Adaptive Frequency-Domain Equalization in Mode-Division Multiplexing Systems

Reduction of the group delay (GD) spread is crucial for minimizing signal processing complexity in mode-division multiplexing. Strong mode coupling and GD compensation (concatenating different fibers with opposing GD ordering) are two approaches for reducing the end-to-end GD spread. In this paper, we study the GD behavior in systems where mode coupling and GD compensation are both present. Using a propagation model in generalized Stokes space, we describe the evolution of the GD variance by coupled differential equations. By integration of these equations, we evaluate the GD variance in GD compensated systems with different mode coupling lengths and GD compensation lengths. When the mode coupling length is much longer than the GD compensation length, a low GD variance can be obtained as a result of GD compensation. By contrast, when the mode coupling length is much shorter than the GD compensation length, GD compensation becomes ineffective, but a low GD variance can be obtained as a result of strong mode coupling. The largest GD variance is obtained when the mode coupling length is comparable to the GD compensation length.

WiMAX physical layer implemented Orthogonal Frequency Division Multiple Access (OFDMA) as one of the medium to conduct data transmission. OFDMA suffers from Intersymbol Interference (ISI) which can distort efficiency of the signal transmitted. ISI emerges in the system because of multipath. Basic Cascaded Least Square (CLS) with Decision Feedback Equalizer (DFE) had been proposed in previous research to minimize ISI in wireless communication system. This research will improve CLS performance in terms of convergence. CLS is a cascaded of two adaptive algorithms which are Least Mean Square (LMS) and Recursive Least Square (RLS). Although basic LMS has low complexity than RLS and has been used widely in wireless environment, LMS is suffering in terms of convergence. Updated Normalized Least Mean Square (NLMS) with training rate applied in this research to overcome disadvantage in LMS algorithm. This research justified that training rate is an alternative method to improve CLS-DFE in wireless communication system.

The application of machine learning techniques for market predictions has been broadly established in the scientific community. Developing high-performance techniques for predicting financial time series is a crucial problem for economists, investigators and analysts. The traditional statistical methods used by economists in the past years seem to fail to capture the discontinuities, nonlinearities and high complexity of the financial time series. Complex machine learning techniques have been used so far to predict the trends of the financial time series. Three major examples are the artificial neural networks (Holland et al., 1995), the random forests (Breiman et al., 2001) and the support vector machines (SVMs) (Vapnik and Cortes, 1995). They provide enough learning capacity and are more likely to capture the complex non-linear models which are dominant in the financial markets. Although machine learning techniques have been widely used in financial indexes forecasting, adaptive filtering techniques have rarely been used. In the present chapter, we achieved high-performance predictions of the trends of the three indexes of DAX30, CAC40 and Euronext with classifiers built based on adaptive filtering techniques. Adaptive filtering techniques do not require previous knowledge of the signal statistics, have a small computational complexity per iteration and converge to a neighbourhood of the optimal solution. Moreover, adaptive filtering techniques are suitable for non-stationary signals and systems, and the trend of a financial index can be characterized as such. Three classical parameter estimation algorithms (least mean squares (LMS), recursive least squares (RLS) and Kalman filter) and one gain adaptation algorithm (incremental delta-bar-delta (IDBD)) were considered. The gain adaptation algorithms have been shown to perform comparably to the best algorithms (Kalman and RLS), but they have a lower complexity (Diniz, 2002). Overall, the adaptive filtering techniques used in this survey are the LMS (Widrow and Hoff, 1960), the RLS (Goodwin and Payne, 1977), the extended Kalman filtering method (EKF ) (Welch and Bishop, 1995) and the IDBD (Sutton, 1992). We use as benchmark/comparative methods the well-known naive strategy, buy and hold strategy, ARMA model and MACD model. Theadaptive filtering techniques have proved to perform significantly better that the classical benchmark techniques. Four different evaluation criteria are used to confirm this superiority (annualized return, information gain, maximal drawdown and correct bidirectional change). The rest of the chapter is organized as follows: In Section 2 a description of the three financial indices is given, and all the traditional and machine learning techniques used are briefly described. In Section 3 the comparative results of the methods are presented and discussed, and in Section 4 some future research directions are proposed.

