mathematical analysis and simulation results are presented to show the performance and efficiency of the proposed techniques.

Low Complexity Implementation of Universal Filtered Multi-Carrier Transmitter

Universal Filtered Multi-Carrier (UFMC) is a promising multi-carrier modulation scheme. It can be considered as the combine of filtered OFDM and Filter Bank Multi-Carrier (FBMC) modulations. The difference among them is that filtered OFDM filters the entire band and FBMC filters the single subcarrier, while UFMC filters groups of subcarriers. It is evaluated their performance for the new conditions. While UFMC offers superior advantages, such as higher spectral efficiency, lower overhead, more robustness and less out-of-band (OOB) emission, the introduction of filters and the application of FFTs for the subband increase the implementation complexity of the transmitter. In terms of complexity, based on the analysis of UFMC transceiver structure, few low complexity implementation methods for UFMC systems are recently put forward. The low complexity solutions need to be found. In this paper, the computational complexity of different UFMC implementation methods based on lightweight structure which combines with the FIR filter and the poly-phase filter is evaluated. It is shown that the computational complexity of the UFMC transmitter can be reduced to be similar to OFDM, which is a promising solution for the advanced waveform. In addition, we analyze the effect of the filter on the power spectral density (PSD) of the proposed structure. Analysis shows that the system can be implemented with better performance by adjusting the filter parameters.

LSRN: A Recurrent Residual Learning Framework for Continuous Wireless Channel Estimation Using Super-Resolution Concept

As only a few parts of wireless resources can be utilized for pilot transmission, channel estimation, especially the interpolation process, has often been recognized as a challenging ill-posed reconstruction problem. To deal with this task, we formulate it as a typical image super resolution problem, and propose a recurrent residual learning framework named LSRN. Our proposed scheme jointly utilizes the advantages of recurrent and residual structure in the machine learning area to approximate the non-linear interpolation relations between the reference signal and surrounding resource elements. In addition, we propose a low complexity implementation scheme called LSRN-L to address the stringent processing delay requirement in the channel estimation tasks. Through numerical examples as well as prototype verification, the proposed LSRN/LSRN-L can easily outperform the convolutional GI plus DFT based interpolation scheme by 10dB in terms of normalized mean square error. Meanwhile, the low complexity LSRN-L can maintain the processing delay within one millisecond.

Low-Complexity Decorrelation NLMS Algorithms: Performance Analysis and AEC Application

In the traditional decorrelation normalized least-mean-square (D-NLMS) algorithm, high computational complexity is mainly caused by finding the decorrelated-vector. To address this issue, this article proposes a low-complexity implementation approach, which cleverly utilizes the periodic update of the decorrelation parameters and delay characteristics of the decorrelated-vector. We firstly develop two low-complexity decorrelation algorithms, (i) fast D-NLMS (FD-NLMS) and (ii) approximate FD-NLMS (AFD-NLMS) which is an approximate version of the first algorithm with even smaller computational requirement. Theoretical performance of the FD-NLMS scheme is also derived. To further obtain low steady-state error in the acoustic echo cancellation (AEC) application, separated-decorrelation AEC structure and robust step-size schemes are designed, resulting in two improved algorithms, namely, fast separated-decorrelation NLMS (FSD-NLMS) and approximate FSD-NLMS (AFSD-NLMS). Finally, extensive simulation study on system identification and AEC is undertaken to verify the efficiency of the proposed methods.

Path Loss Failure Analysis of Unknown Wireless Devices using Software Defined Radio (SDR)

The efficient method for detection of unknown wireless devices using software-defined radios (SDR) in wireless communication is proposed to analyze the power loss. SDR analyze the position of unknown parameter and their transmission of power and energy by sensing the carrier frequency and measuring the signal concerning the Received Signal Strengths (RSS) method. RSS based positioning techniques are useful for implementation of low complexity, and they are susceptible to path loss in the programmable field environment. The RSS based techniques evaluate the position of object and transmission of power from unknown devices by assuming the value of path loss failure before estimation. However, the location of the estimation accuracy depends on the discrepancy of the path loss exponent (PLE) of the object. The novelty of the proposed method introduces a novel approach for path loss failure analysis and calculates the power saving of unknown devices in SDR.

