scholarly journals Performance Evaluation of OFDM System with Insufficient CP Using LMS Equalizer under Harsh Multipath Conditions

2019 ◽  
Vol 15 (2) ◽  
pp. 122-129
Author(s):  
Abolqassem Fakher ◽  
Falih Alnahwi ◽  
Majid Alwan
Keyword(s):  
Orthogonal Frequency Division Multiplexing ◽  
High Performance ◽  
Minimum Mean Square Error ◽  
Lms Algorithm ◽  
Mean Square ◽  
Least Mean Square ◽  
Ofdm System ◽  
Zero Forcing ◽  
Ofdm Signal ◽  
Multipath Signals

This paper presents an insufficient cyclic prefix (CP) Orthogonal Frequency Division Multiplexing (OFDM) system with equalizer whose coefficients are calculated using Least Mean Square (LMS) algorithm. The OFDM signal is passed through a channel with four multipath signals which cause the OFDM signal to be under high inter-symbol interference (ISI) and inter-carrier interference (ICI).8-QAM and 16-QAM digital modulation techniques are used to evaluate the performance of the proposed system. The simulation results have accentuated the high performance of the LMS equalizer via comparing its Bit Error Rate (BER) and constellation diagram with those of the Minimum Mean Square Error and Zero Forcing equalizers. Moreover, the results also reveal that the LMS equalizer provides BER performance close to that of the OFDM system with a hypothetical sufficient CP.

scholarly journals Performance Enhancement of Coherent Optical OFDM System Using LMS Algorithm

2020 ◽  
Vol 1 ◽  
pp. 1-5
Author(s):  
Manisha Bharti
Keyword(s):  
Phase Noise ◽  
Bit Error Rate ◽  
Error Rate ◽  
Orthogonal Frequency Division Multiplexing ◽  
Performance Enhancement ◽  
Lms Algorithm ◽  
Frame Structure ◽  
Least Mean Square ◽  
Ofdm Systems ◽  
Ofdm System

Instability of the local oscillator causes phase noise – a phenomenon that is a disadvantage and is considered to be a major obstacle in the functioning of coherent optical orthogonal frequency division multiplexing (CO-OFDM) systems. An attempt has been made in this paper to reduce the effects of common phase errors generated by phase noise. In this paper, a least mean square (LMS) based algorithm is proposed for estimation of phase noise. Using this proposed algorithm, the major problem of phase ambiguity caused by cycle slip is avoided and the bit error rate is greatly improved. Further, there is no requirement for modifying the frame structure of OFDM using this algorithm. A CO-OFDM system with the 8-PSK technique is used to implement the algorithm concerned. Furthermore, the algorithm, using the 8-PSK modulation technique, is analyzed and compared with the existing QPSK technique and with other algorithms. The investigations reveal that 8-PSK outperforms existing LMS algorithms using other techniques and significantly reduces the bit error rate.

scholarly journals A Novel Signal Detection Algorithm for Underwater MIMO-OFDM Systems Based on Generalized MMSE

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Gaoli Zhao ◽  
Jianping Wang ◽  
Wei Chen ◽  
Junping Song
Keyword(s):  
Computational Complexity ◽  
Signal Detection ◽  
Mean Square Error ◽  
High Performance ◽  
Minimum Mean Square Error ◽  
Multipath Fading ◽  
Detection Algorithm ◽  
Mean Square ◽  
Ofdm System ◽  
Mimo Ofdm

The MIMO-OFDM system fully exploits the advantages of MIMO and OFDM, effectively resisting the channel multipath fading and inter-symbol interference while increasing the data transmission rate. Studies show that it is the principal technical mean for building underwater acoustic networks (UANs) of high performance. As the core, a signal detection algorithm determines the performance and complexity of the MIMO-OFDM system. However, low computational complexity and high performance cannot be achieved simultaneously, especially for UANs with a narrow bandwidth and limited data rate. This paper presents a novel signal detection algorithm based on generalized MMSE. First, we propose a model for the underwater MIMO-OFDM system. Second, we design a signal coding method based on STBC (space-time block coding). Third, we realize the detection algorithm namely GMMSE (generalized minimum mean square error). Finally, we perform a comparison of the algorithm with ZF (Zero Forcing), MMSE (minimum mean square error), and ML (Maximum Likelihood) in terms of the BER (bit error rate) and the CC (computational complexity). The simulation results show that the BER of GMMSE is the lowest one and the CC close to that of ZF, which achieves a tradeoff between the complexity and performance. This work provides essential theoretical and technical support for implementing UANs of high performance.

scholarly journals Estimation of FBMC/OQAM Fading Channels Using Dual Kalman Filters