Mode division multiplexing (MDM) is an emerging optical communication solution for high-capacity wired–wireless applications. Due to the presence of modal crosstalk and link impairments in MDM, this work aims to design a system that provides low complexity, an improved Shannon Capacity limit, and high spectral efficiency. In this work, a quad modal MDM system using an integrated parabolic index multimode fiber and free-space optics (PIMMF-FSO) link is presented. Four linearly polarized (LP) modes, LP01, LP22, LP03, and LP13 based on a 16 × 10 Gbps MDM system offering different sixteen channels, are realized. Results show that the system can sustain a 7.5 dB insertion loss over 100 m FSO and a 100 m fiber range for different LP modes under the impact of clear air, moderate haze, heavy rain and wet snow climates with weak turbulence. A faithful fiber range of 3000 m can be obtained successfully in the proposed system with a −10 dB link loss, −7.62 dBm received power and 10 dB noise. Compared to existing designs, the proposed design offers optimum performance in terms of high channel capacity and a high traffic rate with low complexity and high spectral efficiency. Additionally, high received power, with acceptable noise, link loss, FSO misalignments and fiber nonlinearities, is successfully obtained.

Smart antenna systems are of great importance in wireless communication and radar applications. They effectively enhance the system capacity and reduce the co-channel interference. Smart antenna is an array antenna that uses adaptive beamforming algorithms to steer the main beam toward the desired signal direction and reject the interfering signals of the same frequency from other directions without moving the antenna. This is achieved by continuously updating the weights of each radiating element (antenna). An algorithm with low complexity, low computation costs, high speed convergence rates and better performance is usually preferred. This paper introduces a new performance investigation and comparison between five different beamforming algorithms: Least Mean Square (LMS), Normalized Least Mean Square (NLMS), Sample Matrix Inversion (SMI), Recursive Least Square (RLS) and Hybrid Least Mean Square / Sample Matrix Inversion (LMS/SMI). In this investigation, the number of array elements and the displacement among them are changed in each algorithm. Simulation experiments are carried out, and the results are compared to verify the effectiveness of this investigation. The performance of each algorithm is optimized and demonstrated using MATLAB software package.

In recent times noise cancellation is a vital issue, as it is responsible for reducing undesired disturbances in the process of communication. Active Noise Cancellation (ANC) is the most effective technique to cancel noise. ANC has been an active area of research and various adaptive methodologies have been employed to achieve a better ANC scheme. In the ANC technique, the aim is to minimize the noise interference that corrupts the original signal. ANC has a broad variety of applications in common commercial products, industrial uses and other machinery. An analysis of the prevailing adaptive methodologies is necessary for future research as we need to know the demerits of all the prevailing adaptive methodologies in ANC. This paper provides an analysis of various adaptive algorithms for noise cancellation and a comparison is made between them. The strengths, weaknesses and practical effectiveness of all the algorithms have been discussed. This paper deals with cancellation of noise on speech signal using three existing algorithms- Least Mean Square (LMS) algorithm, Normalized Least Mean Square (NLMS) algorithm and Recursive Least Square (RLS) algorithm and a proposed algorithm- advanced Block Least Mean Square (BLMS) algorithm. The algorithms are simulated in MATLAB platform and a tabular comparison of the algorithms is drawn. Finally, conclusions have been drawn by choosing the algorithms that provide efficient performance with less computational complexity.

A challenge is to measure the impairments of few-mode fiber (FMF) for mode division multiplexing (MDM) system, so as to quantify the few-mode fiber and compensate the impairments, and thus improves the transmission performance of MDM system. In this paper, the experimental setup of impairments measurement with the structure of Photonic Lantern and optical circulators is established. Not only the two kinds of mode coupling (MC) in 9.8 km 3-mode fiber and at splicing of two FMFs (0.9km and 9.8km) with an off-axis distance of 2.0μm and 2.4μm, but also the mode dependent loss (MDL) between LP 01 and LP 11 are successfully measured.

