Constellation Algorithm Research Articles

Routing cost-integrated intelligent handover strategy for multi-layer LEO mega-constellation networks

Low Earth Orbit (LEO) mega-constellation networks, exemplified by Starlink, are poised to play a pivotal role in future mobile communication networks, due to their low latency and high capacity. With the massively deployed satellites, ground users now can be covered by multiple visible satellites, but also face complex handover issues with such massive high-mobility satellites in multi-layer. The end-to-end routing is also affected by the handover behavior. In this paper, we propose an intelligent handover strategy dedicated to multi-layer LEO mega-constellation networks. Firstly, an analytic model is utilized to rapidly estimate the end-to-end propagation latency as a key handover factor to construct a multi-objective optimization model. Subsequently, an intelligent handover strategy is proposed by employing the Dueling Double Deep Q Network (D3QN)-based deep reinforcement learning algorithm for single-layer constellations. Moreover, an optimal cross-layer handover scheme is proposed by predicting the latency-jitter and minimizing the cross-layer overhead. Simulation results demonstrate the superior performance of the proposed method in the multi-layer LEO mega-constellation, showcasing reductions of up to 8.2% and 59.5% in end-to-end latency and jitter respectively, when compared to the existing handover strategies.

Rethinking LEO constellations routing

SummaryThis study investigates the unsplittable multicommodity flow (UMCF) as a routing algorithm for LEO constellations. Usually, LEO routing schemes enable the Floyd–Warshall algorithm (shortest path) to minimize the end‐to‐end latency of the flows crossing the constellation. We propose to solve the UMCF problem associated with the system as a solution for routing over LEO. We use a heuristic algorithm based on randomized rounding known in the optimization literature to efficiently solve the UMCF problem. Furthermore, we explore the impact of choosing the first/last hop before entering/exiting the constellation. Using network simulation over Telesat constellation, we show that UMCF maximizes the end‐to‐end links usage, providing better routing while minimizing the delay and the congestion level, which is an issue today over new megaconstellations.

A robust signal tracking algorithm for GPS and NavIC constellations

A robust signal tracking algorithm for GPS and NavIC constellations

Block Deep Neural Network-Based Signal Detector for Generalized Spatial Modulation

Generalized Spatial Modulation (GSM) is being considered for high capacity and energy-efficient networks of the future. However, signal detection due to inter channel interference among the active antennas is a challenge in GSM systems and is the focus of this letter. Specifically, we explore the feasibility of using deep neural networks (DNN) for signal detection in GSM. In particular, we propose a block DNN (B-DNN) based architecture, where the active antennas and their transmitted constellation symbols are detected by smaller sub-DNNs. After N-ordinary DNN detection, the Euclidean distance-based soft constellation algorithm is implemented. The proposed B-DNN detector achieves a BER performance that is superior to traditional block zero-forcing (B-ZF) and block minimum mean-squared error (B-MMSE) detection schemes and similar to that of classical maximum likelihood (ML) detector. Further, the proposed method requires less computation time and is more accurate than alternative conventional numerical methods.

Research on hierarchical architecture and routing of satellite constellation with IGSO‐GEO‐MEO network

SummaryIn this paper, a three‐layered medium Earth orbit (MEO), geostationary Earth orbit (GEO), and inclined geosynchronous orbit (IGSO) satellite network (IGMSN) is presented. Based on the idea of time‐slot division, a novel dynamic hierarchical and distributed QoS (quality of service) routing protocol (HDRP) is investigated, and an adaptive bandwidth‐constrained minimum‐delay path for IGSO/GEO/MEO hierarchical architecture constellation (BMDP‐HAC) algorithm is developed to calculate routing tables efficiently using the QoS metric information composed of delays and bandwidth. The performance of the IGMSN and HDRP is evaluated through simulations and theoretical analysis. And then, the paper further analyzes the performance of the IGMSN structure and the BMDP‐HAC algorithm with failure satellites.

Satellite Selection with Multi-Objective Genetic Algorithm for Multi-GNSS Constellations

Satellite Selection with Multi-Objective Genetic Algorithm for Multi-GNSS Constellations

An efficient OFDM-based system with an insufficient cyclic prefix via a novel constellation algorithm

In this paper, a new special triangular constellation scheme is introduced to replace the commonly used rectangular QAM constellation in orthogonal frequency division multiplexing (OFDM) modulation. We have shown that this new scheme has 3 major advantages with respect to the well-known QAM. The first advantage is its lower error probability performance, which results from better usage of the constellation space with longer minimum distances. The 2 other advantages are a lower peak to average power ratio (PAPR) and higher noise immunity. Both mathematical analysis and simulation results demonstrate that by applying standard channels in 2 cases, i.e. channels with AWGN and channels with burst noise and also with intersymbol interference (ISI) impairment simultaneously, the proposed constellation exhibits superior performance compared to the well-known QAM. As a result, this constellation is a good choice for high-speed and real-time OFDM multicarrier applications such as WiFi, WiMAX, DVB, and DAB at no extra cost.

