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Orthogonal frequency division multiplexing (OFDM) based wireless communication systems are expected to satisfy the thirst for ever increasing demand on higher spectral efficiency. But, OFDM systems suffer from peak to average power ratio (PAPR) and inter carrier interference (ICI) problems. It is observed that when OFDM is used in the uplink, PAPR problem is more severe and the relative mobility of the user equipments with respect to the base station will cause Doppler spread which leads to ICI. One of the solutions to minimize PAPR and ICI is single carrier frequency division multiple access. But there is a tradeoff in spectral efficiency. The main objective of this paper is to evaluate the performance of fractionally spaced equalizer (FSE) for blind channel estimation based on higher order statistics and to identify any better alternative to improve its performance. Dynamically varying modulus algorithm (DVMA) based FSE is proposed in this paper which is a better alternative for supervised equalization. The simulation results prove that FSE blind equalizer based on DVMA outperform the conventional supervised and blind equalizers.

The practical deployment of Continuous Variables Quantum Key Distribution (CV-QKD) systems benefits from existing optical fiber telecommunication infrastructures. However, optical fibers introduce random variations in the state of polarization, which degrades the system’s performance. We consider a CV-QKD system featuring a polarization diversity heterodyne receiver and the constant modulus algorithm (CMA) to compensate for the polarization drifts in the quantum channel. Our setup can effectively realign Alice’s quantum signal with Bob’s local oscillator for polarization drift variances below 10 −10 . This value is compatible with most experimental implementations, allowing for accurate estimation of the channel transmission and excess noise parameters. Our results establish operational limits for passive polarization drift compensation using a polarization diversity receiver combined with digital CMA, validating its use to compensate for the polarization drift in real-world implementations approximating the ideal scenario of no polarization drift, for polarization drift variances below 10 −10 . This enables long-term stability in CV-QKD systems, eliminating the need for active polarization controllers and manual adjustments.

This paper addresses the self-nulling phenomenon also known as the self-cancellation in adaptive beamformers. Optimum beamforming requires knowledge of the desired signal characteristics, either its statistics, its direction-of-arrival, or its response vector. Inaccuracies in the required information lead the beamformer to attenuate the desired signal as if it were interference. Self-nulling is caused by the desired signal having large power (high SNR) relative to the interference signal in case of the minimum variance distortion less response beamformer, and low power desired signal in the case of the constant modulus algorithm (CMA) beamformer, which leads the beamformer to suppress the desired signal and lock onto the interference signal. The least-square constant modulus algorithm is a prominent blind adaptive beamforming algorithm. We propose two CMA-based algorithms which exploit the constant modularity as well as power or DOA of the desired signal to avoid self-nulling in beamforming. Simulations results verify the effectiveness of the proposed algorithms.

This article presents the decision feedback equalizer (DFE), the maximum likelihood detection (MLD), and the radius-directed equalization (RDE) algorithms designed in MATLAB-R2018a to equalize the received signal in a dispersive optical link up to 120 km. DFE is essential for improving signal quality in several communication systems, including WiFi networks, cable modems, and long-term evolution (LTE) systems. Its capacity to mitigate inter-symbol interference (ISI) and rapidly adjust to channel variations renders it a flexible option for high-speed data transfer and wireless communications. Conversely, MLD is utilized in applications that require great precision and dependability, including multi-input–multi-output (MIMO) systems, satellite communications, and radar technology. The ability of MLD to optimize the probability of accurate symbol detection in complex, high-dimensional environments renders it crucial for systems where signal integrity and precision are critical. Lastly, RDE is implemented as an alternative algorithm to the CMA-based equalizer, utilizing the idea of adjusting the amplitude of the received distorted symbol so that its modulus is closer to the ideal value for that symbol. The algorithms are tested using a converged 5G mm-wave analog radio-over-fiber (A-RoF) system at 60 GHz. Their performance is measured regarding error vector magnitude (EVM) values before and after equalization for different optical fiber lengths and modulation formats (QPSK, 16-QAM, 64-QAM, and 128-QAM) and shows a clear performance improvement of the output signal. Moreover, the performance of the proposed algorithms is compared to three commonly used algorithms: the simple least mean square (LMS) algorithm, the constant modulus algorithm (CMA), and the adaptive median filtering (AMF), demonstrating superior results in both QPSK and 16-QAM and extending the transmission distance up to 120 km. DFE has a significant advantage over LMS and AMF in reducing the inter-symbol interference (ISI) in a dispersive channel by using previous decision feedback, resulting in quicker convergence and more precise equalization. MLD, on the other hand, is highly effective in improving detection accuracy by taking into account the probability of various symbol sequences achieving lower error rates and enhancing performance in advanced modulation schemes. RDE performs best for QPSK and 16-QAM constellations among all the other algorithms. Furthermore, DFE and MLD are particularly suitable for higher-order modulation formats like 64-QAM and 128-QAM, where accurate equalization and error detection are of utmost importance. The enhanced functionalities of DFE, RDE, and MLD in managing greater modulation orders and expanding transmission range highlight their efficacy in improving the performance and dependability of our system.