Recent research has shown that single carrier frequency domain equalization (SC-FDE) is an attractive technology for broadband wireless communications for its advantages such as lower peak-to-average power ratio and reduced sensitivity to carrier frequency offset, when compared to orthogonal frequency-division multiplexing (OFDM) systems. In this paper, we propose a novel structure, FDE-NP, which consists of a linear frequency domain equalizer (FDE) and a time domain noise predictor (NP). It is shown that the proposed scheme has lower complexity and achieves better performance and complexity trade-off than the conventional FDE designs. In particular, by using a simple block interleaver/deinterleaver pair to rearrange the order of the received signals prior to decoding, delayed reliable decisions can be fed back to predict and cancel their post-cursor inter-symbol interference (ISI) effects to the following signals. Simulation results show that the proposed FDE-NP scheme achieves 1.5-2 dB performance improvement over the conventional FD-LE and FD-DFE schemes. Furthermore, we extend the design of unbiased and biased time domain equalizers (TDEs) into the frequency domain and propose the unbiased FDE. It is shown that although the unbiased FDE has a larger MSE than the biased one, it reduces the required SNR by about 0.3 dB in some cases

The performance of linear filters degrade drastically when applied to mitigate intersymbol interference caused by channels with frequent nulls in their spectral characteristics like, e.g., time-dispersive radio channels. In such cases, the well-known decision feedback equalizer (DFE) is one possible nonlinear approach to improve the quality of the receiver. However, adapting the DFE filter coefficients to equalize time-varying channels is computationally intense, especially if the dimension of the observation vector is very high. In this paper, we apply the conjugate gradient (CG) algorithm to a conventional minimum mean square error (MMSE) DFE in order to reduce its computational complexity. Moreover, we compare its performance to the one of MMSE DFE versions which are based on the computationally efficient least-mean-square (LMS) or recursive least-squares (RLS) algorithm, respectively. The analysis additionally includes a detailed investigation of computational complexity with respect to the required number of floating point operations (FLOPs). Simulation results when applied to a digital communications system show the ability of the CG based MMSE DFE to outperform either the LMS and the RLS based MMSE DFE although its computational complexity is even smaller in most of the cases.

SUMMARY This paper proposes an iterative maximum a posteriori probability (MAP) receiver for multiple-input-multiple-output (MIMO) and orthogonal frequency-division multiplexing (OFDM) mobile communications. For exploiting the space, time, and frequency diversity, the lowdensity parity-check code (LDPC) is used as a channel coding with a builtin interleaver. The receiver employs the expectation maximization (EM) algorithm so as to perform the MAP symbol detection with reasonable computational complexity. The minimum mean square error (MMSE), recursive least squares (RLS), and least mean square (LMS) algorithms are theoretically derived for the channel estimation within this framework. Furthermore, the proposed receiver performs a new scheme called backward symbol detection (BSD), in which the signal detection uses the channel impulse response that is estimated one OFDM symbol later. The advantage of BSD, which is explained from the viewpoint of the message passing algorithm, is that BSD can exploit information on the both precedent and subsequent OFDM symbols, similarly to RLS with smoothing and removing (SR-RLS) [25]. In comparison with SR-RLS, BSD reduces the complexity at the cost of packet error rate (PER) performance. Computer simulations show that the receiver employing RLS for the channel estimation outperforms the ones employing MMSE or LMS, and that BSD can improve the PER performance of the ones employing RLS or LMS.