Optimized PAPR Reduction Approach by Partial Approximate Gradient Constellation Algorithm for Better Efficiency

Optimized PAPR Reduction Approach by Partial Approximate Gradient Constellation Algorithm for Better Efficiency

적응 스텝 크기에 의한 CCA 블라인드 등화 알고리즘의 성능 개선

This paper relates with the performance improvement of CCA (Compact Constellation Algorithm) equalization algorithm by adding the adaptive step size control in order to the minimization of intersymbol interference and additive noise effects that is occurs in the channel for digital radio transmissionl. In general, the fixed step size was used in order to adaptation in equalizer algorithm. But in proposed algorithm, the variable step size were adapted that is proposional to the nonlinear function of error signal for equalization. In order to show the improved equalizatation performance, the output signal constellation of equalizer, residual isi, maximum distortion, MSE and SER were used, then it were compared with the present CCA algorithm. As a result of computer simulation, the adaptive step size CCA has more better performance in the every performance index compared to the fixed step size CCA after in the steay state.

THE NEW ADAPTIVE ACTIVE CONSTELLATION EXTENSION ALGORITHM FOR PAR MINIMIZATION IN OFDM SYSTEMS

In this paper, the Peak-to-Average Ratio reduction in OFDM systems is implemented by the Adaptive Active Constellation Extension (ACE) technique which is more simple and attractive for practical downlink implementation purpose. However, in normal constellation method we cannot achieve the minimum PAR if the target clipping level is much below than the initial optimum value. To get the better of this problem, we proposed Active Constellation Algorithm with adaptive clipping control mechanism to get minimum PAR. Simulation results exhibits that the proposed algorithm reaches the minimum PAR for most severely low clipping signals to get minimum PAR.

16-QAM 신호에서 Compact Slice 가중치에 의한 CCA 적응 등화 알고리즘의 성능 비교

This paper compare the performance of CCA (Compact Constellation Algorithm) adaptive equalization algorithm by effect of the compact slice weighting value for minimization of the intersymbol interference in the communication channel. The CCA combines the conventional DDA and RCA algorithm, it uses the constant modulus of the transmission signal and the considering the output of decision device by the power of compact slice weighting value in order to improving the initial convergence characteristics and the equalization noise by misadjustment in the steady state. In this process, it is confirmed by computer simulation that the compact slice weight affects the performance of CCA adaptive equalization algorithm. The performance index includes the output signal constellation, the residual isi and maximum distortion and MSE that is for the convergence characteristics, the SER according to the signal and noise power ratio at the channel is used. As a result of computer, it shows that the large weighting value gives more good in every performance index. But in SER performance, it is known that the small values gives more good in low SNR and the large values gives more good in high SNR.

16-QAM 신호에 대한 CCA 적응 등화 알고리즘 성능 분석

Abstract This paper deals with the performance anlysis of CCA adaptive equalization algorithm, that is used for reduction of intersymbol interference at the receiving side which occurs in the time dispersive communication channel. Basically, this algorithm is borned for the solving phase unrecovery problem in the CMA equalizer, and the comines the concept of DDA (Decision Directed Algorithm) and RCA (Reduce Constellation Algorithm). The DDA has a stable convergence characteristics in unilevel signal, but not in the number of levels in multilevel signal such as QAM, so it has unstable problem. The RCA does not provide reliable initial convergence. And even after convergence, the equalization noise due to the steady state misadjustment exhibited by it is very high as compared to DDA. For the solving the abovemensioned point, the CCA adaptive eualization alogorithm has borned. In order to performance analysis of CCA algorithm, the recovered signal constellation that is the output of the equalizer, the convergence characteristic by the residual isi and MD (maximum distortion), the SER characteristic are used by computer simulation and it was compared with the DDA, RCA respectively. As a result of simulation, the DDA has superior performance than other algoithm, but it has a convergence unguarantee and unstability in the multilevel signal. In order to solving this problem, the CCA has more good performance than RCA in every performance index.

RCA 비용 함수를 개선한 Advanced CMA 등화기 알고리즘

In this paper, the concerned CMA (Constant Modulus Algorithm) adaptive equalizer convergence rate and residual inter-symbol interference using cost function in order to improved to the ACMA (Advanced CMA). The CMA method compensates amplitude but does not compensate phase. On the other hand, The RCA (Reduced Constellation Algorithm) method compensates both the amplitude and the phase but it has the convergence rate problem. MCMA method is a way to solve the phase problem of CMA method compensates both the amplitude and the phase after respectively calculating the real and imaginary components. But it is more than poor CMA method in the complexity of hardware and the compensation performance. The cost function can advantages by improving the CMA and a MCMA (Modified CMA) equalizer so that the amplitude and phase retrieval the equalization steady-state to reduce the error by using ISI and faster convergence rate and performance is good SER (Symbol Error Ratio) was confirmed by computer simulations.