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Mahmoud Aldababseh ◽  
Ali Jamoos
Keyword(s):  
Fading Channels ◽  
Kalman Filters ◽  
Fading Channel ◽  
Minimum Mean Square Error ◽  
Least Square ◽  
Lms Algorithm ◽  
Mean Square ◽  
Least Mean Square ◽  
Recursive Least Square ◽  
Channel Statistics

We address the problem of estimating time-varying fading channels in filter bank multicarrier (FBMC/OQAM) wireless systems based on pilot symbols. The standard solution to this problem is the least square (LS) estimator or the minimum mean square error (MMSE) estimator with possible adaptive implementation using recursive least square (RLS) algorithm or least mean square (LMS) algorithm. However, these adaptive filters cannot well-exploit fading channel statistics. To take advantage of fading channel statistics, the time evolution of the fading channel is modeled by an autoregressive process and tracked by Kalman filter. Nevertheless, this requires the autoregressive parameters which are usually unknown. Thus, we propose to jointly estimate the FBMC/OQAM fading channels and their autoregressive parameters based on dual optimal Kalman filters. Once the fading channel coefficients at pilot symbol positions are estimated by the proposed method, the fading channel coefficients at data symbol positions are then estimated by using some interpolation methods such as linear, spline, or low-pass interpolation. The comparative simulation study we carried out with existing techniques confirms the effectiveness of the proposed method.

scholarly journals MBER Space Time Equalization assisted Multiuser Detection

2012 ◽  
pp. 47-50
Author(s):  
Yogeshwary B.H. ◽  
T.L. Purushottama ◽  
M Z Kurian
Keyword(s):  
Bit Error Rate ◽  
Error Rate ◽  
Minimum Mean Square Error ◽  
Space Time ◽  
Decision Feedback ◽  
Lms Algorithm ◽  
Mean Square ◽  
Least Mean Square ◽  
Estimation Errors ◽  
Time Equalization

A novel minimum bit-error rate (MBER) space–time-equalization (STE)-based multiuser detector (MUD) is proposed for multiple-receive-antenna-assisted space-division multiple-access systems. It is shown that the MBER-STE-aided MUD significantly outperforms the standard minimum mean-square error design in terms of the achievable bit-error rate (BER). Adaptive implementations of the MBER STE are considered, and both the block-databased and sample-by-sample adaptive MBER algorithms are proposed. The latter, referred to as the least BER (LBER) algorithm, is compared with the most popular adaptive algorithm, known as the least mean square (LMS) algorithm. It is shown that in case of binary phase-shift keying, the computational complexity of the LBER-STE is about half of that required by the classic LMS-STE. Our simulation results demonstrate that the MBER ST-DFE assisted MUD is more robust to channel estimation errors as well as to potential error propagation imposed by decision feedback errors, compared to the MMSE ST-DFE assisted MUD.

scholarly journals Performance of Beamforming for Smart Antenna using Traditional LMS Algorithm for Various Parameters

2021 ◽  
Vol 15 ◽  
pp. 8-13
Author(s):  
Vishal V. Sawant ◽  
Mahesh Chavan
Keyword(s):  
Smart Antenna ◽  
Minimum Mean Square Error ◽  
Sensor Arrays ◽  
Weight Vector ◽  
Adaptive Signal Processing ◽  
Lms Algorithm ◽  
Beam Forming ◽  
Mean Square ◽  
Least Mean Square ◽  
Two Factors

Adaptive signal processing sensor arrays, known also as smart antennas .The smart antenna adaptive algorithms achieve the best weight vector for beam forming by iterative means. The Least Mean Square (LMS) algorithm, is an adaptive algorithm .LMS incorporates an iterative procedure that makes successive corrections to the weight vector in the direction of the negative of the gradient vector which eventually leads to the minimum mean square error. Beam forming is directly determined by the two factors. The performance of the traditional LMS algorithm for different parameters is analysed in this paper. This algorithm can be applied to beam forming with the software Matlab. The result obtain can achieve faster convergence and lower steady state error. The algorithms can be simulated in MATLAB 7.10 version.

scholarly journals Hybrid Approach for Channel Estimation Using Iterative Compensation and LMS Algorithm

2018 ◽  
Vol 7 (3.8) ◽  
pp. 34
Author(s):  
Bhanu Partap ◽  
Prabhjot Singh ◽  
. .
Keyword(s):  
Channel Estimation ◽  
Orthogonal Frequency Division Multiplexing ◽  
Hybrid Approach ◽  
Vital Role ◽  
Lms Algorithm ◽  
Mean Square ◽  
Estimation Technique ◽  
Compensation Mechanism ◽  
Least Mean Square ◽  
Frequency Division