Efficient Parameters Estimation Methods for Radar Moving Targets Without Searching

Skilled at representing the chirp signal, Radon-Wigner distribution (RWD) plays an important role in parameters estimation for moving targets. By searching for the best time-frequency angle, the conventional RWD estimation method suffers from the tradeoff between the estimation accuracy and computation complexity. In this paper, an efficient RWD estimation method is proposed by exploiting the geometry information in the RWD domain, and extensive parameters searching is replaced by only two parameters computation, then the computation complexity is effectively reduced from O(MN) to O(2N). The proposed efficient RWD method is applied in moving targets velocity estimation, which is emerging issue for smart city. The non-ideal factors in real circumstance are considered, and three robust methods are proposed to improve the estimation accuracy by exploiting the geometry information in the RWD domain further. A clutter suppression RWD (CSRWD) method is proposed to cancel the clutter but preserve the moving target in the RWD domain, and a learned RWD (LRWD) method is proposed to minimize the estimation error caused by measurement error. Then, CSRWD is combined with LRWD to obtain a unified robust RWD (URRWD), which possesses the advantages of CSRWD and LRWD simultaneously. Simulation and real data processing results validate the proposed methods, and lower implementation complexity is proven under the same accuracy assumption. It can be considered as the geometry information opens a window for the searching-based estimation method to make the original bleak searching process suddenly bright. The concept can also be expanded to the other searching-based estimation methods, such as Radon transform, fractional Fourier transform and so on.

Hybrid two-level MPPM-MDPSK modulation for high-speed optical communication networks.

A hybrid optical modulation approach is described, which layers a continuous wave $M$M-ary differential phase-shift keying ($M{\rm DPSK}$MDPSK) and a two-level ($2L$2L) multipulse pulse-position modulation (MPPM) intensity-modulated signal for improved spectral efficiency. These $2L$2L techniques are a generalization of earlier hybrid MPPM-$M{\rm DPSK}$MDPSK techniques and have the added advantage of reducing transmitter and detector complexities over previous hybrid modulation approaches. The spectral and power efficiencies for the proposed $2L$2L-MPPM-$M{\rm DPSK}$MDPSK modulation techniques are formulated and shown to have the highest spectral efficiency in comparison to other hybrid techniques with lower implementation complexity. The performance of the proposed $2L$2L hybrid techniques is quantified over free-space optical (FSO) networks as well as fiber networks and verified using Monte Carlo simulation. For FSO channels, the proposed $2L$2L-MPPM-$M{\rm DPSK}$MDPSK technique outperforms the traditional MPPM-$M{\rm DPSK}$MDPSK scheme by approximately 2 dB at a bit-error rate (BER) of ${10^{-4}}$10-4 and a spectral efficiency of 2.5 bit/s/Hz. Similarly, in optical fiber, the proposed scheme relaxes the impact of nonlinearity in comparison to traditional MPPM-$M{\rm DPSK}$MDPSK. Specifically, at a ${\rm BER}{=10^{-3}}$BER=10-3, the $2L$2L-MPPM-$M{\rm DPSK}$MDPSK technique outreaches the MPPM-$M{\rm DPSK}$MDPSK by 2000 km at a spectral efficiency of 2.5 bit/s/Hz and an average transmit power of $-{3}\,\,{\rm dBm}$-3dBm.

Mode Selection and Spectrum Allocation in Coexisting D2D and Cellular Networks with Cooperative Precoding.

In this paper, we investigate the mode selection strategies for a new device-to-device (D2D) pair becoming active in a network with a number of existing D2D sensors or users coexisting with cellular users in a D2D-enabled heterogeneous network. Specifically, we propose two selection rules, the signal-to-interference-plus-noise-ratio (SINR)-based and the capacity-based, combined with two sets of different precoding schemes and discuss their impacts on the system under a variety of scenarios. While the cooperative block diagonalization (BD) among the cellular users combined with the zero-forcing (ZF) precoding among D2D users can eliminate interference observed at the new D2D receiving sensor, the maximum signal-to-leakage-and-noise-ratio (SLNR) precoding is often a preferred option due to low-complexity implementations and comparable performance. We note that the two selection rules, the SINR-based and the capacity-based, considered in this paper impact on the system differently, with interesting tradeoff from different perspectives. Finally, we provide insights by simulations into the best selection among the three modes depending on a variety of use cases in the network.