A MSE optimized polynomial equalizer for 16QAM and 64QAM constellation

Recently, a Maximum Entropy (MaxEnt) algorithm and its derivation called WNEW algorithm were presented by the same author. It was shown that the MaxEnt and WNEW algorithm have improved equalization performance compared with Godard’s, reduced constellation algorithm and the sign reduced constellation algorithm. In this paper, a new equalization method is proposed for the 16QAM and 64QAM input constellation based on the WNEW algorithm which is extended with some polynomials of the equalized output and optimized with the mean square error criteria. According to simulation results, the new equalization method leads to over 15 dB advantage in the residual Intersymbol Interference compared to the results presented by Godard, 10 dB advantage compared with the WNEW algorithm and 5 dB advantage compared with the MaxEnt algorithm.

A Novel Discrete Multi-Tone Constellation Algorithm

A Novel Discrete Multi-Tone Constellation Algorithm

A Maximum Entropy approach for blind deconvolution

In this paper we propose a new closed approximated formed expression for the conditional expectation based on the Maximum Entropy principle and Laplace integral method. Our proposed expression does not impose any restrictions (except of even symmetric) on the probability distribution of the (unobserved) input sequence, thus it is suitable for a wider range of source probability density function compared with Bellini's, Fiori's or Haykin's expression. In addition, we propose a new, efficient and noniterative approximation for the Lagrange multipliers related to the blind deconvolution problem. Our proposed Lagrange multipliers are based on our new proposed expression for the conditional expectation and on the mean square error (MSE) criteria. Based on our new derivations, a new blind deconvolution algorithm is proposed with improved equalization performance compared with Godard's, reduced constellation algorithm (RCA), Fiori's and the sign reduced constellation algorithm (SRCA). In addition, a theoretical analysis shows that our algorithm achieves perfect equalization in the real valued and two independent quadrature carrier case. These results imply a significant improvement over Godard's algorithm in the 16 quadrature amplitude modulation (QAM) case.

Fast and low complexity blind equalization via subgradient projections

We propose a novel blind equalization method based on subgradient search over a convex cost surface. This is an alternative to the existing iterative blind equalization approaches such as the Constant Modulus Algorithm (CMA), which often suffer from the convergence problems caused by their nonconvex cost functions. The proposed method is an iterative algorithm called SubGradient based Blind Algorithm (SGBA) for both real and complex constellations, with a very simple update rule. It is based on the minimization of the l/sub /spl infin// norm of the equalizer output under a linear constraint on the equalizer coefficients using subgradient iterations. The algorithm has a nice convergence behavior attributed to the convex l/sub /spl infin// cost surface as well as the step size selection rules associated with the subgradient search. We illustrate the performance of the algorithm using examples with both complex and real constellations, where we show that the proposed algorithm's convergence is less sensitive to initial point selection, and a fast convergence behavior can be achieved with a judicious selection of step sizes. Furthermore, the amount of data required for the training of the equalizer is significantly lower than most of the existing schemes.

The multimodulus blind equalization and its generalized algorithms

This paper presents a new blind equalization algorithm called multimodulus algorithm (MMA). This algorithm combines the benefits of the well-known reduced constellation algorithm (RCA) and constant modulus algorithm (CMA). In addition, MMA provides more flexibility than RCA and CMA, and is better suited to take advantage of the symbol statistics of certain types of signal constellations, such as nonsquare constellations, very dense constellations, and some wrong solutions.

A dynamic convergence analysis of blind equalization algorithms

Blind equalizers do not require a training sequence to start up or to restart after a communications breakdown, making them particularly useful in applications such as broadcast and point-to-multipoint networks. We study in parallel the dynamic convergence behavior of three blind equalization algorithms: the multimodulus algorithm (MMA), the constant modulus algorithm (CMA), and the reduced constellation algorithm (RCA). Using a conditional Gaussian approximation, we first derive the theoretical mean-squared-error (MSE) trajectory for MMA. This analysis leads to accurate but somewhat cumbersome expressions. Alternatively, we apply a Taylor series approximation to derive MSE trajectories for MMA, CMA, and RCA. This approach yields simpler but somewhat less accurate expressions. For the steady-state operation, however we derive even simpler formulas that accurately predict the asymptotic MSE values. We finally study the convergence rates of the three blind algorithms using their theoretical MSE trajectories, computer simulations, and a laboratory experiment.

A blind equalization with the sign algorithm for broadband access

This paper presents an optimal reduced constellation point of sign reduced constellation algorithm (SRCA) for square and nonsquare carrierless amplitude and phase (CAP)/QAM signal constellations. Convergence characteristics of the SRCA algorithm are analyzed and compared to those of the RCA algorithm.