Digital‐domain self‐interference cancellation (SIC) technology effectively mitigates self‐interference by reconstructing the SI signal. Due to the high computational complexity of the traditional recursive least squares (RLS) algorithm in SIC, its real‐time engineering application capability is limited. To overcome this limitation, this paper proposes the application of the DCD‐RLS algorithm to SIC. The proposed method transforms the standard normal equations of the RLS algorithm into auxiliary normal equations and incorporates a bisection search strategy, thereby significantly reducing the computational complexity while maintaining optimal convergence performance. Simulation results demonstrate that the proposed algorithm not only achieves a significant reduction in computational complexity but also retains SIC performance, thus enhancing the implementation value of in‐band full‐duplex (IBFD) communication systems.We propose an RLS‐SIC algorithm based on dichotomous coordinate descent (DCD), that converts the RLS normal equations into auxiliary equations and uses a bisection search to greatly reduce computational complexity while preserving optimal convergence. Simulations show, the method maintains self‐interference cancellation performance with much lower complexity, improving the real‐time feasibility of in‐band full‐duplex communication systems.

This paper analyzes the performance of well known Least-mean-square (LMS) and Recursive-least-squares (RLS) adaptive algorithms in terms of bit error rate (BER) verses signal to noise ratio (SNR) performances on the space-time coded multicarrier CDMA (STC-MC-CDMA) systems in frequency selective environments. For system we consider Alamouti's space-time coding scheme that involves two transmit antennas and provide transmit diversity at transmitting end. We compare the performance of RLS and LMS detector with help of computer simulation. It is found that BER performance of RLS detector is always considerably better than that of LMS detector. Thus, RLS detector outperforms the LMS detector. In this paper, we propose an application of the RLS algorithm for the detection of space-time coded MC-CDMA system. Simulation results presented in this paper confirms the performance analysis.

Speech enhancement is a vital area of research, the performance of speech based human machine applications such as automatic speech recognition system, in car communication depends on the quality of speech communicated. Different methodologies have been used by various researchers to improve the quality of speech signal. In this paper an attempt is made to analyze the performance of Least Mean Square (LMS) and Recursive Least Squared (RLS) adaptive filter algorithm for speech enhancement application. The performance indices used for the evaluations is Mean Square Error (MSE), Signal to Noise Ration (SNR) and execution time. The detail analysis is done and experimentally the results are validated and certain modifications are suggested in the algorithm. The experimentation revels that LMS have fast convergence than RLS. The computational complexity of RLS is very high as compared to LMS.

Transmit power control is indispensable in Direct Sequence Code Division Multiple Access (DS/CDMA) based systems such as the Satellite Universal Mobile Telecommunication System (S-UMTS). Since S-UMTS aims at achieving close integration with the terrestrial component (T-UMTS) in its complementary role, it is going to implement closed-loop transmit power control (TPC) at a slow rate of once every frame. In addition, predictive schemes can be used to mitigate the effects of delay. In this regard, recursive-least-squares (RLS) and least-mean-square (LMS) algorithms are normally employed. The RLS algorithm has a higher convergence rate than the LMS algorithm: an attractive attribute when the fading process abruptly changes. The LMS algorithm, on the other hand, has better tracking property than the RLS algorithm: an attractive attribute when the changes in the fading process are persistently slow. However, the mobile satellite system channel exhibits both attributes: abrupt changes and slow drifts. In this paper, therefore, we compare the performance of predictive TPC based on the RLS and the LMS algorithms for S-UMTS with the conventional TPC as a reference. We demonstrate that the predictive TPC schemes perform better than the conventional TPC scheme. However, the performance gain achieved depends on the predictive algorithm used, the environment in which the user equipment is operating, and loop delays. We show that, in general, the LMS based predictive TPC offers better performance than the RLS based predictive TPC scheme in S-UMTS environment.

This paper discusses the application of iterative versus adaptive equalizers to a Universal Mobile Telecommunications System (UMTS) High Speed Downlink Packet Access (HSDPA) receiver. Among the investigated algorithms are the Least Mean Square (LMS) and Recursive Least Square (RLS) algorithms and their adaptions to Code Division Multiple Access (CDMA), such as Griffith's algorithm, as well as the conjugate gradient (CG) algorithm. We show that adaptive algorithms do not perform well in HSDPA scenarios at medium to high mobile speed, and that the conjugate gradient algorithm achieves higher performance at the same or even lower complexity than any of the investigated adaptive algorithms. We also discuss the necessity to consider oversampling.