Full-Duplex NOMA Transmission with Single-Antenna Buffer-Aided Relays

The efficient deployment of fifth generation and beyond networks relies upon the seamless combination of recently introduced transmission techniques. Furthermore, as multiple network nodes exist in dense wireless topologies, low-complexity implementation should be promoted. In this work, several wireless communication techniques are considered for improving the sum-rate performance of cooperative relaying non-orthogonal multiple access (NOMA) networks. For this purpose, an opportunistic relay selection algorithm is developed, employing single-antenna relays to achieve full-duplex operation by adopting the successive relaying technique. In addition, as relays are equipped with buffers, flexible half-duplex transmission can be performed when packets reside in the buffers. The proposed buffer-aided and successive single-antenna (BASSA-NOMA) algorithm is presented in detail and its operation and practical implementation aspects are thoroughly analyzed. Comparisons with other relevant algorithms illustrate significant performance gains when BASSA-NOMA is employed without incurring high implementation complexity.

Design of a Continuous Signal Generator Based on Sliding Mode Control of Three-Phase AC-DC Power Converters

In recent years, hundreds of technical papers have been published which describe the use of sliding mode control (SMC) techniques for power electronic equipment and electrical drives. SMC with discontinuous control actions has the potential to circumvent parameter variation effects with low implementation complexity. The problem of controlling time-varying DC loads has been studied in literature if three-phase input voltage sources are available. The conventional approach implies the design of a three-phase AC/DC converter with a constant output voltage. Then, an additional DC/DC converter is utilized as an additional stage in the output of the converter to generate the required voltage for the load. A controllable AC/DC converter is always used to have a high quality of the consumed power. The aim of this study is to design a controlled continuous signal generator based on the sliding mode control of a three-phase AC-DC power converter, which yields the production of continuous variations of the output DC voltage. A sliding mode current tracking system is designed with reference phase currents proportional to the source voltage. The proportionality time-varying gain is selected such that the output voltage is equal to the desired time function. The proposed new topology also offers the capability to get rid of the additional DC/DC power converter and produces the desired time-varying control function in the output of AC/DC power converter. The effectiveness of the proposed control design is demonstrated through a wide range of MATLAB/Simulink simulations.

Subcarrier Subset Selection-Aided Transmit Precoding Achieves Full-Diversity in Index Modulation

Index modulation (IM) is a recently proposed multi-carrier transmission scheme, which conveys information both by conventional symbols as well as by the specific subcarrier activation patterns conveying them. However, an impediment of IM is that it lacks transmit diversity gain. In this paper, we circumvent this limitation by proposing a limited-feedback assisted IM transmission scheme. Specifically, Euclidean distance based subcarrier subset selection (ED-SSS) is proposed and its attainable transmit diversity order is shown to be $N_c-N_{IM}+1$ , where $N_c$ is the total number of subcarriers in an IM block and $N_{IM}$ is the number of subcarriers used for IM. Furthermore, the ED-SSS is shown to be amenable to low-complexity implementation owing to the orthogonality of its subcarriers. In order to attain the maximum transmit diversity order of $N_c$ , ED-SSS is further extended with the aid of transmit precoding and its transmit diversity order is quantified. The proposed precoding assisted ED-SSS is shown to subsume several of the existing precoding aided IM transmission schemes. Simulation studies are conducted for validating our theoretical claims and also for quantifying the attainable performance gains of the proposed schemes. Specifically, at a BER of $10^{-3}$ an SNR gain as high as 8 dB is observed in case of precoding when compared to its counterpart operating without precoding.

Trellis Exploration Based Hybrid Precoding and Distributed Antenna Array Structures in Millimeter-Wave MIMO Systems

This paper concerns a downlink single user millimeter wave MIMO system in which the base station is equipped with distributed antenna arrays. Assuming that the fading channel is composite and comprised of large scale fading and small scale fading, this paper investigates mainly the distributed antenna array structures and practical hybrid precoding. In particular, three different layouts of distributed antenna arrays are considered and their spectral efficiencies are analyzed. In addition, a trellis exploration algorithm with a lower implementation complexity and cost is proposed, which can obtain the optimal analog precoder with finite resolution. Simulation results show that the circular antenna layout with a central remote antenna unit is the best choice for the distributed system. Furthermore, the results indicate that our proposed algorithm can achieve the spectral efficiency that is very close to that of matrix factorization algorithm.