In Orthogonal Frequency Division Multiplexing, the concept of channel equalization and estimation plays a vital role to improve the performance of the system by reducing the effects of distortion in the signals that occurs due to fading, multipath, delay spreads. In this study a hybrid channel estimation technique is developed by collaborating the iterative compensation mechanism with Least Mean Square technique. The performance of the proposed work is observed to be more effective and efficient than the Iterative compensation based channel estimation technique in the terms of Mean Square Error (MSE).  

scholarly journals An Effective Adaptive Combination Strategy for Distributed Learning Network

2021 ◽  
Vol 11 (12) ◽  
pp. 5723
Author(s):  
Chundong Xu ◽  
Qinglin Li ◽  
Dongwen Ying
Keyword(s):  
Minimum Variance ◽  
Iteration Scheme ◽  
Estimation Problem ◽  
Lms Algorithm ◽  
Unbiased Estimation ◽  
Mean Square ◽  
Least Mean Square ◽  
Combination Strategy ◽  
Adaptive Combination ◽  
Diffusion Networks

In this paper, we develop a modified adaptive combination strategy for the distributed estimation problem over diffusion networks. We still consider the online adaptive combiners estimation problem from the perspective of minimum variance unbiased estimation. In contrast with the classic adaptive combination strategy which exploits orthogonal projection technology, we formulate a non-constrained mean-square deviation (MSD) cost function by introducing Lagrange multipliers. Based on the Karush–Kuhn–Tucker (KKT) conditions, we derive the fixed-point iteration scheme of adaptive combiners. Illustrative simulations validate the improved transient and steady-state performance of the diffusion least-mean-square LMS algorithm incorporated with the proposed adaptive combination strategy.

scholarly journals A Comprehensive Review on Channel Estimation in OFDM System

2019 ◽  
Vol 5 (3) ◽  
pp. 6 ◽  
Author(s):  
Neha Dubey ◽  
Ankit Pandit
Keyword(s):  
Channel Estimation ◽  
Mean Square Error ◽  
Orthogonal Frequency Division Multiplexing ◽  
Transmission Rate ◽  
Minimum Mean Square Error ◽  
Estimation Method ◽  
Least Square ◽  
Estimation Methods ◽  
Mean Square ◽  
Mimo Ofdm

In wireless communication, orthogonal frequency division multiplexing (OFDM) plays a major role because of its high transmission rate. Channel estimation and tracking have many different techniques available in OFDM systems. Among them, the most important techniques are least square (LS) and minimum mean square error (MMSE). In least square channel estimation method, the process is simple but the major drawback is it has very high mean square error. Whereas, the performance of MMSE is superior to LS in low SNR, its main problem is it has high computational complexity. If the error is reduced to a very low value, then an exact signal will be received. In this paper an extensive review on different channel estimation methods used in MIMO-OFDM like pilot based, least square (LS) and minimum mean square error method (MMSE) and least minimum mean square error (LMMSE) methods and also other channel estimation methods used in MIMO-OFDM are discussed.

Resource Allocation for a Cooperative Broadband MIMO-OFDM System

2010 ◽  
pp. 382-398
Author(s):  
Ibrahim Y. Abualhaol ◽  
Mustafa M. Matalgah
Keyword(s):  
Resource Allocation ◽  
Power Distribution ◽  
Power Efficiency ◽  
Orthogonal Frequency Division Multiplexing ◽  
High Performance ◽  
Ofdm System ◽  
Frequency Space ◽  
Input Multiple Output ◽  
Power Loading ◽  
Mimo Ofdm

In this chapter, a cooperative broadband relay-based resource allocation technique is proposed for adaptive bit and power loading multiple-input-multiple-output/orthogonal frequency division multiplexing (MIMO-OFDM) system. In this technique, sub-channels allocation, M-QAM modulation order, and power distribution among different sub-channels in the relay-based MIMO-OFDM system are jointly optimized according to the channel state information (CSI) of the relay and the direct link. The transmitted stream of bits is divided into two parts according to a suggested cooperative protocol that is based on sub-channel-division. In this protocol, the first part is sent directly from the source to the destination, and the second part is relayed to the destination through an indirect link. Such a cooperative relay-based system enables us to exploit the inherent system diversities in frequency, space and time to maximize the system power efficiency. The BER performance using this cooperative sub-channel-division protocol with adaptive sub-channel assignment and adaptive bit/power loading are presented and compared with a noncooperative ones. The use of cooperation in a broadband relay-based MIMO-OFDM system showed high performance improvement in terms of BER.

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