Pilot contamination is one of the main impairments in multi-cell massive Multiple-Input Multiple-Output systems. In order to improve the channel estimation in this context, we propose to use a semi-blind channel estimator based on the constant modulus algorithm (CMA). We consider an enhanced version of the CMA namely the Modified CMA which modifies the cost function of the CMA algorithm to the sum of cost functions for real and imaginary parts. Due to pilot contamination, the channel estimator may estimate the channel of a contaminating user instead of that of the user of interest (the user for which the Base Station wants to estimate the channel and then the data). To avoid this, we propose to scramble the users sequences before transmission. We consider different methods to perform unitary scrambling based on rotating the transmitted symbols (one Dimensional (1-D) scrambling) and using unitary matrices (two-Dimensional (2-D) scrambling). At the base station, the received sequence of the user of interest is descrambled leading to a better convergence of the channel estimator. We also consider the case where the Automatic Repeat reQuest protocol is used. In this case, using scrambling leads to a significant gain in terms of BLock Error Rate due to the change of the contaminating users data from one transmission to another induced by scrambling.

In this article, we simulate a converged 5G mm-wave analogue radio-over-fiber (A-RoF) system at 60 GHz, and perform offline signal processing to equalize the dispersive optical link with the three most frequently employed algorithms, i.e., the simple least mean square (LMS) algorithm, the constant modulus algorithm (CMA) and the adaptive median filtering (AMF), which are implemented in Matlab. The performances of the different algorithms are compared for various optical fiber lengths with respect to the EVM values obtained before and after equalization. In the case of QPSK in OFDM subcarriers, it is observed that the CMA algorithm performs better than the LMS and MF algorithms, with 2% and 1.4% EVM improvement respectively, while for 16QAM in OFDM subcarriers it is observed that the LMS algorithm has a very small improvement of 0.2% EVM compared to the MF algorithm, while CMA is not suitable for 16QAM modulation in the proposed converged 5G mm-wave A-RoF system at 60 GHz.

In this paper, the concept of blind algorithm with automatic gain control (AGC) is introduced in adaptive antenna system for signal optimization with an aim to estimate the desired response in adaptive fashion. Blind algorithm with AGC is a hybrid two-stage adaptive filtering algorithm; sequentially combining constant modulus algorithm (CMA) and Bessel least mean square (BLMS) algorithm. Blind Bessel beamformer with AGC does not require external reference signal to update its weight vectors and step size for convergence but updates itself from own reference signal obtained from the output of CMA. Similarly, step size is obtained from the correlation matrix which is the product of the signals induced in array elements of antenna. BLMS is the modified version of LMS algorithm; based on the non-uniform step size exploiting the asymptotic decay property of Bessel function of the first kind. The output of CMA provides input and reference signals for BLMS that makes it blind. The contributions of this paper include the development of novel blind theory concept and presentation of an AGC method in order to make the Bessel beamformer blind which can update itself electronically through the correlation matrix depending on the signal array vector with the aim to make the signal power constant.

To improve the performance of the blind equalizer (BE) in impulsive noise environments, a robust maximum likelihood algorithm (RMLA) is proposed for the communication systems using quadrature amplitude modulation signals. A novel robust maximum likelihood cost function based on the constant modulus algorithm is constructed to effectively suppress the influence of impulsive noise and ensure the computational stability. Theoretical analysis is presented to illustrate the robustness and good computational stability of the proposed algorithm under the impulsive noise ambient. Moreover, it is proved that the weight vector of the proposed BE can converge stably by LaSalle invariance principle. Simulation results are provided to further confirm the robustness and stability of the proposed RMLA.

At present, in robot technology, remote control of robot is realized by wireless communication technology, and data anti-interference in wireless channel becomes a very important part. Any wireless communication system has an inherent multi-path propagation problem, which leads to the expansion of generated symbols on a time scale, resulting in symbol overlap and Inter-symbol Interference (ISI). ISI in the signal must be removed and the signal restores to its original state at the time of transmission or becomes as close to it as possible. Blind equalization is a popular equalization method for recovering transmitted symbols of superimposed noise without any pilot signal. In this work, we propose a concurrent modified constant modulus algorithm (MCMA) and the decision-directed scheme (DDS) with the Barzilai-Borwein (BB) method for the purpose of blind equalization of wireless communications systems (WCS). The BB method, which is two-step gradient method, has been widely employed to solve multidimensional unconstrained optimization problems. Considering the similarity of equalization process and optimization process, the proposed algorithm combines existing blind equalization algorithm and Barzilai-Borwein method, and concurrently operates a MCMA equalizer and a DD equalizer. After that, it modifies the DD equalizer's step size (SS) by the BB method. Theoretical investigation was involved and it demonstrated rapid convergence and improved equalization performance of the proposed algorithm compared with the original one. Additionally, the simulation results were consistent with the proposed technique.

A novel methodology is developed for blind equalisation where the output of the linear filter is passed through a nonlinear fractional update term derived from the cost function using fractional calculus. The final weights update is a combination of the conventional constant modulus algorithm (CMA) weights and a fractional update part. As an improvement over the traditional approach, the new fractional strategy helps capture the parameters of the model at a faster rate while keeping the error small. The algorithm is applied for the blind equalisation of flat and frequency‐selective channels. To assess the suitability of the proposed technique, different fractional orders and step sizes are used; the performance metric considered is the mean squared error for a quadrature phase shift keying transmission scheme. The simulation results show that the proposed technique outperforms the conventional CMA, exhibits faster convergence and yields an improved steady‐state response.