Variable Global Optimization min-sum (VGOMS) algorithm of decode-and-forward protocol for the relay node in the cooperative channel

The excessive delay due to the complexity of the decoding process at the relay node results in additional delays at the destination which is a significant issue in cooperative communication channels. A low complexity min-sum (MS) based algorithm called the Variable Global Optimization min-sum (VGOMS) algorithm has been developed to minimise the error corrective performance. The proposed VGOMS algorithm apply the optimization scaling factor at the bit node processing of the variable node operation which have significantly less complexity as compared with the check node operation or both variable node and check node operation. In this study, a straightforward Particle Swarm Optimization approach was used to determine the optimal scaling factor to obtain optimized error corrective performance of the algorithm. The proposed system model uses the channel combining large-scale and small-scale effects which used extensively in cooperative communications. The performance of the VGOMS algorithm performance was compared with four existing decoding algorithms (MS, OMS, NMS, SP) and was found better than the original min-sum (MS) in term of bit error rate (BER) performance and the first algorithm reach optimal BER in error floor region from 3 others existing decoding algorithm (MS, OMS, NMS) after Sum-Product (SP) algorithm. The VGOMS algorithm was therefore shown to have a significantly better compromise between the implementation of good error performance and low computational complexity.

Energy Scheduling for Networked Microgrids With Co-Generation and Energy Storage

This paper proposes an online algorithm for energy storage management in networked microgrids (MGs) with co-generation based on the concept of quality-of-service in electricity (QoSE). The concept of networked MG with distributed renewable energy supply and co-generation makes power supply smarter for electricity/heat using, which has advantages of increasing power supply efficiency and reliability by coordinately scheduling the power supply in a networked way. The demands include quality usage of electricity load and heat. The networked MG central controller aims to minimize the operation cost and guarantee the outage probability of quality usage, i.e., QoSE, by scheduling electricity among renewable energy sources, energy storage systems, co-generation, and external utility market. We formulate the problem as a stochastic programming problem with QoSE and battery capacity constraints. By introducing the QoSE virtual queues and energy storage virtual queues, we transform the original problem into a problem that is applicable to employ the Lyapunov optimization technique. The proposed algorithm is an online algorithm with low complexity for practical implementation, and also provides several deterministic performance bounds. We perform extensive simulations to demonstrate the effectiveness of the proposed algorithm, which exhibits significant efficiency on operation cost reduction compared with an alternative benchmark solution.

Recursive Updating Algorithm for Robust Adaptive Beamforming in a Uniform Circular Array

When the phase-mode transformation technique is used in a uniform circular array, its output performance is known to degrade owing to the approximations applied to the formulation. In this paper, we develop a robust recursive updating algorithm based on the worst-case performance optimization and phase-mode transformation in the uniform circular array, which provides efficient robustness not only against the signal steering vector mismatches, but also against the transformation errors. The proposed algorithm belongs to the class of the diagonal loading technique and the transformation matrix belongs to a certain ellipsoid set. The weight vector is updated by the Lagrange multiplier method, in which the parameters are derived simply. The proposed algorithm has a closed-form solution, in which we analyse the reasonable ranges of two key parameters. The convergence performance and the output SINR performance are also analysed. In additional, the implementation complexity costs of the proposed algorithm and MVDR algorithm are presented in this paper. Our robust algorithm has a low implementation complexity cost and achieves the mean output array SINR consistently close to the optimal one. Simulation results are presented to compare the performances of our algorithm with the conventional algorithms.

A Ripple Reduction Method for Switched-Capacitor DC–DC Voltage Converter Using Fully Digital Resistance Modulation

The output ripple of switched-capacitor dc–dc voltage converter (SCVC) severely degrades the energy efficiency, performance, and robustness of the VLSI system. In this paper, a fully digital resistance modulation (FDRM) technique is proposed to reduce the output ripple of SCVC, which is scalable and compatible with the exiting ripple reduction methods. The proposed FDRM technique suppresses the impulsive charging and discharging effects in the SCVC operation by dynamically modulating its equivalent switch resistances, resulting in a reduced output ripple. The FDRM control signals can be generated from simple logic gates with the interleaved clock signals, realizing a low implementation complexity. The proposed FDRM technique was verified by a fully integrated SCVC in 180-nm CMOS process with an active area of 0.93 mm2. The measurement results show that the SCVC prototype with the proposed FDRM technique achieves an averaged ripple reduction of 31.6% and a peak conversion efficiency of 88.96% under a loading range of 95–190 $\mu \text{A}$ .

Super-Resolution Blind Channel-and-Signal Estimation for Massive MIMO With One-Dimensional Antenna Array

In this paper, we study blind channel-and-signal estimation by exploiting the burst-sparse structure of angular-domain propagation channels in massive MIMO systems. The state-of-the-art approach utilizes the structured channel sparsity by sampling the angular-domain channel representation with a uniform angle-sampling grid, a.k.a. virtual channel representation. However, this approach is only applicable to uniform linear arrays and may cause a substantial performance loss due to the mismatch between the virtual representation and the true angle information. To tackle these challenges, we propose a sparse channel representation with a super-resolution sampling grid and a hidden Markovian support. Based on this, we develop a novel approximate inference based blind estimation algorithm to estimate the channel and the user signals simultaneously, with emphasis on the adoption of the expectation-maximization method to learn the angle information. Furthermore, we demonstrate the low-complexity implementation of our algorithm, making use of factor graph and message passing principles to compute the marginal posteriors. Numerical results show that our proposed method significantly reduces the estimation error compared to the state-of-the-art approach under various settings, which verifies the efficiency and robustness of our method.

Short Prototype Filter With Constrained Frequency Spreading for OQAM/FBMC Systems

In this letter, we propose a short filter (i.e., the overlapping factor is one) with constrained frequency spreading (CFS) for offset quadrature amplitude modulation-based filter bank multicarrier (OQAM/FBMC) systems. In detail, a system of equations in the filter's frequency coefficients is formulated and solved in the sense of least squares, then the filter is obtained through an inverse discrete Fourier transform. Simulation results reveal that the proposed CFS filter is able to achieve comparable performance to existing short filters in the presence of different channel impairments with less frequency spreading, which leads to lower implementation complexity of OQAM/FBMC using frequency spreading structure, thus it is a valuable choice when considering both the CFS and a near perfect reconstruction of the OQAM/FBMC signal.

МОДИФІКУВАННЯ РОБОЧИХ ПОВЕРХОНЬ СТАЛЕВИХ ДЕТАЛЕЙ З ФОРМУВАННЯМ ВИСОКОВУГЛЕЦЕВИХ СТРУКТУР

The urgent task is to create on the surface of steel parts layers that are capable of long time to work reliably under conditions of friction and wear. The tool for increasing the wear resistance of the surface layers of steel parts is chosen to modify the working surfaces by their carbonization. In this case, the hardness of the metal is controlled by the change in the carbon concentration in the surface layers, the content of the alloying elements by thermal and chemical-thermal processing techniques. In the work comparative studies of the efficiency of various technologies of carburization of working surfaces of steel parts and formation of the properties of wear resistance, formation of burrs and gripping according. It has been proposed to carry out comparative studies according to the criteria of performance, cost, and applicability for dimensional parts. The analysis revealed the existence of a number of methods that use the diffusion phenomenon to form wear-resistant high-carbon coatings with metal matrix and carbide reinforcing inclusions or cemented layers with different structural components on the surfaces of steel parts, which are known as “quenching structures”, differing in different technical and economic efficiency. Particular attention is also paid to the method of forming composite high-carbon coating in the mode of self-propagating high-temperature synthesis (SHS). As a result of the carried out researches it is shown that there is a significant shortage of the overwhelming majority of the considered methods of carburizing of working surfaces of parts from low or medium carbon varieties of steel - high cost of production due to low productivity and complexity of implementation. These drawbacks stimulate the development of new high-performance methods of forming on steel details corresponding coatings of considerable depth and low cost.

Full-Angle Digital Predistortion of 5G Millimeter-Wave Massive MIMO Transmitters

In this paper, a full-angle digital predistortion (DPD) technique is proposed to linearize fifth-generation (5G) millimeter-wave (mmWave) massive multiple-input-multiple-output (mMIMO) transmitters with low implementation complexity. It is achieved by compensating the differences of power amplifiers (PAs) in different transmitter chains first and then adopting a common digital block to linearize the whole subarray. Based on this operation, all the transmitter chains can be efficiently linearized simultaneously, providing the merits of full-angle linearization including the main beam and sidelobes. To validate the proposed idea, an mmWave full-digital beam-forming transmitter has been developed, which is operated at the center frequency of 24.75–28.5 GHz to meet the 5G candidate frequency bands. Experimental results show that the proposed method can effectively linearize the mmWave mMIMO transmitter in all directions, which provides a promising linearization solution for 5G mMIMO beam-forming systems.